A special thanks to Lucas and Kevin for their feedback and review.

In 1849, tens of thousands of men went searching for gold in California. Some made out to be kings. Most made out poor. But the greatest success fell upon the merchants building picks and shovels: the foundational tools that would remain important so long as there was gold in the ground. Today’s gold rush is AI. Payments are becoming a foundational part of AI agent workflows, and inevitably, the rails facilitating those payments become today’s picks and shovels. In this paper, we dive deep into the x402 protocol; one particular emerging open agent-native payment rail, including what it is, how it works, where adoption stands today, and what open questions face x402 when machines move money.

A Seismic Shift Is Underway

History is defined by seismic shifts. Sudden, profound transformations that change the very foundation of life itself. The physical geography we inhabit today was formed some 175 million years ago when the Earth’s tectonic plates shifted and broke apart. Tectonic plates are still shifting today, albeit much slower, but the biggest seismic shifts in the modern world have materialized through human innovation.

Foundational inventions, be it the Sumerian wheel, the Gutenberg press, the steam engine yield a similar reinforcing flywheel: humans build machines, these machines yield economic surplus, which leads to more money for building more machines (and better ones at that) which further compounds the output. As technology improves, the cost of experimentation falls while the rate of iteration increases. We’ve seen this dynamic accelerate particularly within the last few decades with computers and the applications and services built around them.

Today this transformative dynamic is materializing through software, particularly artificial intelligence.

AI has been around for a long time. The first paper on neural networks was published as far back as 1943. Expert systems in the 80s and statistical machine learning in the 90s quietly embedded AI into production workflows, from credit scoring to ad targeting.

Spam filters, recommendation engines, and fraud detection have been running on machine learning models since the early 2000s. What changed is the interface. Generative AI lets humans interact with these systems through free-form natural language, and that shift in accessibility is what brought AI into mainstream consumer markets.

OpenAI launched ChatGPT as a simple and intuitive way to interact with its underlying GPT models, which reached 1 million users in 5 days and 100 million in 2 months, the fastest consumer internet adoption in history. Shortly after competing generative AI applications (e.g. Gemini, Claude, Grok) started rolling out, and the ensuing competition drove major AI labs to quickly improve models and ship new product features. On a darker note, fears of job displacement have mounted as corporate executives continue to iterate reducing headcount and increasing investments in AI (though it’s worth noting over half of US workers today are working jobs which didn’t exist in 1940).

No one can state with certainty how AI will ultimately reshape society. But as of late 2025, roughly one in six people worldwide have used generative AI, a technology that barely existed three years ago. While early, a seismic shift is well underway.

But there’s only so much to be done by simply talking to AI. Read access, e.g. letting LLMs look things up, condense large bodies of text, write code, analyze data, is evidently useful but only goes so far. But pairing that with write access, in other words letting AI actually do things across external environments, has unlocked an entirely different set of capabilities.

In 2025, OpenAI rolled out Operator and Codex, Anthropic shipped Claude Code, Perplexity launched Comet, Google embedded Gemini-powered agents across Workspace applications, Microsoft pushed Copilot deeper into Office and Windows. Perplexity, Cursor, Sierra, and Cognition each raised hundreds of millions in new funding and reached new all fun highs in revenue. It’s clear that agents will define the next chapter in how humans interact with AI.

But an overlooked reality is that autonomous agents have been driving economic activity online for years. The digital ads marketplace is one massive agentic economy operating beneath the surface of nearly every ad-supported webpage (which includes most large consumer sites and platforms today).

Whenever someone loads a site, the empty ad space is put up for sale in a mini-auction, and agents operating on behalf of advertisers (this could be any merchant selling goods online) determine how much that ad space is worth to them and submit bids. The highest bid wins and the winning ad is rendered in the slot, within hundreds of milliseconds. By the time the site loads, the ad is already displayed. To put this scale into perspective, consider that programmatic digital ad spend amounts to hundreds of billions annually, which accounts for over 90% of display ad budgets today.

Crawlers are another example. These autonomous agents roam the web, follow URLs, fetch webpages, and feed that content into large-scale indexing and training pipelines. Similarly, scrapers extract raw data from webpages and are actively used in a variety of fields today. On a darker note, “bad bots”, programs set up to exploit business logic online, accounted for as much as 37% of internet traffic in 2025. Collectively, over half of all web traffic is already run by autonomous software agents.

Yet evidently, few people know about these agents, and even fewer actually use them. However useful these agents may be at their particular function, e.g. bidding for ad space, their utility is limited by their purpose-built nature. A crawler crawls, a scraper scrapes, a bidder bids, and nothing more.

When these different capabilities are stacked together into one single program, controlled from one single interface, the nature of the application changes entirely. This is where the general-purpose AI agents most people know today come in. Their most foundational change is their ability to perform many different tasks and operate across multiple apps and environments within one interface. These agents were designed to accelerate human output and productivity: they do more things, at a much greater rate, and as their adoption grows, they generate an ever larger share of web traffic.

So what is exactly an agent? In simple terms, a goal-oriented software program which receives a goal, identifies the steps needed to achieve that goal, and takes action within certain environments using tools available at its disposal to achieve that goal.

A user types a prompt, which is then delivered to an agent’s runtime environment. From there, the agent provides a prompt to its model, which makes decisions and functions as the brains of the operation. The agent uses any available tools it may have access to (i.e. web browsers, APIs, databases, calendars etc) to carry out these tasks, and then carries out a cycle of absorbing information from its actions, reasoning about the information, and using this information to determine its next action or decision until the defined stopping point (e.g. the goal specified in the user prompt) has been reached.

Gartner predicts roughly 40% of enterprise applications will have task‑specific AI agents embedded by the end of 2026. McKinsey estimates AI agents account for $3–5 trillion of global consumer commerce by 2030. Goldman Sachs projects that by 2030, agents could erode meaningful TAM for SaaS by becoming a primary interface for knowledge work even as they expand the overall software market.

Of course, these are forecasts and not facts, and some predictions about the immediate impact of agents have already proven to be too ambitious. Nonetheless, it is difficult (and naive) to ignore the size of the bet being made here: if AI agents are to capture even a fraction of their predicted growth, they start to become consumers. And consumers need, first and foremost, a way to pay for the goods and services they wish to purchase.

Machines Need To Move Money

And so the role of money comes up. Money is a coordination tool: a shared medium of exchange and unit of account that enables humans to coordinate with strangers and settle transactions efficiently. It enables human societies to cooperate at scale; to compound individual effort into part of a greater productive whole.

As AI agents take on more economic responsibilities, they too require access to this shared coordination layer. But agents are a structurally different kind of consumer, with different behavioral patterns and optimization criteria. They don’t face the same cognitive or computational constraints as humans. Their very purpose is to multiply human output by magnitudes: more work, in far less time, though often through more frequent and granular consumption of resources along the way. Agents also aren’t susceptible to the emotional appeals that advertising deploys on human consumers. They are, by design, economically rational actors.

In this context, money plays a familiar but functionally extended role. As a medium of exchange it minimizes transaction friction, enabling seamless value transfer between agents. As a unit of account it provides the standardized measure agents need to conduct precise cost-benefit analyses, evaluating whether the compute cost of a task justifies its expected reward. And as a store of value it allows agents to preserve purchasing power and allocate capital efficiently across time and tasks.

To make use of these functions, however, we must distinguish between the asset itself and the infrastructure that moves it. Money is a coordination tool but payments are its mechanism of action. Stripped to its core, a payment is a way to settle an obligation: the process of reconciling a ledger to prove that value has actually changed hands. For an agent, this is the functional execution of a contract. It is the step that converts a decision into a realized outcome, unblocking access to a resource. Without this settlement capability, an agent is effectively stuck in a read-only state. Agents need a designated way to pay for goods and services online.

But Machines Already Move Money?

To state the obvious, this is already possible. AI agents already have access to infrastructure which trillions of dollars already move online every year through. These systems are built around what we can call checkout-specific flows. A (human) user confirms their purchase intent, a payment token is created for that transaction, and the merchant processes the payment through their respective PSP infrastructure.

Take OpenAI and Stripe’s Agentic Commerce Protocol (ACP) for example. ACP is an open‑source API specification that enables AI agents to complete purchases on behalf of users by standardizing how merchants expose their catalogs, how agents represent offers and carts, and how payment details are delegated back to the merchant’s PSP once the user confirms intent. The clearest implementation of ACP today is Instant Checkout in ChatGPT, powered by Stripe’s Shared Payments infrastructure. Through intuitive natural language language prompts, users can take care of shopping needs (e.g. buy running shoes, order takeout, book reservations) from the familiar ChatGPT interface. On the backend, ChatGPT delivers a scoped payment token to the merchant’s PSP, and the merchant charges for the payment.

Beyond ACP, agent payments are growing as the agentic commerce stack begins to form. Claude Code and Codex run workflows using prepaid balances on user accounts. OpenClaw takes this one step further and enables virtual cards with spending limits to be issued to an agent. Google’s UCP and AP2 use payment tokens linked to user credit cards and email addresses.

This system works. It prompts no immediate reinvention. For human-agent checkout flows, this model performs exactly as designed.

Machines Do Move Money, Just Not Autonomously

However, the point that really matters here is that this is not the the final form for agentic payments. This model becomes strained when applied to continuous agentic workflows at scale.

This emerging ecosystem is what has been characterized as a “virtual agent economy”: a set of linked digital markets where AI agents transact with one another to generate economic value independently of human labor. Critically, in such an economy, agents transact and coordinate at scales and speeds beyond direct human oversight. And this is where key economic and technical constraints begin to emerge within today’s commerce stack.

Protocols like ACP use legacy payment mechanisms: credit cards, digital wallets, BNPL services. Consumers and merchants using these protocols still absorb their economics: 2.9% + $0.30 per transaction, multi-day settlement, 120-day chargeback windows. Annoying but manageable for humans.

Agents, however, transact at a far greater scale. Agentic systems routinely decompose complex problems into granular distributed tasks worth fractions of a cent. Applied across thousands of steps, the cost structure collapses; consider that a $0.30 fixed fee on a microtask worth, say $0.02, is already a 1,500% markup.

Ultimately, this stems from a deeper architectural issue. As Felipe Montealegre articulates in Internet Finance, modern financial systems are just collections of private servers. Moving value requires permissioned administrators to reconcile entries across those servers, a process reflected in settlement delays and layered fees. When an AI agent initiates a purchase via a protocol like ACP, a synchronous chain follows: the request routes to Stripe, which routes to issuing banks. Stripe has its fee layer, Visa and Mastercard have theirs, banks have theirs. Each operates independently.

The efficiency of this reconciliation mechanism sets the speed limit for the entire economy. A 2024 BIS report found that a one-percentage-point increase in digital payment adoption corresponds to a 0.10 percentage-point rise in GDP per capita growth over two years. If removing friction for humans produces that, the implications for agents operating orders of magnitude faster are larger still.

There is also a less visible cost: permission itself. Legacy rails were designed around discrete human-to-human purchases with meaningful transaction sizes (e.g. at least a few dollars’ worth). The compliance architecture built on top, KYC, AML checks, chargeback systems, correspondent banking, reflects rational responses to legal liability at that scale. A $0.004 API call carries different risk than a $50,000 wire transfer, but today’s rails treat them with similar overhead.

Nick Szabo identified a related problem in 1999: mental transaction costs, that is, the cognitive burden of deciding whether a small purchase is worth making. Early micropayment businesses failed not because the technology couldn’t handle small payments, but because humans had to consciously approve each one.

Agentic systems appear to solve this. Claude Code draws from a pre-funded account and proceeds without interrupting the user. But this is a proxy with a budget, not an autonomous economic actor. The agent’s economic range is fixed at setup by its human operator. It cannot negotiate access to new resources, acquire capabilities outside what was pre-provisioned, or carry any economic identity beyond the session. Szabo’s problem reemerges as a ceiling on what the agent can do.

Another key constraint agents encounter with legacy payment rails is identity. In existing human:agent checkout flows, agents essentially borrow their user’s identity to transact. The entire stack assumes the payer is a person with a legal identity, bank relationship, and personal liability, which agents have none of (at least not yet). When Claude Code makes a payment it does so via its user’s Stripe credentials, the merchant’s fraud model, the card network’s chargeback rules, and the bank’s settlement framework.

This also creates unresolved legal questions. How does an agent run an AML check? File a suspicious activity report? Screen for sanctions violations? There may soon be answers to these questions, as this issue is now gaining recognition at the federal level. NIST’s National Cybersecurity Center of Excellence just published a concept paper on AI agent identity and authorization, identifying it as an unsolved infrastructure problem requiring new open-source future standards.

Human-agent checkout flows are genuinely useful. But in their next evolution – an economy of AI agents – cost, autonomy, and identity are three constraints existing systems cannot reconcile. Multi-day settlement and the absence of 24/7 global rails add further friction, though these are secondary to the structural issues above. Solving these constraints starts with understanding how AI agents actually work. Agents are software, they run on the web, therefore perhaps the most natural place to look for a payments layer is within the web itself.

POST /merchant/transfers/payment HTTP/1.1
Host: payments.example.com
Content-Type: application/json
Content-Length: 402

{
  "payment_transfer": {
    "reference": "PAYMENT123456",
    "amount": "1337",
    "currency": "EUR",
    "sender_account_uri": "pan:5299920000000149;exp=2020-08;cvc=123",
    "sender": {
      "first_name": "Amelia",
      "middle_name": "Rosenburg",
      "email": "test123@sender.example.com"
    },
    "recipient": {
      "first_name": "Tyrone",
      "middle_name": "Johnston",
      "email": "test123@example.com",
      "merchant_id": "123"
    },
    "authentication_value": "ucaf:jJJLtQa+Iws8AREAEbjsA1MAAAA",
  }
}

An example of calling a payment API using a POST request to initiate a transaction via HTTP-402.

HTTP 402 was reserved decades ago but never implemented. It anticipated a world where payment could be handled at the protocol level, the same way authentication is handled today. It was simply missing two things needed to make it real: a defined standard, and a form of money that could move at web speed.

We Already Have Web-Native Payment Rails

Just as HTTP moves information freely and permissionlessly across the internet, global value transfer on public blockchain rails is already happening at a meaningful scale. Tens of trillions have already been moved in stablecoins. Circulating stablecoin supply sits at over $308B today.

Mckinsey and Artemis Analytics tracked stablecoin payment volume at $390 billion in 2025, more than double from the year prior, with volume concentrated in B2B and P2P payments, card-linked spending, and B2C payouts. Though most onchain stablecoin activity has historically been trading-driven, chain-specific data shows payment demand emerging as a distinct and growing share.

A stablecoin in its simplest terms is software programmed to maintain the value of a stable unit of exchange, most commonly the US Dollar. A piece of software known as a smart contract is called, and in turn a stablecoin, is either minted or redeemed. Several key properties that make these bits of software uniquely suited for web-native payments, particularly in agentic commerce.

The economic advantage is the most obvious. Stablecoin transactions are orders of magnitude cheaper than legacy payment rails because there is no intermediary taking a cut. A stablecoin transaction is a redistribution of balances on a shared digital ledger. The primary resource consumed is the computing power required to update that balance, paid as a gas fee. Gas fees across most major chains now sit in the sub-cent range. Against the transaction fees associated with credit cards, the cost differential is stark.

A more distinctive advantage is programmability. As Hadfield and Koh point out, agents need “programmable money” to achieve the kind of autonomous cooperation required for complex economic activity at scale. Stablecoins are issued and redeemed via smart contracts in which payment conditions can be encoded directly: conditional releases, payment limits, recurring transfers, etc. Agents can control wallets through cryptographic keys and sign transactions directly, which removes the need for human credentials at the payment layer.

Haseeb Qureshi makes the sharper point: crypto is better suited for agents than it is for humans. An agent can verify transaction details, audit smart contract code, and confirm settlement in seconds. It does not need to trust a counterparty’s legal representation or a jurisdiction’s enforcement mechanism. Code on the other hand is deterministic: given the same inputs, it always produces the same outputs. A smart contract that says “release payment when condition X is met” will release payment every single time condition X is met, enforcement is built into execution.

An agent entering an economic agreement via smart contract knows exactly what will happen under every possible condition before it signs. It can read the code, verify the logic, and confirm the outcome in milliseconds. A stablecoin is just code. A wallet is just code. An agent can hold funds, transact, and enter economic agreements as naturally as it sends an HTTP request.

The Missing Piece

HTTP 402 defines how a server signals that a resource requires payment. Stablecoins provide money that can move at web speed. Together, they start to look less like a checkout flow and more like an HTTP handshake.

Consider a vending machine. It provides one specific item, a soda or a candy bar, but only in exchange for the exact payment amount. The operator does not need to produce anything it sells. It simply provides neutral infrastructure that lets willing buyers access a resource.

This is the model for web-native agentic payments. An agent makes a request, the server responds with a payment requirement, the agent provides payment, and the resource is unlocked. HTTP 402 provides the status code. Stablecoins provide the settlement layer. The missing piece is what ties them together: a web-native vending machine.

x402: A Web-Native Payment Rail

In May 2025, Erik Reppel, head of engineering at the Coinbase Developer Platform, published a whitepaper co-authored with colleagues Kevin Leffew, Dan Kim, and Nemil Dalal that gave HTTP 402 its first real implementation in 29 years: the x402 protocol.

The idea, which this paper takes as its central premise, was simple: the internet has always needed a native way to send and receive payments, and stablecoins now make that possible. Coinbase had been exploring internet payment standards since 2015, but even then the idea was ahead of its time. By May 2025, the conditions had finally aligned. Onchain fees cost fractions of a penny on chains like Solana and Base. Stablecoin supply had grown rapidly and was approaching $300 billion. Agentic commerce had materialized into something concrete enough to build for.

What is x402?

x402 is an open-source payments protocol that standardizes how web clients and servers exchange value using HTTP 402 and stablecoins. It is, in the most literal sense, the missing web-native vending machine: a buyer makes a request, a seller names a price, and payment clears directly through HTTP without a pre-established relationship between the parties.

At its core, x402 separates three things: transport, which is how data moves between parties; payment logic, or the scheme by which value moves; and the settlement network where that value lands onchain. This separation is what makes x402 extensible. HTTP is the primary transport and the 402 status code is the native signal, but the same payment logic can run over MCP or A2A without touching the underlying standard. In practice, x402 plugs into existing web infrastructure as lightweight middleware. Server-side implementation takes a single function call. A client can pay any supported endpoint without a prior relationship with the resource provider.

How Does x402 Work?

In summary, the x402 payment flow works like this:

1. A client requests a paid resource from a server

2. The server returns HTTP-402 with machine-readable payment instructions.

3. The client constructs a signed payment payload and resubmits the request.

4. The payload is verified, and the server fulfills the original request. Note, the payment is first submitted and settled onchain, and the settlement proof passed along via the Facilitator to confirm payment.

5. Once confirmed, the server returns a 200 OK response alongside the requested resource and transaction proof to the client.

There are certain steps involved in which the details vary by specific chain architecture, as we will expand on below. But importantly, each step maps to a specific component split across two layers: an offchain layer where communication occurs, and an onchain layer where payment settles.

The Communication Layer

x402 is transport-native by design. While HTTP is the primary implementation, the protocol complements existing data transportation without mandating additional requests outside a typical client-server flow. Any request-response based system can implement x402 payment flows, which means x402 runs across HTTP for web APIs and REST services, MCP for AI agent tools and resources, and A2A for direct agent-to-agent payments.

The primary offchain components are the client and the server, which apply across all supported transport layers.

The client represents the buyer: the entity requesting access to a paid web resource. This can be a human developer making calls through an HTTP client library, or an AI agent operating as a web client. A client sends the initial request, constructs a valid payment payload, and retries the request with a Payment-Signature header containing the signed payload.

The server represents the seller, or resource provider. Usually an API or content endpoint, but ultimately any digital resource accessible via a supported network. The server responds to client requests with payment requirements, verifies incoming payment payloads, and returns the resource once the transaction confirms.

For agents, no separate integration is required per transport layer. Payments are handled automatically across HTTP APIs, MCP tools, and A2A interactions.

The Onchain Layer

The onchain layer of the x402 protocol is where payment occurs. This consists of a wallet to hold funds and authorize payments, a Facilitator service to verify payloads and submit payments onchain, and the stablecoin or token contract which actually moves between parties.

To execute payments with x402, a client needs a funded cryptographic wallet. A wallet is a public-private key pair. The public key is a pseudonymous identifier that functions as the client’s onchain address, analogous to a routing number. The private key authorizes the client to sign and execute transactions, analogous to a password. When an agent needs to access a paid resource, it constructs a payment payload by signing a transaction authorization with that private key. The signed payload includes the public key so the server can verify the sender and confirm the transaction onchain independently.

To settle payments onchain, a server uses a Facilitator. A Facilitator is a transaction relay and gas abstraction service: it receives a signed payment payload, validates it against the token contract’s authorization parameters, and broadcasts the transfer onchain on the server’s behalf, covering gas costs in the process. When a server receives a signed payment payload, it can either handle onchain settlement directly or POST the payload to the Facilitator’s /settle endpoint. The Facilitator validates the cryptographic signature against the token contract’s authorization parameters, calls transferWithAuthorization the token contract, covers gas from its own balance, and broadcasts the transaction to the network.

Critically, the Facilitator is not a custodian. It executes a transfer that was already cryptographically authorized by the client’s signature. It cannot redirect funds or modify payment terms; it can only execute or reject what the client already signed. Coinbase runs a Facilitator on Base and Solana for USDC payments, but the role is open. Any server can run its own verification logic locally if it prefers not to outsource.

x402: EVM vs Solana

The HTTP handshake in the x402 payment flow is identical across chains. What differs is the onchain settlement mechanism, which is downstream of the architectural differences between EVM-compatible chains and Solana.

On EVM-compatible chains, x402 uses EIP-3009 as its core token transfer primitive. EIP-3009, authored by Coinbase engineers Peter Jihoon Kim, Kevin Britz, and David Knott in 2020, quietly facilitates tens of billions in monthly USDC transfer volume.

The key mechanic is the separation of authorization from execution. EIP-3009 provides a single-use cryptographic signature created offchain, authorizing a specific transfer of a specific amount to a specific address within a defined validity window. That signature becomes the payment payload in x402.

There are two main functions involved here: transferWithAuthorization and receiveWithAuthorization. The client signs a structured EIP-712 message containing the payment details and a random 32-byte nonce. Using random nonces here lets multiple payment authorizations be created in parallel without ordering constraints.

When the server receives the Payment-Signature header, validation is a deterministic checklist: verify the recipient address matches, the payment amount meets the requirement, the token contract is an accepted asset, the validBefore timestamp hasn’t expired, and the EIP-712 signature is cryptographically valid. Because authorization is separated from execution, the server validates the signed payload before submitting anything onchain. An invalid signature, mismatched amount, or expired timestamp gets rejected at zero cost to either party.

If all checks pass, the server or its Facilitator calls transferWithAuthorization on the token contract. The Facilitator covers gas from its own balance, which is why the client only needs to hold the payment asset rather than the chain’s native gas token. Once confirmed, the server returns the requested resource alongside a transaction hash and block number the client can verify independently.

Solana

On Solana, x402 uses a different settlement mechanism that reflects how transactions are structured on the chain. Rather than an offchain EIP-712 signature authorizing a future transfer, the client constructs and partially signs a full SPL token transfer transaction using Ed25519 signing, then encodes it as a base64 payload in the Payment-Signature header. The transaction is partially signed because the client signs the transfer instruction while designating the Facilitator as fee payer, a role that requires the Facilitator’s own signature before the transaction can be broadcast.

Before building the transaction, the client queries the Facilitator’s /supported endpoint to get its fee payer address and confirm support for the requested network. The Facilitator then receives the partially signed transaction, validates it, adds its own signature as fee payer, and broadcasts it to the network. The client pays in USDC without needing to hold SOL for gas.

Both the EVM and Solana flows achieve atomic settlement through different primitives native to each chain. The EVM flow uses an offchain signature authorizing a future transfer the Facilitator executes; the Solana flow uses a partially built transaction the Facilitator completes and broadcasts.

Beyond the mechanics, each chain carries practical advantages. EIP-3009 shifts gas costs from clients to servers, who accept this because payment amounts structurally exceed gas fees. For agents making repeated calls to the same endpoint, servers can batch multiple authorizations into a single transaction, spreading the fixed base gas cost across payments. The authorization-execution split also enables payments in low-connectivity environments: a client can construct and sign an EIP-712 message locally without network access and transmit it when connectivity resumes. Solana’s architecture is optimized for throughput, making it better suited for higher-frequency payment patterns.

That said, x402 is chain-agnostic. The protocol uses CAIP-2 network identifiers so the same handshake handles routing across Base, Solana, Polygon, Scroll, and Avalanche. The server specifies which networks and assets it accepts in the 402 response; the client picks from what its wallet supports. As the Facilitator landscape matures and new chains meet x402’s acceptance criteria, the protocol extends without changes to the underlying standard.

What Does x402 Uniquely Solve?

It’s worth zooming out to revisit what makes x402 valuable.

The ability to transact online is becoming a foundational requirement for AI agents. As software programs that communicate via HTTP and consume web resources continuously, paying for things is central to how they operate. The problem is that the infrastructure underpinning something so fundamental to their function wasn’t built for them. In human-agent checkout flows, agents transact through human infrastructure, borrowing credentials and depending on intermediaries designed for a different kind of consumer. x402 runs on the same web-native rails agents already operate on.

The differences are structural. Cost is the most obvious one; gas fees for stablecoin transactions on the chains x402 currently supports (e.g. Base, Solana) cost fractions of a cent, and represent the only transaction costs within the traditional x402 payment flow. This matches the granular economics with which AI agents operate at (e.g. per-call, per-task etc); which quickly breaks down with a 2.9% + $0.30 fee charged per transaction.

Settlement goes deeper. Because payment conditions are enforced at execution, a transaction cannot finalize unless predefined terms are met. The rails enforce this through code, so the agent doesn’t need to know or trust the counterparty. This is the difference between post-hoc verification and preemptive execution, and it’s what makes delegation to high-stakes workflows safe. An operator defines spending constraints once: daily caps, per-transaction limits, whitelisted addresses. The agent executes within them without requiring approval on each call.

Beyond cost and settlement, there’s identity. Every transaction an agent makes is signed by the same cryptographic wallet address, a consistent and verifiable identifier it owns and controls. Rather than borrowing a human’s credentials for each new service, the wallet travels with the agent, standardizing how it shows up across every interaction.

But in practice, why do these advantages even matter?

Granular pricing structures, e.g. Pay-per-article, pay-per-video, pay-per-stream, were always the intuitive business models for digital content. The early web was built with that vision in mind, but it never materialized because transaction costs made small payments uneconomical and the cognitive overhead of approving each one made them exhausting. Granular pricing can actually match how content is actually consumed rather than bundled into subscriptions that most users only partially use.

For AI agents, granularity is a necessity to how they consume resources in discrete, measurable units (inference per prompt, market data per query, compute per job, API calls per task.) It’s difficult to map these naturally onto subscription pricing, which exists largely because per-unit micropayments weren’t viable. At fractions of a cent per transaction, they are.

x402 can also help reshape the software market for agents. Historically, accessing a capability meant either paying a subscription or building it yourself. As inference costs fall, building is increasingly viable. x402 introduces a third option: an agent mid-workflow can access a capability instantly, paying only for what it uses, rather than spinning up inference to replicate it.

The most expansive implication is what happens to how products get structured. Goods and services are bundled largely because transaction costs make granular pricing impractical. A news article packages interviews, photos, and context together because selling each piece individually wasn’t worth the overhead. An AI agent, meanwhile, can track what a user has already read, coordinate with a content provider to surface only what’s new or relevant, and pay for exactly that. The same logic extends to any digital product with separable components. As micropayments make individual pieces of content economically viable, agent-to-agent commerce could give rise to a retrieval ecosystem where content creators get compensated per use and users get something closer to a personalized product than a standardized one.

This extends to how AI agents actually move through a workflow. When an agent hits a paywall mid-task, the server returns a 402 with machine-readable payment instructions. The agent constructs a signed payment payload, settles onchain, and continues. The workflow never stops for manual authorization. The only prerequisite is a funded wallet, which the operator provisions once upfront.

This becomes particularly relevant in multi-agent systems or swarms. Modern agentic workloads increasingly decompose across specialized agents that coordinate through handoffs: one agent calls another, which calls a service, which calls another agent. When payments need to flow through that same graph, a central billing relationship can become an operational bottleneck. x402’s stateless design means each hop settles independently, such that the payment layer scales with the workflow rather than sitting above it.

All of this is straightforward to integrate. x402 ships with a full reference implementation: core protocol libraries, server-side middleware for Express.js and Next.js, and client libraries for browser and Node.js environments. Server-side integration is a single function call. An agent can hit a paid endpoint, receive a 402, construct a signed payload, and settle onchain without any custom payment logic written on top.

With that in mind, let’s take a look at the current state of x402 adoption today.

The Current State of x402

x402 launched on May 6, 2025, initially supporting USDC on Base. In the nine months since, the protocol has processed 164m total transactions worth $46.4m in volume, across 447k unique buyers and 89k sellers in total.

That growth was not linear; at its peak on November 17, x402 drove 19% of Base’s 17 million daily transactions, a striking demonstration of how quickly the protocol could saturate a single chain’s activity. Since then, amid a broader pullback in onchain and crypto market activity, transaction volume has declined meaningfully, although there has been a meaningful resurgence in transactions and volume count in March once again.

What the data tells us, and what it doesn’t, is worth examining closer.

X402 is a chain-agnostic framework that’s live on a small but growing number of chains today. Base accounts for the majority of x402 activity to date — 125m transactions and $39m in volume — a feat driven in no small part by its early-mover advantage, as well as being home to Coinbase’s native facilitator infrastructure, and the deepest facilitator ecosystem on the protocol with 27 active facilitators. Solana came online in July 2025 and has processed 38m transactions and just under $7m in volume to date. By early February 2026, Solana was gaining rapid share of transaction count (and volume?), accounting for as much as 97% of daily transaction share on February 2nd. Nonetheless, Base still hosts a much larger share of buyers (423k buyers vs Solana’s 23k) and sellers (68k vs Solana’s 20k), suggesting Base still dominates on the demand side even as Solana captures a growing share of transaction count.

Beyond Base and Solana, x402 is actively expanding support to new chains, now live on Polygon, Avalanche, Sei, Optimism, and Arbitrum. These chains are live but collectively still account for a small fraction of activity relative to Base and Solana. It is too early to tell where the most x402 activity will settle, but the answer is far more likely to depend on a specific application on a specific chain rather than the infrastructure of the chain itself.

The facilitator landscape meanwhile, has grown alongside the protocol. Coinbase remains dominant by transactions (78m) and volume ($27.3m), and it’s worth noting their Facilitator offers a zero-fee structure and has a first-mover position. But the ecosystem has started to diversify; PayAI, Dexter, Daydreams, and Virtuals Protocol are among the more active third-party facilitators, which collectively account for the majority of facilitator transactions and volume in more recent months.

But not all agent activity is as it seems. For instance, despite the recent craze around Moltbook, a viral social network exclusively for AI agents which drew lots of coverage about millions of agents posting and coordinating their own lives, much of this early activity was revealed to be agents pattern-matching through trained social media behaviors, with humans involved in many cases.

The same scrutiny applies to x402. Artemis Analytics distinguishes between real and gamed transactions in its x402 dashboard, gamed transactions being indicative of wash trading. One explanation for the incentive to game transactions with x402 is launching a memecoin, driving transaction volume to climb the x402scan leaderboard, and converting that visibility into token buyers.

Though x402 went live in May 2025, the first major surge in activity only took place a few months later around early Q4 ‘25. Much of this activity was memecoin-related however; throughout much of November and early December, gamed transactions accounted for a larger share of total transactions than real transactions.

However, as total x402 transactions have declined in the past few months, real transactions are starting to account for a larger share of total transaction count. February saw 5 straight days in which real transactions accounted for 100% of total count.

Gamed transactions do help stress-test infrastructure, validate settlement mechanics, and prove throughput at scale. But financial incentives and the downstream activity of those incentives dilute the assessment of the quality of x402’s adoption. Whether they are memecoin-related, airdrop farming-related, or otherwise, the bottom line is that gamed transactions represent behavior optimized for financial incentive rather than organic x402 utility.

But numbers, as useful as they are, only tell so much. For a more holistic overview of x402 adoption to date, it’s worth looking at the x402 ecosystem.

The x402 Ecosystem

The x402 ecosystem is made up of the various participants in the x402 payment flow. Clients represent the buyers making requests for web resources to paid endpoints (e.g. AI agents), Servers are the services and paid endpoints (APIs, data providers, content platforms, and any digital resource exposing a paid route via x402), Facilitators are various third-party services handling payment verification and onchain settlement for Servers, and supporting infrastructure and tooling covers any SDKs, analytics, wallets, and developer tooling that make building on x402 practical.

Much of the x402 ecosystem is still early, but the range of service partner integrations and experiments being built on top of the protocol offers a glimpse into its surrounding excitement and interest across different industries and fields.

x402 Within The Broader Agentic Commerce Stack

It’s important to zoom out and recognize that agentic commerce is a wide-ranging multi-layered stack that intersects across several industries. Discovery, consent, checkout, and agent coordination are each being handled by different protocols involving a number of the biggest players across the tech and finance sectors.

On the commerce side: we touched on ACP earlier, which defines how AI agents handle the shopping flow on behalf of human users from product discovery through checkout. Critically, ACP is designed to be processor-agnostic, meaning merchants can plug in whatever payment provider they already use. Google’s UCP (Universal Commerce Protocol) serves a similar purpose for Google’s ecosystem: it standardizes how agents operating inside Google Search and Gemini discover products, negotiate offers, and complete purchases on a user’s behalf. Where ACP is optimized for conversational interfaces like ChatGPT, UCP is designed to work across Google’s broader range of products. The Agent Payments Protocol (AP2), also from Google, sits one layer down from both of these, handling authorization specifically. Before an agent can spend money, AP2 generates a cryptographically signed mandate confirming that a real user authorized this agent to spend up to X for purpose Y. Every transaction gets a tamper-proof record tied to that mandate, which gives merchants and financial institutions an auditable trail to verify the transaction was legitimate. Google’s A2A, now maintained by the Linux Foundation with over 100 supporting organizations, handles a different problem entirely: how agents from different companies and platforms talk to each other. When an agent built by one vendor needs to hand off a task to an agent built by another, A2A provides the common language for that coordination.

X402 complements this stack in several ways. Google and Coinbase built the A2A x402 extension as a production-ready module within AP2 specifically for agent-based stablecoin payments. The split is clean: AP2 provides the authorization mandate, confirming the agent has permission to spend; x402 executes the actual per-call transfer once that mandate is in place, settling in USDC onchain. Cloudflare added x402 into its Agents SDK and MCP server tooling, so any tool or API exposed via MCP can require a 402 payment before granting access. In practice, an ACP-driven assistant might discover a service via MCP, see that the endpoint returns a 402, get operator budget approval, and settle via x402 on every subsequent call while ACP manages the overall order and recordkeeping.

Legacy payment networks have been getting involved with agentic payments too. Visa launched the Trusted Agent Protocol to help merchants identify and verify AI agents during checkout, particularly to distinguish a legitimate shopping agent from a malicious bot. Trusted Agent Protocol gives verified agents a cryptographic signature they can present to merchants during browsing and checkout, built on Cloudflare’s Web Bot Auth infrastructure and backed by partners including Microsoft, Stripe, Shopify, and Worldpay. Visa is working with Coinbase to ensure Trusted Agent Protocol is interoperable with x402. Mastercard launched Agent Pay the same month with its own merchant verification framework: tokenized agent credentials that let merchants register and authenticate AI agents before any transaction clears. While Mastercard has not announced a direct x402 integration, it is a named AP2 partner, and Cloudflare is working with Mastercard on the same Web Bot Auth infrastructure that underpins x402’s authentication layer.

Stripe is another major player that’s been actively ramping up development efforts around agentic payments. In February 2026, Stripe launched Machine Payments in preview, with x402 via USDC on Base as the first supported method, letting developers bill agents for API calls, MCP requests, or HTTP endpoints through the standard PaymentIntents API: the same infrastructure merchants already use for card payments. Alongside this, Stripe introduced Shared Payment Tokens: scoped, time-limited credentials that let agents initiate payments without exposing underlying account details, which non-Stripe merchants can forward to their own processors.

On March 18 the same PaymentIntents API was reintroduced as the foundation for the Machine Payments Protocol (MPP), a new open standard for agentic payments launched alongside Tempo’s mainnet. MPP encodes the same HTTP 402 signaling mechanism as x402 and targets the same fundamental problem but introduces two architectural differences: a “sessions” primitive that lets clients deposit funds into an onchain escrow once and sign offchain vouchers per-request, and multi-payment rail support, featuring native stablecoin settlement on Tempo with Visa, Lightspark, and Stripe extending MPP to cards, Bitcoin Lightning, and fiat methods as well.

x402 and MPP, unlike x402 and other agentic commerce protocols e.g. ACP/ UCP/A2A, are more adversarial than they are complementary. Tempo raised $500M from Stripe and Paradigm, and launched with merchants wielding lots of distribution power including Visa, Mastercard, Anthropic, OpenAI, and Shopify. Their competitive dynamics are worth diving deeper into a future paper; however it is worth acknowledging here that the two protocols are compatible. Cloudflare’s documentation confirms MPP is backwards-compatible with x402: existing x402 flows map directly onto MPP’s “charge” intent, and Stripe supports both.

What’s important to understand with all these different agentic commerce protocols is their division of labor. ACP and UCP enable AI agents to handle online shopping experiences on their human user’s behalf. AP2 handles authorization and audit. Visa and Mastercard handle merchant-facing trust and agent verification. Stripe is working to abstract developer integration across all of it. x402 sits at the execution layer of the stack: it’s a neutral payment API that any agent, any service, any developer can implement in a few lines of code, with no merchant account, processor agreement, or card network required. When an agent hits a paid endpoint (i.e. a raw API call, an MCP tool, or an HTTP resource gated by Stripe’s Machine Payments) the server returns a payment request in the HTTP response, the agent pays in stablecoins, and the request completes. The money moves, the workflow goes on. Onchain rails simply match the granularity of AI agent economics with greater cost, speed, and programmability than legacy payment infrastructure.

Coinbase

Coinbase authored x402 and remains its primary infrastructure provider. Coinbase published the open specification, shipped the reference SDK, and runs the dominant facilitator service. The CDP facilitator processes payments on Base and Solana and currently handles the majority of x402 volume.

Beyond infrastructure, Coinbase’s distribution plays a critical role in x402’s success. Payments MCP is the most significant piece. It packages x402, an embedded non-custodial wallet, and an onramp into a single MCP server that Claude, Gemini, Codex, and other LLMs can connect to directly. A developer can go from zero to a transacting agent without an API key or manual setup. Complementary to this are Agentic Wallets: wallets purpose-built for agents with programmable spending limits, gasless transactions on Base, and a CLI for agent management. Together, Payments MCP and Agent Wallets products form a vertically integrated stack to help developers get started with building on x402.

Major Integrations

Coinbase also co-founded the x402 Foundation with Cloudflare, which has been actively involved with x402; having integrated the protocol into its Agents SDK and MCP tooling to allow MCP-exposed tools to be 402-gated and paid per-call. The practical implication of this shouldn’t be understated; any server using Cloudflare (roughly 20% of all web traffic globally) can become a paid x402 endpoint without modifying backend code.

Amazon Web Services (AWS) is also an x402 Foundation ecosystem member and has published a reference architecture showing how x402 can be layered onto existing HTTP infrastructure using CloudFront and Lambda@Edge. In that flow, CloudFront acts as a global reverse proxy while Lambda@Edge runs payment verification logic at the network edge: when a request arrives, Lambda@Edge checks for a valid payment signature; if missing, it returns an HTTP 402 challenge. The architecture applies x402 to any origin without touching backend code, whether hosted on AWS, GCP, Azure, or on-premise. One of the more interesting capabilities this integration unlocks is the ability to to charge bots while keeping access for human users free. More broadly, this integration means x402 isn’t a crypto-native tool that requires rebuilding infrastructure around it; rather it easily fits into the cloud stack enterprises already run.

Privy added native x402 support, wrapping the full payment flow without browser extensions, seed phrases, or gas tokens. This includes detecting 402 challenges, signing payment authorization with the embedded wallet via a custom useX402Payment React hook, constructing the X-PAYMENT header, and retrying the request. Users only need USDC in their wallet on Base or Base Sepolia; facilitators absorb gas. Privy is also exploring policy-engine integrations for programmable spend controls at the domain, agent, and workflow level, running off-chain. Developers building with Privy can add x402 payment flows without building a wallet layer from scratch, plugging into the 90 million accounts Privy powers today.

X402 and ERC-8004

An important emerging complement to x402 is ERC-8004, an Ethereum standard co-authored by teams at MetaMask, the Ethereum Foundation, Google, and Coinbase, including x402’s own Erik Reppel. Both standards treat AI agents like first-class citizens in a new emerging virtual economy; where x402 provides payments infrastructure, ERC-8004 is designed to provide reputation and identity infrastructure.

The standard is made up of three lightweight onchain registries: Identity, which gives each agent a portable, globally unique identifier via ERC-721; Reputation, which maintains an onchain audit trail of client-authorized feedback; and Validation, which supports cryptographic or crypto-economic proof that a task was actually executed correctly. Together they function as an open credit score and track record for AI agents.

The integration with x402 creates a recursive loop. An agent discovers a service via the ERC-8004 identity registry, checks its reputation and validation history, makes an API call, receives an HTTP 402 code, pays via x402, and submits payment proof back into the reputation registry as a verifiable feedback signal. This way, each transaction helps strengthen the trust record. Over time this enables more sophisticated economic relationships: agent-native credit systems, outcome-based lending, constrained capital deployment where an agent doesn’t receive free funds but specific, verifiable capability.

X402x

x402x (short for x402-exec) is a programmable settlement framework built on top of x402 that turns payment into a complete execution primitive. Where the base x402 protocol handles payment routing, x402x combines payment verification, business logic execution, and facilitator incentives in a single atomic transaction. Standard x402 settles in 500-1100ms across a two-phase flow; x402x reduces that to 200-500ms via a Gateway contract that executes verification, authorization, token transfer, fee deduction, and nonce marking atomically. It adds four hook execution points — beforePayment, afterPayment, beforeBatchPayment, afterBatchPayment — enabling merchants to inject custom TypeScript logic for credential verification, subscription checks, NFT minting, loyalty point distribution, or complex settlement logic without modifying the core protocol. Batch settlement aggregates up to 100 payments per transaction, reducing per-payment gas costs by approximately 92%. A configurable platform fee mechanism gives facilitators an economically sustainable model. Critically, x402x is not a protocol replacement in any way, rather it is a production extension layer that runs fully compatible with the x402 specification.

T54

T54 is building a trust and risk layer for agentic commerce on top of x402. x402-secure is an open-source SDK and proxy layer that adds pre-transaction risk assessment, verified identity, and liability attribution to x402 payments. The system is powered by Trustline, T54’s own risk engine, which captures an agent’s reasoning traces and attaches them to the payment flow as verifiable evidence. Before a transaction settles, independent validators analyze the agent’s reasoning chain, behavioral patterns, and context to produce a risk score. The practical effect is that both buyers and sellers get auditable proof-of-agent intent, which opens a path toward dispute resolution and clearer liability in a protocol that is otherwise irreversible by design. T54 also shipped Claw Credit, an agent-native credit product built on x402 that allows agents to build a credit profile through usage and repayment to then unlock higher spending limits over time. T54’s agentic infrastructure is supported on Base, Solana, and XRPL.

Experiments and Early Builders

Beyond the major integrations, a scrappier layer of builders is exploring what x402 enables in practice. Conway Research’s Automaton is among the most striking examples: an open-source AI agent that owns a cryptographic wallet, pays for its own compute costs in USDC via x402, earns revenue by building and deploying products, and funds child agents when profitable. The agent runs on Conway’s MCP-compatible terminal infrastructure, integrates with Claude Code, Codex, or OpenClaw, and ceases to function if it cannot earn enough to cover its operational costs. Daydreams built xGate as an agent-native discovery layer for x402-enabled endpoints, alongside Lucid Agents, a commerce SDK that bootstraps agents with native x402, A2A, and ERC-8004 support. Developers have built self-generating task markets where agents post, discover, and pay for work autonomously. Agents can now buy their own web domains with x402. Some builders are experimenting with x402 and OpenClaw. There’s agents already using x402 for financial operations entirely through x402-gated endpoints: portfolio queries, market data pulls, swap execution.

Privacy is another open frontier. Onchain rails make every x402 transaction publicly observable: payment flows, cost structures, and purchase intent permanently recorded on a public ledger. Correlating onchain timestamps with server-side metadata is enough to reconstruct behavioral profiles over time, which becomes a nonstarter for enterprise workflows where any AI agent handles sensitive information. Several teams are working on solutions: x402Z, a collaboration between Zama and Mind Network, uses Fully Homomorphic Encryption combined with the ERC-7984 Confidential Token standard to validate transactions without revealing amounts, balances, or intent; Fhenix402 takes a parallel approach using CoFHE, performing computation on encrypted data without decrypting it; Aztec, Payy Network, and Miden, through their respective distinct architectural approaches, feature private settlement embedded natively at the protocol layer. With multi-chain support a core part of the x402 V2 spec, there is a direct path forward for adoption on privacy-native onchain rails.

New AI releases ship every single day, and what’s exciting about x402 is how boring it is: a simple neutral framework doing exactly what it says. That simplicity is what makes it universally applicable. In a world where agents transact autonomously at scale, the payment primitive underneath all of it may be the most important infrastructure layer no one is talking about.

That said, a reality check is in order.

Looking Ahead

Open Questions And Important Considerations

x402 and agentic commerce more broadly are still early. The protocol works, the ecosystem is growing, and there is undeniable value in micropayments and API-native monetization

But the full picture requires both internal development and external adoption to materialize in parallel. Standards like MCP and agentic payment flows still need to mature, and adoption among merchants and payment processors is an external force that no one controls on a fixed timeline.

X402 is not the first attempt to solve machine-native payments over onchain rails, and that history is worth holding onto when evaluating the open questions below. Balaji Srinivasan’s work at 21.co proved in 2013 that the concept of machine-readable micropayments was sound: Bitcoin payment channels could, in theory, enable devices to pay each other autonomously. The infrastructure wasn’t ready: Bitcoin transaction costs made sub-cent payments economically incoherent. L402, developed by Lightning Labs nearly a decade later, got further. It proved the technical viability of the HTTP 402 status code for payment-metered APIs and built real tooling around it. It failed for infrastructure reasons specific to its stack (e.g. Lightning’s statefulness, Bitcoin’s volatility, the two-sided cold-start problem) not because the underlying idea was wrong.

X402 fixes each of those specifically: USDC replaces volatile pricing, Base’s L2 removes channel management, and the facilitator model collapses the integration barrier to a single line of code. But some of the obstacles that slowed L402 including discoverability, compliance, and merchant behavior change have not been solved by upgrading the underlying infrastructure. The following are a few of the biggest questions concerning the adoption and success of x402.

Will credit cards adapt fast enough to make stablecoins unnecessary?

Whether its incumbents can actually be disrupted remains the defining question for x402’s role within agentic payments more broadly. Card networks have largely survived every predicted disruption for fifty years, from PayPal to Apple Pay to crypto, by adapting just enough, just in time. Credit card infrastructure is already deeply embedded, improving rapidly, and both Visa and Mastercard are actively building agent-specific payment frameworks. Agents already transact over card rails today. Virtual agent cards with programmable spending limits and backward compatibility with existing merchant infrastructure are an emerging middle path, which notably requires no stablecoin or crypto exposure whatsoever. If the major card networks lower fees, add programmable controls, and extend their rails to machine-native flows, the cost advantage of stablecoin settlement narrows.

Of course, card networks have built their entire business model on per-transaction fees and interchange, and there is no obvious incentive to destroy that margin to serve a new virtual economy that has not yet fully materialized. Stablecoins are also structurally more programmable, globally accessible, infinitely divisible, and require no chargebacks. The bigger question is whether the friction of crypto adoption is lower than the friction of waiting for card networks to adapt.

It’s worth noting the x402 V2 explicitly added support for legacy payment rails including ACH, SEPA, and card networks. As per the spec, x402 is an open standard for internet native payments which specifically “aims to support all networks (both crypto & fiat) and forms of value (stablecoins, tokens, fiat)”. Even if card networks adapt fast enough to close the cost gap, x402 is designed to work with whatever settlement layer wins.

The crawling and discoverability problem

This is a structural oversight x402 shares with L402, and it has no clean solution yet. Search engine crawlers do not have wallets. When they hit an HTTP 402 response, they treat it as a blocked page. Google’s own documentation confirms that 4xx status codes cause it to stop indexing affected URLs over time. Content protected by x402 is effectively invisible to the open web. Search engines will not index it and will not surface it in results.

Partial mitigations exist. Serving free previews to unidentified crawlers and using structured data to signal paywalled content are both established practices, borrowed from how publishers handle subscription paywalls today. But they add complexity, reintroduce the two-tier access model x402 was designed to eliminate, and don’t translate cleanly to pure API endpoints with no human-readable content to preview.

The longer-term resolution may not come from fixing x402’s relationship with search engines at all. If agents increasingly discover services through other agents, AI interfaces, and protocol-level registries rather than search crawlers, the relevant discovery layer shifts entirely. The SEO problem matters a lot if x402 adoption depends on organic web discovery; much less so if agents find services through agent registries, MCP directories, and AI-native discovery layers instead.

Regulatory and compliance risk

L402 stalled partly on compliance friction: accepting Lightning payments without a regulated gateway created AML exposure most businesses were unwilling to take on, compounding the existing adoption challenges Lightning already faced. x402 faces a version of the same problem, with a more explicit split.

Coinbase’s hosted facilitator includes KYT screening and OFAC checks on every transaction, inheriting Coinbase’s existing compliance stack. A merchant routing payments through that facilitator sits in a position analogous to using Stripe or PayPal: not a money services business, not directly exposed to AML liability. The tension emerges at the edges. Operators running self-hosted facilitators that take custody of assets or perform fiat conversion will, under conservative FinCEN and state interpretations, likely fall into MSB territory and face full BSA compliance requirements. The protocol is designed to be permissionless. The compliance infrastructure is not. If the facilitator layer becomes permissioned enough to satisfy regulators at scale, it concentrates gatekeeping in a way that undermines the protocol’s neutrality. If it stays permissionless enough to be genuinely open, it will likely hit the same OFAC and sanctions walls that complicated L402 adoption.

This is the same unresolved tension that has slowed stablecoin adoption in enterprise contexts for years. x402 does not resolve it, nor is that a failure of Coinbase’s execution. It is a protocol-level constraint: a permissionless payment standard operating in a world that requires permissioned compliance at meaningful scale. Coinbase’s facilitator is currently the most practical answer to that constraint, providing production-ready KYT and OFAC coverage that lets merchants transact without building compliance infrastructure themselves. The open question is what happens as the facilitator ecosystem diversifies and traffic routes through operators without Coinbase’s compliance stack.

Who is liable when an agent makes a bad payment?

Hadfield and Koh raise another structural problem sits underneath all of agentic commerce: current legal systems assume humans can monitor and control their agents, but that assumption breaks down as autonomy increases. When an agent using x402 overspends, sends funds to the wrong address, or is manipulated into paying a malicious endpoint, the liability chain is genuinely unclear. The protocol is irreversible by design. There is no chargeback. The facilitator verified the transaction. The agent acted within its programmed parameters.

This matters more because the same underlying models are deployed across many firms simultaneously. Hadfield and Koh warn that AI agent errors may be more correlated than human errors for precisely this reason: when agents share architecture, training data, or model weights, their failure modes cluster. The 2010 Flash Crash offers the clearest prior example of what correlated automated behavior produces at speed. Algorithmic trading systems briefly erased nearly $1 trillion in market value in under 36 minutes, not because any single actor failed catastrophically, but because systems trained on similar data responded to the same signal in the same way at the same time. The market recovered that day. Human regulators and exchange circuit breakers intervened at 2:45 PM when the CME halted trading for five seconds, breaking the feedback loop.

x402 has no protocol-level circuit breaker. Operators can enforce spending limits and whitelisted addresses at the application layer, and most current deployments still have humans setting those constraints upfront. But as agent autonomy increases and human oversight recedes by design, the window for intervention narrows. A correlated error that propagates through thousands of agents simultaneously would settle onchain before most oversight mechanisms could respond. The settlement finality that makes x402 useful is the same property that makes a cascade hard to stop

What does the future of x402 governance look like?

Coinbase and Cloudflare announced their intent to co-found the x402 Foundation in September 2025, with a stated mission of neutral, open governance. The structure makes sense as a distribution partnership: Cloudflare is one of the largest internet infrastructure providers globally, and Coinbase provides the stablecoin infrastructure and L2. Cloudflare CEO Matthew Prince acknowledged the dynamic directly, saying “Coinbase deserves immense credit for starting the work on the x402 protocol” while framing the Foundation as the path to independent governance.

But Coinbase is also the dominant facilitator, the author of the spec, the operator of the reference SDK, the issuer of USDC on Base, and a publicly traded company with shareholders. That is a significant concentration of involvement in a protocol designed to be a neutral payments layer for the broader web. The Foundation has stated it will welcome additional members from e-commerce platforms, AI companies, and payment providers through an open application process, but as of this writing no formal governance documentation, bylaws, or membership charter has been published.

Open source software has produced some of the most durable infrastructure in the world. Linux is the canonical example, but the Linux Foundation publishes its bylaws, board structure, and membership tiers publicly. The x402 Foundation has not yet done the same. Governance neutrality requires more than a stated mission. It requires structural commitment: published documentation, a clear roadmap for decentralizing facilitator infrastructure, and a governance model that doesn’t concentrate spec authority with the entity that also controls the dominant facilitator, the primary stablecoin, and the reference chain. None of that has materialized yet, as admittedly the Foundation is still young, but much of the difference between x402’s stated aspiration and its real-world impact lies in how effectively it can be governed and led over time.

The advertising fork in the road

In January 2026, OpenAI announced it would begin testing ads in ChatGPT’s free tier, with the first placements appearing in February. The move is understandable financially given OpenAI’s well-documented rate of burning cash, but it surfaces a deeper question about how the agentic economy gets monetized.

If the dominant AI interfaces become advertising-supported, agents face a structural conflict of interest. An agent that recommends a product because a merchant paid for placement is acting as a distribution channel, not in the interest of the end user. OpenAI says ads will not influence ChatGPT’s answers, but whether that holds at scale, under commercial pressure, over years, is a different question.

X402 and infrastructure like it represent an alternative model: agents complete transactions efficiently enough that the economic model does not require selling user attention. But the advertising flywheel has proven itself across every major internet platform for thirty years and generates hundreds of billions annually. The question is whether open payment rails can generate enough revenue fast enough to compete.

From attention economy to preference economy

This extends into something more structurally interesting. Today’s digital economy runs on advertising because there are more products than consumers have time to evaluate, and ads capture attention to guide transactions. In an agentic world where assistants interact with millions of services on behalf of consumers, attention becomes a less constrained resource. What matters more is the algorithm that matches assistants to services.

If strong central intermediaries once again dominate the matching layer, some form of paid prioritization (analogous to today’s advertising) will certainly influence rankings. The truly scarce and valuable resource in that world is not attention. It is high-quality human feedback on goods and services: the signal that distinguishes good services from bad ones, that trains better agents, and that creates a preference flywheel rather than an attention one. The key question is whether agent-to-agent commerce happens inside closed walled gardens controlled by a few dominant providers, or through an open web of agents that freely connect and transact. The answer will determine whether x402 and protocols like it become foundational infrastructure or a niche payment rail inside someone else’s ecosystem.

But merchant adoption doesn’t grow on trees

162 million transactions is a real number, but the majority of that volume flows through Coinbase’s own ecosystem and a small cluster of developer tools and crypto-native APIs. The harder test is whether mainstream API providers, SaaS companies, and non-crypto businesses add x402 alongside their existing subscription and API key models. Cloudflare’s network position reduces the technical barrier significantly, but the ecosystem needs merchants who have a genuine reason to prefer per-request stablecoin settlement over Stripe. That case gets stronger as agent traffic grows, but it is not proven yet.

And markets don’t form spontaneously

A useful corrective from Strange Loop Canon: markets form under coercion or coordination, and when they do exist, strategic sophistication determines who wins depending on how agents are configured. Alignment problems do not disappear just because agents can negotiate with each other. An AI agent economy running on x402 still requires discovery, trust, and security infrastructure among interacting agents. Whoever controls the dominant discovery layer — the mechanism by which agents find services and counterparties — holds enormous structural power regardless of how neutral the payment rail underneath it is. The payment layer is necessary but not sufficient. The governance of the layers above it matters just as much.

There are also several unresolved infrastructure problems that compound the challenges facing x402. The facilitator model has no sustainable economics built in at scale: Coinbase’s CDP charges $0.001 per transaction after a free tier of 1,000 monthly transactions, but third-party facilitators are largely running at zero fees with no clear revenue path. Two-phase settlement adds 500 to 1,100 milliseconds of latency per payment, which compounds quickly: an agent making 100 API calls in a single workflow carries over a minute of payment overhead before any work gets done. And EIP-3009 exclusivity means USDT — the largest stablecoin by market cap — remains incompatible with x402 entirely. Tether’s newer USDT0 variant addresses this technically, but it requires users to hold a different version of an asset they already own. BitGPT built h402 explicitly to preserve the protocol schema of x402 while ensuring the team isn’t “limited by someone else’s tech stack or product roadmap” — a signal that the developer community has noticed these gaps and is hedging against them.

Zooming out, x402 is early, the infrastructure is maturing, and most of these problems have plausible paths to resolution. What they collectively suggest is that x402’s current traction has mostly come from developer-native use cases where friction is low and tolerance for rough edges is high. The harder test is whether the same architecture holds at the scale of HTTP and the web as we know it today.

Closing Thoughts

The protocols that matter most rarely announce themselves. TCP/IP had no launch event. HTTP wasn’t pitched as the infrastructure layer for a trillion-dollar economy. They became load-bearing slowly, by being the path of least resistance for enough people solving concrete problems that adoption accumulated before anyone decided it was significant.

X402 is designed to be a primitive, not a standalone product. It requires one function call on the server and one signed payload from the client. There is no merchant account, processor agreement, or card network inherently required to transfer value. The implementation surface is deliberately small. Cloudflare, AWS, and Stripe aren’t endorsing x402 so much as absorbing it into infrastructure developers already use. That is what the early stages of a load-bearing protocol looks like, though institutional involvement is not the same as widespread adoption.

What makes x402 especially interesting is that it is the only current option that fits how AI agents actually operate. Legacy payment infrastructure requires agents to borrow human identity, human credentials, and operate on human settlement timelines. X402 runs on the same web-native rails agents already communicate over, prices at the granularity agents actually consume resources, and settles in seconds. That asymmetry is modest today. Whether it compounds is the real question.

Infrastructure like x402 “wins” when timing and adjacent development mature together. Facilitator economics need a sustainable model. Regulatory clarity around stablecoin and crypto market structure more broadly is still unresolved. Merchant adoption outside crypto-native developer tooling is not a given. The advertising model, which has monetized every major internet platform for thirty years, is already reaching into the agentic stack. And there is the stark reality that x402 may not be the HTTP-native micropayments solution that ultimately captures the agentic economy after all. The history of machine-native payment attempts is a reminder that the right idea pitched at the wrong time still fails.

What is clear is that AI agents are becoming economically active participants in a new virtual economy, and the infrastructure built for human consumers was not designed for them. In any new economic movement where which applications will succeed remains genuinely uncertain, the highest ROI historically has been foundational infrastructure investments, rather than the applications built on top of it. x402 is an attempt to occupy that position: a neutral payment API, running on open rails, that any agent or developer can implement without a card network or a bank in the middle.

The internet has always moved information freely. At Shoal, we believe it is inevitable that value travels the same way. But the infrastructure driving these movements matters more than any single applications built on top of them. x402 is an early attempt to get that infrastructure right, and whether it succeeds is still an open question. But x402 has one advantage its predecessors didn’t: timing. The conditions that made prior attempts at machine payment impossible, whether it be settlement costs, stablecoin liquidity, or AI agent adoption, are no longer the constraint. A virtual economy of AI agents is forming. Money will move through it. The remaining question is what the underlying rails look like when machines move money.

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

Sources

Data sourced from DeFi Llama, Artemis Analytics, and x402 Scan.

• “Moore’s Law: Transistors per Microprocessor.” Our World in Data, ourworldindata.org/grapher/transistors-per-microprocessor

• Hadfield, Gillian & Koh, Bryan. “Agentic AI and Law.” arxiv.org/pdf/2509.01063

• “Buy It in ChatGPT.” OpenAI, openai.com/index/buy-it-in-chatgpt/

• “Agentic Commerce Protocol Documentation.” Stripe, agenticcommerce.dev/docs

• “AI Agents to Boost Productivity and Size of Software Market.” Goldman Sachs, goldmansachs.com/insights/articles/ai-agents-to-boost-productivity-and-size-of-software-market

• “x402: A Payment Protocol for the Internet.” Erik Reppel, Dan Kim, Nemil Dalal. arxiv.org/pdf/2509.10147

• “x402 GitHub Repository.” Coinbase, github.com/coinbase/x402

• “How x402 Works.” Coinbase, docs.cdp.coinbase.com/x402/core-concepts/how-it-works

• “X402x.” @yq_acc, x.com/yq_acc/status/1987968102605484152

• “Fhenix402.” Fhenix, fhenix.io/blog/fhenix402

• “Monetize Any HTTP Application with x402 and CloudFront + Lambda@Edge.” AWS, builder.aws.com/content/38fLQk6zKRfLnaUNzcLPsUexUlZ

• “Internet Finance.” Montealegre, Felipe. source

• “EIP-3009: Transfer With Authorization.” Ethereum, eips.ethereum.org/EIPS/eip-3009

• “Why the Ad Industry Isn’t Ready to Let AI Agents Spend Ad Dollars.” Digiday, digiday.com/marketing/intentionally-being-cautious-why-the-ad-industry-isnt-ready-to-let-ai-agents-spend-ad-dollars/

Solana’s Block-Building Environment Today

Roughly every 400 milliseconds (a slot period), Solana produces a new block that updates the state of its decentralized ledger. This is what enables you to trade, interact with DeFi, and transact seamlessly.

In theory, the block production process looks straightforward and seems to follow clear rules. Validators take turns being the leader (block producer) for each slot (of 400ms). The leader is responsible for gathering transactions, producing a valid block, and broadcasting it to the rest of the network. Other validators then vote to verify the block’s validity and ensure it adheres to the network’s rules. And this process goes on and on forever.

But in practice, every validator has its own strategy for packing transactions into those 400-millisecond blocks, and how they do so can significantly shape the user experience on Solana. It affects whether a trade goes through, whether a user gets the expected quoted price, and ultimately whether TradFi institutions can comfortably scale their onchain operations.

In other words, the behavior of a small set of validators, deciding in an opaque way how to build and broadcast blocks in order to maximize short-term profit, can create ripple effects across the entire network and impact all stakeholders. At the extreme, this even puts pressure on Solana’s long-term ambition of becoming the home of Internet Capital Markets.

This is a big deal, and it has sparked an increasingly heated debate around how block building on Solana should be structured to best serve the network over the long term.

In this article, we’ll explore the core issues around block building on Solana, the solution Jito is proposing with BAM, and the perspective of a notable outlier in this debate: Harmonic.

Block Building Problems Identified by Jito

Jito, one of the leading infrastructure providers on Solana and a core contributor to its validator and MEV ecosystem, has spent significant time studying how validators construct blocks in practice. Given its direct exposure to validator behavior, transaction flow, and execution outcomes at scale, they are uniquely positioned to have a view on the current block-building process on Solana.

Through this work, they have identified two core issues in the current block-building environment that, they argue, materially impact user experience and put pressure on Solana’s long-term vision: late packing and slot lagging, which we’ll explore in detail below.

Problem number 1: Late packing

Solana is designed as a high-speed streaming system. In theory, validators are expected to pack transactions continuously throughout the slot while broadcasting block data as shreds over Turbine. This allows the rest of the network to reconstruct and replay the block in near real time.

However, Jito observed that some validators tend to concentrate most of their transaction packing toward the very end of the slot. The rationale behind this behavior is simple: delaying transaction inclusion as long as possible allows validators to observe more incoming transactions and, as a result, extract additional MEV or prioritize higher-paying order flow to maximize revenue per block. The issue is that this behavior is often pursued without fully accounting for the second-order effects it can have on the rest of the network.

Before going deeper, let’s just look at different block-building strategies using IBRL, a new explorer released by Jito that shines a light on block packing and provides a score to assess how closely validators adhere to best practices and standards. This allows stakers to inspect how specific validators build and pack their blocks.

Here we see different packing strategy from three different validator, Helius, Figment, and Galaxy:

We observe drastically different block-packing strategies. In this example, Helius has the highest score, which implies stronger alignment, as its blocks are packed much more uniformly throughout the slot. In contrast, Galaxy has the lowest score and clearly exhibits a late-packing pattern.

This lack of a shared standard for uniform and deterministic block packing is a real problem. We’ll explain why in a moment. But first, let’s introduce the second major issue identified by Jito: slot lagging.

Problem 2: Delaying State propagation

Slot lagging refers to the practice where validators modify their Proof of History (PoH) parameters to extend slot times beyond Solana’s default ~360 milliseconds.

Similar to the late packing issue, the rationale is straightforward. By extending the effective slot duration, validators give themselves more time to observe incoming transactions, and pack more profitable transactions into their blocks.

However, this behavior breaks an important assumption of the network: that slots progress as fast as possible and at a consistent pace. Other validators expect blocks to be produced and propagated within a tight timing window. When a leader intentionally runs longer slots, block propagation is delayed, state updates arrive late, and in some cases validators vote on the state of the network later than expected. This also means traders have to wait on new state updates which affect their operations.

Even if only a subset of validators engages in slot timing games, the impact is shared by everyone: confirmation times become less predictable, latency increases, and the network drifts away from its intended high-speed streaming model.

For instance, we can see that Jupiter has a much better slot timing score than Kiln. As a result, even though both validators do not show poor transaction packing behavior, Kiln still ends up with a lower overall IBRL alignment score compared to Jupiter.

The Downstream Implications

At first glance, these behaviors may seem understandable. Validators are economic actors, and it is rational for them to optimize for short-term profit. However, in practice, late packing and slot timing games create downstream effects that harm the network as a whole and ultimately work against Solana’s long-term goals.

Below are the main implications we observed:

Misaligned with Solana’s “Increase Bandwidth, Reduce Latency” vision

If there is one principle consistently emphasized by Anatoly, Solana’s co-founder and that should effectively serve as Solana’s guiding motto, it is this: “increase bandwidth, reduce latency.”

However, when transaction packing becomes end-loaded and validators engage in slot timing games, Solana’s block pipeline stops behaving like a true streaming system. Even if a slot technically finishes on time, state updates are propagated late across the network. As a result, other validators end up voting later than they should, not because of their own processing delays, but because they are waiting for late-arriving data from the block producer. This increases end-to-end latency and degrades overall network efficiency, which runs directly counter to Solana’s core vision.

Moreover, Solana’s consensus mechanism rewards validators for timely votes via vote credits, and there is a bounded window in which votes must be submitted to receive full rewards. While geographically clustered validators may not feel the impact of late propagation as strongly, validators that are further away from the cluster are disproportionately affected. Over time, this creates subtle pressure against geographic decentralization, as validators farther from the network’s core face higher penalties through no fault of their own.

Unfriendly market structure for market participants

From a market microstructure perspective, traditional financial markets operate in a continuous and highly deterministic environment. Orders arrive continuously and are executed on a First-In, First-Out (FIFO) basis by a centralized matching engine. This determinism allows market makers to cancel and update quotes efficiently without constantly risking being picked off. As a result, they can quote extremely tight spreads, even on trades worth millions of dollars, without relying on priority fees.

Late packing breaks this determinism. When transactions are not included continuously throughout the slot, execution becomes less predictable. This increases execution variance, introduces jitter, and makes outcomes around liquidations and auctions harder to anticipate.

A block-building environment that lacks uniformity and determinism is simply not suitable for sophisticated market participants. Over time, this makes it harder for Solana to onboard and retain TradFi and ultimately undermines its ambition of becoming a globally distributed NASDAQ.

Negative impact on user experience

Beyond abstract market structure concerns, late packing and slot timing games have very concrete second-order effects. Block building that maximizes rewards often does so at the expense of applications and users.

When block packing is inconsistent or delayed, users face higher uncertainty around transaction inclusion. A trade that should have gone through may fail. A swap may execute at a worse price than expected. A liquidation or arbitrage transaction may miss its execution window entirely. And a trader who needs the freshest info about the next state transition is operating less efficiently. From the user’s perspective, this shows up as failed transactions, unexpected slippage, or the need to repeatedly resubmit transactions with higher priority fees. Overall, this directly impacts application reliability.

If we assume that the network should improve every day, becoming faster, more reliable, and easier to interact with, then these dynamics are clearly misaligned with that goal.

The Block Assembly Marketplace

To address the block-building issues outlined above, Jito recently introduced the Block Assembly Marketplace (BAM). Put simply, BAM is a new block-building architecture designed to bring verifiability, privacy, and programmability to Solana’s transaction pipeline.

Instead of allowing each validator to independently decide how to pack and order transactions (often in short-term, profit-maximizing ways, as discussed earlier), validators can delegate transaction scheduling to specialized external BAM nodes. These nodes operate inside Trusted Execution Environments (TEEs), which provide strong cryptographic guarantees that the ordering logic is executed exactly as specified.

In other words, validators still validate and vote on blocks, but BAM nodes handle transaction ordering in a way that is verifiable, auditable, and tamper-resistant. The logic behind this is that BAM’s ordering is designed to optimize Solana’s long-term potential by bringing uniformity and determinism to the block-building process and helps create a healthier market structure.

Specifically, BAM is designed around several key properties:

1. BAM nodes receive transactions from clients and schedule them according to transparent, verifiable ordering rules.

2. BAM nodes forward transaction sequences to the leader .

3. The leader executes the transactions exactly in the order they were received, produces the block, and broadcasts it to the network.

4. BAM nodes verify execution results match the forwarded sequences. Leaders who insert or reorder transactions are disconnected.

BAM’s structure is designed to provide transaction ordering which is verifiable and enforced by secure hardware. Since launch, BAM has seen over 122m SOL staked, capturing 28.9% of total Solana stake and 367 validators running BAM.

At a high level, Jito proposed a structural shift in how block building works on Solana by creating a more predictable, fair, and deterministic execution environment with BAM. However, this has also sparked debate within the ecosystem. Some teams argue that this approach gives Jito too much influence over block building and could centralize power around a single infrastructure provider. From their perspective, BAM risks becoming a de facto monopoly over transaction ordering rather than a neutral coordination layer.

This pushback is important, as it has brought alternative viewpoints into the discussion. Notably, one of the strongest responses came from Harmonic, a competing Solana block-building solution developed by the Temporal team.

Harmonic’s Approach To Block-Building

Harmonic works as an aggregation layer that runs multiple block builders in parallel, where each builder constructs its own version of the block and the validator picks whichever one best fits its preferences. In addition to Harmonic, the Temporal team also operates Nozomi, a transaction landing service, and more notably HumidiFi, a leading prop AMM on Solana, having an established presence across multiple layers of Solana’s transaction supply chain.

At a high level, Harmonic’s core disagreements with Jito center around what should be optimized, how it should be measured, and who gets to define the standards in Solana’s block building environment.

Concerns around monopoly dynamics and metric neutrality

One of Harmonic’s core criticisms is that Jito plays multiple roles at once: infrastructure provider, BAM architect, and creator of the IBRL scoring framework. From Harmonic’s perspective, this creates an inherent conflict of interest. They argue that IBRL does not measure “good block building” in a neutral way, but instead measures compliance with Jito’s preferred architecture, and thereby penalizes competing approaches. But we will see that this claim is nuanced.

Disagreement on “late packing”

Harmonic also strongly disputes the claim that their blocks rely on late packing in the sense described by Jito. According to them, the IBRL scoring framework misses a key detail: Harmonic blocks are packed continuously by block builders running parallel block-building processes. Transactions only appear in the final ticks because that is when the auction resolves, not because block construction is delayed. From Harmonic’s perspective, it is not fair that IBRL penalizes this behavior simply because it looks less smooth on the surface.

The only metric that matter for Harmonic: execution

From Harmonic’s perspective, best execution is the only metric that truly matters if Solana is to become the home of Internet Capital Markets. On that front, they argue that BAM does not deliver the optimal outcome. They claim that when looking at execution data across DEX programs, Harmonic shows the strongest results: validators running Harmonic repeatedly outperform the cluster baseline on both mean and median throughput. For them, this is the clearest measure of best execution, and this is what Solana ultimately needs.

Innovation through competition

Harmonic’s final argument is that only open competition can sustainably drive innovation in block building. From their perspective, it is far easier for stakers and validators to hold multiple competing block builders accountable than it is to keep a single dominant architecture in check. Prematurely converging on one standard risks locking in suboptimal design choices and slowing innovation over time. Harmonic argues that Solana should remain a free market for block-building innovation, where different approaches compete on execution quality. In that way, they see BAM as an attempt to consolidate block building around Jito’s architecture in order for them to preserve its leading position.

As an alternative, Harmonic advocates for a Proposer–Builder Separation (PBS) model on Solana. The goal here is to split block production into two distinct roles. Block builders compete to construct blocks, and validators, acting as proposers, no longer build blocks themselves, and instead, choose from blocks submitted by these competing builders.

The idea is to reduce excessive proposer power and create an open marketplace for block building. In theory, this should improve execution quality, limit monopoly control, and allow innovation to emerge organically, rather than being dictated by a single block-building architecture.

Nuancing the Debate

The debate between Jito and Harmonic highlights an important and healthy tension in Solana’s evolution. Both sides raise valid points, but the reality is more nuanced than a simple “BAM vs. competition” framing.

On Monopoly and Neutrality

Harmonic’s concerns around monopoly dynamics are not unfounded. Jito does occupy multiple roles within the ecosystem, which naturally raises questions about metric neutrality. That said, it is important to put this risk into perspective. A block builder that is vertically integrated with a market-making operation, such as Harmonic, arguably poses a much larger systemic risk to Solana. In that scenario, block construction incentives can directly conflict with fair execution and overall network health in ways that are far harder to observe or constrain.

From this angle, Jito’s multiple roles do not necessarily dilute metric neutrality. Jito is not vertically integrated across the full value chain of block building and trading. More importantly, its approach with BAM explicitly promotes verifiability, transparency, and auditable transaction ordering.

Now regarding the scoring system, Harmonic is right to be a critic. Measuring “good” block building is inherently complex, and any scoring framework will necessarily involve simplifications and trade-offs. Jito should be more open to listen to Harmonic feedback in order to improve his scoring. That said, the existence of imperfections in IBRL should not, on its own, invalidate the underlying issues it aims to surface, and as Jito mentioned in its IBRL methodology section, they will continue refining the model and expanding the set of signals over time to improve accuracy.

However, if an actor is intentionally delaying shred propagation, they are, as a matter of fact, slowing everyone else down. And if this behavior is profit-motivated, then that profit is necessarily extracted from the rest of the network.

Performance alone is not sufficient

Harmonic is right to emphasize execution quality. Best execution is undeniably critical if Solana aims to support large-scale, institutional-grade markets, as it directly impacts slippage, liquidity, and overall capital efficiency. But performance alone isn’t enough. Determinism and uniformity in transaction ordering are foundational to healthy market microstructure. Sophisticated trading systems require predictable execution environments.

So yes, Harmonic validators do pack more DEX transactions per slot, but that throughput correlates with longer slot times which is precisely the kind of non-determinism TradFi institutions cannot tolerate: if execution characteristics change depending on which validator is leading, firms cannot scale their operation with confidence and the case for migrating serious flow onto Solana weakens. In that case, we can argue that higher performance at the expense of determinism and uniformity is not a Pareto improvement for the long-term health of the network and also puts bearish pressure on SOL’s potential as an asset. As Mert, CEO of Helius, recently noted on X, late packing may generate incremental short-term gains, but it reduces the probability of sustained SOL appreciation over the long run.

To be clear, these concerns are not hypothetical, they are explicitly recognized in Solana’s own roadmap. In The Internet Capital Markets Roadmap, block-building dynamics and execution quality are identified as critical challenges. While they agree that BAM is an interesting solution forward, it is true that ideally, market structure improvements should come from the protocol itself as it is uniquely positioned to optimize for long-term network health rather than short-term profit incentives.

On competition and innovation

Competition is undeniably a powerful driver of innovation, and Harmonic is right to caution against prematurely locking Solana into a single block-building architecture. Allowing multiple approaches to compete in the open can surface better designs, stress-test assumptions, and reduce the risk of stagnation.

However, market forces tend to be highly short-term oriented. In competitive environments, participants are often incentivized to optimize for immediate gains, even when those strategies degrade long term potential. This dynamic closely resembles a prisoner’s dilemma: if one validator refrains from extracting short-term value, others will not. The rational response at the individual level is therefore to maximize short-term profit, even if the collective outcome is worse for the network.

Without an entity or mechanism capable of setting clear rules and protecting against this dynamic, there is a real risk that Solana’s long-term potential is undermined. Ultimately, this would also hurt validators themselves. If Solana fails to achieve its broader mission, SOL’s long-term value will suffer and SOL price remains one of the most important determinants of validator profitability.

BAM aims to address this problem by shifting it to the application layer through plugins and ACE. In doing so, it ensures that users and applications capture more of the value, rather than having it extracted at the block-builder or proposer layer. The result is a structural shift from value extraction in block building to value creation at the application layer.

It’s also important to be clear about the limits of the PBS path Harmonic is advocating for. PBS does not inherently guarantee fair outcomes from builder competition and introduces new pressure points. To illustrate this, the Ethereum Foundation has spent several years experimenting with proposer–builder separation and open builder marketplaces. While this reduced direct proposer discretion, it introduced new centralization vectors around relays and dominant builders and did not eliminate harmful MEV strategies and instead moved to a different layer of the stack.

In other words, separating proposers from builders changed the structure of extraction, but did not fully solve the underlying incentive misalignment. Even today, more than 25% of DEX trades on Ethereum are still subject to sandwiching. Yes, Solana’s architecture is meaningfully different from Ethereum’s but the incentive problem is not architectural. As long as builder selection optimizes for short-term validator revenue, the pressure to extract rather than create value persists regardless of the underlying consensus mechanism.

Competition at the builder layer alone has not proven sufficient to guarantee fairness, determinism, or long-term alignment. That is an important lesson when considering whether Solana should replicate a similar path.

Closing Thoughts

Solana remains well positioned to become the home of Internet Capital Markets. But its block-building microstructure remains a meaningful unresolved problem. Notably, Jito has identified two major issues that break the determinism and uniformity of block building on Solana: late packing and slot lagging.

To address these issues, Jito introduced BAM, a framework designed to make block building on Solana more transparent and deterministic. Today, over 28% of the network’s stake already runs BAM.

At the same time, this new architecture has faced notable pushback, most prominently from Harmonic. Their core argument is that BAM reduces competition and could therefore slow innovation. They also claim that Jito’s metrics are incomplete, and that the only thing that ultimately matters is best execution.

In this piece, we’ve seen that some of these concerns are well founded, but they also obscure important context. Harmonic is vertically integrated with a market-making operation, which means BAM directly challenges its economic model. And while execution quality matters, determinism and uniformity are the binding constraints for a healthy market structure. Competition without clear rules can easily devolve into a prisoner’s dilemma, where validators optimize for short-term profit at the expense of Solana’s long-term potential.

At Shoal, we believe BAM is a meaningful step forward and an important solution to help Solana become the home of Internet Capital Markets. It shifts the paradigm from value extraction at the block-builder or proposer layer to value creation at the application layer. That said, over the long run the network may need a more native, protocol-level solution to create a safer and more balanced competitive environment. But that is still 12–18 months out, and Solana needs something that works today.

Finally, this is an ongoing and rapidly moving debate. None of this is settled, and there are genuine arguments on both sides. We encourage every SOL staker to look at the data, ask validators directly why they run the block-building software they do, and make their own informed decisions. Where you stake matters. It shapes the execution environment, the market structure, and ultimately the kind of network Solana becomes.

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer. Shoal Research is a Jito delegate. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

Sources

Anza. “The Internet Capital Markets Roadmap.” https://www.anza.xyz/blog/the-internet-capital-markets-roadmap

Helius. “How Solana Works: An Executive Overview.” https://www.helius.dev/blog/solana-executive-overview

Helius. “Solana Slots, Blocks, and Epochs Explained.” https://www.helius.dev/blog/solana-slots-blocks-and-epochs

IBRL. “IBRL Validator Block-Building Explorer.” https://ibrl.wtf

IBRL. “Introducing the IBRL Explorer: Measuring Validator Block-Building Quality.” https://ibrl.wtf/blog/introducing-the-ibrl-explorer-measuring-validator-block-building-quality/

Jito Labs. “Block Assembly Marketplace Explorer” https://bam.dev/explorer/

Jito Labs. “Block Assembly Marketplace Documentation.” https://bam.dev/docs/

Jito Labs. “Introducing BAM.” https://bam.dev/blog/introducing-bam/

Harmonic. “Harmonic Documentation.” https://docs.harmonic.gg/

Harmonic. “Statement on Block Building and BAM.” source

Ruggero. “How Solana Builds Blocks.” https://www.ruggero.io/blog/how_solana_builds_blocks/=

hildobby. “MEV Sandwich Trades.” https://dune.com/hildobby/sandwiches

As ICOs return with renewed momentum, their underlying infrastructure becomes critical. Powered by its own FHE stack, Zama introduces a new Sealed-Bid Dutch Auction that enables confidential participation in public ICOs. In this report, we examine the mechanics of Zama’s Public Auction and its future implications for onchain capital formation.

The ICO Renaissance

In 2013, J.R. Willet set out to solve a very specific problem: raise money on the internet instead of going to VCs in Silicon Valley. To do so, he tried something no one had previously attempted: he published a whitepaper for the project he was building, and convinced friends and strangers online to send him Bitcoin in exchange for receiving a newly issued digital token in return. Within a month, Willet had managed to raise over $750,000, and 60 million tokens were then distributed to investors in a public sale.

Willet’s experiment was called Mastercoin, and the OMNI token sale quietly became the first-ever Initial Coin Offering (ICO). Though online crowdfunding had already existed through websites like Kickstarter and Indiegogo, Mastercoin’s ICO marked the first time a project raised capital by selling a freely tradable digital asset on a public blockchain.

A year later, Ethereum blew Mastercoin’s ICO out of the water, raising 31,000 BTC (worth ~$18.6M at the time) of its own. Shortly after, the ERC-20 standard was introduced: a simple framework that made creating and issuing digital tokens trivial. What followed was nothing short of a Cambrian explosion of ICOs. OMNI was the first, but in hindsight it was obviously never going to be the last.

After a few quiet years, demand for ICOs is coming back in force. In 2025 alone, Pump Fun raised $600M for PUMP in the third-largest ICO ever, and teams like Plasma ($50M), Monad ($269M), and MegaETH ($49M) all completed ICOs, with more sales expected in the coming months. Nearly every major sale in 2025 saw strong investor appetite, oversubscribing and selling out in minutes.

Despite fewer token sales since 2021, the total amount of capital raised has continued to grow, concentrating into fewer, higher-quality projects.

Yet this resurgence surfaces something that today’s ICO infrastructure has not yet been able to solve for: confidential investor participation.

Privacy (Or The Lack Thereof) In ICOs Today

Structurally, an ICO isn’t so different from an IPO: it involves a public sale, a launch event, and a freely traded asset. The fundamental difference is in their underlying rails. ICOs run on public blockchains and settle via smart contracts. Investors send funds to a smart contract, which then allocates them digital tokens programmatically based on predefined terms.

This architecture introduces a high degree of public transparency: anyone can independently verify that the sale executed as intended, that token allocations matched contract rules, and that every contribution and refund was accounted for. But this transparency comes with a greater underlying cost: every contribution, wallet, bid, and allocation is permanently visible onchain.

By design, blockchains offer pseudonymity: users interact through randomly generated wallet addresses which are not directly tied to legal identities. However, once a wallet address is linked to a real individual — whether through an exchange withdrawal, an ENS name, or other onchain activity patterns — their entire sale behavior, spanning from contribution size, wallet balance, bidding and sale patterns, and the rest of their transaction history becomes exposed.

Alongside visibility into sale behavior, public blockchains also expose ICO participants to Maximal Extractable Value (MEV) dynamics: value that sophisticated onchain actors (i.e. block builders, validators, and specialized searchers) can capture by reordering, including, or excluding transactions in a block. Simply put, those with visibility into pending transactions can position their own transactions to capture value by front-running, back-running, or sandwiching other participants’ transactions.

For retail investors, this level of transparency is a privacy leak. A single sale interaction can reveal far more than intended: not just how much they contributed, but how much they hold, when they sell, and how they behave across every other onchain activity tied to that wallet. Over time, these disclosures compound, creating persistent visibility into an individual’s financial position and behavior. In practice, this also increases exposure to MEV, as retail users are the least equipped to protect themselves from adverse transaction ordering and value extraction during highly competitive sale events.

For institutions, this level of transparency is simply a non-starter. Participating in a fully public onchain sale broadcasts order flow, position sizing, treasury capacity, and execution behavior to competitors and counterparties. Even the act of bidding can signal strategic intent well before a position is finalized. Combined with MEV dynamics, this creates unacceptable execution risk, where large bids can be detected, priced against, or exploited in real time. As a result, most institutional capital is effectively unable to participate in public ICOs at all, regardless of demand.

Early internet crowdfunding platforms like Kickstarter and Indiegogo did handle personal data, but access to this data was strictly permissioned: backer identities, contribution sizes, and emails were private to the creator and the platform.

By contrast, most attempts to incorporate some form of privacy into token sales have taken form as private, offchain processes, through whitelisted sales, negotiated allocations, data rooms, SPVs, and manual allocation mechanics. These approaches provide confidentiality for their participants, but only by abandoning the very mechanic that made ICOs powerful in the first place: open global access. The result is a bifurcated market in which institutions can only participate through private deals, while retail is pushed into fully transparent public sales that compromise their privacy.

What’s missing is a middle ground: an open, public ICO rail that preserves fairness and transparency at the system level, while providing confidentiality, by default, at the participant level: the allocation logic would remain publicly verifiable, while sensitive participant data would remain private. This type of platform would allow large-scale, rules-based ICOs, supporting tens or hundreds of thousands of participants, without revealing certain details such as individual contribution sizes or linking to prior wallet activity.

Of course, the next question is what this looks like in practice: how would one build public, auditable, yet confidential token sale infrastructure on top of today’s public blockchains?

The Zama Sealed Bid Dutch Auction

Zama is introducing a new Sealed Bid Dutch auction mechanism to bring confidential participation to onchain ICOs. Built on the Zama Protocol itself using FHE, Zama’s launchpad allows investors to participate in ICOs while encrypting sensitive bid and allocation data.

Understanding Zama

At its core, Zama is infrastructure that enables confidentiality for public blockchains using fully homomorphic encryption (FHE). FHE refers to a cryptographic technique that allows computations to be performed directly on encrypted data without first exposing the underlying values. In practical terms, this means data can remain confidential while still being used inside smart contract logic. Inputs are encrypted before reaching the blockchain, and processed in encrypted form through Zama’s confidential execution framework, and can only be decrypted by authorized parties at the end of execution.

Zama implements this capability through the fhEVM, its core confidentiality framework. A small set of specialized smart contracts is deployed on underlying public chains, enabling applications to validate encrypted inputs, manage encrypted state, and coordinate decryption through Zama’s key management system. These contracts can be integrated into existing wallets and frontends, which users can then easily access. Meanwhile, developers can write confidential smart contracts onchain using Zama’s fhEVM Solidity Library and standard EVM tooling.

As a result, sensitive data remains encrypted throughout its entire lifecycle: from the moment it leaves the user, through onchain computation, until the final result is decrypted by an authorized party. This breakthrough unlocks new classes of onchain applications, one of which is the confidential ICO.

How The Sealed Bid Dutch Auction Works

As with a ‘traditional’ ICO, investors deposit assets and place bids on the token to be sold, and once the auction is complete tokens are programmatically distributed to investors. The critical distinction is that, in Zama’s design, bid data is encrypted by FHE and therefore remains confidential throughout the auction process: whereas the bid price is public, the deposit amount and token allocation are not.

Zama uses a Dutch auction format, meaning the lowest bid price is selected as the clearing price as bids are processed in price-descending order until the available supply is exhausted. During bidding, which lasts a total of 4 days, investors can submit encrypted bids. Once bidding ends, the auction contract calculates the clearing piece by selecting the lowest price at which a bid was filled, and distributes tokens to investors accordingly. Investors can then claim their tokens, or a refund if necessary.

Consider an auction with a total supply of 100 tokens to be distributed:

• Bob bids 40 tokens at a price of $2. His bid is filled, and now there are 60 tokens left to distribute.

• Alice bids 60 tokens at a price of $1. Her bid is filled, and now there are 0 tokens left to distribute.

• Mert bids 20 tokens at a price $0.50, but since there are 0 tokens left his bid is not filled.

In this case, the clearing price is thus $1, given that it was the lowest price at which all the tokens were distributed (Alice’s bid). Therefore Bob receives 40 tokens and a $40 refund, Alice receives 60 tokens and no refund, while Mert receives zero tokens and a $10 refund. All tokens sold from the auction are fully unlocked and freely tradable upon claim.

User Flow

By design, the user flow is relatively simple and straightforward.

Investors first encrypt (or shield) the assets they intend to use for bidding. In exchange, they receive confidential “cTokens” at a 1:1 ratio, representing encrypted versions of the underlying collateral. Investors then submit bids for the token being launched at their chosen price, using these cTokens. Throughout the auction, bids can be monitored and managed directly through Zama’s auction interface, without revealing sensitive bid details onchain.

Once the auction concludes, the contract computes final allocations and clearing price. Investors can then claim their outcomes: successful bidders receive their allocated tokens, while any excess collateral is refunded. At this point, investors may either decrypt their remaining cTokens back into standard tokens or continue using them across other confidential applications within Zama’s broader ecosystem.

Technical Mechanics

At the core of Zama’s protocol is an FHE system. Rather than exposing inputs, balances, or intermediate values to the blockchain, all sensitive data is maintained in encrypted form throughout contract execution. This encrypted state is preserved under a unified FHE key, allowing smart contracts to operate directly on ciphertext without ever decrypting underlying values during execution.

To break this down further, the execution flow follows a well-defined pattern. A user encrypts an input (x) locally, producing an encrypted value E(x). This encrypted input is submitted onchain, where it cannot be decrypted by validators, block builders, or other applications. Smart contracts then compute directly over encrypted inputs, producing an encrypted output E(f(x)). Only authorized parties can decrypt the final result, while all intermediate computation remains opaque to the blockchain itself.

This execution model is implemented through the fhEVM, Zama’s confidentiality execution framework. The fhEVM operates as a specialized layer on top of existing public blockchains, coordinating encrypted computation, maintaining encrypted contract state, and managing controlled decryption through Zama’s key management system. A small set of smart contracts deployed on the underlying chain handle encrypted input validation, encrypted state transitions, and interactions with the key infrastructure, while remaining fully compatible with existing EVM environments.

When applied to the Auction, these mechanics ensure that the auction state is never represented in plaintext. Assets deposited by participants are immediately converted into encrypted state and represented as confidential balances within the auction contract. Bid evaluation, allocation logic, and refund calculations are all performed over encrypted values, preventing observers from inferring contribution sizes, wallet balances, or allocation outcomes during the auction process.

To implement this in practice, Zama introduces confidential “cTokens”, which serve as FHE-encrypted representations of standard ERC-20 assets. These tokens are issued programmatically when assets are encrypted and are redeemed when assets are decrypted, without exposing balances or transaction history onchain. cTokens are implemented using the ERC-7984 confidential token standard, allowing encrypted assets to remain composable across other confidential applications built on Zama’s protocol.

As a result, sensitive data remains encrypted for its entire lifecycle; from the moment it leaves the user, through onchain computation, until the final result is decrypted by an authorized party. In the context of ICOs, this architecture allows auctions to remain publicly verifiable at the system level, while ensuring that participant-level data remains confidential by default.

The North Star: Growing TVS

Zama’s adoption and growth is framed around a new type of metric: the Total Value Shielded (TVS).

TVS refers to the total amount of economic value that is actively encrypted and kept confidential using Zama’s protocol. Just as total value locked (TVL) became a dominant metric for tracking DeFi liquidity, TVS is meant to serve as a proxy for how much meaningful economic activity is taking place under cryptographic confidentiality.

The intuition is simple. The higher the TVS, the more confidentiality is being exercised across assets, applications, and users. As TVS grows, confidentiality evolves from a niche feature to a systemic property of onchain markets: assets become harder to surveil, applications harder to exploit, and users regain more control over the privacy of their onchain activity.

Zama’s strategy for growing TVS is infrastructure-first. Rather than building and owning end-user applications, Zama is positioned as a chain-agnostic confidentiality layer that other developers, protocols, and platforms can build on top of. Zama provides the primitives for encrypted computation and confidential state, while leaving application design, UX, and go-to-market decisions to application developers.

This way, Zama does not need to directly control demand to drive growth. TVS increases as confidentiality becomes a default property across an expanding set of applications and chains. Adoption is reflected in how broadly encrypted computation is integrated and how much economic value is processed under confidentiality, rather than in usage of any single application.

From a deployment perspective, Zama is designed to be chain-agnostic. The protocol is launching first on Ethereum, with plans to expand across EVM-compatible chains and, longer-term, into non-EVM environments such as SVM-based chains. This multi-chain approach ensures that confidentiality is not siloed to a single execution environment, but can follow users and assets wherever onchain activity migrates to.

TVS in Practice: The ZAMA Token Launch

The first live demonstration of an encrypted ICO will be the Zama’s Sealed-Bid Auction will be Zama’s own token sale. The $ZAMA token launch will be conducted on Ethereum, using the Zama Protocol itself to encrypt bids through FHE.

Zama will sell 8% of the $ZAMA supply through this auction. Participants submit bids onchain while sensitive bid data, including contribution size and allocation, remains encrypted throughout the process. Once bidding concludes, the auction clears transparently according to publicly verifiable rules, and tokens are distributed immediately with no vesting or lockups. In doing so, the Auction intends to bring fair price discovery, global access, and confidentiality to ICOs within a single platform.

This auction will mark the first instance of Total Value Shielded (TVS) flowing through Zama’s protocol in production. Capital contributed to the auction is encrypted end-to-end, allocations are computed over encrypted state, and only final outcomes are revealed, thus serving as a real-world stress test for confidential onchain capital formation at scale.

Use Cases and Applications

While confidential ICOs provide a clear and immediate application, the underlying primitives enabled by Zama’s launchpad can, in theory, extend far beyond token sales. At a high level, any onchain mechanism that relies on bidding, allocation, or competition over scarce resources can benefit from encrypted execution:

• NFT Auctions: Encrypted bidding enables sealed-bid NFT sales where bid amounts remain confidential until settlement. This could help prevent bid anchoring, strategic signaling, and enable fairer price discovery.

• Prediction Markets: Encrypted bidding introduces a new design space for prediction markets, where individual positions and strategies can remain private during market formation, while outcomes and settlement remain publicly verifiable.

• Governance: Encrypted bidding can be applied to onchain governance systems where outcomes are influenced by market signals or competitive inputs rather than simple token-weighted voting. By keeping bids or commitments confidential during the decision process, protocols can reduce signaling effects, cartel formation, and strategic coordination that often distort outcomes in fully transparent governance systems. This can be especially useful in futarchy-style governance, where participants express beliefs through markets tied to policy outcomes. In these settings, encrypted bids help preserve genuine price discovery by preventing participants from anchoring or reacting to visible positions before markets resolve.

• Solver Order Flow (PFOF) Bidding: Encrypted bidding can be applied to MEV and order-flow markets, where solvers compete for the right to execute transactions or bundles. This can potentially reduce information leakage around solver strategies, limit copy-trading and reactive bidding, and mitigate winner-take-all dynamics driven via information asymmetries, thus encouraging a more competitive and efficient market for onchain order flow.

• Confidential Block Production or Rewards Auctions: In Proof-of-Stake systems, encrypted bidding could be used to allocate block production rights or reward opportunities over defined time windows. Validators would submit encrypted bids for future slots or epochs, with allocations determined programmatically once the auction clears. By keeping bids private, such mechanisms reduce signaling and strategic front-running among validators, while enabling more market-driven allocation of block production rights.

Taken together, these examples show that encrypted ICOs are just one instance of a broader pattern. Any onchain mechanism that relies on bidding, allocation, or competition over scarce resources can potentially integrate and benefit from encrypted execution.

Closing Thoughts

At Shoal, we believe ICOs are positioned for renewed demand if the underlying infrastructure evolves to support confidential participation. Retail investors can participate in public sales without broadcasting their balances, positions, or trading behavior onchain. Institutions can participate without exposing order flow, signaling strategic intent, or introducing execution risk that distorts broader market activity. Both benefit from reduced MEV exposure during highly competitive bidding processes.

Zama introduces a concrete path toward this future by treating confidentiality as first-class infrastructure and orienting the protocol around a clear objective: growing the TVS across the broader onchain economy.

With this in mind, we look forward to a new chapter of confidential onchain capital formation.

Sources

• Cryptorank ICO Analytics, cryptorank.io/ico-analytics/

• ICO Drops, icodrops.com/

• Cryptoasset Market Coverage Initiation: Network Creation. Bloomberg Research, research.bloomberg.com/pub/res/d28giW28tf6G7T_Wr77aU0gDgFQ

• “Digital Tulips? Returns to Investors in Initial Coin Offerings.” ScienceDirect, sciencedirect.com/science/article/abs/pii/S0929119920302303

• Coin Perspective: J.R. Willett. Medium, medium.com/coin-story/coin-perspective-3-j-r-willet-136ed51a852c

• Zama Documentation. Zama, docs.zama.org/homepage/

• Zama Protocol Litepaper. Zama, docs.zama.org/protocol/zama-protocol-litepaper

• Zama FHEVM Whitepaper. Zama GitHub, github.com/zama-ai/fhevm/blob/main/fhevm-whitepaper.pdf

• Zama Public Auction: auction.zama.org

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the Zama team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

A special thanks to Connan and Mathis for their feedback and review.

Crypto’s Misalignment Problem

One of the first big value propositions of crypto was its ability to significantly lower the friction for capital formation and offer an alternative to the traditional funding model for startups. Instead of relying on VCs, banks, or waiting 10+ years for a potential IPO, crypto introduced a new way for anyone to raise money permissionlessly on the internet and let their project “go public” on day one through a token.

This was a real breakthrough with huge potential. As people began experimenting with this new model, interest exploded, ultimately leading to the historic ICO boom of 2017, where millions were raised and thousands of new tokens were launched.

But while the concept was promising in theory, it was still immature in practice and led to widespread market abuse. Everything was all about hype, and there was a clear misalignment of incentives among the major token stakeholders. Put simply: the more successful an ICO, the greater the incentive for the team to extract what was in the treasury instead of growing the pie. And with no framework to prevent this behavior, it quickly became the default equilibrium and gave rise to countless scams and low-quality tokens.

Apart from SwissBorg and a few others that managed to build something meaningful, most projects from the 2017 era bled to death. While some were genuine projects ahead of their time, many were outright scams, heavy on tech buzzwords to excite people into buying their ICO but with no real substance behind them.

Today, we are still carrying that baggage. While there has been real progress in terms of crypto products and adoption, there has been far less advancement in how to properly structure tokens. And unfortunately, the market continues to suffer from abuse and bad practices, driven by too many short-term opportunists who see crypto as a way to make a quick buck. These actors still treat TGE as exit liquidity rather than the beginning of a project, and focus all their energy on hype instead of building something real: faking followers and metrics, launching at ridiculously overinflated valuations with low float, paying large amounts for tier-1 CEX listings, and relying on sophisticated market makers to engineer “perfect” chart patterns so the project appears successful long enough for teams to exit while leaving retail as exit liquidity.

What’s sad is that these dynamics also put pressure on genuine founders because short-term hype tends to dominate attention. As a result, even serious teams feel pushed to conform to the same practices just to keep attention on their project and remain competitive in terms of mindshare.

Ultimately, all of this has contributed to putting into question the trust and legitimacy of the crypto industry. If nothing is done to correct it, crypto risks becoming irrelevant, not because the technology, products, or underlying promises are not good enough (they are), but because the space has given too much attention to the wrong actors and has failed to provide clear protection for investors against value-extractive practices from insiders.

Fortunately, every problem is an opportunity in disguise. As we speak there are a few very interesting 0-to-1 initiatives being built that aim to solve this token misalignment problem problem and help crypto finally fulfill its potential as the new capital formation layer of the internet. Among them, one project that clearly stands out is MetaDAO.

In the subsequent section, we will explore MetaDAO core value proposition, how it’s positioned to reshape how the internet funds new ideas.

A New Capital Formation Layer

MetaDAO is a fundraising and governance platform that enables anyone on the internet to raise money for their startup ideas, while also simultaneously protecting investors and backers from founder abuse, misaligned incentives, and rugs.

To do so, they are creating a new standard for structuring token launches through what they call “ownership coins,” and they are also pioneering a governance model based on decision markets (and inspired from the concept of Futarchy) to better align incentives between all key stakeholders in a project.

Now, before going deeper into MetaDAO, it is important to introduce how decision markets and futarchy work.

Futarchy 101

Futarchy is a form of governance first put forward by economist Robin Hanson in 2000, which makes decision-making based on prediction markets. In a futarchy, decisions are traded, rather than being voted on.

Instead of debating proposals in endless forums and then voting, futarchy embraces the power of markets to forecast which decisions will be best. Proposals pass when the market speculates that they’re good. Proposals fail when the market speculates that they’re bad.

The core axiom behind this model is that markets aggregate information more efficiently than traditional voting, and that the collective incentives of traders produce better predictions than the opinions of a small group. In fact, we already see this dynamic in practice through the accuracy of prediction markets: during the last presidential election, for example, platforms like Polymarket delivered far more accurate forecasts than professional pollsters.

Now in the MetaDAO case, its decision market governance model is inspired by this same logic, but applies it to the key decisions an economic organization must make.

Let’s take a concrete example to better explain what it is:

Suppose a company wants to undergo a merger acquisition as part of a strategic vertical-scaling plan:

In the current model, a major decision like this goes through a shareholder vote. The board reviews the proposal, issues a recommendation, and shareholders vote. Decisions are slow and inefficient, and they depend on the opinions of a small, often uninformed group, distorted by politics and misaligned incentive. Suppose the deal would temporarily boost the stock price but is clearly destructive over the long term. Executives whose bonuses are tied to short-term stock performance, or board members nearing retirement who want to lock in higher payouts, may vote in favor of the deal. Even though it weakens the company strategically, the short-term price bump benefits them personally.

Now with a decision market model, the organization would open a market to forecast the company’s value if the merger happens and if it does not. Traders buy and sell based on which outcome they believe will create more value. If the “merge” market trades higher, the merger passes. If the “don’t merge” market trades higher, it fails.

How does MetaDAO work?

MetaDAO operates through two complementary pillars built around futarchy: an ICO launchpad to launch ownership coins, and a new governance model using decision markets.

The Ownership Coins Launchpad

Projects can raise money via MetaDAO’s launchpad architecture, which is designed to be “unruggable” in the sense that founders cannot walk away with the money. The idea here is simple: if a launchpad can protect investors from rugs and ensure that founders cannot abuse the system, then it will 1) attract genuine founders and 2) attract genuine capital to fund the right ideas, which is a clear win-win for every one.

Here’s how that works in practice:

1/ The founder sets transparent terms

If a founder wants to raise on MetaDAO, they first need to publish their idea/thesis and define the proposed terms of the token launch. This includes:

• Mission and vision

• Market opportunity

• Minimum USDC target

• Monthly budget

• Optional token incentive package for the team (through a price-based performance design)

2/ Sale goes live

The sale then opens on MetaDAO for a defined period. Anyone can contribute USDC to support a project they believe after evaluating the idea, the team, and the terms. Each sale has a minimum funding target (below which all contributors are refunded) and a discretionary cap, giving founders the option to choose how much of the total raised they want to accept.

3/ What happens if the sale succeeds

If the funding target is met, the founder decides how much USDC they keep, and investors receive their proportional ownership coin allocation.The treasury also automatically deploys 20% of the USDC and 2.9M tokens into liquidity pools to establish healthy market liquidity.Then all the USDC and token minting rights are transferred into a market-governed treasury, which the founders cannot directly access.

4/ Transparent funding from founders

Every month, the founders can spend USDC up to their monthly budget allowance. Everything above that needs to first go through a market proposal to be spent. Furthermore, at any time, anyone can raise a proposal to return USDC to tokenholders and if the proposal passes, then it will be executed.This structure is designed to prevent founders from draining or abusing the treasury and serve as a strong protection guarantee for investors.

While the launchpad is still somewhat gated today where MetaDAO ultimately decides which project can participate based on their own discretionary criteria, the long-term goal of MetaDAO is to make it fully permissionless, meaning anyone with an idea can apply, set their terms, and let the market decide whether it deserves funding.

The Governance Model

MetaDAO’s decision market governance model is automatically embedded into every project that launches on the ICO platform. However, any external project can also choose to adopt it if they believe it is a better alternative to their current governance structure.

In practice, projects can decide whether to delegate their full treasury to MetaDAO, or simply transition from a vote-based governance model to a market-based one.

Once this model is integrated, teams set a minimum amount required to open a proposal market, configure a few basic parameters, and from there anyone can raise a proposal about the protocol: from big cash spending, key strategic decisions, and more. Markets then determine whether the proposal should pass or fail.

• Proposal time: the amount of time a proposal should be active before it can pass or fail. Three days by default. Specified in Solana slots.

• Pass threshold: the percentage that the pass price needs to be above the fail price in order for a proposal to pass.

• Minimum liquidity: to prevent spam, proposers are required to lock AMM liquidity in their proposal markets. The amount they are required to lock, in both USDC and the futarchy’s token, is specified by each DAO.

• TWAP Sensitivity Parameters: The price that gets factored into the TWAP can only move by a certain dollar amount per minute. Each DAO must specify this dollar amount.

For instance, in the example below, Omnipair raised a proposal to spend 64,000 USDC to audit their protocol, and we can see that it passed by a very large margin as the market concluded that this was a good decision.

How Does This Solve Crypto’s Misalignment Problem?

Together, these properties help address the core misalignment problem that is plaguing crypto today:

- At launch, the market cap equals the Net Asset Value (NAV) in the treasury.
This creates a strong psychological and economic floor. If the price trades below NAV for a meaningful period, anyone can raise a proposal to return USDC to tokenholders. This provides strong protection to early investors.

- The ownership coin structure ensures that the intellectual property, the funds, and the authority to mint new tokens are controlled by independent, market-driven governance not by the team. This imbues the token with real value and protects investors from insider abuse.

-The market decides the best path forward for the project and not a small group of insiders. This makes governance more transparent and fair, with decisions driven by what is best for the protocol.

On top of that, the real beauty of MetaDAO’s model is its simplicity. It doesn’t rely on complex and unsustainable financial engineering to make a token valuable. Instead, it works by removing all the vectors that could create negative externalities and aligns incentives around the token in the most straightforward way possible.

The MetaDAO Ecosystem Today

The first ICO launch on MetaDAO took place on April 9th 2025 with MtnDAO. At the time, it was viewed as an uncertain and bold experiment. However, it proved to be interesting enough to capture the attention of the broader ecosystem. Fast forward to today, adoption has accelerated significantly. Both founders and investors are increasingly drawn to MetaDAO’s unruggable ICO design.

As of writing, there have been 8 ICOs launched on MetaDAO with a combined total of $25.6M million USD raised. Below is an overview of all MetaDAO launches so far:

If an investor had allocated $1,000 into each ICO (and managed to receive full allocation), for a total of $8,000 invested, their portfolio would now be worth approximately $26,552 with a theoretical peak value of around $51,728. This represents a current performance of 231.9% and a maximum performance of 546.6%. These stats are unprecedented for any launchpad, especially in current market conditions.

Beyond ICOs, multiple organizations have also begun migrating their DAO structures to a decision market model, and interest continues to grow steadily. We’ve even seen some of the biggest names in Solana like Jito and Sanctum start experimenting with it. Others such as Kyros, ORE, and Drift (just to name a few) are also leveraging MetaDAO’s infrastructure for their governance models.

MetaDAO Business model

Ultimately, MetaDAO is a business: it aims to solve a real market problem, but it also has a token and investors, and aims to generate meaningful revenue from its product.

Right now, MetaDAO earns revenue primarily through AMM fees on token launches.
Every project launched through MetaDAO trades on a MetaDAO-controlled AMM pool, which currently charges 25 bps in fees (with plans to increase this to 50 bps).

In the future, MetaDAO is considering adding a small fee on the oversubscribed portion of a raise, capturing value from high-demand launches. Further, the team is also exploring ways to integrate META into liquidity pools paired against ownership coins, which would further reinforce the place of META within their launchpad.

Together, these components make META directly tied to the success of ownership coins, and therefore to the success of the MetaDAO launchpad itself. And they are already off to a strong start, as the platform has already generated over $1M in revenue to date.

Risks and Important Considerations

Of course, every design comes with trade-offs and potential second-order effects. For now, MetaDAO is far from perfect. While they have managed to build a true 0-to-1 innovation, there are still many iterations needed to go from 1-to-n and drive broader adoption, especially beyond the crypto echo chamber and into the wider startup world as a credible alternative to traditional funding models.

Below are some of the drawbacks we have identified in the current model:

1/ The pro-rata subscription model:

The sales being uncapped and with founders deciding how much they take from the total is a great property. However, this model introduces a major drawback: if a launch is 2× oversubscribed, you are incentivized to put up 2× the USDC for the allocation you want, which then makes it more oversubscribed and reinforces the loop.

This dynamic can also disproportionately favor large players, as smaller investors often cannot “keep up” with the escalating deposits required to maintain their allocation. The result is potential dilution of smaller backers and excessive token concentration in the hands of a few.

Furthermore, because deposits and withdrawals can happen until the very last second, contributors must actively monitor the sale to avoid being underallocated (if others pile in late) or overallocated (if someone withdraws at the last moment).

2/ A permissionless platform

Going permissionless in the sense that any project can leverage their ICO launchpad is great, it fits the ethos of crypto, and it is easy to understand the rationale behind this choice from MetaDAO.

However, removing gatekeepers also opens the door to a decline in project quality. If anyone can launch a project without any filtering, there can be a flood of low-quality launches that may weaken the MetaDAO brand (even though the team acknowledges this risk and prefers it over potentially missing a unicorn idea).

From a user point of view, it can cause a sort of investment fatigue if there are too many projects, and it will also make their job harder when deciding which projects / founders are worth investing in.

3/ The governance model

Let’s be honest, there is still a high barrier to entry in understanding how decision market based governance actually works. For many users, the mechanics of taking positions and interpreting outcomes are not very intuitive. And because the whole model requires putting real money at stake, participation may end up being limited to highly sophisticated users. In practice, this can weaken the “wisdom of the market” argument if not enough traders are watching the market.

While we can argue that MetaDAO decreases friction around capital formation, the model increases long-term friction on capital usage by teams. This is an important consideration. If a founder needs to get something done but the market rejects proposals by a tiny margin because of a small clause in a document and not enough traders are actively trading on it. We can argue that this might not be the best use of a founder’s time, and repeated failed proposals could slow execution rather than accelerate it.

Some Ways Ahead

Several solutions and design enhancements could help MetaDAO overcome its current drawbacks. Below are some avenues the team is already exploring, along with additional suggestions from Shoal on how the product could be improved:

1/ Improving the subscription models

MetaDAO’s founders are already working on solutions to fix the weaknesses of the pro-rata subscription model.

In the short term, they are considering adding fees on commitments over a certain amount to reduce the incentive to over-allocate.

In the longer term, they are exploring a system, where everyone would have a “Ownership score” based on how much “ownership coin” they help, and higher scores receive better allocation. This would help ensure that high-quality, long-term holders are prioritized.

2/ The need for a curation layer

As MetaDAO moves toward a permissionless model, there will be a need for a curation layer.

One potential solution we find interesting is a “verified launch” system (similar to the blue tick on X) where: if a project wants to launch on MetaDAO, it first needs to get publicly “referred” by a trusted partner or a well-regarded founder / voice in the ecosystem (MetaDAO can select the pool of partners). This would act as a first filter, and the referrer would effectively put their reputation on the line creating natural incentives to only endorse projects they truly believe in.

Additionally, it would be valuable for the MetaDAO platform to better aggregate key information about each project to help investors make informed decisions (social graph signals, founder background, key links, optional KYC from founders, etc.).

This would make the screening process far more efficient for potential investors.

3/ Reducing friction on capital usage

In the long run, it’s crucial that MetaDAO does not create unnecessary friction for teams trying to execute. One idea the team is exploring is making “team-sponsored” proposals easier to pass than normal proposals. For example, if the default pass threshold requires the price to move at least +2%, a team-sponsored proposal might still pass even if the market moves –2%. In practice, this gives the team the benefit of the doubt and acknowledges that founders should be trusted unless the market signals strong disagreement.

Another potential improvement is introducing a light filter before proposals go live, either by the team or by a small community council to reduce noise and ensure only meaningful proposals reach the market.

Closing Thoughts

Crypto has enormous potential to improve capital markets and reinvent how capital formation happens on the internet. But for years, this promise has been held back by structural incentive misalignment: too many short-term opportunists have taken advantage of poor token structures to extract value from the market instead of creating it. In practice, this has led to a slow erosion of trust and legitimacy in the crypto industry as a whole.

MetaDAO proposes a genuinely interesting and refreshing solution to this problem through a launchpad architecture that is unruggable by design and that realigns incentives between founders and investors.

While this is a very bold mission, they have already made some very meaningful progress. The performance of their launchpad is one of the most impressive in crypto, and it has managed to attract a strong cohort of high-quality founders. Moreover, MetaProphet, Kollan, and the entire MetaDAO team have proven to be thoughtful, ambitious builders who take feedback seriously and are clearly motivated to keep improving their offer.

At Shoal, we believe MetaDAO stands a real chance of becoming an important pillar for how the next generation of startups, crypto-native or not, fund their ideas on the internet. This is especially relevant at a time when countries like the US are actively moving toward pro-crypto and pro-innovation regulatory frameworks.

Sources

• Elementus. “ICO Market Report - August 2018.” elementus.io/blog-post/ico-market-august-2018/

• MetaDAO. “MetaDAO Project Update.” x.com/MetaDAOProject/status/1909251208336019511

• MetaProphet. “On Improving MetaDAO’s Launchpad Model.” x.com/metaproph3t/status/1985393563153440786

• MetaProphet. “Thoughts on Allocation Mechanics.” x.com/metaproph3t/status/1979243370452258837

• Galaxy Research. Why Futarchy Matters. galaxy.com/insights/research/why-futarchy-matters/

• MetaDAO Documentation. docs.metadao.fi/

The Coming Age of Autonomous Agents

AI agents have advanced beyond simple language tasks. They’re now capable of executing trades, managing capital, making payments, and operating across more complex, multi-step tasks. But as these systems grow more capable, one thing becomes clear. There is a growing mismatch between the infrastructure that enabled today’s AI models and the structural needs of autonomous agents. Systems designed to serve request-response interactions simply aren’t suited for software that needs to make decisions and perform multi-step operations independently.

Consider an autonomous agent tasked with managing a protocol’s treasury. It monitors market conditions across dozens of protocols daily, evaluating risk parameters and executing capital allocation strategies. To do so, an agent needs to query a proprietary API for market data, but the provider can revoke access or modify pricing without notice. Rate limits shift based on provider capacity. The AI model generating trading signals is opaque, as stakeholders can’t verify whether the agent’s decisions actually follow its intended logic. As agents cannot hold assets or sign transactions, they cannot execute actions directly. As a result, there is no shared source of truth to confirm whether an agent acted within their authorized parameters if a dispute occurs.

Why Traditional AI Infrastructure Fails for Agentic Intelligence

Developers building autonomous agents have little choice among infrastructure providers today. Market concentration has consolidated AI infrastructure around a handful of providers controlling access to models and compute. This creates dependency risk in several ways. Pricing becomes unpredictable as terms of service change unilaterally. Rate limits appear and disappear based on provider capacity, and API endpoints shift without warning. For autonomous agents requiring guaranteed uptime and predictable costs, these dependencies represent existential vulnerabilities.

The issue goes deeper than reliability. Centralized platforms cannot provide the transparency and verifiability that multi-party coordination demands. When multiple stakeholders delegate authority to an agent, they need to verify that its decisions followed approved logic. They need audit trails showing how inputs produced outputs, along with guarantees that the agent operated within specified parameters.

Centralized APIs cannot provide this. They return outputs without proofs, processing requests behind closed doors. Users must trust that the model executed correctly, that results were not manipulated, and that pricing was calculated fairly. This level of trust may be tolerable for individual developers building applications but breaks down for autonomous agents coordinating economic activity across organizational boundaries.

Economics introduces another barrier. Agents that generate value cannot capture it directly or participate as economic actors. All revenue must flow through accounts controlled by humans or corporations. Platform operators capture the upside from agent activity while externalizing risks, preventing the emergence of agent-to-agent markets and limiting use cases to narrow, pre-approved categories.

Perhaps the deepest incompatibility lies in how centralized platforms handle permissions and authorization. When multiple parties coordinate through autonomous agents, trust must be explicit and mechanically enforced. Permissions require cryptographic verification that all participants can audit, and execution paths must be provably correct, with disputes resolvable through objective evidence rather than platform policies.

No centralized platform can provide these guarantees. Doing so would contradict centralization itself. The moment a trusted intermediary controls execution, the exact dependencies and trust assumptions that autonomous coordination seeks to eliminate reappear.

Altogether, these limitations show that centralized AI infrastructure cannot support autonomous agents in any environment that requires persistence, verifiability, or cross-organizational coordination. A different architectural foundation is needed.

Talus: A New Decentralized Operating System for Agents

Talus Network aims to address the structural limitations of today’s agentic infrastructure through a decentralized framework purpose-built for autonomous onchain agents. Its architecture supports verifiable workflow execution, cryptographic state management, and programmable economic primitives, enabling agents to custody assets, execute transactions, and coordinate across organizational boundaries through onchain settlement and shared state.

Core Design Principles

Talus is designed on several foundational principles:

• Verifiable computation: Through onchain workflows and cryptographic proofs, Talus ensures that every agent action is auditable.

• Composability: Talus supports modular tool registries and agent “packages,” allowing agents to access and share tool registries and invoke other agents into their workflows without platform-specific integration.

• Economic alignment: Economic incentives are embedded directly into the protocol, with value flowing programmatically and transparently to agents and infrastructure operators.

At its core, Talus offers deterministic workflow execution where each step in an agent’s operation is recorded as a state transition onchain. This creates an immutable audit trail for how decisions were made and executed, while allowing agents to coordinate across organizational boundaries through shared, verifiable state.

The system supports offchain computation with onchain verifiable results, addressing the cost and performance constraints of executing complex AI workloads directly on blockchain virtual machines (VMs). Compute-intensive tasks like model inference occur offchain through a distributed network of execution providers. Cryptographic commitments and state updates settle onchain. This hybrid architecture preserves transparency without sacrificing the performance requirements of production-grade AI systems.

How Talus Actually Works

Talus works via a modular architecture: each component serves a specific role in enabling autonomous-agent coordination. The system couples blockchain consensus with offchain computation, delivering an infrastructure that is both verifiable and efficient. Nexus, the core engine, handles onchain coordination and offchain execution.

A consumer submits a request to a Talus workflow, which is encoded as a directed acyclic graph (DAG). Each step in the DAG corresponds to a tool invocation or state transition. During execution, the Nexus onchain packages coordinate with Leaders offchain to trigger tools, update workflow state, and return results. This separation between workflow specification (onchain) and computation (offchain) allows Talus to remain verifiable without sacrificing performance.

Talus Stack Architecture

Talus runs on the Sui blockchain and utilizes Sui’s object-centric state model and the security of Move. Agents, tools, and workflows are represented as objects with explicit ownership, meaning each resource has a defined owner and can be transferred, combined, or restricted through code. Smart contracts on Sui Move define the structures and protocols for agent and workflow management, allowing developers to then build atop this foundation using different contract package types:

• The Nexus Onchain Package (NOP): The foundational layer maintained by the Talus team, defining core data structures, workflow coordination logic, and the protocol’s primitives.

• Tool Packages sit atop this foundation, allowing developers to publish modular tools — on or offchain — that agents can invoke. Tools expose defined inputs, outputs, and access/payment constraints.

• Talus Agent Packages (TAP): enable developers to compose these tools into complete agent systems with custom business logic. This layered structure creates a clear separation of responsibilities while enabling composability across the ecosystem.

The relationship between Talus agents, the Nexus onchain packages, the Leader network, and the tool ecosystem is shown below. Talus agents route requests to Nexus, Nexus updates workflow execution onchain, and Leaders perform offchain computation while triggering tools across both environments.

Nexus sits at the center of execution flow: agents submit workflow requests onchain, Nexus updates workflow state, and Leaders coordinate offchain execution by invoking the appropriate tools and returning results onchain. This separation between onchain coordination and offchain computation is what enables Talus to remain both verifiable and performant.

Workflow Engine and Deterministic Coordination

Talus encodes agent behavior as directed acyclic graphs (DAGs) stored onchain. Each node within these graphs represents a specific action, whether reading data, calling a tool, or modifying state, and the dependencies between nodes establish which operations must complete before others can begin. Because the workflow specification itself lives onchain, anyone can audit what an agent is authorized to do before execution even begins.

Computation, however, is offloaded. The Leader network performs the actual work by pulling node definitions from Nexus, executing the corresponding tool, and submitting a verifiable receipt back to the chain. This hybrid model preserves performance for heavy workloads while ensuring that state, permissions, and results remain transparent.

Workflow Execution States

Each workflow execution creates a DAGExecution object that tracks progress through walk states. A walk represents the execution of a single node in the workflow graph. Walks transition through four possible states: Active (currently processing), Successful (completed and ready to activate next nodes), Failed (encountered an error), or Consumed (completed but waiting for other inputs to converge).

This state system ensures workflows handle errors gracefully and prevents deadlocks through fallback mechanisms. If a tool fails, the workflow can trigger fallback edges that route execution to alternative paths, maintaining liveness even when individual components encounter issues.

Talus Tools and Modular Capabilities

Tools represent functional modules that agents can invoke during workflow execution. When registered onchain, each tool declares its expected inputs, produced outputs, pricing structure, and access requirements. This registration system creates an open marketplace where developers contribute capabilities and agents discover services organically.

Tool execution can happen in two environments:

• Onchain tools interact directly with Sui smart contracts.

• Offchain tools execute compute-heavy tasks (LLM inference, analytics, image work, math routines) coordinated by Leaders.

Leaders orchestrate these calls and return results onchain with proofs or commitments, keeping the entire workflow auditable.

Payment and Gas Model

Every workflow execution requires economic coordination across multiple service providers. Users deposit payment into a gas budget within a globally unique gas service object. Before execution begins, Nexus verifies sufficient funds to cover all potential tool costs based on the workflow’s maximum execution path.

As the workflow progresses, each tool consumes its designated payment from this budget only after successfully completing its work and passing any required verification steps. This delivery-versus-payment mechanism ensures tools receive compensation for honest work while protecting users from paying for failed or incomplete operations.

The protocol implements a dual-track revenue system. According to current protocol design, when overall protocol revenue falls below a threshold, priority fee income is burned, reducing token supply and supporting token economics. When revenue exceeds the threshold, fees are distributed to Leaders performing coordination work.

Data Storage with Walrus

Essential workflow metadata lives on Sui’s blockchain, ensuring its integrity and availability. However, storing everything onchain becomes prohibitively expensive. Walrus, a decentralized storage platform from Mysten Labs, handles supplementary data including agent memory, operational context, and intermediate results.

Agent functionality depends critically on maintaining context and memory across multiple sessions, allowing them to learn from past interactions and build upon previous decisions. Walrus provides this storage layer with configurable verification mechanisms.

Tools can reference data stored on Walrus through onchain commitments, enabling workflows to pass large datasets between operations without incurring excessive gas costs. This distributed storage approach maintains verifiability for intermediate values while achieving practical scalability.

Leader Network and Distributed Execution

Leaders serve as the coordination layer between onchain workflow specifications and offchain computational resources. They monitor the blockchain for workflow execution requests, identify which tools need to run, coordinate their execution, and submit the results back onchain as verifiable receipts.

Execution Flow in Practice

Consider an investment agent making a portfolio adjustment.

The user triggers the workflow by submitting a transaction to the agent’s smart contract. This creates a DAGExecution object and emits an event that Leaders monitor. A Leader picks up this event, retrieves the first tool’s information from the onchain registry, and discovers it needs to fetch current market prices. The Leader calls this price oracle tool, receives the data, and submits it back onchain along with payment for the tool. This activates the next workflow node, perhaps an analysis tool that recommends allocation changes. The Leader orchestrates this second tool’s execution, submits results, and continues until the workflow reaches a terminal node that executes the actual trades through onchain DeFi protocols. Each step produces an auditable record while the Leader handles all coordination overhead.

Security and Progressive Decentralization

The network maintains security through economic incentives. Leaders stake collateral that faces slashing if they behave maliciously or fail to maintain liveness. The system rotates execution responsibilities across multiple Leaders, preventing any single operator from dominating the network. These mechanisms create fault tolerance and resistance to coordinated attacks.

The Leader network architecture evolves progressively. It currently operates with initial trusted Leaders to ensure reliability while the platform establishes product-market fit. Future phases introduce TEE-based distributed execution, where Leaders run inside trusted execution environments that provide cryptographic proof of correct operation. The final phase reaches a fully permissionless model where anyone can stake tokens and participate as a Leader. This gradual transition maintains service quality while steadily increasing decentralization and minimizing trust assumptions.

The combination of object-based state management, flexible security levels, hybrid execution, and economic coordination addresses specific technical constraints that emerge when agents need to operate across organizational boundaries.

Building a Market for Autonomous Intelligence

Talus creates economic infrastructure where agents transact as independent participants rather than subordinate tools. The $US token is designed to serve as the coordination and incentive layer across the network. It functions as payment and settlement for workflows, tools, and agents, and as the medium for tool registration and Leader network participation.

Within this system, agents compensate other agents for specialized services based on computational requirements and market demand. Revenue flows programmatically to tool developers through usage, to model providers through invocations, and to Leaders through execution work. $US holders secure the network through staking and participate in governance over protocol parameters.

The result is a market-driven coordination system where intelligence operates as a commodity. Stakeholders capture value proportional to their contribution. Economic sustainability derives from network activity rather than external subsidy.

Use Cases: What Talus Makes Possible

Taken together, these primitives (workflows as DAGs, tools with flexible security, hybrid execution, and programmable payments) address a specific class of applications that existing infrastructure cannot support.

Agent-Driven Prediction Markets

Prediction markets aggregate distributed information through financial incentives, but their efficiency depends on participants who can continuously process diverse data sources and execute trades instantly. Human traders cannot simultaneously monitor onchain oracle feeds, analyze social sentiment, cross-reference polling data, and adjust positions across multiple markets 24/7. Talus agents eliminate these constraints:

• Polling oracles provide official results with confidence intervals

• LLM tools analyze sentiment from social platforms

• Statistical models weight historical accuracy and detect trends

• Execution tools adjust positions across market contracts

• Risk modules enforce portfolio constraints

Traditional platforms like Kalshi operate as black boxes where users cannot inspect internal algorithmic behavior. Talus workflows, by contrast, can record decision steps onchain. When an agent is configured to trade based on polling data and sentiment, the DAG execution trail provides a verifiable onchain record of which tools were invoked, in what sequence, and how their outputs influenced the final action.

Computation-heavy analysis runs offchain within the Talus agent framework, while trades and key coordination events settle onchain. Market resolution can follow the same pattern: agents query multiple oracles, check for consensus conditions, and trigger settlement only after verification.

Verifiable DeFi Agents

Consider an MEV protection bot executing trades across decentralized exchanges. Today, users must trust the operator’s claims about routing logic. With Talus, the agent produces cryptographic receipts for every step: route selection, price impact calculation, final execution path. Users verify that the actual transaction matched the stated strategy without requiring blind trust.

Cross-protocol yield strategies work similarly. When an agent moves capital between lending markets, stakes proceeds, and rebalances positions, the Leader network generates proofs for each interaction. The complete execution trace becomes auditable. If something goes wrong, participants can verify exactly where and why through cryptographic evidence recorded onchain.

Agent-to-Agent Tool Markets

A trading agent needs machine learning predictions but doesn’t want to train models itself. On Talus, it hires a specialized prediction agent, verifies the model’s identity through cryptographic attestation, and pays per inference onchain. The predictor earns revenue automatically through usage. No platform middleman, no API contracts, no trust required beyond cryptographic verification.

This creates markets for specialized intelligence. Agents that build valuable capabilities, whether ML models, data analysis tools, or execution strategies, monetize directly through usage. Tool development becomes economically sustainable without platform dependency.

Cross-Chain Autonomous Portfolios

Today, moving assets across blockchains requires custodial bridges or trust assumptions. Talus enables coordinated operations across chains through verifiable state queries. A portfolio agent could check prices on Ethereum, execute a swap on Base, and stake proceeds on Arbitrum, all within a single coordinated workflow.

Each step occurs within the same execution context. The Leader network provides cryptographic proofs that operations happened correctly across different chains. This eliminates custodial bridge risk since agents read and write state directly rather than locking assets with intermediaries.

Accountable DAO Governance

Treasury management agents prove their risk calculations through transparent workflows auditable before vote execution. Stakeholders verify that proposed allocations derive from stated logic. Emergency response agents act autonomously within smart contract-enforced constraints. All authorization logic remains verifiable onchain. This creates accountability for autonomous governance while maintaining automated response speed.

Privacy-Preserving Enterprise Operations

Through TEE integration, agents process sensitive data without exposing it. Healthcare agents analyze patient records, financial agents evaluate credit risk, and compliance systems audit transactions, all while keeping underlying data encrypted. Only the TEE decrypts inputs during computation, and cryptographic attestation proves correct processing. This enables autonomous intelligence in regulated domains where privacy is mandatory.

Economics

Talus creates a self-reinforcing agent economy where growth compounds across the network. As tools expand capabilities, agents become more powerful. More capable agents attract users with sophisticated needs. Rather than relying on centralized rent-seeking, the network distributes value programmatically to contributors while capturing protocol-level revenue through usage-based fees.

Value Flow and Network Effects

When a workflow executes, users fund its execution through onchain fees that cover computation and coordination. As execution completes and verification passes, fees distribute to workflow participants according to protocol rules:

• Leaders earn for coordination work

• Tool developers earn from usage

• Agent developers earn from deployment and performance

This creates reinforcing network effects. Each additional tool increases what agents can do, attracting users with more sophisticated needs. Increased usage generates more fee revenue for successful capabilities, incentivizing developers to build specialized services. The flywheel accelerates as growth in one area drives growth everywhere else.

Token Value Accrual

$US sits at the center of this economy as the primary unit of value and coordination. (Note: $US has not yet been listed on exchanges as of this writing.) Network activity creates ongoing demand for the token to pay for usage, access features, and participate in governance. Users can pay base gas fees in SUI, with the protocol routing settlement and incentives through $US. Direct $US payment enables priority execution and enhanced features.

Supply Dynamics and Fee Structure

The protocol implements a capped supply with a fee model that links token scarcity to real usage. When protocol revenue falls below a threshold, priority fees are burned rather than distributed. This permanently reduces circulating supply. As network activity scales, this creates a value-accrual loop where demand acts on shrinking supply. When revenue exceeds the threshold, fees distribute to Leaders performing coordination work, aligning rewards with network growth.

Staking and Participation Incentives

Staking forms the foundation of Talus’s security model. Leaders stake $US to participate in coordination and earn fee revenue. Stakes face slashing for malicious behavior or failures. Tool providers stake when registering capabilities, aligning their incentives with service quality. As the marketplace grows, staked tokens lock supply while usage-driven demand continues.

Long-term holders gain governance rights and access to protocol features. This creates incentives to participate in the ecosystem rather than purely speculate. The combination of staking requirements, governance participation, and usage-based rewards aligns all participants with network health and sustainable growth.

Limitations & Open Challenges

Talus addresses coordination problems that existing systems cannot solve, but introduces distinct constraints in doing so. Understanding these limitations matters for realistic deployment and protocol development.

Technical Constraints

Verification adds latency to every workflow. Each step requires cryptographic proof generation and onchain settlement, creating overhead that centralized API calls avoid. Offchain execution handles compute-intensive tasks efficiently, but the coordination layer still adds milliseconds to seconds depending on blockchain finality. For high-frequency trading or real-time response systems, this latency may prove prohibitive.

When agents hire other agents as subcontractors, workflow dependencies multiply across the system. A failure in one sub-workflow cascades through dependent operations, creating unpredictable failure modes. Debugging becomes difficult when execution spans multiple tools, chains, and offchain providers. Tooling for monitoring and diagnosing multi-agent systems remains immature.

Economic and Governance Risks

Incentive misalignment emerges at protocol boundaries. Leaders might prioritize high-fee workflows, delaying execution for lower-paying users. Tool developers could extract value through opaque pricing or degraded service quality. Agent owners might exploit protocol mechanics for individual gain at collective cost.

Token holders must balance competing interests across tool developers, agent operators, and end users when making governance decisions. Protocol upgrades require consensus among stakeholders with different time horizons and risk tolerances. Without effective mechanisms, the system risks capture by concentrated interests or stagnation through disagreement.

Security Concerns

Malicious agents pose systemic risk. An agent could exploit tool vulnerabilities, manipulate other agents through adversarial inputs, or drain assets through legitimate-looking workflow logic that contains hidden exploits. Permissioned tool registration and workflow verification provide safeguards, but determined attackers may find edge cases.

Despite slashing penalties, a coordinated attack on the Leader network could compromise workflow execution during the current trusted phase. The progressive path toward full decentralization addresses this over time, but the trusted setup introduces temporary centralization risk.

Ethical and Societal Implications

Autonomous agents operating in the real world raise questions infrastructure cannot answer. An agent managing capital might make economically rational decisions that cause social harm. Agents coordinating across domains could amplify systemic risks or trigger unintended consequences no single operator foresaw. The protocol verifies what agents do, not whether they should do it. This gap between technical capability and ethical judgment remains an open challenge for autonomous systems.

Future Outlook: The Path Toward Decentralized Intelligence

The trajectory of AI development points toward a fundamental reorganization of economic activity. As agents gain autonomy and operate across decentralized infrastructure, they will evolve from tools that execute commands into participants that negotiate, transact, and coordinate independently. Talus positions itself as the coordination layer for this transition.

Agents as Economic Participants

Current systems treat agents as extensions of human operators. Future systems will treat them as independent actors. Agents will hold assets, evaluate counterparties, negotiate terms, and execute agreements without human intervention at each step. An agent managing a treasury will adapt strategies based on market conditions rather than following predefined rules. It will hire specialized agents for analysis and coordinate with others to achieve objectives.

This requires infrastructure where agents can transact trustlessly. Traditional finance operates through intermediaries that verify counterparty creditworthiness and enforce settlement. Decentralized systems replace intermediaries with cryptographic guarantees and programmatic enforcement.

Talus provides this foundation through verifiable workflows and onchain settlement. As agent-to-agent commerce grows, the protocol could function as settlement infrastructure similar to how SWIFT coordinates interbank transfers or Visa processes card transactions.

Expansion of Decentralized AI Infrastructure

Security and privacy requirements will drive technical evolution. Zero-knowledge machine learning and trusted execution environments will enable privacy-preserving verification. A healthcare agent could prove it analyzed patient data correctly without exposing the data itself. A trading agent could verify strategy execution without revealing its algorithm.

Interoperability will expand beyond blockchain ecosystems to encompass traditional systems. Agents will read state from legacy databases, execute operations through conventional APIs, and write results to blockchains. The distinction between onchain and offchain execution will become less important than whether operations are verifiable and settlement is trustless. Marketplaces for tools and agent services will mature from experimental protocols into established infrastructure with standardized interfaces and competitive pricing.

The Long-Term Vision

Decentralized intelligence may evolve into global utility infrastructure. Just as cloud computing commoditized server access and the internet commoditized information distribution, autonomous agent platforms could commoditize intelligent coordination. Multi-agent ecosystems might manage supply chains, coordinate energy grids, or optimize resource allocation across organizations.

Talus is positioned to serve as backbone infrastructure for this future. The protocol does not attempt to solve all problems but provides coordination primitives that other systems can build upon. If agents become economic participants at scale, they will require neutral infrastructure for settlement and verification. Whether Talus captures this role depends on execution, but the architectural foundation exists. The question is not whether autonomous agents will reshape economic activity, but which infrastructure will coordinate them.

Closing Thoughts

The infrastructure supporting AI development has reached a critical juncture. Centralized platforms delivered the current generation of AI models, but they cannot support the next phase of autonomous agent deployment. Their constraints are structural, not temporary: opaque execution, unilateral control, and no way for agents to act as independent economic actors prevent the coordination mechanisms autonomous systems require.

Talus addresses these limitations through architectural choices that leverage blockchain properties for verifiable execution, permissionless participation, and programmable economic alignment. Rather than trying to rebuild the whole AI stack, Talus offers coordination primitives: composable infrastructure that other systems can build on.

The path forward involves more than technical specs. Verification latency must shrink. Multi-agent debugging tools must emerge. Incentives must align across stakeholders. Security must decentralize progressively. These are real challenges, but they are addressable through iterative development.

What remains uncertain is adoption trajectory, not technical feasibility. The architectural foundation exists for agents to operate as independent economic participants. Whether this vision materializes at scale depends on execution, ecosystem development, and whether builders choose verifiable coordination over centralized convenience.

Autonomous agents will reshape economic activity. Which infrastructure coordinates them remains an open question. Talus positions itself through verifiable workflows, agent-first architecture, and programmable economic primitives. Success will be measured not by technological sophistication alone but by whether the protocol becomes essential infrastructure for the autonomous systems that follow.

At Shoal, we believe autonomous agents could reshape economic activity at scale. What matters is the infrastructure that supports them. Adoption will determine its ultimate role, but with verifiable workflows, native asset control, and modular coordination primitives, Talus’s architecture is designed in a way that gives it a credible path toward becoming foundational rails for the agentic economy.

Sources

• Talus Documentation. Talus Network, docs.talus.network

• Talus Network Litepaper. Talus Network, talus.network/litepaper

• Nexus Whitepaper. Talus Network, talus.network/whitepaper

• Talus Combines Walrus and Sui to Enable Onchain AI Agents. Walrus, walrus.xyz/blog/talus-builds-onchain-ai-agents-walrus

• What Is Talus ($US)?. Whales Market, whales.market/blog/what-is-talus/

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the Talus team. Some members of the team have material holdings of SUI and Talus. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

DeFi solves the information asymmetry problem that has riddled TradFi’s capital markets, though still more in theory than in practice. The value, and difficulty, of coordination, only becomes more evident in decentralized systems, and despite its transparency, self-custody, and operational efficiency, liquidity in DeFi still remains scarce and mercenary as there is no shared coordinating framework. In this piece, we explore the need for onchain liquidity coordination and how Turtle approaches this challenge.

The Capital Coordination Challenge

For all its innovation and growth, DeFi has long been plagued by mercenary, non-sticky liquidity. This is ultimately a product of structural misalignment between foundations and liquidity providers (LPs). Foundations allocate token emissions to incentivize aligned, multi-cycle liquidity for their protocols, but may have no idea what type of LPs they will attract nor how long they will stick around for. Meanwhile LPs, seeking a return on their capital, have little visibility into the organic activity or trading volume a protocol can sustain over time, therefore it becomes rational to exit once emissions end rather than remain committed. As such, most protocols and LPs stand little chance against the self-reinforcing cycle of short-term incentives and mercenary liquidity that defines much of DeFi today.

Information asymmetry is not new to capital markets by any means. The distribution of knowledge around asset quality, risk, and value between borrowers and lenders has never been proportional for as long as capital markets have existed. Investment banks emerged to solve this underlying coordination failure, conducting diligence, structuring credit into tranches by risk appetite, and distributing that credit across a syndicate of lenders accordingly. Borrowers gained access to consolidated capital through a single negotiation, while the IBs earned fees for aggregating information, standardizing risk, and coordinating supply and demand.

This process, formally known as syndication, transformed credit markets by separating origination from investment. Large banks would originate loans but distribute the debt to non-bank investors such as hedge funds, pension funds, and collateralized loan obligations (CLOs). The Basel II and III frameworks reinforced this dynamic by incentivizing banks to transfer credit risk off balance sheet and optimize capital efficiency. What began as bespoke loan placements evolved into a standardized system for converting private credit assessments into tradable financial products. Though complex and costly, syndication has helped standardize processes for diligence, distribution, and ongoing risk management, building the infrastructure that enabled credit to be assessed, distributed, and maintained at scale. More importantly, it demonstrated the value of coordination in capital markets by turning a fragmented, bilateral lending process into a scalable, trillion-dollar asset class.

DeFi sought to rebuild these financial coordination architectures with code. AMMs and lending markets encoded core functions of intermediation (i.e. price discovery, collateralization, settlement) into smart contracts. Anyone with an internet connection and capital could supply liquidity or borrow assets on shared, observable infrastructure where transaction settlement was compressed from days to minutes or even seconds. This transparency, however, has not produced coordination.

Protocols can issue incentives, but they cannot determine or predict their outcomes. Token emissions attract liquidity, but they do not specify who participates, how long that capital will remain, or under what conditions it will leave. Each program operates in isolation, competing for the same pool of capital with no shared framework for evaluating risk or aligning expectations. Liquidity flows toward the highest nominal yield, not toward sustainable participation. When incentives end, deposits leave, and the process begins again

Information asymmetry in DeFi thus re-emerged in a different form. Onchain data made transactions visible but that barely means much if investors and LPs still cannot easily assess governance quality, operational integrity, or the long-term economics of a protocol, nor if foundations, for their part, cannot distinguish between speculative and committed capital.

DeFi protocols have demonstrated that smart contracts and code can replace costly, operationally inefficient human intermediaries across a range of financial products and services. But removing intermediaries has also made it that much more difficult to coordinate how liquidity is distributed across these systems in a way that aligns incentives for all participants.

With this in mind, we can reasonably assert the opportunity for DeFi’s ‘investment bank’, a protocol dedicated to structuring, allocating, and sustaining liquidity across markets. Rather than replacing existing protocols, it would provide the coordination infrastructure through which foundations and liquidity providers can negotiate and maintain capital at scale. In effect, such a protocol would serve the same function investment banks did for credit markets: aggregating information, matching the right participants, and standardizing how liquidity is structured and deployed.

Turtle: DeFi’s Investment Bank Layer

Turtle is an onchain liquidity distribution protocol connecting lenders (i.e. onchain LPs) with borrowers, including protocols and foundations seeking liquidity. Through curated campaigns, custom deposit vaults, and embedded distribution across partner frontends, Turtle aims to standardize how Turtle standardizes how liquidity is sourced, coordinated, and deployed across DeFi. Since its MVP launch in May 2024, Turtle has coordinated over $5.5B in total liquidity, nearly $300m in boosted campaign deposits, grew its treasury to over $13.7M, and has over 400k connected wallets alongside 15k campaign LPs.

Functionally, Turtle operates as a liquidity distribution layer beneath chains, bridges, and applications onchain. It integrates through APIs, wallets, and smart contracts to track liquidity positions and coordinate deployment across multiple ecosystems. Foundations define their liquidity terms, including total amount, duration, and under what conditions. Turtle works with foundations to help structure the best possible deal in order to incentivize LPs long term and ensure liquidity is not mercenary. LPs meanwhile can review terms and deposit liquidity directly to the foundations through Turtle’s non-custodial vaults. Together, this structure aims to improve upon standard token emission models through organized dealflow, functioning as an Investment-Bank-style coordination layer across the broader DeFi ecosystem.

Turtle’s architecture revolves around curated campaigns and embedded distribution, the two mechanisms through which it coordinates liquidity:

• Curated Campaigns: Structured liquidity programs negotiated with protocol teams, each with defined capital targets, reward schedules, and durations. These campaigns bring consistency and transparency to how liquidity is sourced and rewarded.

• Embedded Distribution: Integrations that surface campaigns directly within wallets, exchanges, and analytics platforms, allowing users to discover and participate in opportunities through familiar interfaces.

Campaigns are standardized and published through Turtle’s deal interface; deposits are managed through non-custodial structured vaults; and participation data flows through embedded dashboards and leaderboards that coordinate rewards and feedback. To better understand how these components all work together, it’s worth unpacking them in greater detail.

Discovery and Dealflow Layer

The Discovery and Dealflow layer, known collectively as Turtle Deals, structures how liquidity opportunities are sourced, negotiated, and distributed through the protocol. Partner protocols define their key campaign parameters, namely their target capital, eligible assets, reward terms, and duration, which are then verified and published on the Turtle Deals dashboard. This is designed to provide a standardized and transparent framework to onchain liquidity sourcing.

Within this system, Boosted Deals represent campaigns co-incentivized by the Turtle DAO, where LPs (Turtle Members) earn both native rewards from partner protocols and additional rewards, paid out in TURTLE tokens, proportional to their participation. Partner protocols provide the DAO with a defined boost payment, and the DAO distributes those boosts back to Members pro-rata, the first distribution happening during Turtle’s TGE.

This process ties liquidity incentives to real-time activity. Partner protocols gain structured, predictable access to capital under defined parameters, while LPs are compensated transparently according to duration and engagement. The Turtle DAO functions as the coordination and settlement layer, tracking deposits, managing boost payouts, and maintaining auditability across campaigns.

Ecosystem-As-A-Service: Turtle Vaults And Campaigns

Turtle’s Vaults and Campaigns infrastructure forms the operational layer through which liquidity is committed, distributed, and tracked across DeFi. It organizes the flow between foundations, vault providers, and liquidity providers into a repeatable framework that any protocol can deploy as a service.

Each vault represents a campaign-linked deposit contract coordinated by the Turtle DAO and operated by Vault Curators. A vault is a smart contract at its core: foundations define their desired liquidity parameters, including the target amount, eligible assets, lock-up duration, and reward logic, and the vault is created to enforce these specific terms onchain. Turtle Members supply liquidity directly to the partner, where rewards accrue natively. Each vault is designed to remain fully non-custodial. Liquidity providers sign participation approvals through their own wallets, while assets stay in the partner protocol’s native contracts. Participation and performance are tracked through onchain activity.

This design allows campaigns to launch without introducing custodial risk or manual oversight. Fees and boost rewards flow transparently between participants: partner protocols pay a liquidity fee to the DAO, the DAO redistributes boost rewards directly to LPs, and all activity remains visible through onchain data via the smart contract vaults. Vaults also simplify capital allocation for LPs, enabling them to earn yield passively without manually rotating across protocols or campaigns.

At the campaign level, Turtle coordinates between Partner Protocols, Vault Providers, and Vault Curators to align incentives and operational tracking. Campaign vaults distribute liquidity directly to whitelisted protocols, where users earn both base and boosted rewards. Optional lock-ups synchronize capital duration with protocol needs, and integrated tracking captures data on performance, duration, and aggregate TVL.

Protocols can launch structured liquidity programs through a single integration; vaults manage capital logistics and tracking; and the DAO oversees coordination, auditing, and reward distribution. Meanwhile LPs can easily discover campaigns, deposit through Turtle’s interface, and receive both native and TURTLE rewards without relinquishing custody of assets.

Turtle Earn

Turtle Earn provides the distribution layer through which liquidity campaigns reach their designated end-users. It integrates directly with frontends such as wallets, exchanges, and analytics platforms, allowing them to surface active campaigns within their own interfaces. This structure enables users to review terms and participate in campaigns without leaving the environments they already use.

Distribution partners connect their user base to Turtle campaigns and receive a share of deal revenue proportional to the liquidity they help onboard. Turtle Members deposit directly to partner protocols, earning native rewards from those protocols and any additional boosts allocated by the Turtle DAO.

The DAO manages coordination and accounting between all participants. It sources dealflow from partner protocols, collects associated boost revenue, and redistributes it to Members and distribution partners based on verified activity.

Embedded Distribution and Tooling

Turtle’s embedded distribution layer connects the protocol’s liquidity infrastructure to the broader DeFi interface network, integrating directly into existing user frontends.

The Earn Widget embeds active Turtle campaigns within these applications, allowing users to discover and participate in verified opportunities natively. Deposits, campaign data, and reward parameters are synced in real time, creating a consistent interface across multiple protocols.

Participation and performance are tracked through Turtle’s Leaderboard system. Wallets earn points relative to their total deposits, referral activity, and lockup participation, with boosted weighting for referred liquidity. Weekly snapshots then determine the distribution of TURTLE rewards and campaign bonuses. For foundations and partners, client portals and dashboards provide visibility into campaign performance, emissions tracking, and reward distribution, ensuring liquidity programs can be monitored and adjusted in real time without requiring manual oversight.

Together, these tools extend Turtle’s coordination framework into user-facing environments. Campaigns appear directly within existing interfaces, participation is recorded onchain, and reward distribution is managed through a standardized system.

Tokenomics and Value Flows

Turtle’s coordination framework is anchored by TURTLE, an ERC-20 utility token deployed on Ethereum, BNB Chain, and Linea. TURTLE coordinates liquidity incentives and governance within the Turtle network, governing how liquidity incentives are distributed, how campaign revenue circulates through the Turtle DAO, and the decisions made about the protocol’s development.

TURTLE is transferable and classified as a functional utility token under Swiss law. It carries no ownership, profit-sharing, or redemption rights.

Value Flow

The Turtle DAO manages capital between three participants: partner protocols, liquidity providers (LPs), and the DAO treasury.

1. Partner Protocols fund liquidity campaigns by paying boost and integration fees.

2. LPs deposit through verified vaults and earn rewards based on their deposit amount.

3. The DAO Treasury collects campaign proceeds, and reallocates it toward future campaigns.

In theory, this all works together through a self-reinforcing feedback loop: protocols fund liquidity programs on Turtle, the Turtle DAO distributes rewards, and proceeds flow back into future operations.

Governance

Governance is executed through Turtle DAO, using OpenZeppelin’s Governor contracts and Tally’s interface. Holders can stake TURTLE to mint sTURTLE, which represents voting and delegation rights for proposals related to campaign parameters, integrations, and protocol operations. Proposals are subject to quorum and creation thresholds to maintain procedural integrity. Treasury execution remains under DAO oversight, with progressive decentralization planned over time.

TURTLE standardizes how liquidity programs are funded, distributed, and governed. Protocols pay to access coordinated capital; LPs earn verifiable rewards; and the DAO manages redistribution and governance through TURTLE.

Closing Thoughts

DeFi has solved for access but not for coordination. Anyone can deploy a market or a vault, and liquidity can move freely between them. Yet each system operates under its own parameters, incentives, and time horizons, leaving no shared structure for how capital is discovered, priced, or maintained over time. In practice, this leads to the aforementioned self-reinforcing cycle of short-term incentives and mercenary liquidity that defines much of DeFi today.

Turtle is designed to address this coordination gap. Its framework standardizes how campaigns are structured, how deposits are tracked, and how incentives are distributed. Rather than creating new markets and custodying assets, it provides the organizational layer through which existing ones can coordinate capital more efficiently.

To reiterate, DeFi has thus far proven that markets and other financial services can run without intermediaries; but coordination remains a central challenge among decentralized networks, and the scarcity of liquidity in DeFi is a prime example. An opportunity emerges for protocols capable of encoding commitment: to make capital deployment measurable, recurring, and auditable across ecosystems.

With the shift in the regulatory environment surrounding digital assets, the demand for bringing net new capital onchain is slowly but surely growing. With new capital coming onchain comes the need for coordination. Through its curated campaigns, structured vaults, and embedded distribution framework, Turtle makes a compelling case to capture this opportunity and become DeFi’s Investment Bank layer.

Sources

• Scott, Zachary. The Syndicated Loan Market. zacharyscott.com/syndicated-loan-market/

• The Syndicated Loan Industry Statistics. Coinlaw, coinlaw.io/syndicated-loan-industry-statistics/

• “Investment Banking.” Wikipedia, en.wikipedia.org/wiki/Investment_banking

• “Asymmetric Information.” Investopedia, www.investopedia.com/terms/a/asymmetricinformation.asp

• The Ten Commandments of Liquidity Incentives. Caladan Labs, caladan.xyz/report-the-ten-commandments-of-liquidity-incentives/

• Turtle Frontend. app.turtle.xyz/

• Turtle Documentation. docs.turtle.xyz/

• Turtle MiCA Whitepaper. drive.google.com/file/d/1DGhHqPv8RVX2XYgVb1yjPFQJQ4Y4961R/view

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the Turtle team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

The Convenience Premium: A Tradition Rooted In History

“Civilization advances by extending the number of important operations which we can perform without thinking of them.” ― Alfred North Whitehead

Throughout history, humans have relentlessly pursued convenience. Nothing illustrates this better than the convenience premium: the additional amount that a consumer is willing to pay for goods and services that save time, effort, and make their lives easier.

There has always been a market for convenience: a willing buyer and a willing seller. Roman merchants charged higher prices in busy forums than in remote markets. Before refrigerators were invented, American households paid significant premiums for ice delivery. Even money itself has evolved for convenience: from barter to coins to paper to digital assets, each step has been around making money easier to carry around and transact with.

Today, the convenience premium lies in digital experiences. Online shoppers are increasingly seeking shorter delivery windows at checkout. Hundreds of millions of people pay for an annual membership to have Amazon deliver almost anything to their door the next day. Food delivery apps continue to see record users and order numbers despite regularly charging 30-40% markups on orders.

Time has shown that while the means have changed, the ends sure haven't. People gravitate towards convenience, and they’re willing to pay the price for it. It is no wonder why, across numerous industries, application-specific solutions have historically evolved into universal, general-purpose solutions as they mature. The question then becomes who delivers convenience and captures these premiums in the digital age.

The Rise Of The Aggregator

The post-internet era has been dominated by aggregators.

Platforms like Google, Meta, Amazon, among many others, curate information, content, and services from other providers, offering consumers a simpler way to access what they are already looking for. Billions of web pages are searchable through Google. Millions of products are browsable on Amazon. Friends’ photos and life updates appear in one place on Facebook or Instagram.

Crucially, these companies do not own what they sell. They own something far more valuable: the user relationship.

Before the internet, this would have seemed an implausible business model. Distribution was physical, therefore distribution costs were higher, supply was scarcer, and generating outsized profits required controlling most if not all of the supply chain.

The internet transformed these dynamics. The cost to distribute information about digital or physical goods to a global audience became nearly free. A single website could handle hundreds, thousands, sometimes even millions of customer transactions at once, with far less overhead than physical stores.

Ultimately this reshaped consumer marketplaces by shifting power towards the user. Owning the user relationship became key to gaining a competitive advantage and making outsized profits with the internet, and the business model revolves around user experience above all else. With abundant supply now at their fingertips, what consumers valued most was discovery and curation: finding the best goods and services in a single place.

So what lies next? As mobile becomes the new user interface, and consumers demand more integrated digital experiences, the next evolution of aggregators is evolving into Everything Apps.

Aggregating The Aggregators: Everything Apps

Everything Apps, also called ‘Super Apps’, are platforms that bundle multiple services and utilities into a single cohesive interface, most commonly a mobile app.

On a desktop, users could manage multiple tabs simultaneously without much friction. This doesn’t quite translate to smartphones, as smaller screens, app-switching delays, repeated logins, lost context, all create friction. History shows the pattern: whenever convenience can be improved, someone builds the solution, and someone else is willing to pay for it.

These mobile pain points point to the need for more integrated, multi-service experiences on mobile. The model benefits all participants:

• For consumers, Everything Apps provide a simplified, all-in-one experience. Users can access a variety of services without having to leave the app. One login, an integrated payment system, and personalized experiences based on their user preferences.

• For businesses, Everything Apps can help reduce user churn and lower Customer Acquisition Costs, while strengthening network effects and creating the opportunity to capture multiple revenue streams from the same user base.

The concept was first articulated publicly by Blackberry founder Mike Lazirdis at the Mobile World Congress in 2010, where he envisioned “everyday apps that people would use because they provided such a seamless, integrated, and efficient experience.” The first live example would come a few years later, when Tencent’s WeChat - a messaging app at the time - would launch WeChat v5.0. This update introduced a handful of new features including payments and gaming, marking WeChat’s shift from messaging to a robust multi-service platform.

Today, the most successful Everything Apps combine messaging, personal finance, payments, social networking, e-commerce, and plenty more. Unsurprisingly, they have flourished in “mobile-first” regions where smartphones are the primary - and in some cases the only - gateway to the internet.

So Why No Everything Apps In The US (Yet)?

Despite their widespread success in Asia and other mobile-first regions, Everything Apps have yet to truly take root in the US and Europe. The reason is straightforward: structural headwinds..

Digital ecosystems in the US and Europe are crowded with entrenched incumbents: specialized apps which dominate their respective fields and benefit from strong brand loyalty. Users love Venmo for payments, Uber for ride-hailing, Robinhood for trading. Habits like these are difficult to break, and app-store dynamics only further reinforce them. Platform restrictions such as Apple's notorious 30% App Store tax, and restrictive control over native capabilities, made aggregation even more difficult to build and sustain over time. Furthermore, data privacy laws like GDPR and CCPA, combined with aggressive antitrust enforcement, have created compliance burdens that actively prevented platforms from dominating multiple sectors.

But the demand IS there. The gravitational pull of convenience keeps growing. A new digital banking report recently revealed that 60% of users add a second finance app specifically because their primary app lacks essential features, while 91% would gladly consolidate to a single app if it offered all the services they needed.

And slowly, this shift is happening, in a way. Companies like Meta and Google continue layering in more messaging, shopping, and payment features to their apps. Elon Musk has openly shared his long-term vision for X as ‘the Everything App’, which recently integrated Polymarket, the kind of vertical integration that can very well be a stepping stone for future expansion.

Despite regulation, platform politics, and consumer inertia, the Everything App vision is alive in the West.

Aggregators in Finance: A Natural Evolution

If there’s any sector primed to capture this transformation next, it’s finance.

Financial services remain fragmented across thousands of banks, brokers, and financial products today. This complexity has always made aggregation valuable. Historically, banks themselves were the original aggregators, combining checking, savings, credit, and investing under one roof. However, they aggregated financial services through regulation, branches, and charters. As technology evolved though, this monopoly would start to crack.

The internet would then enable the next generation of financial aggregators. PayPal and Stripe aggregated payment processing. Plaid then followed suit by aggregating bank accounts. Robinhood aggregated trading stocks, options, and crypto assets.

From there, the ambition only grew. The following generation of financial aggregators sought to aggregate the entire financial stack. On the consumer side, apps like Revolut and Cash App bundled banking, payments, and trading. On the enterprise side, solutions like Ramp and Brex combine payments, banking, credit, payroll, and expense management under one roof.

Fintechs have proven the aggregator model works in finance: bundling services creates stickier products, stronger network effects, and captures greater market share. But they’re still boxed in by regulations, jurisdictional barriers, and the tech debt of legacy infrastructure. These pain points beg the question: today’s financial aggregators being built on the right rails? Or are we at an inflection point, a time to explore new rails, new regulations, and unlock new opportunities?

New Rails

Crypto protocols are well-suited for financial aggregators because they are composable, open, and permissionless. Unlike legacy infrastructure, integrating these protocols doesn’t take years of licensing battles or contracts to sign, just code to write and deploy. This programmability makes it easier to embed and integrate more financial services at scale. Today’s blockchains, the rails which crypto protocols live on, also enable significantly lower fees and faster speeds for financial transfers than legacy systems.

New Rules
“Plain and simple: securities intermediaries should be able to offer a broad range of products and services under one roof with a single license..” - Paul Atkins, SEC Chairman

Regulators are now dismantling the barriers that previously prevented Everything Apps from flourishing in the US. Today, a financial platform needs separate licenses for each individual service: broker-dealer license for trading, money transmitter licenses in all 50 states for payments, banking charter for deposits, and somehow, the list goes on. This is one major point the SEC's Project Crypto aims to address. As per recommendation from the White House Digital Asset report, the SEC is now actively developing a ‘Regulatory Super-App Framework’, which would allow an SEC-registered platform to offer multiple financial services under one platform or license. This extends beyond traditional securities to crypto asset staking, lending, payments and more.

New Opportunities

Altogether, crypto rails unlock access to financial services on a broader, more global scale than its legacy predecessors. Regulations are now being developed to ensure this can develop in a compliant and secure manner. But more importantly, the real unlock is the innovation that the composability of crypto protocols and transparency of blockchains enables.

Every new protocol becomes a building block for others, enabling products and services that are difficult for financial services to assemble on their own. Borrowing and lending, payments, trading, identity, can be integrated with one another with code. A financial system built on the internet, for the internet.

The most impressive part about this system is that it’s already live. There is already over $300 billion in value on public blockchains today. $9.5 trillion has already been traded - in a non-custodial manner - on decentralized exchanges (DEXs). Over $274 billion has been issued in stablecoins to date, which have actively surpassed Visa, Paypal, and legacy remittance systems in monthly volumes since November. Yet DeFi is still largely niche and unknown- why is that?

The Missing Ingredient: The Mobile Moment

Today, over 5 billion people use the internet, the vast majority of this activity taking place on mobile devices. Mobile has become the dominant user interfaces across the globe, and mobile apps the new pipes through which industries reach mass adoption:

We’ve seen this playbook before. The explosion of mobile in the early 2010s went hand-in-hand with the rise of social media, as mobile internet access quadrupled between 2011 and 2015. The same pattern would unfold into e-commerce, which grew from less than 5% of retail purchases in 2010 to over 18% in 2020. In 2025, smartphones are projected to account for nearly 60% of global retail e-commerce sales, or roughly $4 trillion in mobile commerce revenue.

16 years since Bitcoin’s Genesis Block, crypto’s Mobile Moment is overdue.

Signs are certainly starting to appear. A16z estimates there are now 34.4m monthly active mobile wallet users. Coinbase has previously topped all finance apps on the iOS App Store and even broke the top 10 free apps overall. Jupiter, the largest DEX aggregator on Solana and one of the largest in crypto broadly, launched its mobile app in late 2024, allowing users to onboard with familiar payment systems like Apple Pay. Solana Mobile recently shipped out over 250k orders of the Solana Seeker second-gen phone to more than 50 countries.

Across industries, mobile has been the unlock for mass adoption. Crypto will be no different. The next major opportunity to aggregate financial services is clear: a truly crypto-native Everything App.

DeFi App: The Crypto-Native Everything App

Every breakthrough technology needs its killer app. For social, it was Facebook. For AI, it was ChatGPT. DeFi App aims to be that catalyst for crypto.

Today, DeFi ultimately functions as a consortium of protocols built on top of one another. DeFi App simply aggregates these multiple layers between chains and their respective applications into a single mobile interface.

These layers make up what we call the DeFi Value Chain:

• Assets: These are digital assets that live on blockchains and represent some monetary value.

• Protocols: These are smart contracts that provide financial primitives and ultimately aggregate user assets together.

• Protocol Aggregators: These are protocols built to aggregate other crypto protocols, optimizing to find the best rates, prices, and execution for users.

• Interface aggregators: These are the applications that provide the consumer touchpoint to access all underlying layers and services.

In a sense, DeFi resembles an open set of legos. An Everything App would be the box they're packaged in. And as other industries in the post-internet era have proven, whoever controls that box - and defines how users interact with those legos - owns the user relationship and becomes crypto's ultimate aggregator.

Early Traction

DeFi.App has achieved significant early traction since launching in February:

• 3.7M+ wallets created across 1.38M active users

• $2.87M in protocol fees generated since April.

• 80M+ HOME staked by 7.7K+ unique stakers, the majority choosing 12-month lockups.

So far, the protocol generated $18.8B in total volume, with the majority ($15.2B) coming from Swaps.

Perps meanwhile, launched in May and have already generated $3.66B in total volume.

But exactly how does it all work under the hood?

Here’s how DeFi App intends to capture this opportunity.

What does DeFi App Offer?

DeFi App provides a simple, cohesive interface for users to access core DeFi services:

Swapping

• Spot trading across multiple DEXs on multiple chains with aggregated liquidity to find the best rates.

• DeFi App users can access more assets on DEXs than on traditional CEXs, and better rates depending on the asset and its onchain liquidity.

By using an intent-based system, DeFi App users can express their swap preferences with less manual configuration.

Leverage Trading

• Perpetual contracts (Perps) trading, accessible through a simple interface built on a Hyperliquid backend.

• Users can access institutional-grade liquidity, transfer fees lower than on CEXs, and maintain custody of their assets.

• Users get a dedicated ‘Perp Account’ which they can fund from their existing DeFi App wallets. From there, they can long or short any supported asset, and add leverage if they so choose. It’s important to note that leverage trading can be significantly riskier than spot trading.

Earn

• Users can easily stake the HOME token and access various DeFi yield strategies with transparent risk assessment.

Lending/Borrowing

• Users can borrow or lend supported assets through a simple interface.

• DeFi App aggregates multiple borrowing and lending protocols to offer users the best rates across the DeFi ecosystems and chains it supports.*

To address common pain points in the onchain user experience, the platform offers:

Self-custodial Wallet Management

• DeFi App creates a wallet for users on all its supported chains to eliminate new wallet set-up hassle for users. This is especially useful for chains that operate on different VMs, like Ethereum and Solana, both of which DeFi App supports.

• While CEXs provide this service for users, they custody the assets held in those accounts. Though it’s been almost 3 years since FTX imploded, the principle hasn’t changed much. Not your keys, not your crypto.

• With DeFi App, users avoid wallet set-up hassle but still maintain custody of their assets.

Easy onboarding/on-off ramping

• Users can easily fund their DeFi App accounts using familiar payment methods like Apple or Google Pay.

Gasless Transactions

• Users don’t pay gas fees when transacting. Gas fees are either sponsored by DeFi App directly or swapped from other tokens on the user's behalf.

Integrated Cross-chain UX

• All swaps are executed for users “behind the scenes”, including those involving different assets on different chains. This helps eliminate the need for users to manually bridge their assets, which could involve multiple steps and transactions otherwise.

*Note: this feature is not live yet.

System Architecture

Stripped down to its purest elements, DeFi App is a crypto-native interface aggregator. It combines and integrates different smart contracts, Solvers, Smart Account infrastructure, APIs, and adapters through a single cohesive interface layer.

• Smart Accounts: Creates seedless wallets on user’s behalf through AA

• Gas Abstraction: A paymaster to sponsor gas fees for transactions.

• Transaction Execution: An aggregator system for routing transactions using intents.

• Frontend interface: Combines multiple DEXs, lending protocols, and yield sources into one interface.

Smart Accounts

DeFi.App lets users sign up and manage crypto wallets on their behalf using familiar authentication methods like email and social apps. Instead of directly managing private keys and gas fees on multiple chains, embedded Smart Accounts let DeFi.App handle gas, bridging, and signing under the hood for users.

Smart Accounts are built on the principle of Account Abstraction. The core principle is to separate how an account is verified from how its transactions are actually processed on Ethereum. In simpler terms, it means a public key (a wallet address) is no longer tied to a single private key. Instead, smart contracts can be written to define custom rules for verification, recovery, and fee payment.

The vision has been around for a while now, dating back to Vitalik Buterin’s EIP-86. The core concept was to make wallets more flexible by allowing accounts to operate like smart contracts rather than just externally owned accounts (EOAs) controlled by private keys. The first major implementation was realized through multi-signature (multi-sig) wallets pioneered by Safe, which demonstrated early forms of AA by separating asset custody from transaction authorization. Instead of just one signature with a single private key, protocols could set up contracts to require multiple signatures before a transaction is approved. This proved beneficial for protocols and teams, but less tangible to retail.

With ERC-4337, AA resurfaced strongly as a key upgrade concept: an additional layer, though not one that altered the core Ethereum protocol. ERC-4337 wrapped user transactions into a higher-level construct known as a ‘UserOp’, short for UserOperation. Any transaction generated from an ERC-4337 enabled wallet would be a UserOp, and UserOps had their own Mempool. From there, Bundlers would aggregate UserOps into regular transactions to be posted to Ethereum mainnet.

A core UX unlock that was long envisioned with AA was gas abstraction. EIP-4337 enabled this through the use of Paymasters, specialized smart contracts which can be programmed to either sponsor fees on behalf of an account or accept gas fees in any ERC20 token besides native ETH.

ERC-4337 provided the toolkit for advanced Smart Account logic, but it did not standardize it in a way that was more universally applicable. It also required that users create a new Smart Account instead of being able to import their old Externally Owned Address (EOA). This would change with EIP-7702, as part of the Pectra upgrade. EIP-7702 allowed any existing EOA to temporarily function as a Smart Account with additional features like transaction batching, gas sponsorship, alternative gas fee payment.

In short, Smart Accounts enable apps like DeFi.App to sponsor gas fees for users, let users pay gas in any ERC20 tokens, and support alternative and more familiar verification mechanisms beyond the standard ECDSA commonly used in crypto today. And central to Smart Accounts and Account Abstraction is proper and secure key management.

Key Management

DeFi.App integrates several providers for secure key management of newly-generated user accounts. The implementation varies slightly between EVM and SVM environments, but together create a smooth onboarding and transaction flow for users:

Turnkey

Turnkey is the custody and signing provider that underpins account creation on both Solana and EVM chains.

• On Solana, Turnkey directly manages the user’s wallet where assets are actually held. Because funds live in this wallet, importing the key into another wallet provider, like Phantom, would display a balance.

• On EVM chains, Turnkey generates a signer key for each user. This signer key doesn’t hold any funds itself - but it does authorize transactions via a linked smart account. In practice, Turnkey provides the signature layer while funds themselves live in a generated smart contract.

Zerodev

ZeroDev provides DeFi.App’s Smart Account infrastructure for EVM chains.

• User funds on EVM chains are stored in an EIP-4337 smart contract wallet managed by ZeroDev.

• The wallet is controlled by the Turnkey signer (described above), but custody of assets lives entirely within the ZeroDev smart contract.

Effectively, ZeroDev handles the vault and execution layer for EVM assets, while Turnkey supplies the signer that secures it.

Dynamic

Dynamic is the wallet connector and onboarding layer that ties the system together, managing user authentication mechanisms.

When a new user signs up, Dynamic provisions two embedded wallets:

• A Solana wallet (Turnkey-managed, holds funds directly).

• An EVM Wallet consisting of a Turnkey signer + ZeroDev smart account (signer controls, smart account holds funds).

Dynamic also allows users to export their keys. For Solana, this means exporting the Turnkey wallet. For EVM, users can export the signer key, but balances won’t appear in a standard EOA wallet since funds live in the ZeroDev contract.

Besides simpler user onboarding, Smart Accounts also enable another major UX breakthrough for DeFi.App: the ability to pay transaction fees in any token.

Gas Abstraction

It’s critical to understand that users still pay a fee to DeFi.App when transacting. Transactions and blocks cannot be uploaded to a blockchain without a fee to cover the associated computational costs. These fees are paid in the native token of that blockchain, which in itself is a core part of the token’s utility. While it is not possible to avoid gas fees, it is possible for application developers to make sure their users don’t need to worry about them.

DeFi.App enables gas abstraction across all its supported chains, though the implementation varies by the execution environment (VM) of that chain.

On EVM chains, gas fees are sponsored by ZeroDev architecture and funded by Defi App transaction fees.On Solana, this is handled by a custom paymaster contract. The gas fee is quoted to the user transaction, and swapped from the paying token into SOL.

Intent-based Execution

DeFi.App implements an intent-based system for transaction execution, designed to give users a simple experience with optimal pricing.

An “intent” has no formal definition, but it functions similarly to a transaction, only instead of specifying the exact execution path, the user specifies their desired outcome. This approach focuses on removing the complicated parts of the onchain transaction experience like sourcing liquidity, ensuring sufficient gas, and bridging if another chain is required. For example, a user may want to swap 100 USDC for SOL, but doesn’t know if the best prices are on Orca, Raydium, or elsewhere.

How does this work on DeFi.App?

DeFi.App runs a Solver transaction aggregator that taps into multiple liquidity sources across chains and protocols, presenting users with the best available price. The platform integrates with 0x, 1inch, Bebop, Debridge, Dodo, Enso, Jupiter, LiFi, Odos, Open Ocean, Paraswap, Relay, and Stable. Proprietary routing logic in the backend evaluates these options in real time, ensuring users see a single, streamlined execution flow.

Cross-chain support is native to this system. Traditionally, swapping USDC on chain A into USDC on chain B (or even USDC → USDT across chains) would require multiple bridging steps. With DeFi.App’s solver network and routing logic, cross-chain swaps are presented to the user as a single transaction, no different from a same-chain swap.

MEV-Protection with Jito

One particular underrated feature DeFi.App enables is to let users opt-in to protect themselves from MEV on Solana.

MEV is often captured through sandwiching/arbitraging unsuspecting users.

Jito built the Jito-Solana Validator Client to help mitigate this on Solana. Users can submit transaction bundles to Solana validators running the Jito client. These transactions get executed atomically, in order, therefore either they all execute or fail altogether. Tips (bonus fees) are attached to bundles to incentivize validators, priority going to the highest bidder.

Broadly speaking, DeFi.App supports three transaction modes: Classic, Jito, and Auto. Classic mode provides standard routing to validators running the Agave validator client. Jito mode routes transactions to validators running the Jito-Solana client, while Auto mode selects the best option for the user at the time.

DeFi.App Users can opt-in to MEV protection by simply toggling “Jito Mode”, which will then route their transactions through Jito-enabled validators, leveraging the bundling and tipping system.

Capturing The Convenience Premium: DeFi.App’s Business Model

Earlier, we highlighted the impact of the convenience premium throughout history: people are willing to pay a premium for goods and services that make their lives easier, plain and simple. Aggregators dominated the post-internet era by focusing on owning the user relationship and experience. In an increasingly mobile-first world, the next evolution of this model is the Everything App.

The next question that follows is how exactly do the aggregators, the harbingers of convenience that don’t own what they sell, actually generate and accrue value?

Let’s examine DeFi.App’s business model.

The HOME Token

DeFi.App and its ecosystem is powered by the $HOME token. HOME launched on June 10, 2025, and currently lives on Solana, Base, and BNB Chain. There are 10 billion HOME tokens in total.

Token Utility

HOME plays several key roles across the DeFi.App ecosystem:

• Governance: HOME stakers can create and vote on any proposals that come up around the DeFi App protocol, whether it’s revenue sharing models, token buybacks or yield to $HOME stakers, which feature comes next for the platform, and so on. Companies can also buy and stake HOME to accelerate their own integrations into the ecosystem. Over 5.9k votes have been cast over the first 5 proposals.

• Gas Abstraction: Users who hold HOME in their DeFi.App wallets can transact on any supported chain and pay gas fees in HOME. Defi.App’s treasury automatically purchases HOME from the user at open market rates and uses that HOME to subsidize that user’s gas fees on their behalf. This is facilitated through the HOME Staking contract, which provides collateral to support gas subsidies and conversions.

• Incentive Coordination: Users can lock HOME to earn XP, which accrues on a daily basis and may be a qualifying criteria for future HOME airdrops. Locking HOME can activate up to a 3x multiplier on XP gains, with more XP for longer lockups.

Data shows that the majority of HOME token holders are on Base (83k holders), with ownership on BNB Chain (6.4k) and Solana (1.9k) notably lower.

DeFi.App’s Business Model

DeFi.App generates revenue through protocol fees. Every swap, deposit, leveraged trade - any user action that generates a transaction - generates a protocol fee. Think of this fee as the convenience premium that DeFi.App charges.

The business model is designed to work the following way:

1. A user joins DeFi.App.

2. Each time they swap, borrow, lend - each action generates a protocol fee.

3. Fees are allocated however governance sees best fit.

4. Fees are distributed, often through buybacks or allocated for future incentives.

5. As more HOME is staked, token supply decreases, interest grows, and…

6. A new user joins DeFi.App.

DeFi App charges 0.02% on swaps, and 0.01% on perps. Today, as much as 80% of DeFi App’s net fee revenue is allocated towards HOME buybacks for its treasury reserve, as was recently voted on in DIP-004.

Analysis

It’s time to unpack what we just learned and reflect on the challenges and opportunities Shoal Research sees ahead for DeFi.App.

Challenges And Considerations

DeFi.App is building a solution to a very real problem: a lack of simple, beginner-friendly access to DeFi. This isn’t a new problem by any means though, and the DeFi.App team are not the only ones actively building to solve this problem.

The biggest, high-level challenge we foresee DeFi.App facing is competing with bigger “crypto-adjacent” firms that have a strong distribution advantage and bigger user-base. Firms like Coinbase and Robinhood come to mind. This may even extend beyond crypto firms: major fintechs and even banks can push for their own Everything App, incorporating crypto and DeFi alongside other non-crypto services.

Still, most of these firms require users to fill out KYC verification beforehand, which can be a major onboarding pain and bottleneck. DeFi.App has an upper-hand here: users can quickly sign up with their email and fund their account with Apple Pay. However, this is where major wallet providers like Phantom and Metamask, who also very much stand to benefit from launching their own ‘Everything App’, stand to be a bigger threat and concern. In a way, these apps already function as “crypto-native Everything Apps” and offer multiple integrated services similar to DeFi.App. Therefore, non-custodial wallets are currently the biggest competitors to DeFi.App we see as of today as they can provide similar offerings relatively easily. That said, while there may be less room to compete on service offerings, there will always be room to compete on pricing and execution rates against wallet providers and other protocols. After that, it becomes a race to who can support more chains, more assets, and offer more services to users.

Moving on to more crypto-native challenges, DeFi.App, like many other crypto protocols, need to make sure they can scale to keep up with the expanding multichain landscape. There are currently 300 chains, some believe we will have 1000s. DeFi.App currently only serves 6. While many of these chains are not active or have not generated any real sustained activity, users are continuously traveling to different chains and ecosystems. Corporations like Circle and Stripe are launching their own chains. Who knows who else will follow. As an “aggregator of aggregators”, it’s in DeFi.app’s best interest to be able to serve their users wherever they want to go.

This is a hard problem to solve, but if users can access virtually any chain through DeFi.App’s simple interface, this can be a major advantage and differentiator. Conversely, if DeFi.App chooses to keep their overall chains and ecosystems limited, someone else will expand to serve users of any asset on any chain. It even becomes advantageous for infrastructure-focused teams, such as those building interoperability protocols, to create their own user-facing application to let users access any asset on any chain. The easiest solution for DeFi.App to hedge against this risk is to integrate a major interoperability protocol, like Hyperlane or LayerZero, and tap into their existing distribution and scale with them. A much more difficult approach will be to build a proprietary solution. Either way, we believe DeFi.App needs to dream big, and embrace aggregating “everything”. This starts with providing users access to any asset on any chain.

Exciting Opportunities and Outlook

Overcoming these challenges will be critical for DeFi.App’s short and long-term success, but there is plenty to be excited for. By building around aggregation and the mobile-first experience, DeFi.App is following the same playbook that has kickstarted major growth and adoption cycles in other digital industries as it sets out to build the first crypto-native Everything App.

Consumers have expressed a strong preference for mobile-first experiences in today’s age. DeFi.App is building a mobile app for that. Interest in crypto will likely continue to grow as enterprises and major institutions continue to either experiment with or fully embrace blockchain solutions. And they’re actively piling in because for the first time in crypto’s history, the US is actively developing pro-innovation guidelines and regulations to legitimize this technology. This alone won’t bring “the next million users” onchain, but the path to mass adoption all starts from there.

It doesn’t hurt that aggregators have done quite well overall, either. Once Google had integrated search results with user data, they were able to offer targeted advertising, and have made hundreds of billions in revenue over the years. Meta had a similar approach, integrating their feed with user’s data to unlock targeted advertising, and generates over $100b annually, with most (97%) of that revenue coming from targeted ads. WeChat, the poster child for Everything Apps today, has earned hundreds of billions over the years from in-app purchases and more.

In DeFi, numerous protocols have already demonstrated the revenue potential of frontend applications. Photon and Pump Fun have both seen upwards of $6m in daily fees before. Axiom hit over $250m revenue in 8 months.

Though maybe not all apples-to-apples comparisons, these examples demonstrate there is lots of demand and value to capture in frontends and aggregators.

Ideas And Suggestions from Shoal

Some ideas and suggestions Shoal Research believes could help contribute to DeFi.App’s success moving forward:

• Expand to support more chains and assets over time. Dream big: every asset, every chain should be accessible through DeFi.App’s mobile interface.

• Integrate an underlying cross-chain messaging protocol so DeFI.App’s chain coverage can easily scale with them.

• Integrate an AI chatbot UX to let users type out their preferred action. Bonus points for voice mode to make it even more convenient.

• Enable non-users to onboard easily via text message. Text your friend some money through a link. They sign up for DeFi.App in seconds.

• Enable a “Privacy Mode”: Integrate a proven privacy solution like Railgun to let users transact privately when they want to, the same way they can toggle “Jito Mode”. This may be trickier to manage from a legal perspective, but can be a massive benefit and differentiator for DeFi.App long-term.

• Offer MEV protection on all chains, not just Solana. Integrate a Flashbots/MevBlocker RPC endpoint for EVM chains.

• Productize (or even better - open source!) a plug-in that teams can use to easily add their protocol or service to DeFi.App’s interface.

Importantly, these are merely ideas we suggest the DeFi.App team take into consideration. Some of these ideas are more practical and urgent than others. Some may not make sense right now, but may become critical in a few months or a year. Time will tell.

Closing Thoughts

History has repeatedly shown us that the convenience premium is timeless. In 2025, aggregators have captured this premium and dominate consumer marketplaces by delivering superior user experiences. Mobile devices have taken over the majority of internet traffic as they have evolved to offer more services and information than desktops, conveniently fitting in everyone’s pocket.

At Shoal, we believe the same plot will inevitably unfold on crypto rails. Fintechs have proven the demand for convenient, aggregated access to financial services, but they still face centralization risks and remain barred to geographical barriers. Crypto protocols are open, programmable, and permissionless to integrate, and have already moved upwards of twelve figures in value across the globe. Major enterprises and institutions are adopting stablecoins for payments. Countries like the US are actively pushing to establish pro-crypto and pro-innovation regulatory frameworks. It’s becoming clear that the time to build crypto’s everything app is now.

Sources

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• de la Mano, Matías. Open Platforms and Innovation: The Case of the Linux Operating System. London School of Economics, 1999, eprints.lse.ac.uk/22471/1/wp20.pdf. Accessed 24 Oct. 2025.

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• “New Study Reveals Using Multiple Financial Apps Is Necessity, Not Choice, for Most Users.” Nordic Fintech Magazine, nordicfintechmagazine.com/new-study-reveals-using-multiple-financial-apps-is-necessity-not-choice-for-most-users/. Accessed 24 Oct. 2025.

• “Statistics That Prove How Your Delivery Speed Impacts Your Business.” Meteor Space, 13 Feb. 2025, meteorspace.com/2025/02/13/statistics-that-prove-how-your-delivery-speed-impacts-your-business/. Accessed 24 Oct. 2025.

• Thompson, Ben. “Aggregation Theory.” Stratechery, 17 July 2015, stratechery.com/2015/aggregation-theory/. Accessed 24 Oct. 2025.

• Thompson, Ben. “Defining Aggregators.” Stratechery, 24 Apr. 2017, stratechery.com/2017/defining-aggregators/. Accessed 24 Oct. 2025.

• “Uber Eats Statistics.” Business of Apps, www.businessofapps.com/data/uber-eats-statistics/. Accessed 24 Oct. 2025.

• “10 Years of WeChat History.” Eggsist, www.eggsist.com/en/insights/10-years-wechat-history/. Accessed 24 Oct. 2025.

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• “Superapps.” Venture Vine, Medium, medium.com/venturevine/superapps-6a425de7db87. Accessed 24 Oct. 2025.

• “The Era of Provable Software.” Succinct Writings, writings.succinct.xyz/provable. Accessed 24 Oct. 2025.

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer. This post has been created in collaboration with the DeFi App team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

The Experience Is The Product

Over the past three decades, the World Wide Web has reshaped the world and how it functions as we know it. As software gradually replaced physical distribution channels, the user experience has become the product. Look no further than the role which aggregators like Google, Amazon, Facebook (among plenty others) play in everyday life for evidence. By delivering cohesive user experiences, these platforms came to dominate consumer markets.

It can certainly be argued that DeFi faces a similar fate. The early internet began as a patchwork of disconnected webpages and hyperlinks, and so too, the broader DeFi landscape today spans hundreds of chains, tens of thousands of protocols, and millions of tradable assets, many of which are forks. This replicability is indeed a feature, not a bug. As Richard Stallman laid out, the right to reproduce, modify, and share code freely is a pillar of truly free software. In practice, however, DeFi’s open-source, permissionless design has created as much fragmentation as it has driven innovation.

The Unified Margin Opportunity

A majority of today’s DeFi volume and activity still concentrates around a few primitives: swaps, perps, staking services, and money markets. Yet there’s no simple, intuitive way to access them all through a single margin account. To trade perps, users deposit collateral to a specific account on a specific exchange. To borrow or lend, they need to withdraw and then deposit to a new position on another protocol. These DeFi primitives operate in isolation, fragmenting liquidity and leaving large amounts of capital idle and unproductive.

The lack of a unified margin account hasn’t been a conceptual problem so much as it’s been a structural one. Different DeFi protocols maintain their own accounting and risk parameters. An AMM tracks LP positions and pool reserves. A lending market tracks deposits and debt and evaluates protocol health. A perps exchange needs to track margin balances, open interest, funding rates, and liquidation thresholds. Each protocol has its own requirements in order to remain solvent. They can integrate with one another via smart contract calls, but they can’t share balance sheets.

This localized risk design is what makes DeFi safe in isolation but inefficient in aggregate. However, by building these primitives around a single shared risk engine, the opportunity to build a unified margin account emerges. This design would enable a user’s deposit to support multiple roles across trading, lending, staking, while maintaining consistent solvency rules. Collateral that once sat idle could be reused across products. A trader’s gain in one market could balance a loss in another, tightening spreads and reducing redundant margin.

Still, connecting DeFi primitives does not address the inefficiencies in their underlying mechanics.

DeFi’s Static Trap

The DeFi primitives we know today have processed trillions in volume, supported hundreds of billions in liquidity, and generated billions in protocol revenue. Like the internet, their greatest strength lies in being borderless, transparent, and always-on. Most protocols were designed as a set of smart contracts with fixed parameters that can run autonomously, for an indefinite period of team. Some teams have introduced governance or upgrade hooks for managing in critical situations, but between constant-product AMMs, fixed loan-to-value ratios, and passive liquidity provisioning, much of DeFi still operates on static logic.

These design choices made sense initially. Fixed parameters gave users trust: DAI’s stability, Uniswap’s simplicity, and Aave’s predictable risk. In stark contrast, however, traditional financial systems, which handle more volume in a single day than DeFi does in a month, operate on dynamic parameters that adjust to real-time conditions. Banks, market-makers, and derivatives desks continuously reprice risk, widen spreads, and adjust leverage based on market activity.

Building DeFi primitives that respond to real-time market conditions unlocks better performance, safer risk management, and more efficient and productive use of capital. These protocols can adjust key parameters such as collateral ratios, borrow-lending rates, and execution routing based on real-time market conditions.

This responsiveness matters most during market stress. When asset prices move sharply, parameters can tighten automatically to prevent unnecessary liquidations and preserve solvency. During more stable conditions, they can loosen to improve capital utilization and allow collateral to work more efficiently. In both cases, risk settings evolve with the market rather than remaining static.

The question then becomes how this looks like in practice.

A Deep Dive Into Flying Tulip

Flying Tulip introduces an onchain financial system designed to standardize pricing, credit, and risk across a unified suite of markets. The protocol brings together spot trading (AMM + CLOB), lending, perpetuals, insurance, and a protocol-native stablecoin within a shared margin framework.

Flying Tulip is built around three core design goals. First, to make collateral reusable across multiple functions. A single deposit can support several forms of activity, earning base yield, margin for trades, or loan collateral, without being locked into a single utility or module. Second, price risk using real, executable liquidity instead of static references or delayed oracles. The protocol measures depth and volatility across its markets in real time, and those readings feed directly into parameters like collateral ratios, funding rates, and liquidation thresholds. Third, to recycle protocol cashflows back into the system. Revenue from trading, borrowing, and settlements flows toward the FT token through programmatic buybacks or user distributions, linking network activity to value capture in a transparent way.

These design goals shape two outcomes: better yield and better user experience. Better yield comes from capital efficiency. The same deposit can earn base yield while simultaneously securing loans, resting limit orders on the CLOB, or providing margin for derivatives. Assets stay productive across the stack rather than sitting idle. Better UX, meanwhile, comes from integration. Core mechanics are encoded directly in contracts and applied consistently across products. The same depth-aware inputs that determine trade execution also inform borrowing limits, funding adjustments, and liquidation logic, ensuring that all parts of the system respond to market conditions in the same way.

Flying Tulip’s design builds on the original Deriswap concept Andre Cronje introduced in 2020. The concept he laid out was simple: to merge multiple DeFi functions into a single, more capital-efficient protocol. Deriswap proposed that the same liquidity pool could be used for swaps, lending, and options simultaneously in order to reduce idle liquidity. which proposed reusing liquidity across swaps, lending, and options. Here, that principle extends to the full financial stack. The protocol’s architecture connects trading, credit, and risk management within a single environment, allowing capital and exposure to be managed through one unified framework.

Flying Tulip will launch natively across multiple chains, beginning with the Sonic L1, and expanding to Ethereum, Avalanche, BNB Chain, and Solana. Each chain will feature its own local instance of Flying Tulip, ensuring liquidity and settlement remaining within native environments across all deployments.

The Flying Tulip Stack

Flying Tulip consists of an integrated suite of DeFi components. ftUSD, lending, spot, and perpetuals operate from a shared liquidity base and unified margin layer. Parameters across the system adjust to market conditions in real time, keeping pricing, collateral, and risk consistent across products.

ftUSD: The FT- Native Stablecoin

To avoid dependency on third-party stablecoins for bootstrapping liquidity, Flying Tulip introduces ftUSD, a protocol-native, dollar-pegged stablecoin. that serves as the primary margin and settlement asset across the protocol’s AMM pools, order book, and derivatives markets. ftUSD is minted by depositing supported stablecoins or blue-chip assets and can be held as a stable unit of account or staked to sftUSD to earn yield from Flying Tulip’s underlying yield strategies. Staking represents a conversion into the yield-accruing pool, with distributions reflected through a variable exchange rate between sftUSD and ftUSD.

Collateral deposited to mint ftUSD is deployed across delta-neutral strategies across several venues, executed programmatically onchain to remain transparent and auditable. Assets are allocated across money markets, staking pools, and hedging positions, and the yield generated on these positions is then distributed to sftUSD holders while proceeds from unstaked ftUSD are retained by the protocol to fund operations and liquidity.

How It Works

ftUSD maintains its dollar peg through a delta-neutral strategy, wherein collateral backing ftUSD is deployed to earn yield onchain while keeping net exposure to market movements near zero. Yield for ftUSD originates primarily from the interest earned on supplied collateral in money markets i.e. Aave, plus staking rewards generated on the long side of hedged positions. Positions are rebalanced periodically and constrained by caps, sizing bands, and venue limits to preserve solvency and reduce volatility transmission.

The yield, realized after deducting borrow costs, fees, and safety buffers, is distributed proportionate to the amount of sftUSD held. Distributions to sftUSD holders are reflected through a variable exchange rate between sftUSD and ftUSD. Unstaked ftUSD does not accrue yield; its proceeds are directed to the protocol’s treasury to fund operations, expand liquidity, and support the token-first model.

This structure allows collateral to remain productive while minimizing directional risk. The long (staked) and short (borrowed) legs are calibrated to offset one another under normal conditions, reducing the likelihood of liquidation during volatility. The approach prioritizes conservative sizing and transparent, programmatic management. All strategies and rebalancing logic are executed onchain and are auditable through public dashboards.

ftUSD’s underlying yield strategies operate fully onchain, with key parameters such as pool exchange rates and collateral exposures remaining transparent to track performance and risk composition. All positions are marked to market and managed under the same delta-neutral framework, with periodic adjustments based on observed liquidity and volatility conditions.

A native stablecoin like ftUSD allows Flying Tulip to price risk, settle trades, and manage liquidity under a single unified framework. Anchoring market activity to such a stablecoin enables Flying Tulip to unify collateral management across its AMM, CLOB, and derivatives markets. ftUSD serves as the base unit for margin, settlement, and lending, allowing the same collateral to support multiple positions without leaving the system. Yield generated from these activities, such as funding payments or lending carry, remains within the protocol’s liquidity base and is routed either to sftUSD stakers or to operational reserves according to policy.

FT Lend: A New Money Market Structure

Flying Tulip introduces FT Lend, a native money market integrated directly into the protocol’s unified margin system. It allows users to borrow and lend assets using the same collateral that supports trading, settlement, and derivatives positions across the protocol. FT Lend is designed from the ground up to operate within an adaptive framework informed by real-time market data on liquidity depth and volatility.

Most of the major money markets today rely on fixed loan-to-value (LTV) ratios and over-collateralization thresholds. Borrowers deposit collateral exceeding the value of their loan, maintaining a margin buffer that depends on the price of the underlying asset. Liquidation occurs when the debt obligation surpasses the collateral’s adjusted value.

Static LTV parameters are chosen for their simplicity and the predictability they offer. They let protocol designers fix a borrow threshold for each asset, defining the maximum debt a borrower can take relative to posted collateral. These thresholds typically assume worst-case volatility and enforce over-collateralization, ensuring lenders remain protected during rapid price movements. However, this design also limits capital efficiency and can have cascading effects. When markets are stable, capital remains under-utilized; when volatility spikes, static parameters amplify liquidation cascades as prices fall and collateral thresholds are reached across the protocol. Static risk parameters, by design, cannot differentiate between stable and volatile market conditions.

FT Lend replaces static assumptions with adaptive parameters that adjust continuously to market conditions. Borrow limits, interest rates, and liquidation behavior are derived directly from onchain data rather than fixed governance rules:

• Trade-Weighted LTV (TWAR). Borrowing power scales with position size relative to pool depth. Smaller loans receive higher limits, while larger ones face progressively lower caps to account for execution impact. The collateral-to-pool ratio determines the maximum permissible LTV, which tightens predictably as position size or volatility increases.

• Volatility-Aware Rates. Borrow costs adjust according to realized dispersion. Assets are classified as stable or volatile, and rate formulas respond automatically: during high volatility, rates rise and borrowing capacity contracts; during calm periods, rates compress and available credit expands.

• Snapshot LTV. When a borrower opens or modifies a position, borrowing limits are locked to current market conditions. Existing positions remain under those limits even as policy parameters change, preserving predictability while allowing future loans to reflect updated market states.

• Market-Aware Liquidations. Liquidations are executed through a Liquidity Engine that aggregates AMM reserves and order book depth to minimize slippage. Before execution, the engine simulates price impact, segments large positions into smaller trades, and prioritizes resting CLOB orders when available. This approach reduces volatility transmission and ensures orderly liquidation behavior under stress.

• Unified Collateral System. The permissionless layer mirrors the AMM: each trading pair hosts its own lending market, parameterized by live depth and volatility. The permissioned pool governs a curated set of cross-margin assets. Deposits in this pool remain productive across the system—earning base yield, backing open orders, and securing perpetual positions simultaneously. A debt-netting mechanism offsets delta-neutral exposures, mitigating conventional liquidation paths and reinforcing the collateral logic that also underpins ftUSD.

All parameters across LTVs, rates, and utilization update onchain in real time. The AMM publishes time-weighted (TWAP) and reserve-weighted (RWAP) price windows that feed directly into lending logic, ensuring collateral values reflect executable market conditions. Borrowers pay interest; suppliers earn it. Cashflows are settled through the token-first model, where lenders receive FT-denominated yield distributions and policy fees flow into protocol reserves and buyback mechanisms.

Flying Tulip’s protocol research demonstrates that collateral utilization adjusts in line with both market volatility and liquidity depth, as illustrated above. As volatility rises or liquidity thins, the maximum permissible LTV declines along a predictable curve, allowing leverage to expand and contract with market stability rather than remain fixed. This relationship directly informs how FT Lend calibrates borrowing power and liquidation behavior in real time, ensuring that capital allocation across the protocol remains both responsive and risk-aware.

Adaptive AMM-CLOB Spot Trading

Flying Tulip offers spot trading through a dual-engine exchange that combines an adaptive AMM with a central limit order book for execution. The two systems operate within a unified framework, enabling liquidity to route dynamically between continuous and limit-based liquidity depending on market conditions. Orders execute along the path offering the best realized price and minimal impact, designed to maintain consistent pricing across the protocol.

The AMM is curve-adaptive, referencing short-horizon volatility inputs including realized volatility (rVOL), implied volatility (IV), time-weighted average price (TWAP), and reserve-weighted average price (RWAP) to continuously reshape its pricing curve. In stable conditions, the curve flattens toward a constant-sum form to minimize slippage and compress bid-ask spreads. During volatility spikes, it steepens toward constant-product behavior to preserve depth and maintain price integrity against rapid order flow. This allows markets to self-regulate without manual intervention, adjusting curvature to prevailing liquidity and volatility conditions.

Price and flow data are smoothed through exponential moving averages (EMAs), dampening the effects of short-term swings while allowing the protocol to respond to structural market shifts. Guardrails limit how far the curve can migrate during a single window, ensuring predictable behavior for traders and liquidity providers alike. The result is a trading engine that adapts continuously to changing environments while avoiding instability.

Before execution, each swap is simulated onchain to assess its price impact relative to current pool depth and defined pool thresholds. Large trades are automatically broken up into several orders to smoothen market impact. If better pricing is available through the CLOB, orders are routed there by priority; while any remainder clears through the AMM. Fees are dynamic, designed to lower in stable markets to encourage activity, while raising during volatility to compensate LPs for their risk accordingly.

LPs can supply assets across full or concentrated ranges. A full-range position provides consistent fees on a x*y=k basis. Concentrated liquidity allows higher fee density while prices remain within range but requires active rebalancing when markets trend. The adaptive curve moderates these dynamics: spreads tighten when markets are stable and widen when volatility increases, reducing divergence loss during stress periods.

Fees function as a dynamic policy variable rather than a fixed parameter, adjusting with observed market conditions. The schedule moves toward its lower bound in calm regimes, attracting flow, and rises toward its upper bound in volatile conditions, compensating liquidity providers and slowing adverse flow. All bounds and coefficients are publicly visible and updated on-chain.

By unifying AMM and CLOB execution under a shared liquidity and risk framework, the protocol maintains consistent price discovery across conditions and avoids fragmenting depth. The AMM also serves as the foundation for other components; perpetual markets, lending, and liquidations, all of which reference AMM-derived price and depth windows for funding, collateral valuation, and impact-aware execution.

Flying Tulip’s protocol research demonstrates that AMM curvature dynamically adjusts to market volatility, as illustrated above.

This figure compares the simulated performance of several LP strategies across range-bound, uptrend, and downtrend regimes. Concentrated and trigger-based strategies generated higher fee income during stable periods, while passive LPs preserved value through directional moves. The results highlight the trade-off between reactivity and exposure: adaptive strategies outperform in equilibrium markets but converge toward passive outcomes when volatility rises sharply.

This relationship underpins how the protocol’s adaptive curve manages liquidity under stress. By adjusting curvature in response to volatility, the AMM preserves depth, stabilizes fee generation, and supports collateral assets such as ftUSD, ensuring yield and price stability across the broader protocol.

By unifying both market types under a shared liquidity and risk framework, Flying Tulip’s spot module maintains efficient pricing across conditions and avoids fragmenting AMM and orderbook liquidity. The CLOB also incorporates volume-based fee scaling, maker rebates, and a referral system that rewards LP and trading activity, designed to align user incentives with liquidity depth.

FT Perps

Flying Tulip introduces a native perps engine designed to let users take long or short positions on assets with leverage, settling directly to prices discovered within the protocol’s own AMM and CLOB. FT Perps derives these values internally from the protocol’s live trading activity. Prices, funding rates, and liquidation thresholds reference actual executable liquidity rather than delayed or externally sourced data.

This design allows the protocol to align mark prices with real trading depth. If an asset changes hands at a given price on the AMM or CLOB, that becomes the effective settlement price. Prices are updated on a continuous basis. Funding rates adjust automatically based on borrowing demand, position imbalances, and the effective cost of holding long or short exposure within the protocol. When longs are effectively borrowing stable assets to hold, and that borrowing becomes expensive, the funding rate reflects it; when the imbalance reverses, funding direction flips. All funding inputs and parameters are transparent and observable onchain.

Perpetuals can be traded in isolated or cross-margin modes. In isolated mode, collateral supports a single position, containing risk within that market. In cross-margin mode, users can post collateral from the permissioned Lend pool, allowing a single deposit to back multiple positions across the Flying Tulip protocol while continuing to earn its base yield. Borrowing power and leverage limits are determined by live depth and volatility metrics and are snapshot at the time a position is opened or adjusted to prevent retroactive changes.

Liquidations follow the same logic that governs the rest of the protocol. When a position approaches its liquidation threshold, the engine simulates the next execution step on the AMM’s adaptive curve. If the modeled impact exceeds safe limits, the position is unwound gradually, crossing resting CLOB orders first and breaking up the remainder across the AMM, to minimize price disruption.

All positions settle in ftUSD, Flying Tulip’s native settlement currency. ftUSD itself does not yield, but users can stake to sftUSD to receive protocol distributions. Liquidity providers who opt into settlement pools earn per-settlement fees, supplying ftUSD to back the perps engine.

Because pricing, funding, and liquidation logic all derive from internal markets, FT Perps operates as an integrated component of the broader protocol. It shares the same liquidity base, depth windows, and collateral pools that support the spot and lending layers, ensuring that leverage, pricing, and liquidation behavior remain consistent across the protocol. This structure creates a reinforcing trading environment: market data informs pricing, pricing informs leverage, and both evolve within the same collateral and liquidity framework.

Insurance

Flying Tulip introduces a protocol-native insurance layer with onchain protection for investors against unprecedented protocol incidents. These span across smart contract exploits, faulty liquidations, or other technical failures across the protocol’s trading, lending and settlement layers. The insurance engine operates as a continuous market between coverage buyers - investors seeking to protect their capital - and capital providers that earn yield through the premiums they collect.

Coverage is structured as an active position in a shared pool. Buyers post collateral and pay a streaming premium for the duration of coverage. Providers deposit capital in USDC, which serves as the pool’s base asset and payout currency. In return, they pool tokens representing their proportional share of assets and accumulated premiums, allowing protection capacity and pricing to adjust automatically as utilization and risk conditions change.

Premiums function as variable rates that respond to real-time data. Utilization, coverage demand, and internal risk indicators, which are derived from the protocol’s AMM and order book, jointly determine rate adjustments. When pool utilization rises or volatility increases, premiums move higher to attract liquidity; when conditions normalize, they ease back down.

If an incident occurs, an external verifier such as UMA’s optimistic oracle assesses whether it meets predefined on-chain conditions, including the affected system, time frame, and loss threshold. Once verified, users with active coverage positions can redeem their insured amount in USDC directly from the pool. If no qualifying event occurs, coverage can be closed at any time, immediately halting premium payments.

The insurance layer integrates with the same collateral and pricing systems that govern lending and trading. Assets deposited in Lend can back both open positions and coverage, while the same liquidity and volatility metrics that drive funding and liquidation logic also inform premium and capacity adjustments.

Capital providers assume exposure to verified payout events; coverage buyers depend on oracle adjudication. Both remain exposed to typical smart contract and liquidity risks, with temporary withdrawal limits possible during claim settlement.

In this model, insurance functions as part of the protocol’s internal risk infrastructure rather than an external add-on, allowing protection capacity, premiums, and liquidity to evolve directly with system conditions.

FT & Economics

Flying Tulip’s economic structure centers around its native FT token. Each protocol component – ftUSD, Lend, Perps, AMM, and Insurance – generates transaction fees, funding payments, and yield; which is then used to repurchase and burn FT. This reflects FT’s core design principle: to reflect protocol performance through net token supply over time.

The FT launch introduces a new kind of structure in DeFi, in which 100% of token supply is allocated to private and public participants, at the same valuation at the same time, through the Public Capital Allocation. FT has a fixed supply of 10 billion tokens and no set inflation schedule, and the founding team receives no upfront allocation; any exposure is earned later through open-market buybacks funded by protocol revenue. Minting occurs only through the Public Capital Allocation (PCA) at a fixed ratio of 10 FT per $1 contributed. If $500 million is raised, exactly 5 billion FT are minted; once the 10 billion cap is reached, issuance ceases permanently.

Participants in the raise receive their FT position as an onchain Perpetual PUT. While the option is active, holders can redeem FT at par in the original collateral asset, permanently burning the redeemed tokens; or withdraw FT, invalidating the option and releasing the backing capital for market buybacks and burns.

Each PUT functions as an NFT and encodes specific redemption rights to the participant’s underlying deposit, wherein if a holder withdraws 50% of the tokens allocated to their NFT, they will still be able to redeem the remaining 50% of their invested capital. This enables partial redemptions for investors with transparent onchain accounting, though it also means redemptions are irreversible, i.e. tokens cannot be re-deposited into the NFT once they’ve been withdrawn.

Capital raised through the PCA is deployed into conservative, onchain yield strategies, spanning Aave V3, stETH, jupSOL, AVAX staking, and sUSDe. Yield from these deployments is first distributed across the protocol and its operational costs; while the surplus is allocated towards FT buyback-and-burn. This structure is designed to enable protocol revenue and capital yield to sustain itself without relying on new issuance.

Both paths reduce circulating supply, either directly through redemptions or indirectly through repurchases funded by released reserves. Secondary-market FT carries no such redemption right; if holders redeem their underlying capital, their corresponding FT is burned, reducing circulating FT supply and concentrating more value to existing holders.

Flying Tulip’s Business Model

Revenue generated across the FT product stack is used for FT buybacks:

• ftUSD yield flows to the treasury and is used for FT repurchases.

• Lend contributes its net interest margin, the spread between borrower and supplier rates.

• Perps forwards a portion of trading and funding fees to the same buyback mechanism.

• Insurance directs its share of active premiums there as well.

• When holders withdraw FT from the Perpetual PUT, the capital released from that redemption reserve funds additional market buybacks.

Repurchased FT is then split between two outcomes: one portion is burned, permanently reducing that given portion of circulating FT supply; the other part is unlocked for distribution.

Unlocked tokens are governed by revenue‑funded buybacks and follow a fixed allocation schedule. When protocol revenue funds buybacks, the Foundation, Team, Ecosystem, and Incentives unlock 1:1 in a 40 : 20 : 20 : 20 split.

Just as Flying Tulip’s products are designed to adapt to real-time market conditions, the protocol’s business model is designed to adapt to real-time protocol activity. When protocol utilization rises, whether through higher trading volume, borrowing activity, or insurance demand, the aggregate revenue available for buybacks increases proportionally. When utilization slows, supply contraction continues but at a reduced rate, as no new issuance occurs.

In practice, FT functions as the coordination layer for value across Flying Tulip’s native ecosystem. Product-level revenue and conservative yield form the inflows; buybacks and burns are the outflows; and unlocks, when revenue-backed, maintain contributor alignment. The result is a closed capital circuit in which operational output, user activity, and liquidity depth all converge into measurable changes in supply, effectively tying FT’s value directly to the protocol’s realized performance and operational efficiency.

Outlook

DeFi hasn’t yet crossed the chasm, but it is far closer than it was a few years ago. The foundations of an Internet Financial System are already in place: 24/7 global settlement, transparent ledgers, composable infrastructure, and the ability to move capital without intermediaries.

However, DeFi’s current architecture remains too fragmented for the open, composable financial system it was intended to be: each primitive manages its own accounting, risk parameters, and collateral base. This isolation preserves protocol solvency but limits broader capital efficiency.

This issue isn’t unique to crypto, nor is it a new one by any means. A brokerage account at Fidelity can’t seamlessly interact with assets held at Charles Schwab. Transferring securities between them involves settlement delays and manual coordination. Even within a single institution, moving funds between accounts, say, rolling over a 401(k) into an IRA, requires intermediary steps and time-consuming processes. In contrast, however, DeFi protocols operate on shared, public infrastructure. Open standards and transparent state make composability a native property rather than a feature built through integration.

Flying Tulip lies uniquely at the intersection of two structural shifts in DeFi: aggregation and real-time adaptivity. The need for unified margin architectures that bring DeFi services i.e. trading, lending, derivatives under shared liquidity and collateral is becoming glaringly obvious as DeFi becomes more and more crowded. At the same time, the need to build DeFi products and services that respond to real-time market conditions in order to adjust parameters such as collateral ratios, borrowing limits, and funding rates, is becoming more apparent.

Flying Tulip aims to capture both in a single protocol. It connects DeFi’s core primitives: stablecoins, trading, lending, derivatives, and insurance, designed to adjust risk and pricing dynamically with real-time liquidity and volatility, through a unified margin account. Undoubtedly, if Flying Tulip’s launch and early growth are successful, there will be dozens of imitators building and selling a similar product. Nonetheless, first-mover advantage is a force to be reckoned with in DeFi. At Shoal, we believe Flying Tulip is well-positioned to become the leading real-time DeFi stack.

Sources

Data is sourced from DeFi Llama.

• Richard Stallman, Free Software, Free Society: Selected Essays of Richard M. Stallman, 3rd Edition https://www.gnu.org/doc/fsfs3-hardcover.pdf

• Flying Tulip, Flying Tulip Documentation https://docs.flyingtulip.com/

• Flying Tulip, Internal Research Threads. Retrieved from:

  • https://x.com/flyingtulip_/status/1898432266499416429

  • https://x.com/flyingtulip_/status/1898785006249513246

  • https://x.com/flyingtulip_/status/1898839079757996287

  • https://x.com/flyingtulip_/status/1898784803249410508/photo/1

  • https://x.com/flyingtulip_/status/1899466570981691502

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the Flying Tulip team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

A Transformation Is Overdue

Innovation tends to be scarce in an environment as entrenched, risk-averse, and burdened by technical debt as the financial services sector. Yet history suggests that advances in financial infrastructure have constantly moved in tandem with periods of exponential economic growth. Double-entry bookkeeping laid the foundation for modern accounting and credit. The joint-stock corporation, introduced in the 17th century, opened the door to large-scale investment and capital formation. SWIFT and ACH helped standardize interbank transfers and cross-border payments. Since the Industrial Revolution, global GDP has only accelerated.

Naturally, this raises the question: where and how does the next opportunity for financial innovation emerge? As Blackrock and Fidelity move hundreds of billions onto blockchain rails, landmark digital-asset legislations continue to be passed, and even retirement-plan managers begin allocating capital to crypto, it’s becoming clear a growing number toward one particular direction: tokenization.

At its core, tokenization means encoding assets of economic value - cash, securities, real estate - and their associated ownership claim onto blockchains. These distributed ledgers record every transaction as a transparent and immutable event, maintaining a single source of truth synchronized across a decentralized network of nodes. Offchain assets are represented as digital tokens; when ownership changes (i.e. assets are created, transferred, or destroyed), the changes are first validated by the nodes, and then permanently stored onchain.

The most prominent and recognizable example of this phenomenon today is the stablecoin. Designed to represent the value of stable reference assets, most commonly the US dollar, stablecoins are digital dollars issued and transferred on blockchain rails. They provide a stable, borderless unit of exchange in digital form: just as anyone with an internet connection can use WhatsApp to message globally in seconds, anyone can move money globally with stablecoins.

Given >99% of circulating stablecoin supply is made up of US Dollar backed stablecoins, we can effectively think of them as tokenized dollars: their supply onchain is backed 1:1 by reserves held in assets like dollars and treasuries. Stablecoins have stood the test of time and have seen remarkable growth in supply, which sits at just over $300B today. That said, if we observe monthly issuance volume of tokenized assets 5 years ago vs year-to-date, it’s clear the appetite for tokenization extends well beyond just digital dollars now.

To better understand why demand for tokenization is growing, it helps to first examine the limits of today’s market infrastructure.

Why Finance Needs New Rails

Few words capture the state of today’s financial infrastructure better than archaic. The systems that move trillions each day; payment networks, clearinghouses, settlement systems, were designed more than half a century ago.

History doesn’t repeat itself, but it often rhymes. Throughout much of the 20th century, America’s pre-1935 corporate giants dominated the market. They lost ground slowly, shedding about 2.2% of value each year to rising competitors. Then came the 1970s. New technologies, oil shocks, and inflation broke that inertia, and the pace of decline nearly doubled to 3.4% annually, sometimes even spiking above 10%.

That decade marked a structural inflection. The electronic systems that replaced paper trading and manual clearing ushered in a new era of efficiency, standardization, and scale. Networks like SWIFT, ACH, and the DTCC digitized the plumbing of global markets, transforming how capital moved and was recorded. Yet half a century later, those very systems now constrain the markets they once modernized. The infrastructure that enabled global finance to operate electronically has become its primary bottleneck.

T+1, T+2 (And Counting)

Most global securities markets today rely on delayed, multi-day settlement cycles, typically one to two business days between trade and final transfer. A trade executed on Monday is not settled until Wednesday. A trade placed on Friday may not be settled until next week. It’s as outdated as it sounds; this standard emerged from the paper-certificate era when trades required physical delivery and manual reconciliation. While trades themselves can be executed electronically in seconds today, the final transfer of their ownership still takes days. Until that moment, each trade is merely an entry in another institution’s siloed private ledger.

It’s naive to downplay this structural lag as mere operational inconvenience: delayed settlement represents one of the largest hidden costs in global markets. Delays mean asset ownership remains in flux, exposing participants to credit and counterparty risk and tying up that capital that could otherwise be productively deployed. In aggregate, trillions of dollars remain effectively idle and unproductive because the underlying infrastructure has failed to adapt.

The DTCC reports that tens of billions of dollars in daily settlement failures. These failures rarely occur in isolation; when one security fails to arrive, it often has already been pledged as part of another transaction. That second trade then fails, potentially a third after it, effectively creating a cascade of failures which are only amplified in markets with high leverage and thin liquidity.

Even when trades do eventually clear, structural inefficiencies remain. The longer the settlement window, the greater the collateral required, as clearinghouses demand higher margins to account for the additional risk. Meanwhile, banks and brokers often struggle to determine which securities are truly available in real time, and settlement delays often lead to over-allocation and inventory mismanagement. On top of this, settlement systems stop when markets close, meaning their daily operating hours and operational days per week are always limited.

These frictions only amplify across borders. Different regions operate their own central securities depositories with their own rules and timelines. Transferring assets across them involves multiple intermediaries and reconciliation steps. The regulatory patchwork adds another layer of friction. Compliance requirements differ by jurisdiction, forcing firms to spend heavily on reconciliation and reporting. Market operating hours rarely align, creating dead zones where trades cannot move forward. A current example is Europe’s delayed adoption of T+1 settlement, scheduled for 2027, meaning the EU will remain out of sync with the US and other regional markets, imposing new costs on participants that operate across both markets.

With this in mind, it’s important to recognize that the evolution of financial infrastructure more often than not is a reactionary one:

• In the 1960s, back-office paperwork overwhelmed Wall Street, leading to NASDAQ’s creation in 1971 as the first electronic stock market.

• In 1973, the Depository Trust Company centralized recordkeeping to eliminate the physical transfer of certificates.

• After 9/11, Congress passed the Check Clearing for the 21st Century Act, allowing digital images to replace paper checks.

• In 2012, Hurricane Sandy flooded DTCC vaults, destroying 1.7 million certificates and accelerating the transition to fully digital records.

Infrastructure most commonly evolves when inefficiency or crisis leaves no alternative. Today, the pattern repeats. The shortcomings of today’s market infrastructure are not abstract. They translate directly into costs, UX pain points, and risks that are borne by end-users.

The internet already enables information to move globally in milliseconds. There is little reason why money and securities should remain constrained by regional ledgers and business-hour settlement when the technology for instant, continuous transfer already exists. It is only natural that value should move as freely as information: accessible globally, operating continuously, and secured by transparent, programmable infrastructure.

All Roads Lead to Tokenization

Tokenization is designed to directly address the structural lag in modern financial systems.

By recording ownership and transfer on shared ledgers, it removes the need for multiple intermediaries to reconcile the same transaction across fragmented databases. Settlement becomes a matter of redistributing balances on a digital ledger rather than routing instructions through correspondent banks and clearing houses. The primary resource consumed in this process is computing power, paid as a transaction, or gas fee, a cost that continues to decline as low-cost or near-zero gas transactions have now become table stakes for chains today. As a result, the marginal cost of value transfer for tokenized assets will continue to gravitate to zero.

By contrast, legacy payment systems remain burdened by institutional overhead. A single wire or ACH transfer may pass through several intermediaries; sending and receiving banks, correspondent institutions, and clearing houses, each extracting fees and introducing settlement latency. Global cross-border transfers still average 6.49% in transaction costs, a persistent tax on capital mobility that tokenized rails largely eliminate.

The idea of tokenizing assets is not new. It has resurfaced repeatedly over the past decade in different forms. As early as 2012, Meni Rosenfeld’s Colored Coins proposal introduced the idea of attaching metadata to fractions of Bitcoin to represent other assets such as stocks, bonds, or commodities: a digital bearer asset could embed both monetary value and an external ownership claim. A few years later, DigixDAO in Singapore sought to bring physical assets like gold onchain. Built on Ethereum and IPFS, it introduced a Proof of Provenance model that allowed anyone to verify an asset’s existence through its custody chain. Digix was among the first to combine asset-backed tokens with decentralized governance via a DAO.

The broader environment, however, was not ready to scale. Between 2017 and 2020, onchain liquidity was scarce. Average daily volumes on many exchanges were under $10 million, and quoted bid-ask spreads in smaller tokens often exceeded 4-8%, setting up for unreliable price discovery and therefore limited adoption.

The supporting infrastructure for tokenization, across custody, settlement, and compliance, was equally immature. In 2018, only a handful of licensed custodians operated globally, and most lacked regulatory clarity. The SEC noted that few met depositary standards under Rule 206(4)-2 of the Advisers Act, leaving persistent uncertainty around asset segregation and safekeeping. On/off-ramps between banks and blockchains were also unreliable. Until 2020, fiat-crypto conversion failure rates averaged nearly 50 percent across major providers, driven by inconsistent KYC processes and recurring payment rejections. Infrastructure diversity was minimal: early entrants like MoonPay, Ramp, and Transak collectively supported fewer than 60 fiat currencies globally, excluding a good portion of emerging markets and locales.

The concept of tokenized finance was sound, but the market lacked the architecture, incentives, and trust mechanisms to sustain institutional participation. It would take another half-decade, driven by advances in compliant custody, the rise of stablecoin liquidity, and the emergence of cross-chain settlement frameworks, before tokenization began to take form at the scale it was once envisioned to.

At the foundation of this shift is a principle of fluidity. In any monetary system, assets that are highly divisible and low-friction tend to circulate rather than sit idle. Tokenized assets follow that same logic: they are designed to move, integrate, and settle across networks in real time. Blockchains function as public digital ledgers, and automated reconciliation reduce the cost of movement, while fractional ownership lowers participation thresholds and deepens liquidity.

The Tokenization Landscape Today

Total tokenized value now sits at just over $320 billion, spanning more than 220 issuers and roughly 400,000 onchain addresses. Still, only a fraction of that value circulates actively within DeFi protocols. The difference highlights a key point: much of tokenized value today remains in custodial or permissioned environments rather than freely composable onchain.

That definitional caveat aside, the trendline is clear: while still small relative to the $100T+ traditional markets, tokenized asset value has grown sharply in the past year. The rise of tokenized assets has been most present in two particular asset classes: stablecoins and tokenized Treasuries.

The State of Stablecoins

Stablecoins remain the clearest proof of concept for tokenization. With a circulating supply of more than $300 billion, they show that there is demand and utility for programmable, globally transferable, always-on cash works at scale.

Over the past five years, stablecoin adoption has accelerated sharply. Tens of trillions of dollars in value have already moved through stablecoin rails, and supply continues to set new all-time highs.

By token, issuance remains concentrated in USDT and USDC, which together account for the vast majority of supply. New entrants such as FDUSD and PYUSD, backed by major exchanges and payment networks, signal a gradual institutional pivot toward regulated issuance, while DAI, USDS, and USDe demonstrate persistent demand for decentralized, DeFi ‘native’ stablecoins.

By chain, Ethereum still leads in aggregate supply and transaction value, but Tron, Solana, and Base have grown rapidly.

These distributions reveal two different types of utility. Ethereum dominates in DeFi-related usage (i.e. liquidity pools, lending markets, and settlement) while Tron leads in peer-to-peer transfer volume, especially across emerging markets where low fees and quick confirmations make it ideal for payments and remittances.

More impressively, though, stablecoins are already eroding legacy payment networks. Aggregate transfer volumes now reach into the trillions per month, rivaling networks like Visa, Mastercard, and SWIFT, but with near-instant settlement and materially lower transaction costs.

The regulatory landscape around stablecoins is evolving in tandem. A new class of regulated money-market fund (MMF) tokens, including names like BUIDL, BENJI, USDtb, and Cap Money (which aggregates them), illustrates how stablecoin design is converging with traditional cash-management structures. In effect, these instruments merge the accessibility of stablecoins with the yield of Treasuries, hinting at the next phase in tokenized assets.

A Rising Phoenix: Tokenized Treasuries

If stablecoins effectively functioned as a blueprint for tokenization, treasuries are emerging as the rising phoenix. At the time of writing, tokenized Treasuries have reached upwards of $8B marking more than an 80% increase YTD. That growth has well outpaced the early trajectory of stablecoins, reflecting growing demand for yield-bearing, low-risk assets that can serve as collateral and liquidity instruments across global markets.

At a high level, the tokenized treasury landscape today already has:

• $8.3B total value

• 50 total assets

• 52.7k total holders

*As per rwa.xyz

Treasuries are debt instruments issued by the U.S. Department of the Treasury to finance government spending. Backed by the U.S. government, they occupy a central position in global markets, functioning as cash equivalents for banks, corporations, and sovereigns. The four main categories - Treasury Bills, Notes, Bonds, and TIPS - differ in maturity but share two defining traits: liquidity and yield.

While stablecoins provide price stability, Treasuries offer something GENIUS-compliant stablecoins cannot: interest income. With short-term yields above 5% since the Federal Reserve’s tightening cycle in 2022/23, onchain investors have increasingly sought exposure to the U.S. risk-free rate. The logic is simple: idle capital held in stablecoins can instead earn yield through tokenized Treasuries without sacrificing liquidity or transparency.

Demand has also been shaped by broader structural forces. The rise in rates ended the ‘easy-money’ era that defined much of DeFi Summer. Liquidity mining returns fell sharply as incentives disappeared and the unsustainable yields of prior cycles collapsed. Tokenized Treasuries emerged as an alternative source of yield which itself stems from interest-bearing government securities with clear legal protections and transparent collateral.

It’s worth noting tokenized Treasuries come with a distinct limitation: they are legally classified as securities, which restricts their composability across open DeFi systems. Compliance requirements, investor whitelisting, and regulated custody frameworks introduce friction that prevents them from circulating as freely as stablecoins. Even so, the pace of growth speaks for itself. Tokenized Treasuries surpassed $7 billion in supply far faster than stablecoins did in their early years.

A Deep Dive Into OpenEden

OpenEden represents one of the first credible attempts to build a functioning framework for institutional-grade tokenization. Its mission is straightforward: to provide investors with continuous, regulated access to U.S. Treasuries onchain, combining the legal protections of traditional markets with the operational efficiency of blockchain infrastructure.

Founded in 2023, OpenEden is a tokenization protocol providing structured access to short-duration U.S. Treasuries. It is the first tokenized-Treasury issuer to receive independent third-party bond ratings, earning an “A-bf” from Moody’s and “AA+f / S1+” from S&P Global Ratings.

OpenEden Digital (OED), a licensed subsidiary of the OpenEden Group, serves as the platform’s regulated issuer. Operating under a Digital Assets Business License granted by the Bermuda Monetary Authority, OED is authorized to issue, sell, and redeem digital assets within a clearly defined legal framework.

Growth and Adoption

OpenEden’s footprint has expanded steadily since launch, though adoption has notably accelerated in 2025.

Total value locked, which currently sits at $531M, has climbed sharply YTD, driven by consistent inflows into TBILL, OpenEden’s tokenized Treasury product, and USDO, its stablecoin, which each have amassed ~$266M and ~$264M in TVL respectively.

To date, OpenEden has generated $7.29M in total fees, which thus far has amounted to $1.79M in protocol revenue.

Most OpenEden assets reside on Ethereum, however, liquidity is also growing across other chains like Solana, Arbitrum, and XRPL.

Transaction data shows a steady rise in usage, with daily transfer volumes across OpenEden’s products now reaching parity between TBILL and USDO, the latter of which was notably launched at a later time.

Key Features

OpenEden’s framework is designed to combine the reliability of regulated financial structures with the efficiency and transparency of onchain settlement. The TBILL fund’s underlying assets are custodied by The Bank of New York Mellon (BNY), while the fund itself is investment-managed by BNY Investment Management (BNYIM), marking the first time BNY is managing a tokenised fund on a public blockchain. Each tokenized instrument represents a verifiable claim on a corresponding pool of short-term U.S. Treasuries, and the protocol automates issuance, redemption, and settlement through smart contracts while maintaining regulatory compliance.

​​OpenEden’s model rests on several core principles:

• Regulated and licensed issuance: OpenEden assets are backed by real U.S. Treasuries held by qualified custodians under a BMA–licensed framework.

• Self-custody: Investors hold their OpenEden assets directly onchain.

• Low-cost, programmable settlement: Onchain rails minimize reconciliation costs and enable cheaper, faster, and more transparent settlement.

• Smart contract automation: Core fund operations, namely minting, burning, and exchange-rate calculation, are governed by verifiable smart contracts.

• DeFi Composability: ERC-20 compatibility allows OpenEden assets to integrate across broader DeFi ecosystems.

• Multichain deployment: Multichain support for assets expands accessibility and network reach including Ethereum, Solana, Base, Arbitrum, BNB Chain, and XRPL.

In practice, these design choices allow OpenEden to combine the trust and governance of traditional markets within an onchain environment, where issuance, custody, and settlement are managed programmatically under a regulated framework.

OpenEden Core Products

TBILL

OpenEden’s flagship product, TBILL, provides tokenized access to short-duration U.S. Treasury Bills. It is available on Ethereum and Arbitrum, and is backed 1:1 by a pool of short-term U.S. T-Bills and dollar reserves held with regulated custodians.

Growth and Fees

Since launch, TBILL has seen consistent growth in both deposits and fees generated. At the time of writing, TBILL TVL sits at $264M, up over 130% YTD and 2300% since launching in 2023 ($11M).

TBILL has also consistently generated tens of thousands in fees since early 2024, amassing $624K total fees to date.

Structure and Issuance

TBILL is issued through the TBILL Vault, a permissioned smart-contract that governs how investors onboard, deposit capital, and receive tokenized exposure to short-term U.S. Treasuries.

The vault is investment-managed by BNY Investment Management (BNY IM), while the underlying assets, namely U.S. Treasury Bills, are custodied by BNY, ensuring that collateral remains fully auditable and compliant with global custody standards.

The investor onboarding process begins once investors complete full verification. Whitelisted wallets are granted permission to interact with the vault, allowing them to deposit USDC and receive tokenized exposure to short-term Treasuries.

TBILL is minted and redeemed through the TBILL Vault smart contract. USDC deposits on supported chains are converted into TBILL, while redemptions are processed back into USDC. Each TBILL token represents a proportional claim on the net asset value (NAV) of the underlying fund. Minting and burning are handled automatically by the vault’s smart contract, which updates circulating supply onchain in real time.

All issuance and redemption activity is publicly verifiable onchain, while underlying assets remain secured through traditional custodians

Subscriptions (Aka Minting)

Onboarded investors can “subscribe” to receive TBILL tokens by depositing USDC; which can be done through the standard interface or manually through the smart contract.

Subscriptions begin once USDC has been deposited. TBILL tokens are minted and delivered to the investor; the exact number is determined as a function of the TBILL/USDC price, fetched from TBILL’s price oracle contract, as well as the amount of USDC deposited.

tbillMinted=usdcDeposit/tbillusdcExchangeRate

Initial deposits must be at least 100,000 USDC, with subsequent deposits at least 1,000 USDC.

Redemptions

Investors may redeem TBILL for USDC at any time. Redemption requests are placed in a First-In-First-Out (FIFO) queue. During redemption, TBILL tokens are burned, and the corresponding USDC amount is minted and returned to the investor’s whitelisted wallet.

usdcReceived=tbillRedeemed*tbillusdcExchangeRate-TransactionFee

This process ensures redemptions are orderly and transparent, with NAV-based pricing enforced by the vault oracle.

Vault Strategy

The fund managing TBILL maintains a portfolio of short-term U.S. Treasury Bills with a weighted-average maturity of less than three months.

T-Bills are issued at a discount to par value. For instance, an investor might spend $950 to buy a $1,000 bill and redeem it at maturity for a ~5% yield. For TBILL holders, this structure translates to a predictable, low-risk yield that accrues through the vault’s automated portfolio management.

Fees

Two primary fees apply to TBILL: the Total Expense Ratio (TER) and the protocol transaction fee.

Total Expense Ratio (3 bps or 0.03%)

The TER is charged daily on the fund’s total value (TVL) and represents the cost of managing the underlying T-Bill portfolio. It covers:

• Maintaining the portfolio’s weighted-average maturity below three months.

• Rolling over matured Treasuries into new issues and compounding generated yield.

• Meeting regulatory and reporting obligations for the BVI fund.

• Commissioning third-party audits for both fund assets and smart contracts.

• Custodian and banking fees charged by BNY Mellon and affiliated institutions.

Transaction Fee (5 bps or 0.05%)

Applied during minting and redemption, this fee is calculated in USDC and processed onchain. It offsets operational costs such as gas usage, bank wire fees, and offramping costs associated with fiat conversion.

Together, these fees sustain both the onchain and offchain components of the TBILL system, ensuring the vault remains compliant, functional, and capital-efficient.

Token Price

The TBILL token price reflects the Net Asset Value (NAV) per token, tracking the appreciation of the underlying T-Bills as they approach par value:

tokenPrice=totalAssets - feeClaimable/ circulatingTokenSupply

At launch, TBILL was priced at 1.0000, appreciating gradually as underlying assets accrue yield.

Price Oracle and Governance Controls

OpenEden has implemented a set of onchain mechanisms to ensure the security of the TBILL Vault. To start, OpenEden uses an in-house price feed that transmits NAV data to an onchain oracle, safeguarded by a price guard that halts minting or redemption if deviations exceed predefined thresholds.

• Timelock: Whenever the TBILL Vault’s treasury wallet address [changes], a 24-hour window is implemented to [allow admins to offramp funds if the change in address is unintended].

• Multisig: The TBILL Vault uses a multi-signature (multi-sig) mechanism to authorize onchain transactions (i.e. minting/redeeming TBILL).

• Price Guard: A circuit-breaker mechanism designed to track the TBILL token price and respond when needed, namely when the price deviates beyond predefined thresholds. In the case of attempted oracle manipulation attacks or failures, this mechanism halts minting/redeeming of assets via the TBILL Vault.

Onchain, TBILL Vault smart contracts are publicly viewable and verifiable on Etherscan. Offchain, the TBILL Vault’s U.S. T-Bills are held in custody by regulated custodians. Any remaining USD fiat currency reserves will be held in regulated custodians or be in transit between the on/off service providers, the prime broker, and the custodian account.

USDO: The OpenDollar

Another core component of OpenEden’s product suite is the OpenDollar, or USDO.

USDO is a regulated, yield-bearing stablecoin issued by OpenEden Digital (OED). The rebasing yield is distributed directly in the form of additional USDO to holders’ balances, and is available on Ethereum, Base, and BNB Chain.

USDO has seen faster growth than TBILL, both in supply and adoption. TVL has climbed steadily since launch, nearly mirroring that of TBILL with $264M of its own.

The number of USDO holders has also increased rapidly, surpassing TBILL’s user base within months of launch (400 USDO holders vs 59 TBILL holders).

As a result, USDO is also generating fees and revenue at a much greater rate than TBILL, its $6.6m in fees accounting for >90% of OpenEden’s aggregate total fees ($7.29m).

Rebasing mechanism

USDO rebases daily while maintaining a fixed $1 price. This rebasing mechanism allows holders to earn yield generated from reserves backed by U.S. Treasury Bills and reverse repurchase agreements (repos).

Yield is distributed directly in the form of additional USDO, meaning holders see their balance increase daily, rather than the price changing.

The rebase is governed by a bonus multiplier, which updates automatically each day (including weekends):

usdoBalance=shares*bonusMultiplier

Like traditional fixed-income products, USDO’s yield is predetermined by OpenEden Digital and may be periodically adjusted to reflect prevailing market conditions. For instance, if short-term Treasury yields rise, USDO’s rate may be increased accordingly; if they decline, the protocol may lower it to maintain parity with underlying asset performance.

USDO Minting & Redemption

At a high-level, USDO can be minted against either USDC or TBILL collateral deposits. Similar to the TBILL token, USDO can only be minted by authorized users who pass KYC and KYT checks.

• To mint USDO, an authorized user initiates the Instant Mint function, selecting their collateral token.

• The USDO smart contract calculates the equivalent amount of USDO to be minted.

  • USDO minted at a 1:1 rate to USDC

  • USDO minted based on the prevailing TBILL exchange rate at the time.

• The Liquidity Manager contract then mints and transfers USDO tokens to the authorized recipient’s wallet address.

USDO redemptions follow the same logic in reverse. Tokens are first burned, and the equivalent amount of USDC is transferred back to the redeemer’s wallet.

Two redemption modes exist:

• Manual Redemption, wherein USDO is transferred to OED’s wallet and processed on a First-In-First-Out (FIFO) basis. Requests settle during U.S. business hours, typically within 1–2 business days

• Instant Redemption: The USDO Liquidity Manager contract sends the underlying TBILL to the TBILL Vault, which redeems USDC from Circle’s BUIDL contract using its BUIDL allocation. The resulting USDC is transferred to the redeemer’s wallet onchain

USDO Reserves

USDO is backed by a diversified pool of regulated tokenized Treasuries issued by major financial institutions:

• TBILL (OpenEden)

• BUIDL (BlackRock)

• BENJI (Franklin Templeton)

• VBILL (VanEck)

These assets are backed by short-duration U.S. Treasuries themselves. OpenEden integrates Chainlink Proof-of-Reserves for real-time, onchain verification of collateral holdings. As of the latest data, USDO reserves sit at ~273,196,076.72, with over 99 % of circulating USDO residing on Ethereum.

Fees are charged on all transactions involving the minting and redemption of USDO:

• Minting Fee: 3 bps (0.03%)

• Redemption Fee: 10 bps (0.10%)

These fees are calculated in USDO and deducted directly from the total USDO amount being minted or redeemed.

They are designed to cover the Vault’s operational costs, including:

• Onchain gas usage

• Bank wire fees
  Off-ramping and fiat conversion costs

Together, these transaction fees are designed to sustain both the onchain and offchain components of USDO’s infrastructure, while preserving the asset’s liquidity and peg stability.

cUSDO

USDO is designed to provide secure, regulated yield. However, its structure as a rebasing token makes it difficult to integrate with a broader range of DeFi protocols. As such, OpenEden introduced cUSDO, a wrapped and compounding version of USDO.

cUSDO represents the same underlying exposure as USDO but accumulates yield internally, causing the token’s price to increase over time rather than its balance. Each cUSDO token reflects an increasing claim on the underlying pool, ensuring it remains fully composable across DeFi protocols. Conversion between USDO and cUSDO is permissionless and handled through a public smart contract, allowing users to wrap or unwrap at any time.

Available on Ethereum, Base, and BNB Chain, cUSDO extends the reach of USDO’s regulated yield into the broader DeFi ecosystem.

Collateralization ratio

To ensure each USDO token remains redeemable at $1, OpenEden maintains a real-time onchain collateralization ratio that compares the total value of reserves to circulating supply:

CR=ReserveNAV/usdoCirculating

As of the latest reporting period, USDO is 103.7719% collateralized, implying a 0.37719% over-collateralization buffer. This margin accounts for operational frictions, such as price movements or settlement delays, while ensuring full redemption coverage.

The collateral ratio is continuously updated and verified through Chainlink’s Proof-of-Reserves integration, which tracks the real-time Net Asset Value (NAV) of the underlying Treasury and repo instruments. Should the ratio approach parity, the system is designed to restrict new issuance or trigger rebalancing actions to maintain full coverage.

EDEN and Economics

OpenEden’s business model is built on the fee revenue generated from its products, primarily TBILL and USDO. These fees, derived from minting, redemption, and ongoing management, are collected as protocol revenue by OpenEden Group, the platform’s operating entity. This is made up of TBILL’s Total-Expense-Ratio (TER) and transaction fees (0.03% and 0.05%), plus USDO’s minting and redemption fees (0.03% and 0.10% respectively).

To date, OpenEden has generated $7.29 million in total fees and $1.79 million in revenue, according to DeFiLlama. The revenue base grows in tandem with product adoption and transaction activity, as both TBILL and USDO generate recurring volume-driven fees tied to their respective vaults. Again, it’s worth recalling that USDO has generated a more meaningful portion of these fees ($6.6m in fees or >90%) and despite having been around less than TBILL, which has generated $624K in fees respectively.

The EDEN token sits at the center of OpenEden’s ecosystem across several functions:

• Staking, to support protocol operations and economic security

• Governance, allowing holders to participate in decision-making on upgrades and parameters

• Incentives and emissions, to bootstrap liquidity and adoption of new products

• Utility features, such as fee discounts or rebates for active participants

The OpenEden Group is also exploring mechanisms such as buyback-and-burn programs, funded through a portion of protocol revenue or treasury reserves, to reinforce token value capture over time.

The OpenEden Ecosystem

OpenEden’s ecosystem combines onchain integrations that extend the usability of its tokenized Treasuries with offchain partnerships that provide regulated market access and infrastructure support. The design reflects a dual objective: to make short-duration U.S. Treasuries operable within DeFi while ensuring the legal, custodial, and compliance standards required by institutional investors remain intact.

Onchain Integrations

OpenEden’s onchain footprint spans the main environments where DeFi activity occurs today. Its assets are deployed across Ethereum, Base, BNB Chain, Solana, Arbitrum, and XRPL, designed to maximize distribution by allowing broader user access from different ecosystems.

Within the broader DeFi landscape, OpenEden has integrated a number of bluechip liquidity venues including Curve, Balancer, Aerodrome, and money market protocols including Pendle, Morpho and Euler among others. These integrations allow cUSDO (and therefore indirectly USDO) to operate as productive collateral or yield-bearing components within existing financial primitives including liquidity pools, vaults, and structured yield products, offering USDO investors a way to maximize the productivity of their capital.

Offchain Partners

Offchain, OpenEden’s network centers on custodial, brokerage, and infrastructure providers that establish the operational and regulatory base for its products. Custodians including Binance, BitGo, Fireblocks, Hex Trust, and Propine handle storage and segregation of client assets within regulated frameworks, while HiddenRoad handles OpenEden’s brokerage needs, This ensures institutional holders can manage exposure through their existing compliance and risk channels.

The infrastructure and services layer, spanning providers including Chainlink, Kaiko, and Archblock, and supports data integrity, market transparency, and pricing reliability. Together, these integrations form the scaffolding that allows OpenEden’s assets to operate in both regulated and onchain environments.

Binance Banking TriParty

OpenEden has partnered with Binance Institutional through Binance’s Banking Triparty framework, allowing cUSDO to be used as yield-bearing collateral within Binance’s trading environment. The setup lets institutional clients hold cUSDO with approved custodians while using those balances on Binance for trading and settlement purposes.

The arrangement preserves segregated custody, wherein assets remain off-exchange with custodians such as BitGo, Fireblocks, and Hex Trust, while Binance mirrors the positions internally for operational use. This reduces the need to move assets between custodians and the exchange, lowering counterparty and settlement risk.

In effect, the partnership connects OpenEden’s tokenized Treasury products to existing exchange infrastructure without altering their underlying regulatory or custodial structure. It offers a straightforward mechanism for institutions to deploy tokenized Treasuries as collateral with institutional-grade compliance and risk management.

Analysis & Outlook

Having detailed OpenEden’s design and ecosystem, the next step is to evaluate how its hybrid structure, anchored by regulated issuance and DeFi composability, shapes its prospects in the growing market for tokenized Treasuries and beyond.

Competitive Landscape

The tokenized-asset market has grown crowded in a short period of time. Growth accelerated notably after late 2024, when renewed institutional optimism and a post-election risk-on environment drove inflows into yield-bearing digital products. Unlike prior cycles dominated by crypto-native experiments, today’s competition includes some of the largest names in global finance, with over 115 tokenization platforms in production today.

In tokenized Treasuries, OpenEden competes most directly with Securitize, Ondo, Backed, and Matrixdock, alongside institutional entrants such as BlackRock (BUIDL), Franklin Templeton (BENJI), and WisdomTree (WUSDY). Each offers exposure to short-duration U.S. government debt through regulated structures and onchain issuance.

Stablecoins present a different challenge. Supply remains highly concentrated in a few dominant issuers (USDT, USDC) which together account for well over 90 percent of circulating volume. However, zooming out, if we exclude USDT, USDC, and BUSD supply, it’s becoming evident that stablecoins, like treasuries and the rest of the tokenization landscape, is only going to become more competitive soon.

How Does OpenEden Differentiate?

With $531M in TVL and $7.29M in fees thus far, OpenEden’s raw numbers, albeit notable, are not reflective of its positioning. OpenEden’s strength lies in the overall quality of its protocol structure and how it merges together institutional-grade regulatory compliance with onchain programmability and DeFi composability.

Importantly, OpenEden is the first tokenized Treasury issuer to receive third-party credit ratings, (an A-bf from Moody’s and AA+f / S1+ from S&P Global) attesting to the institutional standards of its fund structure and custody model. In a sector still dominated by self-reported audits, third-party credit ratings mark a meaningful step toward formal institutional adoption.

Investor onboarding follows a permissioned framework: wallets must complete KYC and KYT checks before interacting with vaults. Once approved, all transactions (i.e. mints, redemptions, and fee accruals) are visible onchain. This setup combines regulatory traceability with public transparency, creating a model where compliance and verifiability reinforce each other rather than conflict.

At the same time, OpenEden’s assets are built for interoperability. Both TBILL and USDO follow the ERC-20 standard, allowing them to move freely across DeFi protocols. TBILL functions as a yield-bearing Treasury asset, while USDO extends that yield into a rebasing stablecoin format. Regulated issuance with open integration illustrates how tokenized securities can exist within DeFi infrastructure without compromising oversight requirements.

OpenEden’s roadmap points toward a shift from individual product issuance to a standardized infrastructure model. The aim is to simplify how institutions bring regulated assets onchain, abstracting the operational and compliance overhead behind tokenization itself. In this sense, its long-term vision resembles a “Shopify for tokenization”: a modular framework that enables others to issue and manage tokenized products within defined regulatory and technical boundaries.

Where We Suggest OpenEden Focus Next

There are several key emerging catalysts could strengthen OpenEden’s position within the tokenization landscape:

DeFi-Nativitiy as a Differentiator

As institutional capital becomes more comfortable operating on public blockchains, the expectation will shift from passive exposure to productive onchain deployment. Assets like Treasuries, which already serve as low-risk collateral in traditional markets, can increasingly be used within DeFi to generate incremental yield or support liquidity provision.

OpenEden’s architecture is already compatible with major DeFi protocols, and as the regulatory perimeter around tokenized assets expands, this interoperability will matter more. Institutions will likely prefer frameworks that allow their assets to circulate safely across lending markets, structured vaults, or automated liquidity systems, without losing compliance guarantees. In that environment, composability becomes a form of capital efficiency, and OpenEden is positioned to benefit from that shift.

Privacy: The Next Institutional Unlock

The next major frontier for institutional adoption is likely transaction privacy. Institutions are unlikely to transact meaningfully on public blockchains until confidentiality, data protection, and counterparty privacy can be guaranteed. Zero-knowledge proofs, encrypted state systems, and privacy-preserving chains are quickly maturing, and the first regulated issuers to adopt them will gain a substantial lead in onboarding capital that cannot move through transparent rails today.

For OpenEden, integrating privacy infrastructure, or even pursuing strategic partnerships or acquisitions in that domain, could become a defining differentiator. Privacy-enabled settlement would allow institutions to interact with tokenized assets while maintaining compliance and auditability through selective disclosure. The ability to conduct verifiable yet confidential transactions would mark a decisive step toward large-scale institutional use.

Closing Thoughts

Tokenization has momentum, but its footprint remains small: tokenized assets including government bonds, credit, and equities still represent only a fraction of global financial markets, estimated at less than 0.2% of the roughly $500 trillion in total assets worldwide. Stablecoins continue to dominate onchain liquidity, with over $300 billion in aggregate supply dwarfing the combined value of all tokenized assets on public chains. The advantages of tokenization are clear: operational efficiency, near-instant settlement, 24/7 access, transparency, and programmability for functions like automated compliance or dynamic asset management.

However, though magnitudes more efficient than legacy financial rails, tokenization still encounters its fair share of structural limits. Derivative markets such as perpetual futures protocols already offer efficient onchain exposure to the price or yield of offchain assets without requiring direct ownership or custody. These instruments capture much of the same demand that tokenization seeks to serve, often with greater capital efficiency and fewer regulatory constraints.

Furthermore, value capture remains uncertain. John Pfeffer argued that much of the economic benefit from tokenization will flow to users rather than intermediaries. Open systems are difficult to monopolize. The same qualities that make blockchains effective in coordination and record-keeping also make them incompatible with business models built on control and opacity.

Even so, the catalysts on which today’s surge in tokenization adoption hinge on suggest it really may be different this time. The ICO period lacked regulatory guardrails; that foundation now exists. Institutional trust and adoption, while still unfolding, is being accelerated by major asset managers like BlackRock, Franklin Templeton, and WisdomTree deploying and issuing regulated onchain funds. Tokenized assets are now used as collateral in protocols including Aave and Maple Finance, demonstrating the composability that yield-bearing instruments can get from DeFi rails. As adoption grows, capital is likely to move up the onchain yield curve, from stablecoins to Treasuries and eventually into tokenized credit and equity, expanding the potential tokenized asset value well into the trillions.

At Shoal, we think it’s clear by now that tokenization is no longer a matter of “If?” or “When?” but rather “How?” The more pressing question now becomes what kind of products and services prove to be the most useful over time, which hinges on a) the quality and efficacy of their underlying structure; b) the exogenous value they provide to users, DeFi, and the broader DeFi and financial services sectors; and c) their ability to capture value from the services they provide and redistribute to investors and token holders in a sustainable way.

In practice, this resembles a tokenization platform which manages to efficiently integrate compliant, regulated financial services with smart contracts and onchain rails; lower barriers to entry for institutions to bring regulated assets onchain by abstracting the operational and compliance overhead; and coordinate and align value capture from the organic demand for the services it provides through a native token.

OpenEden offers one of the most practical attempts to build this exact framework. Its regulated structure, emphasis on short-duration Treasuries, and institutional custody integrations anchor it within existing financial infrastructure, and independent credit ratings reinforce its credibility. A growing presence among the broader DeFi ecosystem ensures its assets are composable, productive, and scalable. OpenEden wasn’t the first attempt, nor will it be the last, but we believe it is one of the best-positioned teams to capture the tokenization opportunity ahead.

Sources

Data is sourced from DeFi Llama, Rwa.xyz, and Artemis Analytics.

• Dune Analytics, RWA Report 2025 Dashboard. Retrieved from https://dune.com/rwareport2025

• Theia Research, Internet Finance. Retrieved from https://x.com/theiaresearch/status/1876618725547233417?s=46

• Modern Treasury, History of ledgers. Retrieved from https://www.moderntreasury.com/journal/history-of-ledgers

• Gresham Technologies, Why trades fail: Understanding the cost of reconciliation errors. Retrieved from https://www.greshamtech.com/blog/why-trades-fail-reconciliation

• TD Securities, Cross-border implications of T+1 settlement. Retrieved from https://www.tdsecurities.com/ca/en/cross-border-implication-of-t-1-settlement

• John Pfeffer, An investor’s take on cryptoassets. Retrieved from https://hostingfilesonline.co.uk/An%20Investor’s%20Take%20on%20Cryptoassets%20v6.pdf

• Teej Ragsdale, Jack Chong, Mukund Venkatakrishnan, An Unreal Primer on Real World Assets https://docsend.com/view/u53utyp2j4ycg7r6

• Carter, Nic, The Stablecoin Duopoly Is Ending. Retrieved from https://x.com/nic__carter/status/1973399535092171216

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the OpenEden team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

The way we move money has always evolved, from barter to gold to paper to digital dollars. As stablecoins are set to eat global payments and work their way up the rest of the financial services stack, purpose-built stablecoin infrastructure becomes critical. In this report, we explore how Plasma is building towards global rails for the future of money movement.

The Digital Dollar Arc

Money rarely sits still, and neither do the ways we move it.

Few things are as elementary and timeless as the transfer of value from one individual to another. When ancient herders needed grain or tools, they traded cattle and goats. If medieval farmers needed a pair of shoes, they’d head down to the local village market with bushels of wheat. Over time, it became clear that carrying bulky items and livestock wherever one traveled was inconvenient, so everyone turned to precious metals like gold and silver bars. It didn’t take long to realize those too were not practical nor convenient, and so bars were melted into coins, and coins eventually gave way to paper cash, IOUs, and banknotes. As technology became a bigger part of people’s day-to-day lives, the ways in which money moved only accelerated.

Today, someone in the US can send digital dollars to a friend in Peru or Thailand in a matter of seconds, while paying a penny to do so. This isn’t something they could do through their local bank or Western Union; this kind of borderless, low-cost, near-instant money movement is made possible with stablecoins.

Stablecoins are digital dollars issued and transferred on blockchain rails. They provide a stable unit of exchange in digital form: the same way anyone with an email address or WhatsApp can instantly communicate across the globe, stablecoins enable anyone with internet access to move money globally.

Tens of trillions of dollars have already been moved through stablecoins. They continue to grow and reach new all-time highs in supply. Though not new by any means, stablecoin adoption has recently accelerated at a pace few anticipated.

Impressively, stablecoins aren’t just growing. They’re already eroding legacy payment networks.

At Shoal, we believe it is inevitable that stablecoins work their way up the rest of the financial services stack beyond just payments, eventually disrupting consumer banking, lending, B2B settlement, payroll systems, and beyond. But this all begs the question: what’s so special about these digital dollars?

The Stablecoin Value Prop

“It is possible to create tools to allow end users to create currency protocol layers which have a stable value, pegged to an external currency or commodity. In this way, users of these currencies can own stabilized virtual currency tied to U.S. Dollars, Euros, ounces of gold, barrels of oil..” - Mastercoin whitepaper (2012)

Fundamentally, stablecoins are digital assets programmed to maintain the value of an underlying asset it is pegged to. Most commonly, stablecoins are pegged to fiat currencies, particularly the US Dollar (this is why we can call them digital dollars). To maintain this peg and stable value, stablecoin issuers implement specific mechanisms to ensure each stablecoin is backed 1:1 by its underlying asset. The most common way to do this is by holding reserves in cash and short-term US Treasury bills, matching the circulating stablecoin supply. For every new stablecoin minted, users deposit dollars to the issuer. These funds are safeguarded in regulated financial institutions and custodians. When stablecoins are redeemed, an equivalent amount is withdrawn from these reserves and returned to the redeemer, and the corresponding stablecoin supply removed from circulation, or “burned”.

*Quick aside: There are other stablecoin peg stability mechanisms, some of which are represented by OG leading stablecoin protocols like Maker DAO (now Sky) who built a CDP protocol to let anyone borrow DAI. Conversely, there are also examples like Luna/UST which have failed tremendously and wiped out tens of billions of dollars. Overall, the design of stablecoins can be a fascinating rabbit hole to fall down, but it’s worth staying grounded to the reality that over 80% of stablecoin supply is in USDT and USDC, both of which are USD-backed stablecoins maintained and operated by central issuing entities.

The stablecoin value proposition is not one-dimensional by any means:

• Global, 24/7 Settlement: Stablecoins can be transferred globally at any time of day, any day of the week. They operate on blockchain rails, designed to settle in minutes if not seconds. For reference, legacy transfer systems like ACH or international wires often take several days to clear, can only be processed during limited business hours, and can further be delayed by weekends or holidays.

• Superior UX: Stablecoins provide a superior experience to legacy payment systems, on the simple basis of speed and cost. On certain chains, stablecoin transfers can be settled in a matter of seconds. Legacy systems like ACH or wire transfers can take anywhere from hours to multiple business days.

  Stablecoins are also multiples cheaper than legacy payment networks. A stablecoin transaction is simply a redistribution of balances on a digital ledger. The primary resource consumed is the computing power required to update this balance, which is paid as the transaction or gas fee. Nowadays, gas costs are racing to zero as low-cost transactions are becoming table stakes for most chains.

  By contrast, a wire or ACH transfer involves hand-offs between multiple intermediaries - sending banks, receiving banks, clearing houses - all of whom impose a meaningful rake on the money being moved. Legacy cross-border transfers still charge 6.49% on average.

  It is important to note that while stablecoins offer near-instant, low-cost blockchain transfers, converting funds off-chain into bank accounts or cash, or “offramping”, can still involve legacy fees, FX spreads, and delays. But overall, even with this final step, stablecoin-based transfers remain dramatically faster and less expensive than legacy networks today .

• Permissionless Global Access: Stablecoins essentially democratize access to stable currencies, like the US dollar. Anyone with an internet connection can send, receive, and hold stablecoins, regardless if they have a bank account or access to financial services. Despite rising global financial inclusion, over 1 billion adults still remain unbanked today, unable to access formal financial services and often excluded from the digital economy. At the same time, many countries still face hyperinflation and currency devaluation. By enabling permissionless global access to the US Dollar, stablecoins provide a financial lifeline to residents in these developing regions.

• Programmable: Because stablecoins are built on blockchain rails, they exist as smart-contract based tokens. Put even simpler: they are code. This unique design means specific conditions and regulations (i.e. settlement rules, transfer restrictions etc) can be programmed directly into the asset itself. This programmability helps streamline operations as it is much easier and more predictable to write code than to anticipate human error. And because the crypto-native economy is a consortium of open permissionless protocols, stablecoins can naturally plug into, and enhance, nearly any application built on public chains: earning yield through lending or liquidity provisioning, collateral for borrowing other assets, margin for perpetuals trading, betting on bizarre prediction markets, and so on.

Homegrown, Organic PMF

Stablecoins, unlike most other blockchain-native innovations, have stood the test of time because there has always been an organic demand for them. Their history is largely defined by organic adoption arising from clear catalysts at each chapter.

A Trader’s Best Friend

Stablecoins first gained traction among traders looking for a simple way to move value in and out of volatile positions without offramping into fiat. In the early 2010s, there were few things to do with Bitcoin besides trading it, yet traders lacked an easy way to park funds in between trades.

Early references to stablecoin concepts first appeared in the Mastercoin whitepaper, and in 2014 it came into production with the launch of Realcoin (now known as Tether).

USDT was first issued on Bitcoin via the Omni Layer, and became available through exchanges like Bitfinex. When it launched, USDT brought a new utility for crypto traders: move capital between assets, stabilize exposure to volatility, and execute more complex trading strategies without needing to move into fiat then back into crypto. That said, given such a niche audience, adoption was rather limited.

Global Access To Stable Currencies

As stablecoins matured, their impact spread well beyond speculative markets. In countries beset by hyperinflation, where local currencies can be devalued significantly overnight, the value of a stable currency like the US Dollar is hard to underestimate. And today, the best way to get access to the US Dollar is through stablecoins.

Some notable data points:

• Stablecoin purchases accounted for >4% of Turkey’s GDP in 2024.

• In 2024, Argentina led LATAM stablecoin volume share as >61% of crypto transaction volume regionally was attributed to stablecoins.

• $2.36 trillion was transacted through stablecoins in APAC alone from June 2024 - June 2025.

• Sub-Saharan Africa (SSA) grew $205B in onchain value in 2025, reflecting 52% YoY growth in crypto adoption from 2024.

As such, stablecoins have become essential tools for personal savings, payments, and remittances for millions of people across much of Latin America, Africa, Asia.

A Reserve Currency’s Best Friend

As stablecoins have spread across emerging markets, their role in reinforcing US Dollar dominance globally becomes evident. But while the value of access to digital dollars becomes rather self-explanatory in emerging economies, it has been more difficult to translate over in the west, particularly in the US. Most Americans have savings accounts. They can send money instantly through apps like Venmo. Few lose sleep at night because they are worried about the dollar devaluing (20-30%) overnight.

The most compelling reason for US policymakers to endorse stablecoins is geopolitical: to strengthen the global dominance of the US Dollar. The US has long wielded the dollar’s reserve currency status to maintain global financial power, most commonly through the petrodollar system.

Today, this opportunity lies in stablecoins. The idea is simple: dollar stablecoins, nearly all of which are backed by cash and short-term US Treasuries, require real dollar reserves for every token issued. Each stablecoin minted is a new dollar someone overseas chooses to hold, and therefore a new source of demand for US government debt.

One could make the case that the US doesn’t have much of a choice but to embrace stablecoins at this point. In Q4 2024, Tether was ranked as the seventh-largest foreign buyer of US Treasuries, outranking countries such as Canada and Mexico in annual flows. Tether’s latest stablecoin report confirms Tether holds over $127b in U.S. Treasuries, making it one of the largest non-sovereign holders of U.S. government debt.

This dynamic is already reshaping policy. The US passed the landmark GENIUS Act recently, laying the groundwork for a regulatory framework for stablecoin issuance in the US for the first time. Not surprisingly, the GENIUS Act requires dollar stablecoins to be fully backed by [US assets],besides providing monthly reserve disclosures and conducting independent third-party audits. This helps reinforcing a stablecoin-to-debt-financing flywheel of sorts: every new token minted effectively creates another buyer for U.S. debt, therefore stablecoin adoption becomes a powerful strategic tool for maintaining dollar dominance. Treasury Secretary Scott Bessent has publicly said “We are going to keep the U.S. the dominant reserve currency in the world, and we will use stablecoins to do that.”

In short, US dollar stablecoins are evolving from mere crypto products into key instruments for the US’s geopolitical strategy, meaning the US government is highly motivated to see the adoption of US backed stablecoins continue to grow from here on out.

The Stablecoin Landscape Today

These organic catalysts have helped shape and accelerate the adoption of stablecoins to this day. But to better understand where stablecoins go from here, it’s helpful to zoom out and examine the landscape as it exists today.

In the last 30 days alone:

• $3.2T in volume processed in stablecoins

• 1.2B transactions in stablecoins onchain

• 41.4 million addresses have interacted with stablecoins

*Data is from Artemis and referenced at the time of writing.

The total stablecoin supply continues to reach new all-time highs, sitting at just over $291B today. USDT accounts for ~58% of this supply, and has continued to dominate supply throughout the years.

Another important aspect of the stablecoin landscape today is the distribution of stablecoins across different chains. Just as stablecoin supply is dominated by USDT, stablecoin distribution across chains is dominated by Ethereum, accounting for ~55% of total supply today, as is overall stablecoin volume.

That said, it’s important to recognize there is more than meets the eye here.

Ethereum’s dominance, while it cannot be understated, is in large part historical. The first stablecoins like USDT, USDC, and DAI were on Ethereum because there were simply no other blockchain rails to issue and grow programmable digital dollars on. As liquidity deepened on Ethereum, it created a durable moat that persists today.

But the nuances start to emerge when considering specific use-cases: how stablecoins are actually being used on their respective chains.

For instance, if we observe data for purely peer-to-peer transactional volume, which we can map to payments, it’s clear that Tron has steadily grown and become the people’s choice. This doesn’t come as a surprise to anyone who’s actually used the two chains; transactions on Tron are simply cheaper and faster. They are more accessible for everyday users. The numbers account reflect this point.

In short, the stablecoin landscape is broad and expanding, but still heavily concentrated in specific stablecoins on specific chains. At a glance it may seem the winners and losers are already set. Yet the challenges and pain points of today’s infrastructure suggests otherwise.

Current Challenges and Pain Points

Despite their scale and adoption, today’s stablecoin rails weren’t designed to serve a global-scale payments network beyond crypto-native rails. In particular, today’s stablecoin infrastructure falls short in three key areas: large-scale performance, practical user experience, and integrated infrastructure for fiat rails.

Performance At Scale

The standard for chain performance and network capacity has come a long way since Ethereum’s L1 gas wars. Fees for stablecoin transfers, which could cost tens of dollars during peak network congestion on the L1, have dramatically fallen, and stabilized. Even so, most public blockchains still lag legacy payment networks in scalability and uptime. Ethereum and its L2s have roughly average 244 daily TPS. Solana regularly handles upwards of 2-3K daily TPS, and previously peaked around 4.8K (though it is always important to point out the nuance between vote and non-vote transactions). Yet global payment networks are designed to support tens of thousands of TPS, and can’t afford downtime even under peak congestion.

There are lots of promising developments ahead to improve this. Between the rise of app-specific perp DEX chains, Firedancer and Alpenglow coming to Solana, the performance that Monad and MegaEth are expected to deliver, the rise of SVM L2s. Yet none of these are stablecoin-specific. Stablecoin transfers on these chains still compete for more blockspace across swaps, perps, and everything else users can do on those chains as network activity and adoption scales.

User Experience

Even though transactions settle much quicker onchain than through banks, the user experience is still clunky. The Boston Consulting Group’s 2025 stablecoin report identifies “gas-token management for transaction fees” and fragmented liquidity as critical technical obstacles that both retail and enterprise users face when using stablecoins for payments.

The most obvious, low hanging fruit is gas-token friction: someone receiving USDC on Ethereum or SOL on Solana inherently needs ETH or SOL just to move or spend their funds. Onboarding for net new users is still impractical.

Liquidity is another challenge. Stablecoin supply is currently fragmented across hundreds of chains, which makes it harder to move large amounts or guarantee immediate conversion back to fiat outside of Ethereum. This fragmentation also dilutes network effects, forcing users and institutions to navigate bridges, gas tokens, and liquidity sources rather than relying on a single liquid, unified market.

Privacy, too, is lacking on today’s stablecoin infrastructure. Public blockchains expose transaction details by default. While privacy-preserving apps exist, users have to go out of their way to find and use them. There is no integrated “toggle” to enable private transactions. This means everyday payments with stablecoins are far more transparent than they would be with cash or even traditional banking. To the individual consumer, this may just feel invasive. To enterprises and financial institutions, it’s a non-starter.

The onchain user experience has improved significantly, but still leaves room for improvement.

Lack Of Integrated Infrastructure

One of the underappreciated challenges with today’s stablecoin rails is the lack of integrated infrastructure, particularly to fiat rails. Sending a friend USDT is not the same as them being able to spend it at a grocery store. In practice, users still face FX spreads, hidden rakes, and compliance bottlenecks when they try to move between blockchain rails and local payment systems.

Off-ramping involves using centralized exchanges or other intermediaries that require users to create accounts with them, often KYC’d, and pay service fees. This only gets hairier with cross-border transactions, which often introduce more intermediaries and FX Spreads that further eat into the transferred amount.

Until stablecoins integrate seamlessly into fiat rails, they’re great for moving value across crypto, but remain largely impractical for mainstream global payments.

So Where Do Go From Here?

Stablecoins have crossed the chasm. They are no longer a niche by-product of crypto markets. They’re clearing trillions in volume annually, sometimes even monthly, and already powering crypto trading, DeFi, and peer-to-peer markets across emerging economies.

But that level of adoption, as meaningful as it is, remains a different bar than what is required to serve as the backbone for global financial rails. Settlement systems like VisaNet, SWIFT, and ACH operate under far stricter demands: near-instant finality, reliability, and uniform compliance frameworks across varying jurisdictions. Stablecoins already move big numbers, but the chains they run on were never designed to consistently meet these standards at global scale.

One solution is to wait for general-purpose chains to evolve and harden into payments-grade infrastructure. Another is to embrace purpose-built stablecoin infrastructure.

Purpose-Built Stablecoin Infrastructure

Nearly every major industry where reliability, risk management, and performance are table stakes is built around specialized, purpose-built infrastructure.

Global payment networks like Visa and SWIFT are built with specific capabilities to securely move money globally, running in isolated environments optimized for data integrity, regulatory compliance, and uptime. In cloud computing, financial institutions and research centers don’t use AWS or Google Cloud, they still depend on purpose-built setups built to handle petabytes of physics data. In finance, high-frequency trading firms colocate servers next to exchange matching engines because general-purpose infrastructure cannot deliver the latency or reliability needed in markets where every millisecond counts. LLMs trained on sector-specific data in healthcare, insurance, or finance consistently deliver higher accuracy, lower error rates, and stronger regulatory alignment than their general-purpose counterparts.

It’s no different in the world of cryptography. The standards that were critical to the expansion of the internet - RSA for secure communication, SSL for browser authentication, and ECDSA for digital transactions - were purpose-built with singular missions: to secure and authenticate data between counterparties online.

Stablecoins are now at that inflection point.

Applying The App-Chain Thesis

Blockchains follow the same logic. As apps gain traction and activity scales, the limits of general-purpose chains emerge. Unpredictable gas fees, throughput ceilings. Compliance gaps. The list goes on. In response, applications are increasingly motivated to launch their own chains.

We’ve already seen this play out: decentralized exchanges like Osmosis, dYdX and most recently Unichain; gaming and NFT platforms like Immutable; data networks like Pyth; have all adopted some version of the app-chain model. The thesis originates from Cosmos, which argued that applications should own sovereign blockspace, optimized for their needs, while still interoperating with a broader ecosystem.

When a chain is purpose-built, it can deliver deterministic performance and predictable pricing, unconstrained by unrelated blockspace demand. It can embed compliance routines, disclosures, and risk management tailored to regulated products. It can align economic incentives, governance, and value capture directly with the needs of its user base and community.

App-specific infrastructure allows chains to move beyond one-size-fits-all environments for more customization, stronger performance, and better UX. Given the trajectory of stablecoin adoption, we believe it is only a matter of time before stablecoins require the same purpose-built infrastructure. One of the first teams building directly towards this vision is Plasma.

Plasma: A Blockchain Purpose-Built for Stablecoins

Plasma is a new L1 purpose-built for the movement of stablecoins. It combines a custom consensus protocol, an EVM-compatible execution layer, and specialized smart contracts to support high-performance at scale, while posting state roots to Bitcoin through a native Bitcoin bridge for security guarantees.

Plasma’s mission is simple: to transform the way money moves around the world with specialized stablecoin infrastructure. Just as TCP/IP enabled the internet to become a global information hub, Plasma aims to empower stablecoins to become the global payments hub.

Plasma aims to address the pain points of today’s infrastructure - performance, user experience, and integrations- through its core features:

• Day-One Liquidity: Plasma launches with native USDT and $2B in day-one stablecoin liquidity.

• Zero-Fee USDT Transfers: Direct USDT transfers are free through Plasma’s frontend. Gas is subsidized through an in-protocol Paymasters contract, using authorization-based transfers controls for rate-limiting and preventing abuse.

• Custom Gas: Users and apps on Plasma can pay gas fees using whitelisted tokens. At launch, Plasma will enable gas fees in native USDT and pBTC.

• Specialized Architecture: Plasma is built on a modular architecture, combining a custom high-performance consensus protocol and an EVM-compatible execution environment.

• Bitcoin Security: Plasma anchors its state roots to Bitcoin through a trust-minimized bridge for direct BTC-EVM programmability. This bridge also supports native BTC deposits, which are converted to pBTC on Plasma.

• Integrated Infrastructure: At launch, Plasma will support over 100 DeFi apps, including leading protocols like Aave, Ethena, Fluid, and Euler among others, as well as various physical peer-to-peer cash networks. Beyond this, Plasma will support a wide ecosystem of developer tooling and infrastructure across account abstraction, onchain analytics and block explorers, interoperability protocols, oracles, indexers, and RPC providers.

To better understand how Plasma actually delivers these features, it’s worth diving deeper into their core architecture.

System Architecture

Fundamentally, Plasma is a Bitcoin sidechain that maintains its own consensus and posts state proofs to Bitcoin. It is built on a modular, multi-layered architecture combining a custom BFT consensus protocol (PlasmaBFT), a Reth-based EVM-compatible execution environment, a trust-minimized Bitcoin bridge, and protocol-native stablecoin smart contracts.

A transaction on Plasma begins at the user layer, through a supported frontend. It is broadcast to the Plasma chain via RPC. From there it passes through consensus, and once 2/3 PlasmaBFT validators have validated it, the user’s transaction is executed. Plasma then periodically posts state roots to Bitcoin through its native bridge.

Let’s examine these core components up closer.

PlasmaBFT

Consensus is at the very core of onchain systems. It is the coordination mechanism that separates blockchains from banks and fintechs: instead of a single authority, a decentralized distributed network of nodes must validate incoming transactions. This design mechanism, however, introduces latency especially as the network scales and adds more validators, and comes at the cost of performance for many chains today.

To overcome latency bottlenecks, Plasma introduces PlasmaBFT, a custom consensus protocol designed to support high throughput. PlasmaBFT is a pipelined implementation of the Fast HotStuff protocol, written in Rust. Finality is deterministic, and is achieved within seconds.

As its name suggests, PlasmaBFT follows classic Byzantine Fault Tolerance (BFT) assumptions. In practice, this means the network remains secure as long as no more than one-third of validators are malicious (this can be reflected as n ≥ 3f + 1, where n is total nodes and f is Byzantine nodes specifically).

To participate, validators must stake XPL and run supported hardware (2 CPU cores, 4 GB RAM, SSD-based persistent storage). They are then selected to propose and validate blocks on Plasma based on an XPL stake-weighted voting process.

The protocol finalizes blocks through a two-chain commit process. Validators vote on block proposals, and once two consecutive quorum certificates (QCs) are formed, the block is finalized. QCs are aggregated validator attestations; chaining them together enforces a single canonical history.

PlasmaBFT is optimized to support high-performance on the L1. This is made possible with Plasma’s use of pipelining: while one block is being finalized, the next can already be proposed. As such, blocks can be finalized after just two rounds.

This design is derived from Fast HotStuff, a modern BFT protocol designed for, unsurprisingly, being fast. HotStuff introduced a leader-based structure and QC chaining to reduce communication overhead. Fast HotStuff further optimized [chain commit paths for lower latency and higher throughput].

Like HotStuff, PlasmaBFT uses a leader-based round structure, designed to minimize communication overhead while still maintaining fault tolerance. Leaders propose blocks, validators vote, and once enough votes are collected, a QC is generated. If a leader fails or goes offline, the protocol shifts to a new leader using aggregated QCs.

Plasma plans to expand validator participation in several stages. Initially, the biggest priority will be ensuring baseline network stability, therefore Plasma will be secured by a whitelisted set of validators at launch. Over time, the validator set will grow to stress-test performance under larger committees until the final stage, in which participation opens up to the public.

Plasma’s Execution Environment

Virtual Machines (VMs) process transactions, run smart contracts, and keep state in sync across all participants on its underlying chain. A VM reads the current state of the chain, executes new inputs, and then updates state deterministically. It ensures the same code always produces the same result and state is synchronized across all nodes.

Plasma employs a general-purpose Ethereum Virtual Machine (EVM) execution environment. This means developers can deploy existing EVM smart contracts and use familiar tooling and infrastructure.

Plasma’s execution engine is Reth, a modular Rust-based Ethereum client that separates consensus from execution. This enables more efficient updates, clearer boundaries between block production and execution, and better predictability in performance and behavior.

When transactions are submitted on Plasma, Plasma’s execution environment processes them through the EVM, ensuring the relevant smart contracts run and state is updated consistently across all Plasma nodes.

Bitcoin-Level Security

Blockchains normally secure themselves only through their own validator sets. That security is limited by the size of the validator pool and the economic weight behind it. For stablecoins, where large values may be at stake, relying solely on a new or relatively small validator set can be a risk.

To mitigate this, Plasma periodically posts its state roots to Bitcoin. Anchoring to Bitcoin provides an additional settlement guarantee: once a Plasma state is recorded on Bitcoin, altering it would require rewriting Bitcoin’s history. This makes censorship or rollback far less feasible and gives Plasma a stronger security baseline than relying on its validator set alone.

Plasma achieves this through a Bitcoin-native bridge. The bridge consists of a verifier network, each running full Bitcoin nodes to observe deposits and validate state anchoring. Periodically, state roots from Plasma are committed to Bitcoin via transactions (e.g. OP_RETURN), and the verifier network attests that these anchors match Plasma’s canonical chain.

Beyond settlement, the bridge also enables native BTC to flow into Plasma. Users deposit BTC to a designated address, verifiers confirm the transaction on Bitcoin, and Plasma mints pBTC: a fungible ERC-20 token backed 1:1 by BTC. Withdrawals follow the reverse process: users burn pBTC on Plasma, and once verifiers confirm, BTC is released on the base chain.

This bridge makes native BTC usable inside Plasma smart contracts. pBTC is issued as a standard ERC-20, built on LayerZero’s OFT standard to allow cross-chain portability without compromising verifiability against the Bitcoin base layer itself. Users always receive pBTC on Plasma at a 1:1 rate against BTC deposited.

To secure withdrawals, Plasma relies on Multi-Party Computation (MPC). Rather than one party holding a private key, multiple verifiers collectively generate signatures, with no single entity able to unilaterally release funds.

Stablecoin-Native Smart Contracts

On current stablecoin rails, stablecoins exist as generic ERC-20 tokens. They are applications built on top of a base chain. This design works to an extent but friction still emerges: transaction fees paid in separate gas tokens. Every app or wallet has to operate and maintain its own custom Paymaster. Private payments are not feasible.

On Plasma, this functionality is built directly into the protocol. A set of in-protocol contracts, written in Solidity and integrated into the execution layer, give stablecoins first-class treatment. These contracts are written in Solidity, integrated into Plasma’s execution layer, and are compatible with any EVM wallet or contract system, including AA standards like EIP-4337 and EIP-7702.

Plasma is launching with two core modules. The first is the Protocol-Managed Paymaster for Zero-Fee USDT Transfers. This contract sponsors gas costs for direct USDT transfers: transfer() and transferFrom(), so users can send stablecoins without needing XPL. The scope is narrow by design: it applies only to the official USDT and only to direct peer-to-peer transfers, not arbitrary contract calls. To prevent abuse, eligibility is gated through lightweight identity checks such as zkEmail, zkPhone, or captcha systems, and usage is subject to rate limits. The economics are covered by a pre-funded XPL pool managed by the foundation, which pays gas on behalf of users. Developers don’t need custom integrations beyond routing transfers through standard smart account flows, and the system supports both EOAs and smart contract wallets. Over time, Plasma is exploring features like reserved blockspace for eligible USDT transfers, ensuring inclusion even under network congestion

The second module is the ERC-20 Paymaster for Custom Gas Tokens. This contract allows users to pay for any transaction, not just transfers, using whitelisted tokens (this will be USDT and pBTC initially). The process is simple: the user approves the paymaster to spend the chosen token, the paymaster consults oracles to calculate how much of that token equals the required gas, and then pays the validator in XPL under the hood while deducting the equivalent amount from the user. This removes the need for swaps or native token balances, smoothing the onboarding process for new users. Developers benefit because the paymaster is handled at protocol level, so they don’t have to build or maintain their own fee abstraction systems. Wallets only need to surface approvals and error handling, while users see a unified and intuitive UX.

By running these modules at the protocol level instead of leaving them to individual apps, Plasma ensures consistent behavior across apps, enables subsidized gas without external funding tokens, and ties these features directly into block production and execution.

XPL And Plasma’s Economics

At the core of Plasma’s business model is its native token, XPL, which secures the network and subsidizes PlasmaBFT validators.

Every transaction on Plasma requires a base fee, with dynamic demand-based fees. These fees, along with newly issued tokens, form the reward pool that sustains validator incentives.

Plasma takes a unique approach to slashing. Rather than punishing malicious validators, it incentivizes honest behavior through a novel reward slashing mechanism. In this model, validators who misbehave or fail to participate lose their block rewards but keep their principal stake. The penalty is lighter, but it makes participation less risky for institutional operators, where sudden capital loss is often commercially unacceptable. Importantly, Byzantine safety is still preserved under the standard assumption that fewer than one-third of validators act maliciously.

Validators are compensated in XPL for proposing blocks, voting in consensus, and validating transactions. Their rewards come from a mix of transaction fees and token emissions, making validator incentives directly tied to both network activity and the broader economics of XPL.

The Plasma Ecosystem

Surrounding Plasma’s technical work is a growing, integrated ecosystem. Plasma is launching with $2B in stablecoin liquidity and over 100 DeFi integrations, including names like Aave, Ethena, Fluid, and Euler.

Plasma recently announced Plasma One, its main consumer-facing app. It functions as a stablecoin-native wallet and card interface, giving users “one” place to hold, send, and spend USDT. Transfers are gasless, balances can be used directly for payments, and onboarding is quick enough to issue a virtual card in minutes.

On the integration side, Plasma has partnered with Binance Earn, allowing users to access yield products directly from the network. Work is also underway on fiat on/off-ramps and peer-to-peer cash networks in emerging markets, aiming to make the jump between stablecoins and local currency less dependent on centralized exchanges.

Plasma will also launch with a wide ecosystem of developer tooling and infrastructure across account abstraction, onchain analytics and block explorers, interoperability protocols, oracles, indexers, and RPC providers.

The Outlook For Plasma

Given what we now know about Plasma, it’s worth zooming out and reflecting on some of the most important catalysts and considerations impacting how the path forward unfolds from here.

Competitive Landscape

Plasma has an ambitious and important mission: to transform how money moves across the globe. Unsurprisingly, they face competition from several angles.

First, general-purpose blockchains aren’t going anywhere. Ethereum still dominates in stablecoin liquidity and network effects. Tron commands the largest share of USDT transfers and peer-to-peer transaction volume, and Solana is constantly working on developments to improve performance. Meanwhile, new chains like Monad and MegaETH are also competing on the basis of high-performance and full EVM compatibility.

At the same time, we’re seeing the rise of stablecoin-specific chains, or “stablechains”, similar to Plasma. Circle is building Arc, a permissioned chain designed for regulated USDC settlement. Stripe is working on Tempo, focused on embedding stablecoins into merchant payments. Google recently announced Google Cloud Universal Ledger (GCUL), an L1 focused on digital payments and tokenization for financial institutions. Meanwhile others stand out with differentiated approaches and unique value propositions. For instance, Payy also introduces neobanking stablecoin infrastructure, including its own purpose-built chain, but with a core focus on privacy-preserving transfers.

Even outside of crypto, non-blockchain payment rails are already converging to offer much of the same key benefits stablecoins do. That means Plasma not only has to match crypto benchmarks, but also these legacy standards of performance, speed, and reach.

Key Areas of Focus

With that in mind, what are key areas of focus that would help the Plasma team?

For starters, distribution is everything. Like any business, a chain can’t grow without getting its product into the hands of users. For blockchains, this has been mostly limited to crypto-native apps, exchanges, connected chains, and DeFi protocols. Though Plasma launches with over 100 integrations and $2B in liquidity, the harder challenge is pushing beyond crypto-native adoption into retail and enterprise crypto-adjacent use. Plasma is set to support 100+ countries, 100+ currencies, and 200+ payment methods, laying the groundwork for a strong initial distribution footprint. Long-term, though, sustaining real-world adoption will require constant effort: onboarding merchants, working with fintech partners, and leveraging Tether/Bitfinex’s existing networks. Tron’s rise shows how powerful grassroots distribution can be. The question is whether Plasma can retain users and build durable channels beyond its crypto-native base.

Liquidity is another area of focus. For a chain purpose-built around stablecoins, deep liquidity is simply existential. Plasma is launching with billions in native USDT issuance, making it the 8th largest USDT chain on day one. To ensure it can sustain future growth, bridges, ramps, and deposit flows have to be simple and as friction-free as possible.

Privacy is another underrated piece. Confidential Payments are planned but won’t ship with Plasma’s mainnet beta launch. The core idea being explored is to shield sensitive transfer data while remaining composable and auditable. This feature would be implemented as a lightweight, opt-in module at first, though Plasma could very well look to enshrine privacy features at the protocol layer in the future.

With its novel architecture, ecosystem reach, and liquidity base Plasma is in a good position:

• Anchoring to Bitcoin provides a differentiator. Plasma periodically posts its state roots to Bitcoin, giving extra settlement guarantees beyond relying solely on its validator set.

• Additionally, building as a public permissionless chain lets Plasma reach a broader audience, while being able to enforce features like identity-based transfers for specialized experiences like zero-fee USDT transfers.

• Using a custom consensus protocol enables Plasma to support fast execution at a high-throughput rate.

• Launching with over 100 DeFi integrations and $2B in USDT makes it the 8th largest chain by stablecoin supply from day one.

Applications and Use Cases

Plasma provides rails to support a variety of stablecoin-specific use cases and applications.

Global Payments and Remittances

One of the most immediate applications is as a global payment rail. A worker in the U.S. could send $100 in USDT to a relative in Nigeria. The relative could then spend directly through integrated merchants, use directly through crypto cards, or cash out via local OTC desks and exchanges.

In countries such as Nigeria, Argentina, and Turkey, where stablecoins already act as a hedge against inflation and a remittance lifeline, Plasma can reduce friction even further. Success here depends on integrations with local wallets, ATMs, and payment systems. Tron achieved this over years of grass-roots outreach, so Plasma will need similar partnerships, possibly leveraging Tether’s existing networks. If executed well, Plasma could serve as the backend for remittance firms or neo-banks, offering instant dollar transfers without requiring their own blockchain infrastructure.

Merchant Payments and Micropayments

Paying with crypto has been rare due to volatility and fees, but zero fee USDT transfers on Plasma could change that. Merchants could accept stablecoins at the point of sale via QR codes, avoiding credit card fees and chargebacks.

Privacy features would let businesses shield revenue data from competitors, while micropayments become practical. Platforms could charge a few cents per article, stream, or download without fees eroding margins. For merchants, the key will be user-friendly tools and regulatory compliance. Stablecoin payment processors may emerge on Plasma, and over time, even traditional providers could integrate it in high-fee or underbanked regions.

Forex and Cross-Currency Transactions

The stablecoin market extends beyond the U.S. dollar, with Euro, offshore yuan, and gold-backed tokens already in circulation. Plasma could become a hub for on-chain foreign exchange if multiple fiat stablecoins are supported.

A user could, for instance, swap USDT for EURT on a Plasma-based DEX at near-zero cost. This enables cheaper, faster forex trades compared to banks. Imagine a multinational paying European suppliers by converting millions of USD into EUR instantly and settling the transaction on-chain. Deep liquidity will be needed, but zero fees and institutional demand could draw in market makers.

DeFi with Stablecoins and BTC

Plasma’s EVM compatibility opens the door to decentralized finance focused on stablecoins and Bitcoin. Potential applications include:

• Stablecoin DEXes and AMMs: Low-cost, high-volume trading of stablecoin pairs (USDT/USDC, USDT/EURT) or stablecoin/BTC pairs.

• Money Markets and Lending: Platforms could let users lend or borrow USDT against BTC collateral.

• BTC-DeFi Innovations: Bitcoin-backed stablecoins minted on Plasma without custodians.

Institutional Treasury and Settlement

Institutions such as exchanges, fintechs, or even banks could use Plasma as a settlement layer for large transfers. Exchanges may prefer Plasma over Tron or Ethereum for inter-exchange USDT flows, thanks to its speed and zero fees.

Banks or corporate consortiums could also run private overlays on Plasma, settling large interbank transfers with Bitcoin-backed finality. For corporate treasuries, moving $50 million between subsidiaries could take seconds instead of days with SWIFT. Privacy modules would allow selective disclosure for auditors or regulators. If U.S. legislation like the GENIUS Act advances, regulated institutions may adopt public stablecoins like USDT and USDC, with Plasma positioned to capture that demand.

Beyond these, there are more crypto-native use cases that would be interesting to see teams explore building on Plasma. Zero-fee USDT transfers could make a really compelling case to build infrastructure for autonomous agent payments on Plasma. With $2B deployed from day one, Plasma could provide deep stablecoin liquidity prediction markets need to sustain meaningful user activity. Plasma could also serve as a routing layer for stablecoin liquidity across multiple chains.

Closing Thoughts

Plasma’s mainnet beta is now live. We’re excited to see how purpose-built stablecoin infrastructure unfolds from here on out. Stablecoins are set to disrupt global payments and the rest of the financial services stack, but they need dedicated, purpose-built rails to flourish. Though Plasma is not the only one pursuing this north star, it offers one of the most promising solutions to capture the trillion dollar stablecoin opportunity..

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Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author’s own, not the views of their employer. This post has been created in collaboration with the Plasma team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research

Decentralized AI is beginning to cross the chasm. The United Nations Development Programme (UNDP) has long advocated for the sustainable use of AI to empower developing nations and underserved communities as part of achieving its Sustainable Development Goals (SDG). Earlier this week, the UNDP selected FLock - a decentralized AI network - as its strategic AI partner. Why does this all matter, and how does FLock’s decentralized approach address challenges that centralized AI cannot?

AI Is Inevitable

The role of AI continues to grow in our day to day lives. Millions of people are using AI-powered applications like ChatGPT. Enterprises are pouring hundreds billions into developing the infrastructure, training, and integration of AI in their workflows. Governments are actively implementing AI solutions for public operational efficiency with AI.

This is all for good reason: AI stands to unlock a new level of productivity gains not yet seen. It’s already leading to groundbreaking applications across various fields of scientific research. People are automating busy work, leading to more creative initiatives and experiments. A new generation of goods and services is actively being transformed from natural language into existence, what has come to be known as “vibe-coding”. McKinsey projects $4.4 trillion in productivity gains from AI adoption, while Goldman Sachs forecasts up to 7% increase in global GDP by 2030.

There is a caveat though: the AI models we know and use today are black boxes: closed-source LLMs developed and maintained by central, for-profit entities. These entities control how the AI models are developed and trained, and determine the terms and means by which they can be accessed and used. At a glance, these are resourceful, legally compliant companies with a strong economic incentive to keep improving these AI models and listening to their customers’ feedback and requirements. What exactly then is the concern?

Why Not Just Use Centralized AI?

People have been waking up to the dangerous realities of social media algorithms, realizing the extent to which these black boxes maintained by for-profit entities are impacting the way they think and behave. The implications with AI are significantly greater.

Binji’s AI therapist tweet offers a more facetious example to reason about, but it speaks to the concerning implications of speaking to black boxes which operate in an unknown manner behind the scenes. But a shift away from centralized AI is not about realizing utopian cypherpunk ideals: there are legitimate concerns and risks at the individual, enterprise, and national levels:

• Single point of failure: Centralized AI introduces a single point of failure for its users. First, this poses an operational risk, if these facilities go offline due to outages, attacks, or natural disasters. This is not uncommon. Second, there are substantial security concerns, including the risk of leaked user prompts, chat histories, and sensitive data with single-failure points. Such breaches at AI companies could expose everyday user information and become national security liabilities when government or sensitive data is involved.

• Data Protection: Centralized AI apps need to store and maintain user data to improve models and provide personalized experiences. Users are providing LLMs with all kinds of data about themselves, including sensitive and personal information. The concern here is twofold: that data is at risk of being breached. Additionally, the risk is that AI companies monetize user data the same way social media and internet companies have historically monetized their users.

• Value Plurality and Model Output Bias: Model outputs are at risk of bias towards a single entity’s values and perspectives. The Google Gemini incident controversy, though a mostly isolated example, highlighted the potential risks of models providing misinformation to its users.

• Scalability: Processing power limited to a single entity creates potential bottlenecks over time. Network delays, capacity constraints, and overprovisioning challenges can arise as usage scales, and the entire network runs on the assumption that the centralized AI provider alone has enough processing power and capacity to handle it all.

Beyond this, innovation is simply stifled if a limited number of people or entities can experiment and develop new models. Most of the internet’s infrastructure runs on Linux, an open-source operating system that empowers developers worldwide to innovate and adapt freely. Wikipedia, a freely editable and collaboratively maintained knowledge base, serves as a gateway to information for billions of people globally. In the world of computing and software, open source technology movements have repeatedly shown that collaborative development can produce superior, more resilient solutions than closed-source alternatives.

The UNDP: Using AI As A Transformative Force For Good

AI is technology powerful enough to change the world for good. What may be mere quality-of-life improvements in one country can be transformation 0:1 improvements in others. et centralized AI inherently conflicts with its potential as a public good.

Developed in 1965, the UNDP was formed to help eradicate poverty and reduce inequalities worldwide through sustainable development. For years now, The UNDP has been advocating for embracing AI as a transformative force for good. Specifically, the UNDP believes that AI can help accelerate its mission.

The key challenges they are concerned with include:

• Digital public infrastructure: Developing nations may not be able to afford to build Google/OpenAI-scale systems. At the same time, they do not want to be dependent on AI developed by other nations like the US or China.

• Nation-specific needs: Nations need AI solutions tailored to their specific needs and scale, not settle for cookie-cutter general purpose solutions.

• Data sovereignty: Nations need to protect their citizens’ data, especially across healthcare, agriculture, and other sensitive and personal data.

Just earlier this week, the UNDP announced a new strategic AI partner. It wasn’t OpenAI, Anthropic, or Google, but a new decentralized alternative: FLock.

Introducing FLock

FLock, short for Federated machine learning on blockchain, is a decentralized AI network. Instead of a single entity, it enables anyone around the world to contribute models, data, and computing resources and participate in an open, collaborative environment for decentralized AI training. To do this, FLock combines conventional and Federated Learning AI model training with automated blockchain-based reward distribution.

Altogether, this creates an open marketplace of app-specific models which are community owned, built, and led.

How It All Works

To better understand FLock’s approach, it helps to first understand Federated Learning.

Federated Learning, or federated Machine Learning, is a collaborative machine learning technique where multiple participating clients can collaboratively train a global model without exposing any raw data. This approach is designed to improve output quality by enabling AI models to be trained on wider and more diverse data sources, while protecting the privacy of participants’ data.

Federated learning typically involves a central hub server on which the global model lives and is redistributed for local training. Participating clients then train the global model using their local data and only exchange their gradients, the weights and parameters used in their local training, back to the hub server. The hub server then aggregates these gradients to update the global model, and the process is repeated until the training is complete.

Federated learning has proven to be an effective solution to addressing user privacy within AI training, there is a catch with this set up, though. To start, the central hub server, on which the global model lives, is still a single point of failure. Should it stop working for whatever reason, the whole learning stops. Additionally, federated learning still assumes the data clients provide is honest. In other words, it doesn’t address the risk of data poisoning, a technique in which clients provide inauthentic data targeted at diminishing the quality of a model’s training and therefore its output. This is becoming a growing area of concern as more of today’s world becomes reliant on AI models, especially in fields like healthcare.

FLock aims to address these problems by implementing cryptographic proof and economic incentives enforced onchain. So how exactly does that work?

FLock System Architecture

FLock consists of three interconnected systems that form its decentralized AI ecosystem:

• The AI Arena: A decentralized training platform where developers submit models that compete on public datasets. These models are evaluated by a distributed network of validators, with an important feature: validators cannot identify which model came from which trainer, preventing collusion. High-performing models earn rewards while underperforming ones can be refined and resubmitted. This competitive process produces models optimized for real-world performance, with participation requiring token stakes to ensure accountability.

• The FL Alliance: FLock’s Federated Learning client that enables training on sensitive private data without exposing it. The system randomly assigns participants as either proposers (who train models locally) or voters (who evaluate results). When a majority of voters approve a model update, proposers receive rewards. Those who submit corrupted data or false validations lose their stake.

• The AI Marketplace: Successful models from Arena and Alliance are deployed in the Marketplace for production use, with version control, performance monitoring, and automated updates. Model hosts provide infrastructure and set pricing, while users pay for API access beyond free tier limits. Higher-quality models attract more users, generating revenue for creators and hosts - creating market incentives for continuous improvement.

Altogether, models begin in the Arena with public data training, undergo refinement in the Alliance with private data, then reach production in the Marketplace. Usage data feeds back to improve subsequent training cycles. The key innovation lies in how FLock applies onchain mechanisms to coordinate incentives among network participants.

The Onchain Layer

FLock’s key innovation is using blockchain to solve the lack of coordination among participants in federated learning. FLOCK, a token that lives on the Base L2, serves as the key mechanism that aligns incentives across the network.

Proof of Stake

FLock implements a robust incentive mechanism to incentivize honest behavior (and penalize malicious behavior at that) among its federated learning participants. This mechanism, based on a research paper published by the FLock team, utilizes a proof-of-stake system in which participants stake FLOCK tokens to participate. Malicious behavior, i.e. data poisoning, risks real economic value, while honest contributions earn rewards. Rewards are distributed in FLOCK, proportional to stake size and performance quality.

There are three ways to participate:

• Training node operators: stake tokens to compete in model training tasks.

• Validators: stake to evaluate and score submitted models

• Delegators: delegate stake to [training nodes], earn rewards accordingly.

Smart Contract Orchestration

FLock uses a set of smart contracts to orchestrate the training and validation process. When tasks are created, the smart contracts are responsible for:

• Selecting participants using stake-weighted randomness to prevent gaming

• Distributing rewards based on aggregated validation scores

• Slashing stakes of participants who submit poisoned data or false validations

• Managing reward distribution between operators and delegators

More on FLOCK

Live on the Base L2, the FLOCK token serves three functions within the ecosystem:

Utility: FLOCK is staked across different roles, with larger stakes increasing selection probability for training and validation tasks. Task creators can pay additional FLOCK as bounties to expedite their training processes. The system implements time-based multipliers: longer staking periods (up to 365 days) generate gmFLOCK (governance-weighted FLOCK), increasing influence and reward shares from network emissions.

Payment: Users access models trained in AI Arena and FL Alliance with rate limits based on stake amounts, paying in FLOCK for usage beyond these limits. Model hosts stake FLOCK to serve models and set custom pricing structures.

Governance: FLOCK holders participate in DAO governance, voting on task verification (determining which tasks qualify for rewards), parameter adjustments (reward distributions, slashing rates), and protocol upgrades.

Use Cases and Applications

FLock is already being actively deployed across numerous real-world applications where deep, app-specific contextual knowledge, strict privacy guarantees, and decentralization matters:

Specialized Blockchain Development: The Aptos Move LLM

FLock partnered with the Aptos Foundation to develop an LLM that outperforms ChatGPT-4o in generating Move-specific code, from simple "Hello World" scripts to complex programs like yield tokenization with AMM trading functionality. The model was trained specifically on community-contributed Move code datasets, taking advantage of FLock's decentralized training infrastructure.

Initial testing demonstrated superior performance compared to ChatGPT-4o, with greater accuracy and adherence to Aptos-specific requirements. The model, built on top of DeepSeek's foundation and available on Hugging Face, achieves ChatGPT-4-level performance specifically for Move programming.

Community-Specific AI Assistants

FLock enables tailored AI assistants that understand specific contexts:

• FarcasterGPT: Trained on Farcaster protocol data to understand its unique social dynamics, terminology, and user patterns

• ScrollGPT: Optimized for the Scroll L2 ecosystem, understanding its technical documentation, governance proposals, and developer patterns

• DAO-specific models: Custom assistants that understand individual DAOs' governance structures, historical decisions, and community norms

Privacy-Preserving Healthcare Applications

FLock enables healthcare providers to collaborate on diabetes prediction models using linear regression, where each participant retains control over their data while computing model updates locally. Multiple hospitals can train on their local patient records, contributing only mathematical gradients to improve the global model.

This approach is crucial for complying with stringent health data protection regulations such as HIPAA, as patient data never leaves the institution. By integrating data from diverse demographics and geographical locations without sharing raw data, FLock helps develop more accurate and generalized models that account for population variations.

Decentralized Compute Integration: FLock x Akash

FLock has integrated with Akash Network to create one-click deployment templates for running FLock validators and training nodes on decentralized compute infrastructure. This partnership exemplifies the composability of decentralized systems as users can now:

• Deploy FLock validators on Akash with minimal configuration

• Access GPU resources through Akash's marketplace

• Run training nodes using Docker containers with customizable hardware specifications

• Pay for compute using AKT tokens while earning FLOCK for validation

The integration includes pre-configured templates requiring only an API key and task ID, making decentralized AI training accessible to non-technical users.

Natural Language Database Access: Text2SQL

FLock's Text2SQL model enables users to query databases using natural language commands, simplifying SQL syntax. Developed through AI Arena's competitive training process, the model was trained on extensively cleaned and manually reviewed open-source datasets.

The Path Forward

AI represents the next foundational layer of human-computer interaction. However, the practical concerns around centralized AI are becoming harder and harder to ignore. Single points of failure could cripple nations. Data sovereignty violations could compromise millions. Innovation bottlenecks stand to largely favor incumbent interests.

At Shoal Research, we believe this layer must be built just like the internet was: as an open, collaborative effort rather than a corporate, centralized service. We’re excited to see this frontier push forward, and think FLock’s approach to combine Federated Learning with onchain incentive mechanisms and cryptography offers an innovative solution here. Being selected by the UNDP, FLock will mentor five pilot projects across climate finance, inclusive energy, and social protection, proving decentralized AI can address humanity's most complex challenges.

Time will tell how the outcome plays out, but FLock is well-positioned to thrive in a world that embraces AI as a public good and decentralized AI networks as the approach to get there.

References

1. United Nations Development Programme. (n.d.). Sustainable Development Goals. UNDP. https://www.undp.org/sustainable-development-goals

2. FLock. (n.d.). Documentation. FLock Docs. https://docs.flock.io/

3. FLock. (n.d.). Whitepaper. FLock. https://www.flock.io/whitepaper

4. FLock. (2022). FLock: Federated learning on blockchain. arXiv. https://arxiv.org/abs/2211.04344

5. FLock. (2024). Decentralized federated learning: Incentives and mechanisms. ACM Digital Library. https://dl.acm.org/doi/10.1145/3701716.3715484

6. FLock. (2024). Blockchain-enabled federated learning: Privacy and trust in decentralized AI. IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/10471193

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer. This post has been created in collaboration with the FLock team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

Introduction

The Brazilian butt lift (“BBL”) has become a mainstream obsession. In perfect algorithmic, addictive brainrot fashion, I was watching a TikTok where the creator mentioned that BBLs are a form of transhumanism. The creator argued that individuals have a perceived self, which they embody through physical means, and by augmentation, someone can reach their desired “avatar.”

This creator’s offhand remark shows how readily people embrace technology-driven self-modification in everyday life. Such cultural anecdotes hint at a broader phenomenon: an emerging form of “over-the-counter” transhumanism. By over-the-counter, I mean enhancements that are accessible without specialized clinics or implanted devices, practically consumer-grade boosts to human abilities. Notably, these enhancements are increasingly cognitive rather than physical. Just as social media filters and cosmetic procedures allow “one-touch magic” to reshape our appearance, AI tools like ChatGPT offer on-demand augmentation of memory, creativity, and problem-solving.

We’ve been doing it for ages, from writing and printing to smartphones and search engines, each innovation profoundly changing not just how we live but even who we are. But ChatGPT’s arrival in late 2022 felt different in scale and immediacy. Suddenly, a powerful AI, capable of answering questions, writing code, brainstorming ideas, and more, was in the public’s hands. At this pace, the question is no longer whether sustained ChatGPT use makes us a little bit transhuman; it’s whether the tool is now so deeply embedded in that threshold that it verges on pseudo-cyborg technology.

Defining Transhumanism

To ground the discussion, let’s start with a clear definition of transhumanism. In simple terms, transhumanism is the concept of utilizing advanced technology to surpass the natural limitations of the human organism. Transhumanism is described as a movement that advocates using current or emerging tech such as genetic engineering, cryonics, AI, nanotech, etc. In other words, transhumanists want to transcend the normal human state through science and tech, eventually maybe even becoming “posthuman” beings with expanded capacities that an unenhanced human wouldn’t possess, thus enhancing human capabilities to improve the human well-being.

Modern theorists in the transhumanist school of thought offer complementary definitions. Philosopher Max More who helped formalize transhumanist thought in the 1990s, defined it as “a class of philosophies that seek to guide us towards a posthuman condition”. He emphasized that transhumanism shares humanism’s respect for reason and science but “differs from humanism in recognizing and anticipating the radical alterations in the nature and possibilities of our lives” resulting from advancing technologies. Along the same vein, former Oxford futurist Nick Bostrom explains that transhumanists see human nature as “a work-in-progress, a half-baked beginning”. We need not accept our current mental and physical limits as the endpoint of evolution. With “the responsible use of science, technology, and other rational means,” Bostrom says, transhumanists hope humans will “eventually manage to become posthuman, beings with vastly greater capacities than present human beings have”. Yet these ambitions have also drawn sharp criticism. Skeptics argue that the movement’s rhetoric can hide an elitist desire to engineer a “better” class of humans, stoking fears of selective biological enhancement and widening social divides.

In his 2004 Foreign Policy essay “Transhumanism,” Stanford political scientist and author Francis Fukuyama warns, “If we start transforming ourselves into something superior, what rights will these enhanced creatures claim, and what rights will they possess when compared to those left behind?”

In short, transhumanism is about deliberately using technology to overcome our biological limits, whether it longer lifespans, enhanced bodies, or sharper minds. For the fiction nerds, picture the Mentats in Dune, those human “living computers” trained to perform the feats once handled by banned thinking machines.

ChatGPT and the advent of chatbots

At first glance, a chatbot on your computer or phone might not sound as dramatic as cyborg implants or designer genes. After all, ChatGPT doesn’t physically change your physiology rather it’s “just” a very smart tool. Yet the threshold between general tool and a true transhumanist enhancement isn’t always clear-cut.

Transhumanism isn’t limited to physical upgrades. Enhancing cognitive abilities is equally central, and many prominent transhumanists actively pursue it. Just as we accept eyeglasses and prosthetic limbs as bodily augmentations, we can likewise regard an AI assistant as an augmentation for the mind. The key distinction is whether the tool merely offers convenience or if it genuinely expands what we can know or do as humans. A simple calculator, for example, is very useful but arguably just automates a narrow mental task. ChatGPT, by contrast, can generate new ideas, text, and solutions. Such cognitive interactions can converse, explain, and create in ways that feel like having a second brain on call. When a technology begins to so vastly amplify one’s intellectual output or understanding, it starts to cross into the transhumanist realm of qualitatively enhanced capability. ChatGPT, in essence, allows a person to tap into a vast web of knowledge and a reasoning engine adjacent to any individual’s mind. That is why some have described it as a form of cognitive enhancement available to the masses, an “over-the-counter” cognitive boost that doesn’t require a PhD or a billionaire budget to use.
With the right user-driven guardrails in place such as prompt engineering, ChatGPT pushes beyond the ordinary tool threshold of transhumanism and emerges more as a cognitive steroid.

The Extended Mind

Long before AI chatbots, philosophers were already arguing that tools can become literal extensions of our minds. In a famous 1998 paper titled “The Extended Mind,” Andy Clark and David Chalmers proposed that objects like notebooks or computers, when used in the right way, are as much a part of one’s cognitive process as the neurons in one’s brain. They portray this with the case of Otto, an Alzheimer’s patient who relies on a notebook to remember important information. Otto is benchmarked against Inga, who uses her biological memory. When Inga wants to go to a museum, she recalls the address from memory. When Otto wants to do the same, he looks it up in his notebook. Clark and Chalmers argue that the notebook is a constant, trusted resource for Otto, serving the same role memory serves for Inga, and thus can be considered part of Otto's mind. As they put it, "the notebook plays for Otto the same role that memory plays for Inga. The information in the notebook functions just like the information constituting an ordinary belief; it just happens that this information lies beyond the skin".

In Otto’s case, consulting his notebook isn’t fundamentally different from Inga consulting her brain since both are accessing stored information to guide their behavior. This example powerfully suggests that the mind is not limited to the brain. It can extend into the world via devices and tools, as long as they are integrated into our cognitive routines.

The question then becomes: at what point does an external tool count as part of you? Philosophers have offered several criteria sometimes called the “glue and trust” conditions for when we should treat something like a notebook or by extension, an app or AI as an extension of the mind.

• Reliable Availability: The tool must be reliably present and readily accessible whenever you need it . (Otto always carries his notebook everywhere he goes.)

• Ease of Use: Information from the tool should be easy to retrieve almost automatically, without great effort or delay. (In practice, using the tool becomes second-nature, like recalling a memory.)

• Trust: You trust the tool and accept its outputs as true, much like you trust your own memory. (Otto doesn’t doubt his notebook; if it says “MoMA is on 53rd Street,” he believes it, just as Inga believes her recollection.)

• Personal Integration (Prior Endorsement): Ideally, the information in the system was placed there or vetted by you, or in some way, you’ve integrated it into your identity. (Otto wrote the notebook entries himself, so they reflect knowledge he endorsed.)

When these conditions are met, the boundary between “tool” and “mind” gets fuzzy.
Clark and Chalmers explain that our everyday experience fullfills this. For example, a smartphone serves as a memory bank (with our notes, contacts, calendar), a navigation system, and even a “second brain” that we consult without a second thought. In fact, by 2011, even Chalmers noted that his iPhone satisfied at least three of the four criteria for a mind-extension device in skilled users’ hands. For many of us, losing our phone or internet connection feels like losing a part of our mind which is the extended mind thesis in action.

However, building an extended mind also comes with trade-offs and caveats. One obvious issue is that external tools can fail or be wrong. If any of the “glue and trust” conditions break down such as the device isn’t available, or you suddenly doubt its accuracy then the extension breaks down. For instance, experienced GPS users learn not to solely trust their GPS in all cases. They stay alert to correct it when it inevitably makes mistakes or loses signal. Only with skill and critical oversight can a GPS become a helpful extension of one’s navigational mind, rather than a mindless crutch. The same caution applies to AI assistants. Over-reliance on an external memory or skill can lead to cognitive atrophy where we might stop exercising our own memory or problem-solving abilities, becoming too dependent on the tool. Psychologists have observed this with phenomena like the “Google effect”, where people are more likely to forget information that they know they can just look up online. In other words, when a tool is always at hand, we offload mental work to it, which is great for efficiency, but might leave our natural abilities underdeveloped or prone to atrophy. There’s also the risk of false confidence, if the tool feels like part of your mind ,you might accept its outputs uncritically as if they were your own thoughts. With something like ChatGPT, as we’ll discuss, this is a double-edged sword as it can deliver brilliant insights, but also confident-sounding nonsense also known technically by AI researchers as hallucinations. An extended mind must therefore be built on trust and verification. We have to cultivate good “cognitive hygiene” and guardrails with our tools. It is important to know when to trust the AI and when to double-check, much as a good driver knows when to ignore a misleading GPS route. Abiding as a simple “left turn” could be deadly. The ideal is a balanced partnership where the external aid contributes knowledge or speed, and the human contributes oversight and contextual judgment.

ChatGPT as a Cognitive Extension

Alas the king… ChatGPT, the AI chatbot that launched in 2022, that is synonymous with the word AI itself. If any digital tool has the potential to act as a mind extension, ChatGPT is a prime candidate. It often satisfies the key conditions we listed.

Availability: If you have an internet connection, ChatGPT or similar models built into apps and devices is available 24/7, ready to answer questions or generate ideas on demand.

Ease of use: Absolutely, you just converse in plain language, and it responds within seconds. People have found it astonishingly easy to integrate ChatGPT into workflows by asking it to brainstorm topics, explain tricky concepts, draft emails, debug code, etc.

Trust: This is where it gets sticky; we’ll address its pitfalls soon, but many users do report that after some experience, they develop a sense of when to trust the AI’s answers for example, on a familiar topic or coding task and when to be cautious like when it confidently states an obscure “fact” that could be wrong. ChatGPT is often described as “having a super knowledgeable friend always at hand,” which indicates a high level of trust in the tools guidance.

Personal integration: Unlike Otto’s notebook, you didn’t personally write all of ChatGPT’s knowledge store – it comes pre-trained on vast swaths of the internet. However, you can customize the interaction by giving it your context, preferences, or writing style. Over time, the AI’s outputs might start to reflect your own way of thinking or the directions you nudge it toward, creating a kind of feedback loop between your mind and the AI. For example, ChatGPT’s Latent Memory feature makes that augmentation concrete. It quietly retains key details from past conversations, your projects, preferences, and stylistic quirks and uses them to shape future responses. As the model “gets to know” you, a slice of your working memory is effectively outsourced to the chat history, deepening the extended-mind partnership. In effect, when someone uses ChatGPT regularly, it can become a personalized cognitive aide, augmenting their natural abilities.

Empirically, we are already seeing how AI tools boost human cognitive performance. A recent experiment at MIT had hundreds of professionals work on writing tasks (emails, reports, etc.), with half given access to ChatGPT and half working solo. Those with ChatGPT finished their tasks 40% faster, and their output was rated ~18% higher in quality. In other words, the AI made people both quicker and better at their work. Interestingly, it was the weaker writers who benefited the most as ChatGPT helped close the skill gap between people, acting as an equalizer. This suggests that AI isn’t just a fancy convenience but rather can fundamentally alter someone’s cognitive productivity. In creative domains, anecdotal evidence shows AI sparking new ideas. Writers use ChatGPT to overcome writer’s block or generate plot ideas. Coders use it to learn new programming techniques on the fly. Students (unfortunately) use it to get instant explanations of complex topics. In many cases, people report that these tools help them achieve results they couldn’t have achieved (at least not as easily) on their own. For example, a non-programmer building a simple app with ChatGPT’s help, or a novice writer producing a polished essay by iterating with the AI. All of this aligns with the notion that ChatGPT can act as a cognitive amplifier. It extends what we know, effectively giving an individual access to a composite of Wikipedia, a librarian, a tutor, and a creative partner all at once. Little wonder that some have likened it to a “bicycle for the mind” (to borrow Steve Jobs’ old phrase for computers) except now the bicycle sometimes feels more like a jetpack. As AI copilots absorb the repetitive or low-level pieces of knowledge work, our top performers no longer rise by raw technical ability alone; they graduate from hands-on specialists to orchestrators. And in a world shifting from specialization to orchestration, these orchestrators hold the keys: they design the workflows, craft the prompts, select the data sources, and synchronize human talent with machine agents into a seamless whole unlocking outcomes no single expert could achieve in isolation.

The dawn of the orchestrator

Highway to the Danger Zone

That said, we must also address the drawbacks and dangers of relying on such AI extensions. Perhaps the biggest issue with current generative AI is that it can be too confident for its own good. ChatGPT doesn’t actually “know” facts in the way we do. It predicts plausible answers based on patterns in data. This means it sometimes produces fabrications that sound perfectly credible what we call “hallucinations” as described previously. For example, ChatGPT might cite studies or laws that don’t exist, or assert health facts that are utterly wrong but seem right. These inaccuracies are so common that researchers have given them a nickname and are actively studying why they occur. If a user treats ChatGPT’s output as gospel, trusting it like an infallible memory, they can get into trouble. A now-infamous case involved a lawyer who used ChatGPT to help write a legal brief, and the AI invented court cases to cite. The lawyer, assuming the AI’s confidently presented citations were real, submitted the brief and later faced professional sanctions when the judge discovered those cases were nonexistent. In this cautionary tale we can takaway that powerful cognitive extension can mislead you if you don’t remain critically engaged. Over-trust in the AI can lead to mistakes, misinformation, or the erosion of one’s own expertise. This is why many experts stress AI literacy and knowing the strengths and weaknesses of tools like ChatGPT as a crucial skill for the future.

Another consideration is how these tools fit into our broader cognitive habits. Do they complement our thinking or start to substitute for it? There’s a subtle but important difference between complementary and competitive cognitive aids. A complementary aid handles the tedious bits so that you can focus on higher-level thinking. For instance, using ChatGPT to perform basic data entry for you might free up time and mental energy for you to synthesize insights and make creative connections (things the AI might not do as well). In this mode, human and AI form a partnership where each does what it’s best at. By contrast, a competitive aid is one that essentially takes over a task that humans used to do, potentially sidelining the human role. If someone uses the AI in lieu of learning the basics or outsources all their creativity to an algorithm, they might be left with diminished skills and understanding. For example, if students lean on ChatGPT to do all their writing, they might never develop the ability to structure an argument themselves. The AI has “competed” with and supplanted their own cognitive growth. In the workplace, this distinction plays out as well: will AI assistants simply assist workers, or eventually replace those who don’t add unique value? Current evidence suggests that in many cases AI acts as a force multiplier for human talent , but we can’t ignore the possibility that some jobs or skills will be rendered obsolete. Navigating this will require conscious effort and we should embrace the complementary uses of ChatGPT that let us achieve more, while guarding against over-reliance that leaves us complacent or deskilled.

The Cyborg Question

All this talk of human-machine coupling naturally leads to the image of the cyborg. The archetypal transhuman figure who is part human, part machine. So, does using ChatGPT make you a cyborg? The answer depends on how we define cyborg.
Traditionally, the word means a “cybernetic organism,” implying a being with technology integrated into its body or biology. The term was originally coined in the 1960s to describe a person whose bodily functions are aided by implanted devices or biochemical modifications . Classic sci-fi cyborgs have robotic limbs, bionic eyes, brain implants, and so forth. By that strict definition, simply chatting with an AI on your phone doesn’t qualify. There’s no physical fusion of man and machine in the case of ChatGPT; the AI remains an external tool, not a literal part of your organic body. You could put your phone in a drawer and poof the “cyborg” abilities vanish, proving that you and the tool are separate entities. So, in the conventional sense, no, using ChatGPT does not make you a cyborg

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However, some theorists have argued for a broader conception of cyborg-hood that applies to everyday life. Notably, anthropologist Amber Case argues that we are all cyborgs now in as much as we rely on technology constantly to extend our capabilities. You might not have a chip in your brain, but if you rarely go anywhere without your smartphone (your external memory/communication device) and you use digital tools to mediate so many experiences, you are functionally a cyborg. Case points out that we use our phones and computers like “external brains” for communication and memory, effectively becoming a “screen-staring, button-clicking new version of humans” . In this more metaphorical or sociological sense, anyone using ChatGPT as a cognitive crutch is engaging in a cyborg-like merging of human intellect with machine processing. The boundary between user and tool becomes porous during those moments of interaction. Think of how a person might say “I’ll ask my AI,” the way they’d say “I’ll think about it” – it hints that the AI has been incorporated into their cognitive loop. Some bioethicists and futurists use the term “soft cyborg” to describe people who, while not physically augmented, are so intertwined with their personal tech that it’s an extension of their self. By that view, millions of us became soft cyborgs the day we got smartphones, and tools like ChatGPT only deepen that intertwinement.

Even so, ChatGPT remains an external tool unless we literally implant it or physically couple it to our bodies. It’s worth clarifying where the line would be crossed into true cyborg territory. If, for example, in a decade we have a brain–computer interface (BCI) that lets you query an AI with your thoughts and receive answers as instant flashes of neural input, that would pretty clearly qualify as being a cyborg. Companies like Neuralink are already working on high-bandwidth BCIs that, in theory, could one day link human brains with AI systems. A less invasive route might be augmented reality glasses or contacts that overlay AI assistance onto your visual and auditory field at all times basically, an AI that’s always in your perception, not confined to an external screen. At that point, the AI starts to feel like part of your sensory system.

We can also imagine AI-driven neural prosthetics or implants that aids memory, or a language chip that provides real-time translation of foreign languages in your head. Those blur the line between user and device in a much more intimate way than a chatbot app does. ChatGPT, as amazing as it is, still lives in the cloud, you have to physically query it, and there’s a clear boundary (the interface, the screen) where information passes from machine to you. In cyborg terms, it’s one step short of full robocop.

So, calling ChatGPT users full “cyborgs” is mostly a playful metaphor hinting at the near future. A true cyborg enhancement becomes a part of you even when you’re not consciously thinking about it. One might argue like a pacemaker that keeps your heart rhythm steady, or a cochlear implant that continuously provides hearing.

In summary, using AI like ChatGPT moves us toward a cyborg-like existence in a philosophical sense, but it doesn’t make us cyborgs in the literal, physical sense. For that next leap, we’d need tighter integration at the physical level, at the current phase reliably consulting AI still largely requires skill.

The Haves and the Have-Nots: Access and Inequity in AI

If ChatGPT represents a sort of mass-market, over-the-counter cognitive enhancement, we should ask: Who gets to benefit from it? Historically, one concern in transhumanist technologies is that they might create a divide between the “enhanced” and “unenhanced”. For example, only the rich affording life-extending tech or brain boosts. With ChatGPT, the financial barrier is actually relatively low as the basic version has been offered free, but that doesn’t mean access is truly equal. First, there’s the straightforward digital divide: not everyone has a reliable internet connection or a modern device to use AI on. Billions of people still lack broadband access, and even within wealthy countries, underserved and rural communities may struggle with connectivity. Hardware and infrastructure are the baseline requirements for entry into this augmented world.

However, as observers note, a new divide is emerging that goes beyond just owning the tech – it’s about knowing how to use it effectively. Some have started calling this the AI literacy gap, akin to digital literacy but focused on understanding and working with AI. In other words, two people might both have internet access, but if one knows how to craft good prompts, interpret the AI’s output, and integrate it into their tasks, while the other is intimidated or unskilled with the AI, the first person will gain a significant advantage. We’re already seeing this in educational settings and workplaces. Students with strong guidance on using AI can produce better projects and learn faster, whereas those left unguided might either misuse the tool (e.g. simply plagiarize answers they don’t understand) or avoid it altogether out of fear. Similarly, a professional who adapts quickly to using AI tools may outshine colleagues who stick to older methods. This raises the concern that AI could exacerbate existing skill and socioeconomic gaps. Those who are digitally privileged having not just access but also the savvy to use technology can surge further ahead, while others lag behind.

Model Access

Another subtle access issue is the difference between the free versions of AI and the most advanced versions. For instance, OpenAI offers a paid subscription to ChatGPT Plus (with access to more powerful models like GPT-4). Organizations and individuals who can pay for premium AI will get significantly better results, more accurate, less hallucination-prone, with advanced features compared to those using only the free model. Over time, if cutting-edge AI remains behind paywalls, we could see a situation where wealthier users effectively have a “stronger cognitive enhancement” than others. This is not unlike healthcare disparities, except here it’s an intelligence tool. It’s easy to imagine a future where businesses invest in top-tier AI for their employees, training them in its use, and thereby dramatically increasing their output, while smaller firms or workers without such resources fall behind. However, as a hedge, open source models like deepseek have done the unthinkable with launching free models which could be run locally on a person's own computer. Though, enterprise grade hardware is required to run these full models to even spit out a simple question.

So, how do we address these gaps? AI literacy education is one key. Just as basic computer literacy became essential over the past few decades, knowing how to interact with AI should become a widely taught skill. This includes understanding AI’s limitations and ethical considerations, not just technical know-how. Initiatives are already calling for integrating AI training in schools and community programs. For example, learning how to fact-check AI or use it to assist (not replace) one’s learning could be built into the curriculum. Public libraries and online courses can play a role in democratizing knowledge about AI tools, so it’s not confined to tech specialists or elite institutions. Policymakers and organizations like UNESCO have begun discussing “AI literacy as a human right” in the digital age, emphasizing that without proactive measures, AI could deepen inequality rather than alleviate it.

On the infrastructure side, continuing to expand internet access is obviously crucial as every new person brought online is a potential new beneficiary of tools like ChatGPT. There’s also a case to be made for public interest AI: ensuring that advanced AI models are available in forms that the public and underprivileged communities can access perhaps via open-source models or government-provided AI services, rather than having all the best AI locked behind corporate gates.

In summary, ChatGPT offers a glimpse of a future where cognitive augmentation is widespread, but making that future equitable requires conscious effort. Otherwise, we risk a scenario where some minds get much “bigger” with AI help, while others are left behind in the pre-AI paradigm, widening the gulf in opportunity. The notion of transhumanism often dwells on individual enhancement, but we have to broaden that: true progress will be if these enhancements uplift as many people as possible, not just a tech-savvy few.

Conclusion: A Mind in the Making

We stand at a peculiar moment in human history. The tools we've created to extend our minds have become so powerful that they force us to reconsider what it means to be human in the first place. ChatGPT isn't just another productivity app; it's a mirror that reflects our evolving relationship with intelligence itself.

The central tension this essay has explored is not whether ChatGPT makes us transhuman (by most definitions, it doesn't), but rather what it reveals about the porous boundary between tool use and cognitive enhancement. When a technology integrates so seamlessly into our thinking processes that we struggle to separate our thoughts from its suggestions, we've crossed a threshold that demands new frameworks for understanding human capability.

Three critical tensions emerge from this analysis:

First, the enhancement paradox: ChatGPT simultaneously amplifies our abilities and threatens to atrophy them. We become more capable while risking dependence. The solution isn't to reject these tools but to develop new forms of cognitive hygiene: practices that maintain our intellectual muscle tone even as we delegate routine mental labor to AI.

Second, the equity challenge: Cognitive enhancement through AI could either democratize intelligence or create new hierarchies of the augmented versus unaugmented. The determining factor won't be the technology itself but how we structure access to it and, more importantly, education about its effective use. AI literacy may become as fundamental as traditional literacy, yet we're barely beginning to understand what that means.

Third, the agency question: As we transition from specialists to orchestrators, we must guard against becoming mere operators of intelligent systems. True agency in the age of AI means maintaining the ability to think independently, to question AI outputs, and to synthesize insights in uniquely human ways. We must be conductors, not just audience members, in the cognitive symphony.

Perhaps the most profound insight is that we've been augmenting ourselves all along: through writing, mathematics, computers, and smartphones. ChatGPT simply makes this augmentation impossible to ignore. It strips away the comfortable fiction that our tools are separate from our thinking and forces us to confront what we've always been: tool-using animals whose intelligence is distributed across our technologies.

The question isn't whether to embrace or resist this augmented future. It's already here. The question is how to shape it consciously. We need new ethical frameworks that account for distributed cognition, educational systems that teach orchestration alongside traditional skills, and a renewed commitment to human agency even as our tools grow more powerful.

In the end, ChatGPT offers us a preview of a future where the boundaries between human and artificial intelligence blur not through physical merger but through cognitive partnership. How we navigate this partnership with wisdom, equity, and intentionality will determine whether these tools amplify our humanity or diminish it. The choice, as always, remains ours.

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Modern blockchains have achieved scale, but not certainty. While Solana leads on throughput and latency, it still lacks reliable execution guarantees for high-stakes applications. Raiku introduces a programmable coordination layer that restores determinism, predictability, and trust to onchain systems without compromising performance. Built as a sidecar to Solana’s validator network, Raiku enables developers to reserve blockspace ahead of time, schedule transactions precisely, and avoid all the mempool chaos.

This paper explores how Raiku brings execution guarantees, MEV resistance, and pre-confirmation to Solana’s monolithic architecture, offering a system built not just for speed but for reliability in a distributed, adversarial world.

The State of Blockchain Architecture and the Need for Execution Certainty

Global markets demand certainty. Crypto, too often, offers only hope.

Today’s blockchain infrastructure is riddled with architectural compromises. In the pursuit of scale, we've built systems that violate core distributed computing principles, assuming infinite bandwidth, ignoring latency ceilings, and forcing universal state models on applications that don’t need them. The result? Fragile abstractions, unreliable performance, and a developer experience that feels more like survival than innovation.

Imagine launching a modern financial product and being told your transaction might fail, unless you spam it, bribe the validator, or just pray for inclusion.

Blockchain systems are ultimately supposed to abstract that complexity, offering decentralized infrastructure that "just works." But instead, we’ve traded server racks for validator nodes, and latency issues have simply moved from physical hardware to Layer-1 congestion.

This is the daily reality in Web3. Ethereum, the most widely used smart contract platform, handles just 15–30 transactions per second. During high demand NFT drops, liquidations, memecoin seasons, the base layer clogs, fees spike, and L2s are the only escape hatch. These Layer-2 rollups offer massive throughout with reduced gas fees by shifting execution off-chain, but bring new tradeoffs: fragmented liquidity, complex bridging, divergent trust models, and a somewhat fractured user experience.

Meanwhile, Ethereum’s restaking ecosystem introduces even more fragility. Protocols like EigenLayer ask validators to secure third-party systems with Ethereum’s social consensus, effectively betting that if anything breaks, the community will coordinate a bailout. Even Vitalik has flagged this approach as dangerously “high-risk.” Scaling Ethereum is still a 10-year roadmap. Builders need something now.

Solana, on the other hand, pursued a high-performance monolithic design: it keeps consensus and execution in one chain, enabling very fast block times and parallel execution. In practice Solana regularly processes 3,000–4,000 TPS on a global state machine, far outpacing Ethereum’s throughput. This design yields low fees and near-instant finality under normal conditions. However, Solana’s single-chain architecture has also shown stress under extreme load. During periods of surging usage, Solana has experienced congestion and even outages, as every dApp competes for the same global resources.

For example, a dramatic increase in traffic (e.g. a popular NFT mint or arbitrage bots) historically led to stalled blocks and transaction failures. Notably, in Feb 2024 a bug triggered by Solana’s runtime caused a nearly five-hour network halt. Even when the network stays up, users and bots often see transactions fail or time out during peak demand, undermining Solana’s otherwise strong user experience. In fact, high-frequency traders on Solana have observed failure rates up to 75% for transactions when the network is congested. This happens because Solana’s leader schedule and QoS (quality-of-service) prioritization favor certain validators, and when too many transactions flood in, many simply never get included in time.

Beyond throughput limits, both ecosystems struggle with MEV exploitation and unpredictable execution. Solana, despite using a public leader schedule, faces its own MEV dynamics. Solana’s stake-weighted transaction scheduling means large validators or order flow specialists can cut deals to prioritize certain transactions off-book. This lack of transparency leads to centralization concerns, where well-connected bots or firms get fast-lane access while regular users’ transactions struggle. Although Solana has introduced priority fees and solutions like Jito (a specialized MEV-aware client) to bring more open fee markets, the underlying issue remains: when the network is busy, inclusion becomes a bidding war or insiders’ game, and ordinary users face uncertainty and slippage.

On the state side, Ethereum’s rollup-centric model fragments applications across chains. Each rollup becomes a silo, requiring bridges, redundant tooling, and extra security assumptions. Composability, the magic of DeFi, dissolves when liquidity and contracts are no longer co-located.

What’s missing is scale without latency spikes, state fragmentation, or failed trades. The precision global finance requires but blockchains still lack.

This is the design space Raiku occupies.

Born from years of Ethereum infrastructure frustration and a deep respect for clean system design, Raiku introduces a new class of infrastructure to Solana, one built not around more rollups, but around precision execution. It extends Solana’s core by offering programmable building blocks—Edge Compute environments—that give applications their own fast lanes, guaranteed bandwidth, and deterministic settlement without managing a separate chain or breaking composability.

If Solana is the global highway, think of Raiku as the intelligent traffic control system, enabling express lanes for the applications that need them most.

This isn’t another rollup. It’s not a sidechain. Raiku is a rethinking of blockchain execution: fast, modular, programmable, and predictable. A system forged not to chase trends, but to meet the demands of institutions, builders, and global-scale applications that need certainty, not hope.

From Limitations to Vision: The Genesis of Raiku

Raiku was born from a simple observation: not all blockchain workloads need to run in the same crowded kitchen. In late 2023, a team of researchers and Solana veterans began asking how they could offload heavy computations from Solana’s L1 without losing the benefits of being on-chain. This initiative started as a research project (supported by Superteam and the Solana Foundation) out of frustration with the “stagnation” they perceived in Ethereum-centric infrastructure. Ethereum’s ecosystem, while rich in Layer-2s, hadn’t delivered the kind of leap in performance or developer experience these researchers hoped for, rollups were making incremental progress but still tied to a relatively slow and congested base layer. By contrast, Solana represented a chance to try something different: its L1 was already fast and proved that a web-scale blockchain was possible, yet it too could improve by evolving its architecture.

Raiku team noted that by January 2024, many Solana applications were “naturally gravitating” toward an extensions architecture, or, in other words, projects were attempting to build their own mini-rollups or isolated execution layers to scale the base layer throughput even further. Lacking an official framework, some teams tried repurposing tools like the Sovereign SDK (meant for standalone chains) to create Solana extensions or arguably “rollups”. The results were suboptimal: misusing an Ethereum-oriented rollup framework on Solana led to poor performance and lots of friction. Every project rolling their own extension would cause state fragmentation (“basically moving towards all the problems that Ethereum has) and duplicated effort. More importantly, these DIY solutions did not integrate cleanly with Solana’s design, they ran into data throughput limits, timing issues, and inability to share state or accounts with L1.

This pattern highlighted a clear design problem: Solana needed a purpose-built extension framework, not one shoehorned from elsewhere.

Raiku’s founding team, led by Robin A. Nordnes and others, set out to solve this by building an “edge compute” layer tightly coupled to Solana from first principles. They chose Solana after surveying other new L1s like Aptos and Sui, concluding that Solana was uniquely positioned to support their vision. Solana had a critical mass of users, a strong developer community, and fundamentally sound architecture (often likened to an early, rapidly improving Ethereum). Importantly, Solana’s leadership and ecosystem were willing to innovate, core developers shipped updates every couple of weeks, and there was appetite for new approaches to scaling. The idea was not to launch yet another independent L1, nor to create a completely separate shard, but rather to extend Solana in a complementary way.

As one team member put it, “We’re not an L2, we’re not an L1... we’re somewhere in between.” More formally, Raiku positions itself as the block building architecture, an infrastructure protocol that connects validators, applications, and the base chain to orchestrate high-performance execution beyond what the base layer alone can provide. If Solana L1 is the highway, Raiku builds the express lanes and traffic control system that allow certain applications to operate with significantly greater speed and predictability when required.

A key insight driving Raiku’s design is that many high-scale dApps do not require constant interaction with the entire global state. Some applications can run largely isolated (their own orderbook or game engine logic) as long as they can occasionally settle to the main chain and leverage its security and liquidity when needed. As Nordnes explained, “most use cases with large-scale potential do not require state composability at all times”. You can sandbox an application’s execution in its own zone (like an L2), but still get the benefits of the base chain for things like settlement finality, shared user accounts, on-chain price data, and asset security.

This realization underpins Raiku’s architecture: it seeks to give applications their own sovereign execution environments (so they aren’t bottlenecked by others) while maintaining the advantages of Solana’s L1 (one unified asset and identity space, and a high-performance settlement layer). In the Ethereum world, one could try to achieve something similar by launching an optimistic or ZK rollup, but as the team noted, “you can build as an L2 in Ethereum, but you’re still severely restricted by the underlying L1”. Even the best rollup will be limited by Ethereum’s data posting throughput, latency, and upgrade timeline (“fixing the L1 is going to take a decade”). Instead of waiting on that, the Raiku team saw an opportunity to leverage Solana’s strengths today and innovate on top of them.

Raiku would introduce a new layer running alongside Solana’s consensus, operated by (and economically aligned with) the validator community, to coordinate advanced execution features. The vision was bold: make on-chain apps “faster, more reliable, and market-competitive” with Web2 and TradFi systems like AWS or NASDAQ, all without compromising decentralization.

By early 2024, this vision gained traction. The concept of Solana “extensions” became a hot topic, and the community settled on that term (or “network extensions”) instead of “L2s” to emphasize adding to L1 rather than forking away. Raiku emerged as one of the leading projects implementing this concept.

Technically, an extension is similar to a rollup or sidechain, and it’s stigmatized and perhaps downplays what the Raiku team is building. Raiku embraced the term “Edge Compute” to describe its execution zones. The phrase “edge compute” is borrowed from traditional IT (where it means computation done at the edge of the network, closer to where it’s needed). Raiku’s edge compute environments aren’t separate blockchains in competition with Solana, instead, they are modular execution zones at the edge of the Solana network, purpose-built to handle specialized workloads with deterministic performance. It’s a term that resonates with Web2 infrastructure teams and institutional players, bridging the conceptual gap between “rollups” and the familiar idea of edge servers. In essence, Raiku provides custom rollup-like environments (“extensions”) integrated as Solana edge compute zones. This gives developers execution sovereignty, i.e. freedom to run their own logic and scheduling, without the headache of launching an entirely new chain or fragmenting users across ecosystems.

Redefining Rollups: From “Extensions” to “Edge Compute Environments”

It’s worth highlighting how Raiku is reshaping the landscape around custom execution environments. In Solana circles, the term “extensions” emerged to describe these Solana-tailored rollups. However, the Raiku team felt that term was a bit narrow (and even “stigmatized” by early rough attempts). To capture the imagination of both crypto and traditional web2 audiences, what Raiku is building is more than just extensions, they’re building “Edge Compute environments”.

Source: https://x.com/owocki/status/1830621049190560061

With Raiku’s Edge Compute environments, we’re no longer thinking of a Layer-2 as an external add-on that lives on top of L1, but rather an integral part of the network’s architecture, an extension of L1 that lives at the network’s edge, close to the users and applications. By calling them “modular execution zones”, Raiku emphasizes that you can plug in different modules (each module being an execution runtime/VM) to one coherent system. These modular execution zones allow developers to plug different execution runtimes or custom virtual machines into one cohesive system, empowering them to shape low-level application logic with unparalleled control.

The Raiku team believes that performance at scale isn't patched in, it's built in from the outset. Raiku begins precisely where others stop: at the physical boundaries of bandwidth, geography, and time constraints inherent to distributed networks. Specifically, Raiku delivers:

• System reliability that maintains robust operation even under extreme load and pressure.

• Deterministic execution guaranteeing predictable outcomes for every transaction.

• Low latency by placing high-performance edge computing (HPEC) capabilities directly at the edges of the Solana network, enabling transaction processing within milliseconds.

• Developer freedom to customize low-level logic, providing unmatched flexibility and control.

Raiku’s Coordination Engine orchestrates transactions with precision, ensuring they are sent, scheduled, and confirmed swiftly, supported by a sophisticated ahead-of-time blockspace marketplace that guarantees inclusion (more on this in the later sections). Validator Plugins power both ahead-of-time (AOT) and just-in-time (JIT) execution. Combined with proof streaming, these plugins allow for instant pre-confirmation of transactions, turning today’s unreliable, best-effort interactions into dependable and scheduled execution.

Edge compute environments have practical implications. It helps differentiate Solana’s approach from Ethereum’s in the narrative: Ethereum has “Layer-2 rollups”, Solana (via Raiku) has “Edge Computes”. The latter suggests enhancement rather than separateness. It is a term that traditional finance will understand. In enterprise computing, edge compute is a positive concept, it implies faster response by moving compute closer to where it is needed.

Raiku is effectively saying: we move execution closer to the app (in a logical sense) while still being anchored to the main network.

So, in this report we’ve consistently used “edge compute environments”, “extensions”, and “modular execution zones” somewhat interchangeably, to reflect how Raiku’s idea might be referred to. Going forward, as Raiku hits mainnet and more marketing, you’ll likely see “Raiku Edge Compute” as a branded term, much like how Polkadot has “parachains” or Avalanche “subnets”. This terminology also makes it easier to convey new capabilities: for example, Raiku can say “deploy your own edge compute environment on Solana in one week”, which sounds like setting up a cloud environment, thus familiar to developers.

By emphasizing “edge compute”, Raiku aligns itself with the broader trend in web infrastructure where logic is moving closer to the user (edge networks, CDNs, etc.) for speed, except here the “user” is the application transaction, and the edge is a special zone in the network. It’s a powerful analogy that could help more people grasp how Raiku differs from just another scaling hack.

Raiku’s design is guided by a few key principles:

Not all dApps need continuous global state:

Some of the highest throughput applications (exchanges, games, payment networks) can operate in siloed environments for most of their activity, tapping the main chain only when needed. Raiku embraces this by providing opt-in isolation, freeing those apps from the global mempool competition, while still giving them access to L1 liquidity/state when necessary. This stands in contrast to the Ethereum DeFi ethos where everything is highly interwoven on one chain (which is powerful but doesn’t scale when every minor app demands global atomic composability). By recognizing that temporal or contextual composability (only when needed) is enough for many cases, Raiku achieves massive performance gains.

Keep the one-network feel:

Despite introducing modular zones, Raiku strives to avoid the multi-chain UX headaches. The global accounts and orchestration engine ensure that from a user’s perspective, Solana remains one network. You don’t need to manage multiple tokens for gas on different chains/ zones or switch RPC endpoints manually. You interact with Solana, and under the hood Raiku may route your transaction to an extension or the main chain as appropriate. This is a stark difference from, say, the Cosmos app-chain model or even Ethereum’s L2 landscape, where using a new chain means new tokens, new block explorers, and a mental shift.

Raiku’s edge compute zones are much like “network extensions” rather than independent networks, signaling that they extend Solana rather than compete with it. The architectural advantage here is network effects preservation: SOL token’s utility still underpins the whole (fees, staking), and Solana’s community isn’t split among dozens of mini-chains. This addresses a common critique of Ethereum’s rollup-centric roadmap, that Ethereum might become just a settlement layer while user activity migrates to various L2 tokens and ecosystems, potentially diluting Ethereum’s economic security. Raiku’s approach adds capacity and keeps it under the Solana economic umbrella.

Leverage existing security, don’t reinvent it:

Raiku does not create a new base consensus mechanism or require users to trust a brand new validator set with their funds (in fact, Raiku won’t custody funds separately, assets remain on Solana). This is a major advantage over launching a sovereign appchain or a new L1. If a project today chooses to start its own chain (whether via Cosmos SDK, Avalanche subnet, or some sovereign rollup), it faces the monumental task of bootstrapping validators, incentivizing them (often via inflationary new token rewards), and securing bridges back to other ecosystems. Raiku shortcuts this by building on Solana’s validator community and bridging natively via global accounts.

There’s no separate bridge contract, the extension is logically part of Solana. This dramatically lowers the security risk and development overhead compared to sovereign chain approaches. For example, teams who tried Sovereign SDK on Solana ended up with state fragmentation and poor performance because Sovereign SDK wasn’t made for Solana’s context. Raiku’s custom solution avoids those pitfalls and maximizes reuse of Solana’s battle-tested components (like its networking, validator incentives, etc.).

Predictability and transparency as core features:

Both builders and users value knowing what will happen. Raiku bakes predictability at the protocol level. Inclusion signals remove the guesswork from transaction submission. MEV is handled by design (no private mempools, everything goes through auctions or known channels. This fosters a healthier ecosystem.

On Ethereum, despite improvements, users still fear being sniped by an arbitrage bot the moment they send a Uniswap trade. On Solana, users fear transactions “not going through” when the network is busy. Raiku wants to eliminate those fears, making the blockchain feel reliable and “boring” in the best way, like AWS infrastructure where if you schedule a job, you trust it runs on time. This is a key selling point for institutional adoption (who require SLAs and predictability) and for broad consumer use (no one wants to spam “Submit” hoping one transaction sticks).

Real-World Use Cases Enabled by Raiku’s Edge Compute Design

What can developers actually do with Raiku that they couldn’t before? The answer: build on-chain applications with the speed and guarantees of off-chain systems, and deploy new types of services on Solana that previously might have required a separate chain or centralized solution. Let’s explore a few illustrative use cases that Raiku’s team and community have envisioned, highlighting how the edge compute approach makes a difference:

High-Frequency Trading and Exchanges (Drift Protocol’s Swift as an example):

Drift is a leading Solana-based perpetuals exchange that processes large volumes of trades. In early 2025 they introduced Swift Protocol, an on-chain ultra-low-latency matching engine built directly on Solana. It keeps the order book and matching logic on-chain, then routes filled trades to Drift’s perpetuals program for settlement. While innovative, Swift still faces a limitation: when it comes time to settle those matched trades on Solana L1, it’s subject to the usual network conditions and could experience delays or contention (especially during volatile market moves when many exchanges are active).

Enter Raiku: a Drift-like DEX could deploy an edge compute extension specifically for its trading engine. In this extension, orders could be matched and immediately finalized on-chain (in the extension) with microsecond precision, far faster than even Solana’s 400ms block time. The extension could use Light SVM optimized for trading, enabling thousands of operations per second (e.g. matching bids and asks, updating trader positions) with deterministic timing. Crucially, using Raiku’s guaranteed inclusion, once a trade is matched, it can be scheduled to settle on the next Solana block with zero uncertainty. No more racing the clock or praying your transaction gets in, the trade settlement is reserved and confirmed ahead of time.

This means Drift could offer execution guarantees akin to NASDAQ, something unheard of in DeFi. Swift Protocol currently can’t guarantee exactly when a Solana settlement lands or that it won’t be front-run. With Raiku, they can guarantee inclusion and timing, solving that problem. In effect, Raiku could turn a decentralized exchange into a low-latency trading platform where market makers and arbitrageurs can operate on-chain with confidence.

Beyond Drift, any exchange or AMM could benefit, imagine an on-chain order book that updates prices every 100ms in a dedicated extension without bogging down the mainnet. Raiku’s design even supports HFT (high-frequency trading) strategies: as stated on their website, you could execute “thousands of cross-exchange trades per second with microsecond precision and guaranteed settlement timing,” giving quantitative traders an institutional-grade DeFi infrastructure. This is exactly the kind of performance unlock that could bring serious trading firms into DeFi, when they no longer have to worry about network jitter or unpredictable failure rates.

Payments and Fintech Infrastructure (e.g. “Stripe-like” payments, Squads):

Payment applications demand both high throughput and high reliability. Consider a scenario like Stripe on Solana, a service handling thousands of transactions per second for merchants, payroll, micropayments, etc. On Solana L1, this is possible in theory (given the high TPS), but in practice if the network gets congested or if a program in one payment workflow eats too many compute units, others might fail.

With Raiku, one could create a payments extension, essentially a specialized (edge compute) zone for payment transactions. This extension could be optimized for simple token transfers, including specialised/optimised environments or Light SVM for maximum efficiency. Through Raiku’s bandwidth reservation, a payment operator (like a stablecoin issuer or a CBDC platform) could reserve throughput for, say, 500 tps continuously to ensure their transactions always go through regardless of external demand. Users sending funds would get instant inclusion confirmation (no stuck transactions).

For enterprise or institutional use, Raiku can enable private settlement networks on Solana: “private settlement channels between major financial institutions with deterministic finality and encrypted flows”.

Think of big banks settling FX trades or securities on a shared Solana extension, they could have their own Raiku extension with transactions visible only to the parties (via encryption, yet still verifiable) and guaranteed finality. This unlocks use cases like cross-border payments, remittances, or inter-bank settlements on a public chain, but with the predictability of SWIFT or FedWire.

On the consumer side, something like SquadX (a popular Solana multisig and coordination tool) could use Raiku to guarantee that multi-signature transactions (which might involve many instructions) execute reliably even during peak network times. A major pain point for DAO treasuries or multi-sigs is when you try to execute a complex transaction and it fails due to network issues, Raiku could eliminate that by allocating a dedicated slot when all signers have approved, so the multi-sig transaction goes through atomically. Essentially, Raiku can provide institutional-grade reliability for any critical transaction flow, “five nines” of success rate, as they say in cloud, which is vital if blockchain is to be trusted for payments at scale.

Moreover, with RFQ-style liquidity integration, Raiku could empower new payment models: for instance, a decentralized payment app could query market makers via Raiku’s RFQ system to get the best conversion rate for a currency swap as part of a payment, all settled within one extension with no slippage or MEV. This is analogous to how Stripe routes payments through various banks to optimize fees and success, Raiku could let crypto payments route through various liquidity sources in a controlled, deterministic manner.

DeFi Protocols and Services (beyond trading):

Beyond exchanges, many DeFi protocols can benefit from Raiku’s edge compute environments. Lending platforms could use them to perform instant liquidations via dedicated channels (Raiku even enables “automated risk management with microsecond precision”,meaning a lending protocol could monitor positions and execute liquidation transactions in a guaranteed time window, reducing bad debt). Options and derivatives platforms could coordinate complex multi-leg strategies across venues by using an edge compute zone as a co-ordination hub, with Raiku guaranteeing all legs execute atomically in consecutive slots.

For instance, an options DEX could ensure that when a user rolls over a position (closing one option and opening another), both transactions happen back-to-back with no gap for price movement. This level of control is currently only feasible in centralized systems. Stablecoin issuers could benefit as well: imagine USDC could use a Raiku extension to manage its mint/redeem flows in large batches with priority, ensuring big redemptions don’t clog the network or get front-run. By allocating blockspace, they maintain smooth operation even in stress times.

Hybrid CEX/DEX and Institutional Access:

Raiku could blur the line between centralized exchanges and DeFi. Its architecture allows something like a “regulated DeFi zone” where only KYC’d entities (say institutions) participate, enabling compliance while still settling on Solana. The site hints at “hybrid CEX/DEX exchanges with superior UX”, a scenario where a central venue (or consortium) provides liquidity but trades settle on a public network extension. With edge compute, such a platform could offer the speed of a CEX (matching engine on dedicated hardware) and the transparency and custody benefits of DeFi (settlement on Solana). Institutions could use this to trade on DeFi markets without dealing with retail traffic or uncertain fees, since they might have their own extension or guaranteed lanes. Over time, this could attract significant volume from traditional finance into the Solana ecosystem, as Raiku can meet their performance and privacy needs (e.g., encrypted order flows in a private extension).

Non-Financial Apps (Real-time Games, Social, AI Agents, IoT):

While Raiku’s focus seems to start with DeFi and the financial markets, the framework is broadly applicable. For real-time games or virtual worlds, Raiku could support an extension that handles rapid game state updates (imagine a fully on-chain fast-paced game, Raiku’s scheduling could guarantee actions happen in a timely tick system). For social networks or messaging apps on Solana, Raiku could provide throughput to handle spikes in activity (say a viral post causing thousands of reactions, handled in an extension so the main chain isn’t flooded).

The website’s use cases explicitly mention bringing “AI Agents, DePIN, Social Networks, Payment Infrastructure, Capital Markets, HFT Trading, The Internet to Solana”. This sweeping vision suggests Raiku sees itself as enabling any high-demand application that today might think a blockchain can’t handle their scale or speed.

For example, AI agents that perform dozens of on-chain actions per second (perhaps bidding in auctions, rebalancing portfolios, etc.) could utilize Raiku to get those actions processed reliably. DePIN (projects like Helium or decentralized Uber/Airbnb concepts) often require lots of micro-transactions and device interactions, Raiku could ensure IoT devices have reserved throughput to log data or settle payments consistently.

In all these cases, the common theme is that Raiku enables a level of performance and trust that wasn’t possible on a single L1. It lets developers think bigger, to imagine on-chain services matching the responsiveness of centralized servers.

An example: if Solana is already very fast for blockchain, Raiku makes “fast” the default even under load, much like a well-provisioned cloud server that auto-scales capacity for your app.

The edge compute model is particularly attractive to projects that considered launching their own chain for performance reasons: now they can get similar benefits while plugging into Solana’s ecosystem, with a shared global state and full composibility. We may see projects from Ethereum L2s or other ecosystems “orbit” to Solana via Raiku. In fact, Raiku explicitly plans to court EVM-compatible protocols in its go-to-market, showing them that they can deploy on Solana with minimal friction and huge performance gains.

Raiku’s Novel Approach: Execution Consensus Separation with Predictable Performance

Raiku introduces a new block-building architecture that improves Solana’s base layer from within. It does this by enabling programmable coordination, allowing validators and applications to engage in more deterministic execution without introducing separate consensus or fragmented environments.

One can think of Raiku’s network as a specialized execution layer that runs in parallel to Solana’s main chain, powered by the same validator set, that simply opts in by running the Raiku sidecar. By doing this, Raiku frees intensive application logic from contending with all of Solana’s traffic, yet it’s not an island, it remains tightly synced with Solana for finality and data availability.

Unlike other ecosystems still wrestling with modular tradeoffs or fragmented execution models, Solana offers a high-performance, monolithic base layer. It delivers sub-second block times, ultra-low fees, and parallelized execution via Sealevel. These characteristics make it the most likely chain where ideas like just-in-time (JIT) or ahead-of-time (AOT) block auctions can be implemented at scale without prohibitive latency.

While Solana delivers fast block times, low fees, and parallelized execution through Sealevel, the current block construction process still has limitations:

• Validator profitability remains volatile, heavily reliant on native inflation subsidies and occasional MEV spikes.

• Transaction inclusion is unpredictable, particularly for applications that require atomicity, deterministic ordering, or pre-confirmations.

• Out-of-protocol coordination is rising, with protocols like Jito stepping in to address the lack of robust auction mechanisms and bundle execution guarantees.

What’s been missing is a programmable coordination layer, one that gives validators more consistent revenue opportunities, empowers dApps with reliable execution features, and plugs into Solana’s native architecture without compromising its low-latency, high-throughput properties.

Solana’s incentive and economic framework is evolving rapidly, guided by a series of critical Solana Improvement Documents (SIMDs). These proposals reshape core validator incentives, transaction priority mechanisms, and reward distribution, setting the stage for a more competitive and robust ecosystem:

• SIMD-0096: Redirects 100% of priority fees to validators, significantly increasing validator profitability and discouraging off-network side deals.

• SIMD-0123: Introduces a native, scalable protocol mechanism for validators to distribute rewards directly to stakers, enhancing economic alignment and transparency.

• SIMD-0228: would have introduced a dynamic, market-driven emission schedule by adjusting inflation according to staking participation levels to improve economic efficiency and security. (However, the proposal failed to reach the required two-thirds supermajority in its March 2025 vote and has not been activated)

While these changes focus on incentive alignment, they also amplify competition among validators, driving them to find new external revenue streams, especially important in bear markets when MEV and priority fee opportunities decrease.

This ongoing evolution in Solana’s market structure lays the foundation for Raiku’s orchestration engine, designed precisely to enable reliable, predictable, and high-performance decentralized execution.

The benefits of this execution–consensus separation are profound.

First is predictable inclusion: where normally a user’s transaction might sit in a mempool or queue and hope to be included in the next block (or be dropped due to a spike in load), Raiku’s design aims to offer hard guarantees on inclusion and timing. Transactions submitted via Raiku can receive an “ahead-of-time” confirmation of inclusion, effectively a reservation in an upcoming block. This is achieved through Raiku’s novel scheduling and auction mechanisms (which we’ll discuss shortly). For users and dApp developers, it means no more spamming transactions or anxiously waiting to see if a critical trade gets mined, you can know within milliseconds that your transaction is scheduled for a specific future slot. This focus on deterministic, predictable execution is a key differentiator. Traditional L2s on Ethereum can improve fees and throughput, but they generally cannot guarantee exactly when a transaction will land on L1 (especially for optimistic rollups with challenge periods). By contrast, Raiku provides time-slot guarantees on Solana, an L1 known for its 400ms block times. Raiku essentially extends Solana with a “global scheduler” that applications can tap into for reserved blockspace.

Another major advantage is failure isolation. In a monolithic L1, if one application (say, a popular NFT mint program) suddenly consumes huge resources or crashes, it can degrade or halt the entire chain. We’ve seen this on Solana, one dApp’s workload can cause network-wide slowdown. With Raiku, applications run in isolated execution zones, a.k.a. edge computes. If one of these zones faces an issue, for example, a runaway program consuming excessive compute, it won’t directly clog Solana’s main chain or other zones. The fault is contained to that extension environment. Solana’s consensus remains unaffected, and other extensions continue normally. This failure isolation is akin to having multiple “sandboxes” on the network: each application (or group of applications) can utilize a dedicated slice of capacity and even have custom parameters, without risking the stability of the whole. Traditional L2s offer some isolation (a fault on Arbitrum doesn’t halt Ethereum), but because they ultimately depend on L1 for proofs and bridging, a severe failure (or exploit) on a popular rollup can still have systemic effects (e.g. mass withdrawals through L1, or social pressure on L1 to intervene in case of a huge hack. Raiku’s close integration with Solana means extensions are first-class citizens of the ecosystem, but any given extension’s troubles don’t spill over. It’s like partitioning traffic into separate high-occupancy lanes, an accident in one lane won’t gridlock the entire highway.

Crucially, Raiku’s architecture preserves what both L1s and L2s aspire to: security and sovereignty. Each Raiku extension environment has execution sovereignty, meaning the application developers can customize the execution logic, VM, and parameters to their needs (it’s their “own chain” in that sense), but they do not need to bootstrap a new set of miners or validators from scratch. Raiku leverages a validator network that works in sync with Solana’s validator set. In practice, Raiku validators will be Solana validators opting to run Raiku’s software (a sidecar for the validator client), potentially earning additional fees for doing so. This means security is professional and robust from day one, you have an experienced validator set at the helm, and there isn’t a need for a separate token for security.

By separating consensus (still handled by Solana’s PoH/PoS for finalizing blocks) from execution (handled by Raiku’s scheduling and validator network), throughput scales up significantly. Solana no longer has to execute every single instruction of every program itself; it can outsource execution of certain programs to Raiku extensions and simply verify outcomes or proofs. This is a different philosophy from Ethereum rollups which fully verify proofs on L1 (often incurring high costs). Solana can instead trust its own validator-integrated network to do much of the heavy lifting, given the alignment of incentives and real-time cooperation.

In summary, Raiku is neither a standalone L1 nor a typical L2, it is an execution layer for Solana that introduces:

(a) execution–consensus separation (freeing apps from L1’s throughput limits),

(b) predictable inclusion and scheduling (no more probabilistic mempool games), and,

(c) robust fault isolation (one extension’s issues don’t threaten the whole)

It transforms Solana from a single-tier network into a multi-tier system: the base layer for consensus and global state, and an upper layer for high-performance application-specific execution.

Inside Raiku’s Technology Stack: Deterministic Settlement and Modular Execution

To deliver on its promises, Raiku introduces several novel components and transaction types. These can be thought of as infrastructure building blocks that work together to enhance Solana. Let’s break down the key elements of Raiku’s tech stack:

1. Ahead-of-Time Block Auctions & Inclusion Signals:

At the heart of Raiku is a new way of managing blockspace. Instead of the ad-hoc first-come-first-served mempool model, Raiku implements a slot auction marketplace. Applications or users can bid for upcoming slots on Solana (or rather, on Raiku’s coordinated schedule) in advance, securing priority for their transactions. The winning bids receive “inclusion signals” ahead of time, essentially a guarantee that their transaction (or bundle of transactions) will be included in a specific future block or sequence of blocks. These auctions are stake-weighted and atomic, meaning the scheduling respects the Solana stake distribution (validators with more stake have more capacity to include reserved transactions, aligning incentives) and transactions can be reserved in bundles that execute sequentially without interruption. The outcome is that a Raiku user can get a faster confirmation now that their “ticket” for execution is secured.

Compare this to the traditional experience: on Ethereum, you send a transaction and hope a miner picks it soon (maybe boosting the fee if you’re desperate), and even on Solana, you might spam several transactions to ensure one lands during congestion.

With Raiku, the process becomes akin to booking a train seat in advance rather than elbowing through a crowded platform. This system dramatically reduces failed transactions and uncertainty, one of Raiku’s core objectives is guaranteed execution.

Proof Streaming: Unlocking Large Payload Execution via Sequential Blockspace

One fundamental limitation in Solana today is strict per-block data constraints, designed to preserve fast block propagation. This can become a bottleneck for applications that need to submit large state updates, such as settlement engines or ZK rollup proofs.

Raiku addresses this through sequential blockspace reservations, a concept enabled by its Ahead-of-Time (AOT) block auction model. By securing a sequence of upcoming slots, applications can reliably stream large proofs or payloads in smaller, verifiable chunks, without hitting Solana’s per-block cap.

The idea is to break up large transactions or proofs into smaller pieces that can be streamed and verified over multiple slots, bypassing Solana’s strict per-block data limits. Practically, this means an application could submit a very large state update or proof (for example, a zero-knowledge proof or a batch of hundreds of trades) through Raiku, and Raiku will handle feeding this data into Solana in chunks that validators can handle.

Rather than submitting a large transaction that risks failure or bloat, applications can schedule and stream structured data across multiple slots, while validators process and verify it in a controlled manner.

2. Fast and Deterministic Settlement (“Guaranteed Execution”):

Many of the next-generation applications Solana aims to support, such as high-frequency trading platforms, real-time gaming systems, and institutional payment networks, demand strict guarantees that transactions will land exactly when and where they are expected. In these domains, execution uncertainty is not just a UX flaw; it is a deal breaker.

Unpredictable network congestion and mempool dynamics can result in transaction failures, reordering, or delays. For advanced use cases like automated liquidations, synchronized asset swaps, or arbitrage strategies, this unpredictability leads to missed opportunities and capital inefficiency.

Raiku addresses this gap with Guaranteed Inclusion through Ahead-of-Time (AOT) and Just-in-Time (JIT) slot reservations. For example, bots acting on real-time price movements may prefer JIT inclusion, while external systems may opt for scheduled AOT slots. In both cases, users pay for precision, both in timing and bandwidth (using a combination of Raiku tokens and SOL).

When a Guaranteed inclusion transaction is submitted through Raiku, it is assigned a reserved execution window, ensuring it is processed at the intended time and never dropped or reordered due to validator behavior or network congestion. While only the slot leader can include the transaction, all validators running the Raiku sidecar help propagate and acknowledge the schedule ahead of time. Raiku uses a pre-consensus scheduling system to coordinate transaction plans, which the slot leader then executes during block production.

By reserving blockspace in advance and assigning deterministic execution slots, Raiku mitigates peak failure scenarios where high-frequency users on Solana have historically seen failure rates exceeding 90 percent. It delivers guaranteed bandwidth, latency precision, and settlement predictability, even during periods of extreme load.

Guaranteed Execution also introduces MEV resistance by design. Since transactions are scheduled in advance and confirmed across the network, frontrunning is mitigated, and sandwich attacks are neutralized by incorporating expected value extraction into the auction mechanism itself. Private order flow deals that previously operated outside the protocol are no longer necessary. Instead, inclusion is handled transparently via a fair scheduling auction or reservation system.

For developers, Guaranteed Execution means you can offer your users a web2-like guarantee: e.g. a trade on a Solana DeFi app can be confirmed in milliseconds with certainty it will settle, no more “transaction dropped, please retry” errors.

3. Global Account Model and Unified State:

One of the most groundbreaking aspects of Raiku is its Global Accounts Module. This component (planned for V2 alongside proof streaming) tackles the state fragmentation problem head-on. The idea is to allow users and applications to maintain a unified identity and state across multiple execution environments.

In practical terms, a user would still have one Solana wallet/address that they use on the main L1 and across any Raiku extension they interact with. Assets and data can move seamlessly between the main chain and extensions without traditional “bridging.” The global account model will enable composability across extensions, so two Raiku extensions can interoperate or access a shared user state if needed.

This is a significant differentiator from typical L2s, where each rollup is a walled garden requiring a bridge to move assets and where accounts/contract addresses might be chain-specific. With Raiku, since extensions are more like “zones” within the Solana ecosystem, the user experience remains unified. Developers can deploy in an extension environment and still easily integrate with Solana-native programs or accounts.

For example, an order placed in a Raiku-powered orderbook extension could settle into a user’s main Solana wallet or be recognized by a program on L1, thanks to unified accounts. Technically, this might be achieved by having Raiku share Solana’s account address space and signature verification, or by having a mechanism to synchronize state roots between the extension and L1.

The outcome is that state fragmentation is solved, you get multiple execution environments with one combined state. As the Raiku team describes, this allows composability across extension environments, which is something neither Ethereum L2s (all separate) nor earlier Solana rollup attempts achieved. It’s a first-principles approach to ensure that scaling out doesn’t mean splitting the user base or liquidity.

The Global Accounts Module also supports multi-VM functionality. Raiku isn’t limited to Solana’s native VM (SVM), it is being built to potentially host different virtual machines under the same umbrella of coordination. In fact, Raiku aims to support EVM-compatible extensions, allowing projects from the Ethereum world to deploy their Solidity code as a Solana extension.

The mention of “projects like Arbitrum Orbit to deploy on Solana” suggests an Ethereum L3 or custom chain could effectively plug into Solana via Raiku. This is huge: it means an Ethereum dApp could enjoy Solana’s performance and user base without abandoning its codebase. All of this is enabled by global accounts, users wouldn’t even need to switch wallets or bridge tokens; the EVM extension would recognize their same wallet (possibly via an address derivation or mapping) and could use Solana’s native assets.

This suggests Raiku has a somewhat layered node architecture: normal users/apps talk to an Ackermann node (or cluster) which then interfaces with validators to schedule execution. It’s an interesting design for scaling input handling and ensuring the system can handle bursts of transactions by distributing them across validators effectively.

Concluding thoughts

Raiku’s emergence signals a turning point for Solana and blockchain architecture at large. It offers a vision where a decentralized network can achieve the reliability, speed, and flexibility that were once the exclusive domain of Web2 cloud or traditional finance systems. By introducing a coordination engine with deterministic execution, Raiku enables Solana to transcend the label of “just another L1” and become a platform for truly mission-critical, high-performance applications.

Consider what this means for developers: With Raiku, building on Solana can feel like building on a scalable cloud service.

• Need more throughput? just spin up an extension and reserve the slots you need.

• Need custom execution logic or a different VM? plug it in as an edge compute zone.

• Worried about user experience during peak times? offer guaranteed tx inclusion so users never see a failed transaction again.

The developer experience is significantly improved. Developers can build with confidence, knowing that the infrastructure will meet their app’s demands instead of the other way around. The base Solana chain acts as a stable backbone, while Raiku provides the agile, programmable scaffolding to push the limits.

This has the potential to attract not only crypto-native developers but also Web2 developers who have performance-sensitive use cases. They can come to Solana without worrying that the network might slow them down. In effect, Raiku could make Solana the preferred platform for any application that requires both decentralization and high throughput.

On the institutional front, Raiku could be the key that unlocks serious enterprise adoption of Solana. Banks, hedge funds, gaming companies, social media upstarts, all could finally feel comfortable building on-chain because the performance and control they require are available. Solana has already been one of the more institution-friendly chains (with firms like Jump investing in its tech); Raiku turbocharges that appeal by promising fine-grained execution control and high reliability. Institutional-grade means things like 99.999% uptime, transaction deadlines, privacy where needed, compliance hooks – all of which Raiku can facilitate (through isolated extensions, scheduling, etc.). We might see pilot projects like a stock exchange settlement network on Solana, or a large payment processor integrating Solana via Raiku for instantaneous global transfers.

The journey is just beginning, Raiku is in testnet, with mainnet slated for late 2025,but the foundational narrative and architecture are in place. It addresses the pain points we outlined at the start: scaling limitations (solved by adding modular throughput), MEV exploitation (minimized through predictable ordering and auctions), restaking challenges (avoided via a more contained approach), fragmented state (solved by global accounts), and performance issues (solved by guaranteed inclusion and fault isolation). Raiku thus emerges as a cohesive solution, not a piecemeal fix.

References

1. CoinShares. (n.d.). Ethereum’s major problem: Fragmentation. CoinShares Blog. https://blog.coinshares.com/ethereums-major-problem-fragmentation-aa80c71be455

2. CoinShares. (n.d.). Integrated vs. modular: Solana vs. Ethereum. CoinShares Blog. https://blog.coinshares.com/integrated-vs-modular-solana-vs-ethereum-b0438ef1e1dc

3. Cointelegraph. (n.d.). Solana vs. Ethereum: A detailed comparison of blockchains. Cointelegraph. https://cointelegraph.com/learn/articles/solana-vs-ethereum-blockchain-comparison

4. Hedera. (n.d.). Blockchain and edge computing. Hedera Learning Center. https://hedera.com/learning/data/blockchain-and-edge-computing

5. Helius. (n.d.). Solana permissioned blockchains. Helius Blog. https://www.helius.dev/blog/solana-permissioned-blockchains

6. Helius. (n.d.). What is Firedancer? Helius Blog. https://www.helius.dev/blog/what-is-firedancer

7. Kiln.fi. (n.d.). Ethereum L2 challenges and fragmentation. Kiln Blog. https://www.kiln.fi/post/eth-l2-challenges-fragmentation

8. Kiln.fi. (n.d.). What are Solana network extensions, and how are they different from Ethereum layer-2s? Kiln Blog. https://www.kiln.fi/post/what-are-solana-network-extensions-and-how-are-they-different-from-ethereum-layer-2s

9. Medium. (n.d.). AMA with Raiku founders: Problems and roadmap. Medium. https://medium.com/

10. Medium. (n.d.). MEV exploitation on Ethereum and Solana. Medium. https://medium.com/

11. Medium. (n.d.). Raiku technical offerings: Guaranteed Inclusion, Inclusion Signals, Proof Streaming, Global Accounts. Medium. https://medium.com/

12. Raiku Labs. (n.d.). Raiku Docs – Conceptual documentation. Raiku Documentation. https://docs.raiku.com/

13. Raiku Labs. (n.d.). Raiku Docs – Technical documentation. Raiku Documentation. https://docs.raiku.com/technical-docs

14. Solana Foundation (Colosseum). (n.d.). Raiku: Introduction and features. Colosseum Blog. https://blog.colosseum.org/

15. Solana Foundation (Colosseum). (n.d.). Solana congestion and failure issues. Colosseum Blog. https://blog.colosseum.org/

16. Terminal Technology. (n.d.). Network extensions #2. Termina Technology Blog. https://www.termina.technology/post/network-extensions-2

17. Unchained Crypto. (n.d.). Are Solana’s network extensions just like Ethereum’s layer-2s, but by a different name? Unchained Crypto. https://unchainedcrypto.com/are-solanas-network-extensions-just-like-ethereums-layer-2s-but-by-a-different-name/

18. Vitalik Buterin & Blockworks. (n.d.). Vitalik’s caution on restaking risks. Blockworks. https://blockworks.co/news/vitalik-buterin-restaking-risks

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    Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

    Disclosures. All posts are the author's own, not the views of their employer. This post has been created in collaboration with the Raiku team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

Summary

By combining personal wearables, AI coaching, and blockchain-based data ownership, CUDIS builds a transparent health economy that transforms wellness from a private endeavor into a collective, open ecosystem. Its flagship CUDIS smart ring tracks vital health metrics like steps, sleep, heart rate, and stress, feeding data into a personalized AI coach that has delivered over 1 million tailored insights.

With 20,000 rings sold and 200,000 users across 103 countries, CUDIS demonstrates strong traction. The $CUDIS token powers an earning model that rewards healthy behaviors and enables governance, staking, and premium access. Unlike competitors like Oura, CUDIS prioritizes user data ownership through Longevity Decentralized Identifiers (LDIDs), allowing health data to be minted as NFTs or stored privately via IPFS.

Backed by a $5 million seed round from Tim Draper, SkyBridge Capital, and the Solana Foundation, and bolstered by partnerships with UCLA Athletics and WalletConnect, CUDIS addresses privacy concerns and gamifies wellness. Its scalable infrastructure and DeSci alignment position it to democratize longevity, potentially transforming health data into a user-owned asset class, making CUDIS a compelling contender in the $50–80 billion anti-aging market.

The Quest for Longevity

Mayflies survive just a single day, while Galapagos tortoises can live up to 170 years. Topping the longevity charts, the Greenland shark holds the record with a lifespan exceeding 400 years.

However, this has led to one of humanity's most universal desires: to live long and experience the fullness of old age. It has been believed that this has been achieved by comparatively few throughout history, and the “why” behind these enormous swings and the quest to harness longevity for humans has driven fevered attempts (and billions of dollars in research spending) to slow or stop aging. This has even led to some thought-seeking controversial questions like, “If this is achieved, what happens to our humanity, beliefs, and systems?”

On the topic of longevity research, for the first time, breakthroughs in aging research, cellular biology, genomics, and personalized health tracking have made extreme longevity a tangible possibility, with studies suggesting humans could live beyond 150 years in optimal health, as noted in research from Nature Communications and Scientific American. Innovations like senolytics and AI-driven diagnostics are pushing boundaries, but advanced treatments can cost up to $250,000 annually, limiting access to the wealthy.

McKinsey’s 2025 Future of Wellness survey values the global wellness sector at $2 trillion. In the United States, millennials and Gen Z account for 41 percent of spending while representing just 36 percent of adults. Eighty-four percent of U.S. consumers now rank wellness as a top priority, and nearly one-third of Gen Z increased their wellness spend “a lot more” year-over-year. These demographics favor personalized, data-rich solutions, creating tailwinds for platforms like CUDIS that combine wearables, AI, and user-owned data.

In 2022, a McKinsey Health Institute report estimated that humanity could gain 45 billion additional years of higher-quality life, or about six extra healthy years per person, if proven interventions were scaled and health were addressed holistically across physical, mental, social, and spiritual dimensions. Despite this potential, the average person still spends roughly half of their life in suboptimal health, a ratio unchanged in 50 years. These findings underscore the need to redirect investment toward prevention and early detection.

The anti-aging industry, valued at around $50–80 billion (not $600 billion, likely a confusion with the broader beauty market), is projected to grow significantly, possibly reaching $247.9 billion by 2030. Yet, misinformation, unproven therapies, and a lack of real-time progress tracking make longevity feel like just a theory. And while strong evidence shows that advanced health interventions contribute to increased human longevity, such as CRISPR-based gene editing to correct genetic disorders and stem cell therapies to repair damaged tissues, It still contrasts with the natural longevity observed in Blue Zones, driven by community practices like balanced diets and physical activity, and provides a more affordable and sustainable alternative to these high-tech interventions.

Despite rising outlays, large gaps persist. Younger cohorts report the greatest shortfalls in mental resilience, cognitive health, and longevity services. For instance, 42 percent of U.S. Gen Z and millennials place mindfulness as a very high priority, yet feel current offerings miss the mark. High-growth subcategories include functional nutrition, healthy aging products, and AI-powered biomonitoring, precisely the value stack CUDIS delivers by converting biometrics into actionable insights and tokenized rewards.

Source: The Secret of Okinawan Longevity

Aside from religious narratives describing humans living for hundreds of years, which are not widely accepted due to diverse belief systems, the most credible examples of longevity are found in Blue Zones like Okinawa, Japan. Residents there often live beyond 100 years, remain free from common age-related health issues, and maintain the vitality of adults in their 40s.

The Okinawan mystery begins with the question, “How do the people of this island live such long and healthy lives?” Researchers and scientists studying the Blue Zones attribute their longevity to a key combination of diet and exercise. A key principle followed by Okinawan residents is Hara Hachi Bu, which translates to eating until they are 80% full, promoting mindful consumption and sustained health.

The universal pursuit of longevity, like the Okinawan residents, has spurred prominent companies, including Whoop, Oura, Headspace, Strava, Levels Health, Apple Fitness+, and ZOE, to develop innovative apps. These platforms serve diverse purposes, from fitness tracking and metabolic health monitoring to sleep optimization and mindfulness practices, all aimed at enhancing health and extending life.

This proliferation of innovative apps reflects a global push for longevity, yet it sets the stage for examining why today’s longevity solutions, despite their promise, often fall short.

Why Today’s Longevity Solutions Fall Short

The digital longevity health revolution is transforming how individuals track and optimize their well-being, with over 1.1 billion users engaging with fitness and wellness platforms projected to reach $1.6 trillion by 2033. The rapid adoption of wearable technology, used by more than 600 million people who generate over 1 GB of health data daily, including heart rate, sleep patterns, and activity levels, has significantly accelerated this shift. However, despite this vast data volume, users cannot fully harness it for longevity optimization, as most health information remains locked in corporate silos, limiting individual access and benefit.

In a separate evaluation, after using over 10 fitness and longevity-themed applications on the App Store and Play Store for a few weeks daily, a recurring pattern emerges: while these apps promise innovative features, their delivery is often undermined by poor user experience and design. Moreover, some apps are entirely unresponsive, while others provide inaccurate, non-actionable data. A less noticeable issue is that user fitness data is frequently collected and sold to third-party data companies. Stepping back, several key flaws become apparent:

• Poor user experience: The apps often feature clunky interfaces and confusing navigation, making it difficult for users to fully utilize their features. This lack of intuitive design frustrates users and discourages regular engagement.

• Inaccurate data measurement: Many applications provide unreliable fitness or health metrics, such as step counts or heart rate readings, due to faulty sensors or algorithms. This inaccuracy renders the data untrustworthy and limits its practical value.

• Unauthorized sale of user data: User fitness data is frequently collected without explicit consent and sold to third-party companies, raising privacy concerns. This practice prioritizes corporate profit over user trust and control.

• Lack of incentives to keep users engaged in their fitness journey: The apps fail to offer rewards, challenges, or personalized goals to motivate continued use. Without such incentives, users are less likely to stay committed to their health and fitness routines.

In contrast, Oura stands out by prioritizing user experience and addressing many of these issues effectively with its sleek, intuitive design that enhances user engagement. The app delivers accurate health metrics through optimized algorithms and sensors, providing reliable data that users can trust for informed decisions. Oura also emphasizes user privacy by offering transparent data management, reducing concerns about unauthorized sharing. Furthermore, its personalized insights and actionable recommendations empower users to stay motivated on their wellness journey, setting a high standard in the fitness app market.

Oura’s Ring has contributed to its solid growth, with over 2.5 million units sold by 2024, indicating steady consumer interest in health and wellness tracking. The company has raised $550 million in total funding across multiple rounds, including a $200 million Series D round in December 2024, bringing its valuation to $5.2 billion, supported by investments from firms like Fidelity Management, Dexcom, and Forerunner Ventures. It also received recognition, such as being named one of Time magazine’s "100 Best Inventions of 2020" and earning the "Best Consumer Wellness Company" award from the UCSF Digital Health Awards in 2020 for its smart ring technology. We will compare it later in the article with that of the CUDIS Ring.

However, Oura still falls short in two critical areas essential for long-term user adoption.

1. Firstly, despite its emphasis on privacy, user data continues to be sold by the company to third-party service providers for profit, rather than being monetized in a way that directly benefits users, leaving many feeling their health insights are exploited rather than rewarded.

2. Secondly, the app lacks incentives, such as rewards to encourage ongoing usage, which could otherwise motivate users to remain engaged with their wellness journey over time.

This gap in user empowerment and engagement hints at a potential shift in the wearable tech landscape, where a new contender is emerging on the horizon. The CUDIS Ring suggests it might address these shortcomings, but the full story of its potential to redefine health tracking remains to be unveiled. We delve deeper into its promise in the next paragraph.

Here is CUDIS

CUDIS is an emerging player in the wearable health technology sector, carving a niche with its innovative approach to wellness by integrating artificial intelligence (AI), blockchain, and decentralized technology. Launched in April 2024 by BeatBit Wellness Lab, the company has quickly gained traction, pre-selling over 6,000 smart rings across more than 50 countries and building a community of 50,000 members within its first year.

This rapid growth is further evidenced by a successful $5 million seed funding round in September 2024, led by Draper Associates, with additional investments from Borderless, Skybridge, Penrose, SNZ, Mozaik, NGC, Foresight Ventures, OGBC, Monke Ventures, DraperDragon, and notable individuals like Sean Carey of Helium and Adam Jin of the Solana Foundation. CUDIS aims to deliver over 1 million rings in the next 18 months, signaling ambitious plans to expand its global footprint and enhance its AI-driven health solutions. At the helm is Edison Chen, the co-founder and CEO, a seasoned entrepreneur with a background in blockchain, AI, gaming, and e-commerce, and a UCLA graduate whose vision is to empower users with ownership of their health data. Chen, also the Vice-President of Plum Ventures, brings a wealth of experience from investing in over seven unicorn startups, driving CUDIS’s mission to revolutionize wellness through user-centric innovation.

CUDIS’s success is supported by a small, stealth team of passionate professionals, whose identities remain undisclosed to maintain a competitive edge. Strategic partnerships with entities such as UCLA Athletics, the Italian GT Championship, Padel and Fitness events, and Web3 companies like Solana Mobile and World Chain App have bolstered its credibility and market reach. These collaborations, including in-game signage at UCLA basketball games and the Pro Athlete Ambassador Program featuring Olympians and endurance athletes, highlight CUDIS’s growing influence beyond the crypto space into mainstream wellness.

The company’s flagship product, the CUDIS Wearable Ring, sets it apart with its AI-powered health tracking and blockchain-based rewards system, monitoring metrics like heart rate, sleep patterns, and activity levels while allowing users to monetize anonymized data on the Solana blockchain. This ring, available in models like the CUDIS 002 launched in December 2024, offers up to 10 days of battery life and integrates with a personalized AI coach, providing tailored wellness insights without monthly fees, a stark contrast to competitors like Oura.

Complementing the ring is the CUDIS Super App, a versatile platform that extends beyond ring users to offer a unified hub for health data management, integrating with services like Apple Health and Google Fit. The app features social challenges, an AI coach, and plans for a decentralized data network, enabling users to mint health data as NFTs and earn rewards, positioning CUDIS as a pioneer in the HealthFi (Health Finance) space. With its focus on data ownership, privacy, and incentivized wellness, CUDIS is poised to challenge established players, though its small team and reliance on future tokenomics and market education will be critical to sustaining its momentum.

A Super App

The CUDIS Super App is nothing short of a groundbreaking masterpiece, redefining the future of longevity and personal wellness with its all-in-one platform that empowers users to take complete control of their health data like never before. Launched in April 2024 as part of the innovative ecosystem, this app has already captured the imagination of health enthusiasts worldwide, amassing an impressive 200,000 downloads since its beta release, a testament to its rise and the growing demand for cutting-edge wellness solutions.

Seamlessly integrating with a vast array of major health platforms, including Apple Health, Google Health, Strava, and MyFitnessPal, the CUDIS Super App transforms fragmented health data into a unified, intuitive interface that serves as the ultimate command center for comprehensive health management. This isn’t just an app; it’s a lifestyle revolution, designed to unlock the secrets of a vibrant, extended healthspan for millions, blending cutting-edge technology with an inspiring vision of wellness that’s accessible to all.

At the heart of the CUDIS Super App lies its unparalleled ability to track and optimize healthspan and physical health, pushing the boundaries of what’s possible with a bold goal of enabling users to thrive well into their 140s. Powered by state-of-the-art AI-driven coaching, the app delivers personalized, science-backed recommendations 24/7, analyzing biometric data, behavioral patterns, and lifestyle choices to provide actionable insights tailored to each user’s unique journey.

Whether it’s fine-tuning sleep patterns, boosting physical activity, or enhancing nutritional habits, the personalized AI coach acts as a virtual wellness guide, adapting in real-time to guide users toward their longevity goals with precision and care. Beyond coaching, this AI coach opens doors to exclusive resources of curated longevity products, programs, and services, offering everything from advanced supplements to expert-led wellness courses, ensuring users have access to the tools they need to maximize their health potential. This holistic approach sets CUDIS apart, creating a dynamic ecosystem where wellness isn’t just tracked but actively nurtured and rewarded.

One of the app’s most exhilarating features is its rewards-based longevity challenges, turning health optimization into an engaging, gamified experience that motivates users to stay committed. By participating in activities like wearing the CUDIS Wearable Ring, logging workouts, or achieving personalized health milestones, users earn Health Points—a proprietary incentive system in the CUDIS Beta phase. These points can be redeemed for raffle spins, offering chances to win rewards like USDC, Edamame NFTs, or even a CUDIS 002 ring, making every step toward better health a step toward tangible benefits.

This innovative blend, built on the Solana network, allows users to mint their anonymized health data as NFTs, opening a new frontier where personal wellness becomes a valuable, monetizable asset. This feature alone has sparked widespread excitement, drawing a global community eager to embrace a future where health data ownership drives both longevity and financial empowerment.

Another essential feature of the CUDIS Super App is its built-in wallet, powered by Multi-Party Computation (MPC) technology and LDID, which ensures secure transactions, robust identity protection, and financial sovereignty while enabling seamless engagement within the CUDIS ecosystem.

Despite its early success, the CUDIS Super App is still in its beta stage, and users have noted some challenges with UI design that occasionally hinder the seamless experience it promises. Clunky navigation and occasional lag in certain features have been reported, reflecting the app’s ongoing development as a work in progress. However, these imperfections are overshadowed by its immense potential, as the beta phase allows the small, stealth team at CUDIS to refine and enhance the platform based on real-world feedback.

As it continues to grow and refine, its potential to redefine wellness technology is limited only by the imagination, making it a must-watch contender in the evolving landscape of health optimization.

CUDIS Wearable

The global adoption of wearable devices has skyrocketed, with tens of thousands of units sold worldwide, signaling a transformative shift in how individuals monitor their health. CUDIS has seized this opportunity, integrating seamlessly with 70+ mainstream wearable brands and bringing thousands of wearable users on-chain to benefit from its innovative CUDIS protocol. Yet, rather than following the crowded path of wristbands, CUDIS opted for a ring design for its proprietary crypto wearable, the CUDIS Ring, tailored for the crypto-native community seeking continuous biometric tracking of critical real-time biomarkers like heart rate variability, stress levels, sleep patterns, and vital signs.

This choice was deliberate: the fingers, packed with a dense red capillary bed, excel at detecting blood volume changes, providing unparalleled accuracy; their low muscle movement delivers clearer signals for heart rate pulse wave velocity; and their stable contact ensures a snug fit during sleep or active use, offering flexibility that wristbands, despite popular belief, struggle to match. This strategic pivot underscores CUDIS’s commitment to precision and user comfort, setting the stage for a wearable that redefines health tracking.

The CUDIS Ring itself is a marvel of engineering, pairing effortlessly with the CUDIS Super App via a fast Bluetooth connection that ensures real-time data synchronization with pinpoint accuracy. Designed for all weather, this sleek ring is fully waterproof, capable of withstanding swims, showers, and intense workouts without compromising performance, making it a reliable companion for any lifestyle. Its power supply is equally impressive, boasting a robust 10-day battery life on a single charge, thanks to optimized energy management that minimizes downtime and maximizes tracking consistency, ideal for users who demand uninterrupted insights.

The latest sporty version, unveiled in June 2025, introduces a vibrant palette of colors, jet black, lunar silver, cosmic blue, and fiery red, drawing inspiration from the business theory behind the success of the iMac in 1998. Just as Apple’s introduction of colorful iMacs allowed users to express individuality and boosted sales by 40% that year, CUDIS leverages this psychology to enhance user engagement, turning the ring into a stylish statement piece that resonates with the crypto community’s dynamic identity.

This innovative design ties seamlessly into the robust infrastructure powering CUDIS’s ecosystem, which we’ll explore in the next paragraph. Priced at $369, the new sporty version reflects its advanced features and sporty appeal, a slight increase from the original CUDIS Ring’s $349, underscoring the brand’s evolution and commitment to delivering cutting-edge value. As the wearable market continues to expand, the CUDIS Ring stands poised to lead, blending technology, style, and community-driven innovation into a product that’s as empowering as it is transformative.

Building the Infrastructure for a Reward-Driven Healthspan

CUDIS is dedicated to revolutionizing longevity by transforming it into a measurable, achievable, and financially rewarding pursuit, while ensuring its benefits are accessible to everyone through a robust, cutting-edge infrastructure designed to fulfill every promise. By integrating artificial intelligence, blockchain, and community-driven models, CUDIS builds a foundation that not only tracks and enhances healthspan but also aligns individual well-being with tangible financial rewards, creating a sustainable ecosystem poised to democratize longevity on a global scale.

Its infrastructure, known as the CUDIS Data Network, seamlessly integrates blockchain, NFTs, and a secure processing framework to deliver a robust platform for data ownership, rewards, and privacy preservation. This network comprises four pivotal components: LDID, which manages users’ comprehensive health data with robust privacy safeguards; the CUDIS Data Aggregator, which generates unique, immutable health data records; the CUDIS DataHub, facilitating flexible and equitable data transactions; and the CUDIS Processing Nodes, ensuring transparent and efficient data processing.

Delving deeper into CUDIS’s infrastructure reveals a layered system built to prioritize user empowerment, data integrity, and secure exchange. At its core, LDID ensures robust privacy protection for sensitive health data, while the CUDIS Data Aggregator leverages blockchain to create unique, immutable records, laying a secure foundation for personal data ownership. Complementing this, the CUDIS DataHub facilitates flexible and equitable data transactions, and the CUDIS Processing Nodes enable transparent, efficient computation. Together, these components form a dynamic, user-centric data economy designed to advance longevity through trust and innovation.

This divides the CUDIS infrastructure into two complementary pillars, each reinforcing the other.

Foundations of Data Integrity and Privacy

This pillar is composed of the CUDIS LDID (Longevity Decentralized Identifier) and the Data Aggregator.

CUDIS LDID

The CUDIS LDID emerges as a sophisticated privacy-preserving identity layer, elegantly anchoring user data ownership and control across the diverse landscapes of Web2 and Web3. By ingeniously binding digital identities to Non-Fungible Tokens (NFTs) and Soul-Bound Tokens (SBTs), LDID crafts verifiable, non-transferable profiles that seamlessly integrate multi-chain addresses and third-party accounts, such as social media or fitness platforms, into a unified digital persona. This modular framework not only facilitates secure identity aggregation and self-authentication but also establishes a robust, permissioned gateway for users to manage access to their personal health and wellness data with unparalleled precision and trust. Enhanced by features like PSD-KV (Private Data Key-Value Storage) on IPFS, which safeguards sensitive information like AI agent memories, LDID lays a resilient foundation that prioritizes privacy and empowers individuals to dictate the terms of their data’s use.

Building upon this, the CUDIS Data Aggregator elevates the LDID-linked data into a realm of tokenized digital assets, redefining the potential of health information with elegance and innovation. This harnesses state-of-the-art encryption and metadata protocols to encase user-controlled health records within immutable NFTs, enabling secure exchange, monetization, and ownership preservation on the blockchain. The Aggregator seamlessly inherits identity and access parameters from LDID, ensuring that every data transaction remains user-permissioned, fully traceable, and aligned with a rewards ecosystem, thus forging a continuous pipeline from identity establishment to the realization of data value. Optimized through the Metaplex Protocol, it introduces tailored enhancements for health data’s unique characteristics, promising privacy, integrity, and economic incentives, and positioning the aggregator as a pivotal bridge that transforms LDID’s identity framework into a dynamic, user-empowered marketplace for wellness insights.

Empowering Transactions and Efficiency

This pillar is composed of CUDIS DataHub and CUDIS Progressing Nodes.

Flowing seamlessly from the aggregator’s output, the CUDIS DataHub emerges as a pioneering next-generation marketplace that harnesses tokenized health data, providing a decentralized platform where users can securely exchange and utilize their NFTs with complete control and privacy. Built on cutting-edge decentralized technologies, this innovative hub rewards users for sharing their data while ensuring transparent, secure, and efficient transactions, whether transferring access rights or ownership, unlocking the intrinsic value of personal health insights. This dynamic ecosystem not only empowers individuals but also fosters a vibrant community for innovation in health analytics and longevity research, transforming health data into a catalyst for scientific advancement and personal gain.

Complementing the DataHub’s marketplace prowess, the CUDIS Processing Nodes (CPN) redefine data handling by offering a decentralized, secure, and scalable environment, powered by Kubernetes (K8s) container orchestration, which ensures transparency, user control, and trust in managing sensitive health data. This robust infrastructure revolutionizes the processing, analysis, and sharing of health information, delivering efficient computation that supports a wide range of data-driven applications with unwavering reliability. Together, the CPN’s advanced capabilities and the DataHub’s marketplace dynamics forge a privacy-first, user-empowered data ecosystem that benefits individuals while propelling breakthroughs in longevity science, envisioning a future where health data fuels both personal wellness and collective progress.

The CUDIS Longevity Hub

The CUDIS Longevity Hub is the ecosystem’s launchpad for science-backed health applications. Powered by the core CUDIS infrastructure stack (LDID → Data Aggregator → DataHub), it serves as a decentralized accelerator and community hub for longevity-focused products, programs, and DApps.

For developers, it removes the friction of building from scratch. Each project deployed through the Hub benefits from integrated privacy tools, including zero-knowledge data access, NFT-gated consent, and reward mechanisms. This allows founders to focus on innovation rather than backend setup.

For users, the Hub acts like an app store for living longer. Token-gated access unlocks personalized supplement plans, biomarker assays, early disease screening tools, and more. These services are coordinated around an individual’s LDID profile, with the CUDIS AI Coach integrating them into personalized, real-time wellness protocols.

From an economic perspective, the Hub establishes a positive feedback loop. Projects pay listing fees in $CUDIS, share revenue with data contributors, and can bootstrap liquidity through CUDIS-token pairs in on-chain pools. Users benefit through early access to new tools, participation in trials, and opportunities to contribute to community-led initiatives.

This creates a self-sustaining ecosystem that transforms health data into a valuable resource and broadens access to the future of longevity.

Token Economics

The CUDIS token forms the financial and operational backbone of the entire ecosystem, facilitating data transactions, incentivizing participation, and enabling the tokenization of trillions of dollars in digital health assets over the coming decades.

With a total supply of 1 billion tokens and 247.5 million in circulation at TGE (24.75%), the token is designed for equitable and sustainable value distribution. Strategic allocations—such as 25% for community, 15% for ecosystem development, and 17% for investors—are governed by thoughtful vesting schedules, ensuring long-term alignment. As the primary medium of exchange across the network, CUDIS tokens are used for settling data uploads, accessing AI insights, unlocking ecosystem features, and interacting with sub-data token trading pairs. This seamless, transparent mechanism supports a sustainable, user-centric longevity protocol, making health optimization both rewarding and accessible.

$CUDIS Use Cases

Token holders of $CUDIS are entitled to the following benefits:

1. Governance: CUDIS token holders play an active role in decentralized governance, helping shape the future of the ecosystem. Through community-driven voting, they influence key decisions such as fee structures, feature integrations, and protocol upgrades. This governance model promotes long-term stability, fairness, and continuous innovation, empowering the community to guide the evolution of CUDIS with transparency and collective ownership.

2. Premium Access: The CUDIS tokens provide access to a premium suite of health and longevity solutions. Users can securely manage their wellness data, protect privacy, and access advanced tools through token-based data settlement. Additionally, token holders benefit from personalized AI-driven insights and exclusive access to top-tier longevity clinics, expert consultations, and cutting-edge treatments.

3. Medium of Exchange: The CUDIS token serves as the primary medium of exchange within the ecosystem, enabling seamless transactions and service access. It covers network fees for data uploads, processing, and operations, ensuring long-term sustainability. Additionally, CUDIS tokens function as the default currency for accessing services and act as the base trading pair for subdata tokens, supporting liquidity and fractionalized ownership of valuable digital health assets.

$CUDIS Incentive Framework

The Incentive Framework integrates the staking mechanism, reward system, and overall $CUDIS economic model into a unified structure that drives ecosystem growth and sustainability.

At its core, the CUDIS staking model strengthens engagement, security, and governance by encouraging users to demonstrate long-term commitment through token staking, which earns them additional rewards and fosters community stability. Simultaneously, node operators and data contributors stake tokens as collateral to uphold ethical data sharing practices and maintain high integrity standards within the network. This staking also empowers committed stakeholders by granting increased voting rights, amplifying their influence over critical ecosystem decisions, and ensuring a decentralized, community-led governance model.

Complementing staking, the CUDIS Token functions as both the primary medium of exchange and an incentive mechanism that fuels activity across the ecosystem. Users earn tokens by uploading, storing, and sharing wellness data, while partners such as research institutions and AI developers utilize tokens to access data processing and analytics services, driving innovation. Node operators, providing computational power and storage, are likewise compensated with token shares. To maintain operational sustainability and support ongoing platform growth, CUDIS employs a balanced fee structure: users pay upload fees for on-chain data storage, nominal listing fees for minting and listing data NFTs, and processing fees for AI-driven analytics that sustain the CUDIS Processing Nodes (CPN). Marketplace transaction fees on NFT sales and project launch fees further finance platform maintenance and future ecosystem development. Together, these interconnected mechanisms establish a robust, scalable, and user-aligned economic model that underpins the continuous evolution and success of the CUDIS ecosystem.

Key Drivers of Mainstream Adoption

CUDIS stands as a pioneering force with a first-mover advantage in the Web3 wellness space and beyond. As one of the earliest decentralized health platforms built on the Solana blockchain, CUDIS uniquely integrates AI-powered wearable technology, token-based incentives, and user-controlled data ownership to create a groundbreaking ecosystem. This innovative combination of blockchain security, real-time AI guidance, and token rewards sets CUDIS apart from traditional health apps. Positioned at the intersection of emerging Web3 technologies and mainstream wellness, CUDIS is redefining how people manage their health, empowering users worldwide with tools to extend longevity and improve quality of life. With its unique approach and scalable model, CUDIS is poised to lead the future of decentralized, data-driven wellness on a global scale.

The technological foundation of CUDIS is a critical factor in its potential for mainstream adoption, particularly its reliance on Solana's high-speed infrastructure. Solana, capable of processing over 65,000 transactions per second at costs below $0.01, ensures that biometric data can be recorded and processed on-chain in real time, a necessity for handling billions of data points from users across 103 countries. This scalability, compared to slower or costlier blockchains, enables CUDIS to deliver seamless, instant feedback and reward issuance, making the platform accessible to both tech-savvy and non-technical users. The platform's integration with Solana's growing ecosystem, including DePIN (Decentralized Physical Infrastructure Networks) and DeSci (Decentralized Science) projects, creates additional synergies that could amplify its reach. For instance, recent developments, such as the token launch on June 5, 2025, and partnerships with entities like WalletConnect, as seen in an X post, highlight its expanding network, which could facilitate onboarding millions more users to the longevity ecosystem.

User engagement is another pivotal driver, driven by CUDIS's innovative earning model, underpinned by the $CUDIS token. This model turns healthy habits into economic value, with users earning points for daily activities tracked via the smart ring and app, redeemable for token rewards. The initial airdrop of 50 million tokens catalyzed user engagement, with future seasons structured to incentivize long-term retention. Beyond earning, tokens enable users to unlock advanced AI features, stake for boosted rewards, and participate in governance, blending DeFi mechanics with behavioral science. This gamification, combined with the AI coach's ability to deliver over 1 million personalized insights, sets CUDIS apart from traditional wearables like Oura, which lack both financial incentives and decentralized ownership.

Finally, CUDIS's credibility and growth potential are bolstered by significant investor backing and strategic partnerships, positioning it as a scalable solution with mainstream appeal. Recent developments, such as listings on major exchanges like Binance Alpha, Bybit, Bitget, LBank and MEXC, announced on June 5th, respectively, indicate market momentum. With over 20,000 smart rings sold and 200,000 users as of date, CUDIS demonstrates strong product-market fit, suggesting it could evolve into a real-time, user-owned health database powering AI-driven longevity research, making it a compelling investment for the future of wellness.

Future Vision

CUDIS’s roadmap from Q3 2025 to 2027 envisions a transformative shift in the longevity ecosystem, leveraging its decentralized infrastructure, AI advancements, and community-driven governance to make health optimization accessible, rewarding, and globally impactful. In Q3–Q4 2025, CUDIS will lay the cornerstone of its longevity ecosystem with the beta launch of the CUDIS Longevity Launchpad, a platform to onboard longevity-focused projects, researchers, and programs.

This initiative will foster collaboration between scientists, developers, and users, enabling the creation of decentralized applications (dApps) that leverage CUDIS’s secure data network. The public launch of the Longevity Data Marketplace will allow users to monetize their anonymized health data as NFTs, creating a user-empowered economy where wellness data drives both personal gain and scientific progress. Simultaneously, the release of CUDIS AI Agent Coach v2 will enhance personalization, using multi-modal AI to integrate behavioral, biometric, and environmental data for hyper-tailored longevity strategies. These developments, built on Solana’s high-speed blockchain, will solidify CUDIS’s position as a pioneer in HealthFi, attracting millions to its ecosystem by addressing the limitations of centralized platforms like Oura, which lack incentivization and data sovereignty.

In 2026, CUDIS will focus on community growth and governance to sustain its momentum. The beta release of the token-gated Longevity Hub community will provide exclusive access to research-backed products, premium AI coaching, and longevity services, thereby fostering a vibrant and engaged user base. The $CUDIS token will play a central role, enabling governance through token-weighted voting, allowing users to shape platform features, research funding, and fee structures.

This decentralized governance model aligns with the DeSci movement, empowering users to drive longevity science forward. The development of a multi-agent AI system will further personalize health strategies, combining data from wearables, environmental sensors, and user inputs to create dynamic wellness plans. Major brand awareness campaigns, leveraging partnerships with entities like UCLA Athletics and Solana Mobile, will expand CUDIS’s reach beyond crypto-native users to mainstream health-conscious audiences. By 2026, CUDIS aims to onboard 1 million users, capitalizing on its scalable infrastructure and growing exchange listings on platforms like Bybit and MEXC to drive adoption.

By 2027, CUDIS envisions a global, decentralized longevity ecosystem that redefines wellness. The establishment of a Global Longevity Center will serve as a hub for research, education, and innovation, collaborating with universities and health institutions to advance anti-aging science. A Longevity Scientist Committee will oversee community-funded research, ensuring transparency and alignment with user priorities.

The Longevity Foundation will democratize access to advanced treatments, subsidizing costs for underserved communities through token-driven funding. These initiatives will transform CUDIS from a wellness platform into a global movement, potentially managing trillions in tokenized health assets. With over 4 billion steps and 40 million heart-rate records already processed, CUDIS’s data network could power real-time, AI-driven insights for longevity research, rivaling centralized databases while prioritizing user control. Strategic partnerships and investor backing from Tim Draper and others will fuel this expansion, positioning CUDIS to lead the $247.9 billion anti-aging market by 2030.

Conclusion

Longevity isn’t just about adding years—it’s about adding healthy, high-quality years. Yet despite torrents of biometric data from wearables and steady progress in aging science, most people still have no infrastructure for turning those insights into real-world gains. CUDIS aims to close that gap.

CUDIS’s future vision is both bold and attainable, built on its demonstrated ability to seamlessly integrate cutting-edge technology, incentive mechanisms, and community-driven governance. In an industry often hindered by poor user experiences, data exploitation, and a lack of meaningful rewards, CUDIS stands out by offering a scalable, user-centric alternative that prioritizes privacy, engagement, and value creation.

Positioned at the intersection of wearable tech, blockchain, and AI-driven health, CUDIS is uniquely poised to become the next major player in the wearable technology space, transforming wellness from a solitary endeavor into a decentralized, inclusive movement. Its ambitious roadmap from 2025 to 2027 aims not only to democratize access to longevity-enhancing tools but also to empower individuals worldwide to take control of their health data and futures. With its innovative approach and growing ecosystem, CUDIS represents a compelling investment opportunity capable of reshaping how we understand and pursue long-term health and vitality.

References

1. Official Cudis Whitepaper. 2025. Source.

2. Reviewing the Cudis Smart Ring Health Tracker. Lightspeed. Source.

3. Science is making anti-aging progress. But do we want to live forever? The Harvard Gazette. Source.

4. Official CUDIS Smart Ring and UCLA Athletics Partnership – Athletech News
   https://athletechnews.com/cudis-crypto-smart-ring-ucla-athletics

5. CUDIS Raises $5M Seed Round Led by Draper Associates – Chainwire
   https://chainwire.org/2024/09/18/cudis-secures-5m-seed-led-by-draper-associates-to-challenge-oura-ring-in-the-wearables-market-with-blockchain-and-ai

6. Oura Raises $200M, Reaching $5.2B Valuation – Fierce Healthcare
   https://www.fiercehealthcare.com/health-tech/oura-closes-200m-round-boosting-smart-ring-makers-valuation-52b

7. Oura Ring Official Website
   https://ouraring.com

8. UCSF – Fitness Trackers Could Help Spot Illness Early
   https://www.ucsf.edu/news/2025/06/430166/your-fitness-tracker-could-help-doctors-spot-health-risks-early

9. NCBI – Applications of CRISPR–Cas9 in Regenerative Medicine and Aging
   https://pmc.ncbi.nlm.nih.gov/articles/PMC10980556

10. Scientific American – Could Humans Live to 150?
    https://www.scientificamerican.com/article/humans-could-live-up-to-150-years-new-research-suggests

11. McKinsey Health Institute – Adding Years to Life and Life to Years
    https://www.mckinsey.com/mhi/our-insights/adding-years-to-life-and-life-to-years

12. McKinsey – Future of Wellness: Consumer Trends
    https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/future-of-wellness-trends

13. Research and Markets – Complementary and Alternative Medicine for Anti-Aging Forecast
    https://www.researchandmarkets.com/reports/5526495/complementary-and-alternative-medicine-for-anti

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer.This post has been created in collaboration with the CUDIS team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research

Crypto is at a crossroads. The last decade brought remarkable innovation in defi, gaming, and tokenized assets, yet the user experience remains fundamentally broken. Exploits are rampant, manipulation is normalized, and meaningful institutional participation remains out of reach. What was supposed to be an open financial system has become a high-stakes battleground, where sophisticated actors consistently outmaneuver honest users. Institutions cautiously watching from the sidelines cite systemic data leakage, integrity risks, and compliance uncertainties as persistent blockers to meaningful adoption.

Crypto, despite all its potential, remains plagued by toxicity.

One of the most overlooked gaps is the lack of confidential and compliant stablecoin infrastructure, a foundational layer for real-world payment and liquidity flows. While stablecoins are gaining traction as programmable, borderless money, their usage remains highly transparent by default. This is a non-starter for enterprises handling sensitive transactions like payroll, settlement, or treasury operations. Institutions require flows to remain confidential yet audit-ready, a balance that current protocols fail to strike. Some of the major players, like Circle, are actively looking for a solution.

The scale of this issue cannot be overstated. In the past year alone, DeFi lost over $1 billion to hacks and exploits, while an additional $2 billion evaporated due to manipulative tactics like front-running or other predatory behaviors. This toxicity goes beyond financial damage, it's eroding trust and credibility, essential pillars for sustained growth. Every transfer on public blockchains, payroll transactions, or strategic trade were visible to the entire world, which is the reality for crypto today. Traders routinely see their carefully planned strategies compromised, gamers find their moves publicly exposed, and enterprises risk sensitive commercial strategies leaking onto public blockchains. This nature of public blockchains, far from being empowering, often empowers only the most sophisticated, opportunistic actors who thrive on exploiting market inefficiencies.

In the Web2 world, confidentiality is given. Banking, healthcare, and communications all rely on it. Web3, however, has struggled to balance confidentiality with decentralization and regulatory compliance. The absence of onchain confidentiality has stifled the growth of essential applications, from private transfers to secure financial services. Yet emerging technologies like decentralized dark pools (pools that conceal transaction details until settlement) and modular onchain confidentiality frameworks offer hope. These solutions promise to protect user anonymity while enabling regulatory oversight, fostering an environment where confidentiality and compliance coexist.

Detoxifying Web3: Toward Trustworthy and Corruption-Free Markets

On most transparent public blockchains, every pending trade, bid, game move, or transaction intent is public knowledge before it is finalized. This open mempool (or memory pool of pending transactions waiting to be picked up) and state allows opportunists to exploit information that should have been confidential. The absence of confidentiality has resulted in several well-documented issues that degrade user experience and discourage participation:

• Institutional and Enterprise Reluctance: Banks, hedge funds, and corporations have strict confidentiality needs. On public chains, anyone can observe wallet balances, trading strategies, or business contracts. It’s no surprise that many institutions avoid public blockchains for sensitive operations; no serious trading firm wants to broadcast its positions in real-time, and privacy-sensitive users are similarly deterred. In short, transparent blockchains “cut out” institutions and confidentiality-conscious users from participating​, limiting the ecosystem’s growth into traditional finance and Web2 industries.

• Frontrunning and Forced Liquidation: Sophisticated actors (like arbitrage bots or miners) can see your pending transactions and insert their own to profit at your expense. For example, they might detect a large buy order and purchase the asset first, driving up price (a classic frontrun), or sandwich your trade between their own orders. Such tactics have siphoned value from regular users and are only possible because transaction details are exposed before execution​. This is akin to someone eavesdropping on a sealed-bid auction and then outbidding you with knowledge of your bid – an unfair advantage built into transparent ledgers.

• Broken Onchain Gaming and Applications: Many interactive applications (Pari-mutuel betting, AI agents) require secrets to function correctly. Consider a strategy game where players’ moves need to be hidden until a reveal or an AI application that uses proprietary data. On a public chain, all moves and data are exposed, so opponents can cheat. Developers are essentially prevented from bringing popular internet applications onchain because any secret logic or data would be immediately exposed​. This has stunted use cases like fair onchain gaming, confidential auctions, or AI models that require hidden data or verifiable randomness.

• General Data Exploitation and User Tracking: Outside of outright attacks, transparency means user activity is fully traceable. Malicious parties or even marketers can monitor everything you do onchain – trades, transfers, votes – building profiles and potentially using that against you​. Every onchain interaction leaks information. This “public information leakage” leads to asymmetric advantages where those with analytics tools can manipulate or influence less-informed users​. It’s the opposite of the level playing field that decentralization promised.

Crypto shouldn’t require complex safeguards to uphold trust and fairness. Preventing corruption should be intuitive and integrated by default.

This is where Fairblock steps in: the world's Incorruptible Computer.

Fairblock is on a mission to enable incorruptible markets and machines using dynamic confidential computing. Unlike simple cryptographic libraries or privacy-focused chains, Fairblock provides a modular and programmable confidentiality solution that integrates seamlessly into existing blockchain ecosystems. It is not a privacy patch. It’s a programmable coordination layer that unlocks new classes of applications through built-in confidentiality, compliance, and composability, by leveraging cutting-edge cryptographic techniques such as threshold identity-based encryption (IBE), homomorphic encrypion (HE), and multi-party computation (MPC).

Web3 has struggled to balance confidentiality with decentralization and compliance, stifling crucial applications from secure transfers to institutional finance. Fairblock offers a path forward: dynamic confidentiality that protects onchain interactions until verifiable events (e.g., price thresholds, block heights) occur, after which data can be transparently revealed and audited.

Unlike traditional dark pools, which rely on centralized operators, Fairblock’s approach ensures that confidentiality and compliance coexist through selective disclosure and decentralized compliance frameworks. This means that pre-transfer compliance checks and post-execution auditability can be enforced without revealing transaction details to the public, a fundamental improvement over existing privacy solutions. To put it simply, Fairblock isn’t just about privacy, it’s about efficiency, security, and enabling fair markets. Much like how CoW Swap and Flashbots solved real user problems by protecting traders from frontrunning and several other market inefficiencies, Fairblock gives users a secure, compliant, and decentralized way to transact without the risks of frontrunning, information leaks, or regulatory uncertainty.

FairyRing is Fairblock’s native blockchain. Currently live on testnet across EVM ecosystems and Cosmos appchains, it is designed to help developers build confidential applications. By providing threshold cryptographic techniques and selective disclosure mechanisms, Fairblock empowers users and institutions to interact with privacy, fairness, and security without sacrificing the benefits of decentralization.

Blockchain’s evolution into a credible financial infrastructure depends on solving the privacy crisis. Fairblock’s modular confidentiality solutions pave the way for large-scale adoption by ensuring that confidentiality is not just a feature but a fundamental pillar of the decentralized economy. Like the car replacing the horse, confidential transactions will soon be the default and not just an option, but a necessity.

These issues are not theoretical. They are direct consequences of the transparent-by-default design. As the Fairblock team succinctly put it, today’s blockchains “leak the contents of your messages, decisions, and strategies… before they are executed,” giving privileged actors undue advantages and “negatively impacting blockchain experiences” in multiple ways​. Without some form of confidentiality, blockchains will continue to be plagued by frontrunning bots, hesitant institutional adoption, and an inability to support many real-world applications beyond simple token transfers. The need for a solution is clear: if we want decentralized networks to truly compete with Web2 systems (which do keep most data confidential by default), we must bring confidential computing into the blockchain world.

In this article, we will delve into Fairblock’s unique approach to onchain confidentiality, its key components like FairyRing and FairyKit, and how its modular, programmable confidentiality solutions are setting a new standard for blockchain confidentiality with compliance and decentralization.

Before we get started, it’s important to define a few terms that will come up often:

• Threshold IBE​: An identity-based encryption is a form of encryption based on some identifying conditions. Fairblock uses threshold IBE so that no single party controls the decryption keys. Instead of "identity," Fairblock uses "conditional IDs," which are less about actual identity and more about various variables, often related to a chain's global state, such as block height. This approach introduces the minimal bandwidth compared to other MPC and FHE schemes.

• Threshold encryption​: Threshold encryption is a form of cryptography that allows a private key to be derived as long as a certain threshold of network participants come together to aggregate their private key shares. Fairblock uses Threshold IBE, which allows validators to generate a share of the private key using their Master Secret Keyshare once a condition (ID) is met onchain. Validators then submit their private key shares to FairyRing, which can construct the derived private key once the threshold of validator key share submissions is met.

• Public key​: A public key is a large cryptographic value that is used to encrypt data. The public key is publicly visible and used to identify some encrypted data. The public key is derived from the private key, a randomly generated cryptographic value that is needed to decrypt data. In asymmetric encryption and its derivatives, public keys are openly known, while private keys are only made available to owners or receivers of some data and signify true owners of data. In Fairblock, we use a Master Public Key (MPK) that is used to encrypt every transaction within an epoch. The MPK is discarded after the end of an epoch.

• Block height​: The block height is the current length of the blockchain network. It is the number of blocks preceding the current block in a network. The height of the genesis block of a blockchain is zero.

• Derived private key​: The derived private key is a unique key corresponding to a certain encryption condition (ID) and is required for a chain to decrypt encrypted transactions. The derived private key is obtained by aggregating a threshold number of derived private key shares.

• Frontrunning​: Frontrunning is an example of predatory MEV (Maximal Extractable Value), where MEV searchers can observe a user's unexecuted profitable trade in a mempool and manipulate the ordering of transactions within a block. The most common type of frontrunning is sandwich attacks.

Fairblock: The Missing Piece in Crypto’s Tech Stack

Fairblock emerges to fill the critical gaps discussed above with its Incorruptible Compute a decentralized cryptographic engine designed to detoxify Web3 by transforming markets, stablecoins, and machines into secure, fair, and performant environments. Whether it's trading, lending, or auction execution, Fairblock ensures each action proceeds without manipulation or information leakage.

Fairblock solves this by acting as the confidentiality backbone of Web3. It encrypts any user interaction, such as swaps, loan bids, or private data exchange, and stores the decryption keys with a decentralized validator set. The data remains confidential until a predefined condition, like a block height or price threshold, is met. Once triggered, the information is decrypted and the transaction is settled. The entire process remains fully verifiable after execution for audit and compliance purposes.

Much like how Cloudflare secures internet traffic, Fairblock delivers confidentiality as a service for Web3. It ensures incorruptibility without compromising transparency, setting the foundation for trust, institutional access, and next-generation applications.

Blockchains open up the world of financial instruments and manipulation, which are controlled by corporate bodies and governments, aiming to return power to individual users. This provides users with the tools and opportunities to correct inefficient, flawed financial practices that were widespread in the past. Additionally, blockchains were designed in a way that makes them easily manipulable; for instance, immutable transactions can still be affected at the mempool level.

Furthermore, the future of blockchains require more than what we are used to: memecoin pumps, centralization trends, unused applications, and less secure blockchain-based applications that throw businesses and users into turmoil as they occasionally face public leaks of on-chain activities and data. For instance, despite the launch of numerous applications on the blockchain, there is a scarcity of compelling, user-friendly applications that display benefits or tackle key issues as preached in the past. On the other hand, many blockchain-based applications have begun exhibiting centralizing tendencies either through governance models or token distributions. This contradicts the core principles of decentralization and damages the vision of the community.

Fairblock solves all the issues stated above via its key products, FairyRing and FairyKit. Tackling “onchain confidentiality, which is the greatest unsolved crisis in crypto,” as stated by founder, Peyman Momeni, Fairblock has a unique approach. It does not rely on slow or centralized, one-size-fits-all solutions; rather, it tailors and optimizes the security and performance of the cryptographic network for each specific DeFi and AI application. For instance, it hosts multimodal Multi-Party Computation (MPC), Homomorphic Encryption, Trusted Execution Environment (TEE), Zero-Knowledge (ZK) solutions to optimize performance and guarantee confidentiality for various use cases, including private transfers, lending, clearing markets, hidden information GameFi, confidential AI, and private data monetization. .

The use of MPC cryptographic computations on Fairblock allows it to easily run DeFi, AI, and gaming economies for cheap and fast execution that allows applications to thrive in ecosystems such as EVM (L2s included), Solana, and Cosmos. To solve the issues around onchain confidentiality, Fairblock comprises two core elements: FairyRing and FairyKit. FairyRing abstracts away intensive cryptographic operations for applications built within its chain as well as those built on alternative blockchains. For external partners, FairyRing distributes confidential execution via FairyKit, which plugs into any protocol and application to bring one-click confidentiality to any app or user that requires it.

FairyRing & FairyKit

FairyRing is a dynamic and decentralized network that hosts applications optimized for secure and performant confidential computing. This means when apps (either native apps or external apps) are integrated into the Fairblock ecosystem, they have active communication with the FairyRing chain and can frictionlessly integrate with any chain. Having this interconnected network of chains deepens liquidity between apps and bridges the gap of fragmentation. This helps to increase the robustness of the network as it abstracts away cryptographic computations, including condition verification, threshold key generation, and execution of encrypted transactions.

The FairyRing network is built on the Cosmos SDK, incorporating both the benefits of the Cosmos chain and its own novel features. One of the key advantages is that FairyRing does not require relayers, since the confidential computation and contract callbacks are native functions of the hosting chain. Another key benefit is that applications built on FairyRing inherit the Cosmos chain's high performance. Thanks to Tendermint’s horizontal scaling, validators can run multiple chains in parallel, ensuring that workloads are distributed and throughput remains high. Consequently, any application launched on the FairyRing can operate efficiently, free from common bottleneck issues.

FairyRing allows developers to build confidential applications. In EVMs and other blockchains, every transaction, contract call, and state change is public, which carries aforementioned risks like frontrunning and data leaks. FairyRing eliminates these risks by leveraging a suite of confidential computation schemes. A flagship primitive is threshold identity-based encryption: transactions are encrypted, stored onchain, and decrypted only when preset conditions such as reaching a target block height, are met.

FairyKit is a module that extends the same confidential guarantees to both native Fairyring applications and external chains. It is a one-click integration package that can be plugged into any protocol and application. There are two main integration routes that the chains must follow:

1. The use of “FairyKits” is specific to the blockchain that is integrating with Fairblock. This allows for custom setups, in which the partner network handles some or all of the decryption and encryption. No changes to the core protocol architecture are required.

2. In the “co-processing” route, FairyRing handles the all the cryptographic computations adn execution, and returns the end state to the integrating network. For instance, an auction application that allows users to submit encrypted bids can off-load all cryptographic processing to FairyRing, which will return the final state to the auction app.

FairyKit ships adapters for EVM, Cosmos, XRP Ledger and Rust-based chains, delivered as modules, smart contract libraries, or execution-layer hooks, depending on the needs of the application's logic and architecture.

The Fairblock Ecosystem - Application First Approach

The Fairblock ecosystem is made up of several native and external applications. These applications showcase the variety of use cases for onchain encryption across DeFi, AI, gaming, randomness, and governance.

As Web3’s confidential processing layer, Fairblock recognizes that infrastructure is only a means to an end: applications. From day one, the team is launching a suite of DeFi products powered by confidentiality. These serve both as proof-of-concept and as valuable services in their own right, showing users what’s now possible with onchain confidential computing.

Fairblock specifically offers tailored solutions in three of the most impactful verticals:

Incorruptible DeFi

Fairblock enables a new class of high-integrity DeFi applications through programmable confidentiality. By introducing encrypted intents and sealed-bid auctions, Fairblock replaces legacy coordination methods with trustless, manipulation-resistant infrastructure, that is ideal for trading, lending, and token distribution.

• Encrypted Intents & Bids for Solvers
  Fairblock allows users and solvers to submit encrypted messages, ensuring no information is leaked before execution. This not only supports confidenital limit orders and swaps, but also enables a novel use case: sealed-bid auctions for solvers, an upgrade over off-chain RFQ models used by platforms like Anoma, Squid, and UniswapX. With Fairblock, solver bids remain confidential until decryption triggers are met (e.g., block height or escrow lock), preventing censorship, favoritism, and price manipulation.

• Sealed-Bid and Leaderless Auctions: Fairblock’s leaderless sealed-bid auctions eliminate sniping, bid shading, and collusion by keeping all bids hidden until the auction closes. This mechanism is used for:

  • Fixed-rate lending, where both borrower bids and lender asks are encrypted, producing a market-driven clearing rate without backend games.

  • Fair token launches, replacing Dutch auctions with a single clearing price.

• Front-running and forced-liquidation protection: Applications like FairyCow Swap protect transaction details until execution, preventing exploitation through front-running or forced liquidations.

• Private lending and clearing markets: Confidential markets like Fairates and SettleX offer robust lending primitives with transparent, decentralized, and trustless execution without backend games.

Institutional Confidential and Compliant Stablecoins

• Large institutional transfers without market impact: Fairblock’s confidential solutions enable institutions to move significant sums securely, minimizing market disruption.

• Addressing confidentiality blockers for LPs: Ensuring privacy to overcome institutional barriers related to data leakage and confidentiality concerns.

• Private balances and transfers compliant through decentralized selective disclosure (MPC): Fairblock combines MPC and ZK proofs to enable compliant confidential transfers, suitable for normal and institutional users.

• Fairblock enables encryption of transaction amounts, unlike ZK-only solutions, which typically only obscure address linkages while still exposing amounts and activities. With Fairblock, address anonymity is optional, and confidentiality can be fully customized.

• Fairblock’s MPC and ElGamal-based approach offers significantly higher efficiency and decentralization compared to FHE counterparts, which often suffer from performance bottlenecks and centralization risks in practical deployments.

Incorruptible AI

• Inference on encrypted data (e.g., trading agent, balancing pools): Enables AI models to operate securely on encrypted data, preserving confidentiality and integrity during computations.

• Unruggable AI models through MPC: AI models protected by MPC and TEEs, ensuring secure, unstoppable operations.

• Private data marketplace: Fairblock allows secure monetization of private data for AI model training without compromising data confidentiality.

To implement its solutions in the above-mentioned verticals, Fairblock's integration and distribution strategy includes two complementary approaches:

1. Dine-In

2. Delivery

The Dine-In ecosystem features native confidential applications (cApps) developed directly within Fairblock’s FairyRing chain, already live on testnet with over one million transactions. Flagship offerings include credible DeFi products such as fixed-rate lending markets, sealed-bid auctions, and dynamic intent matching (collaborations with Anoma, Squid, and CoWSwap). Additionally, Fairblock hosts unstoppable AI models through MPC and TEEs (e.g., Phala), confidential lending and clearing markets (Fairates, SettleX), compliant confidential transfers, private data monetization for AI, and engaging hidden-information GameFi applications.

The Delivery motion extends confidentiality solutions via strategic partnerships, starting with Arbitrum. Fairblock’s dynamic confidentiality modules integrate natively with EVM rollups (Plume, Syndr, Xai, OP Stack), appchains (Celestia's Rollkit, AltLayer, Polymer), and AltL1s (Monad, Osmosis, Sei). In future phases, Fairblock will leverage coprocessor architectures to extend dynamic confidentiality directly into Ethereum and Solana ecosystems, significantly broadening the reach and impact of onchain confidential computing.

First, let's examine some of the use cases made possible by leveraging Fairblock's infrastructure and capabilities. Then, we'll explore the specific dine-in applications within the broader Fairblock ecosystem.

Confidential Stablecoins & Real World Assets For Institutional Adoption

Stablecoins are rapidly becoming the backbone of global financial infrastructure, yet the current transparent nature of blockchain transactions poses significant barriers for institutional adoption. Enterprises handling payroll, treasury operations, settlement flows, and strategic financial transactions require confidentiality not in terms of anonymity, but specifically regarding transaction amounts and strategic details.

Fairblock addresses these critical needs through a unique cryptographic solution built on Threshold ElGamal decryption combined with Multi-Party Computation (MPC). Unlike zero-knowledge (ZK) solutions, which primarily obscure the link between addresses but leave amounts and transaction patterns exposed, Fairblock encrypts transaction amounts directly. This targeted confidentiality is complemented by the integration of lightweight ZK proofs to validate the input amounts. Crucially, businesses typically prioritize preventing sensitive data leakage over address anonymity due to compliance considerations and practical needs for auditability.

Fairblock’s technology significantly surpasses Fully Homomorphic Encryption (FHE) based projects such as Zama, Inco, and Fhenix in terms of speed and decentralization. FHE methods require computationally heavy operations and typically rely on centralized coprocessors, optimistic assumptions, and trusted relayers, introducing points of centralization and potential security risks. Fairblock’s MPC and Threshold ElGamal approach eliminates these centralized dependencies, ensuring robust decentralization, efficient batch decryption, and selective disclosure, delivering greater scalability and performance.

Fairblock’s solution has attracted the attention of major industry players, aligning with recent market trends where leading stablecoin issuers and payment giants are actively seeking confidentiality enhancements. PayPal’s recent interest in integrating confidential transfers on Solana and Circle’s acquisition of on-chain privacy-focused protocol Gateway illustrate the increasing importance of confidentiality in mainstream financial transactions.

Fairblock is actively collaborating with key blockchain and stablecoin infrastructure partners, including Noble, Ripple, Plume, Hyperliquid, Arbitrum Foundation, Dowgo, Axelar, and Cosmos Hub, to deploy its encrypted transfer capabilities broadly across ecosystems.

Additionally, Fairblock’s confidentiality infrastructure unlocks innovative auction-based TradFi use cases, such as Treasury bill auctions and other institutional asset distributions. Leveraging Fairblock, institutions can securely execute sealed-bid auctions without leaking strategic bidding data, enhancing market integrity and fairness.

Another notable example within the Fairblock ecosystem is SettleX, the first confidential clearing and settlement solution specifically built for institutional stablecoin use. Cross-chain stablecoin flows are currently fragmented, costly, and highly visible, exposing sensitive trading information. SettleX mitigates these issues by netting transactions across chains, securely settling only net deltas, significantly reducing capital inefficiency, bridging costs, and protecting counterparty privacy. By combining MPC with Trusted Execution Environments (TEE), an approach Fairblock has also implemented with Cycles, SettleX provides robust security without solely relying on hardware-based TEE or MPC assumptions alone.

Through these targeted confidentiality solutions, Fairblock is enabling a new era of institutional adoption and strategic financial operations on-chain, redefining how real-world assets and stablecoin transactions can securely and compliantly scale within decentralized ecosystems.

Incorruptible fixed-rate lending on Arbitrum and Hyperliquid

Imagine a lending protocol where interest rates or loan terms are determined via a sealed-bid auction among lenders, rather than a fixed formula. Lenders submit bids (e.g. the lowest interest rate they’re willing to offer or the highest they’re willing to pay to borrow) encrypted via Fairblock. Because bids stay confidential until the auction ends, no participant can adjust their bid reactively and no “last-minute sniping” or collusion is possible. The result is a fair market-driven rate discovery for loans.

This sealed-bid auction framework, internally referred to as DeBid, represents Fairblock’s general-purpose auction engine deployed on Arbitrum One and Orbit chains using Stylus. Fairates, a fixed-rate lending market, is one of its first real-world implementations. In fact, Fairblock’s solution for sealed-bid on-chain auctions was recently recognized in Arbitrum’s development initiative – “Fairblock is revolutionizing onchain auctions by building sealed-bid infrastructure using Stylus… enabling new privacy-preserving use cases that weren’t previously possible onchain.” By applying this to lending, we get more efficient and equitable lending markets that could attract institutional lenders who otherwise fear information leakage in DeFi.

Intent-Based Trading and Dynamic Price Discovery

In collaboration with intent-centric protocols like Anoma, CoWSwap and Squid , Fairblock’s tech can power hidden “intent” orders. A user might express an intent to trade asset X for Y under certain conditions, which on current DEXs would be translated into a visible limit order or a transaction in a mempool. Instead, using Fairblock, the intent can remain encrypted until it is matched with a counterparty. Various market makers or counterparties can bid to fill the intent, also in a sealed-bid manner, and once a match is found and finalized, the trade is executed and only then details are revealed. This creates a dynamic price discovery process where the market finds the best execution price for a trade without broadcasting the intent to everyone. Such functionality is invaluable for large trades (to avoid price slippage from advance knowledge) and is aligned with what leading projects are exploring. By partnering with Anoma (an intent-focused layer-1) and integrating with cross-chain liquidity hubs like Squid, Fairblock is ensuring these confidential intents can even operate across multiple networks, further scaling the liquidity and reach of decentralized economies.

Sealed-Bid Token Launches (Fair Token Distribution)

When new tokens are released (through IDOs, ICOs, or auctions), savvy players often game the process by creating gas wars on Ethereum or Solana. Fairblock enables a better model: sealed-bid token launches in which each participant submits a bid for the token (how much they’re willing to pay or how many tokens they want) confidentially. All bids are revealed simultaneously at the close, and an algorithm determines the clearing price and allocations. Because no one knows others’ bids in advance, every bidder plays on equal footing, and outcomes can’t be skewed by real-time insider info. This approach can prevent the typical frenzy and network congestion of token launches, leading to more stable price discovery. Fairblock unites champions for “fair token launches” and makes dynamic pricing for intents far simpler with sealed-bid, leaderless auction mechanics . The idea is to bring IPO-like fairness to crypto token distributions, which will fuel broader participation and build trust in these events.

Some specific examples of dine-in apps

FairRates: Fixed Rates, Not Fixed Games

FairRates, built by Fairblock, brings a new paradigm to fixed-rate lending, one where confidentiality and decentralization converge to create fair, efficient lending markets. Like some of the existing applications today, FairRates uses sealed-bid auctions to discover market-clearing interest rates, ensuring that all borrowers and lenders settle at the same rate without favoritism or last-minute sniping.

But FairRates stands apart in two critical ways. First, it’s built for high-performance EVM DeFi hubs like Arbitrum and Hyperliquid, drastically reducing gas costs and enabling real-time participation. Second, and most importantly, it eliminates reliance on centralized infrastructure. Other systems like Term rely on centralized servers to reveal encrypted bids, introducing trust assumptions and potential points of failure.

In contrast, FairRates uses Fairblock’s MPC infrastructure via FairyRing to securely decrypt and reveal bids only when predefined auction conditions are met. This ensures confidentiality throughout the bidding process, trustless execution, and zero reliance on fallback mechanisms or centralized servers.

The result is a robust lending primitive: one rate for all, transparently discovered, and zero backend games.

FairRates is actively supported by the Arbitrum Foundation and is being deployed on both Arbitrum One and Hyperliquid as part of Fairblock’s broader effort to bring sealed-bid infrastructure to next-gen DeFi ecosystems. It represents a foundational use case for intent-based lending, delivering the performance, integrity, and decentralization that DeFi was always meant to embody.

Explore FairRates: https://fairates.fairblock.network

FairyCow Swap

The FairyCow Swap is a demo application of Fairblock's modular programmable encryption added to an existing intent-based EVM application (CoW Swap) limit orders. Encrypting trade intents ensures that transaction details such execution time, amount, liquidation trigger, stop-loss price remain confidential until execution and cannot be exploited. FairyCoW Swap is available on the Sepolia testnet, demonstrating how programmable encryption can be applied to existing decentralized exchanges.

Explore: FairyCow Swap

FairEx

FairEx introduces an interesting confidentiality-focused trading solution on Arbitrum, enabling users to encrypt their swap orders and protect sensitive trading information from premature exposure. Operating as a UniswapV2-style decentralized exchange on Arbitrum's Orbit testnet, the platform specializes in handling encrypted limit orders that remain confidential until specific market conditions are met. Users can specify precise limit order conditions, such as executing a transaction when the ETH/USDC price reaches $500, with their orders remaining encrypted until the price trigger. When the specified price threshold is reached, the system triggers a decryption key request to FairyRing. The validators then generate and verify the decryption key. Once validated, the key is transmitted to Arbitrum's testnet, enabling secure order execution while maintaining confidentiality throughout the entire process.

Time Machine: Confidential Prediction Markets with Delayed Decryption

Time Machine is a prediction market application built using Fairblock’s programmable confidentiality infrastructure. Unlike traditional onchain prediction markets where all bets and outcomes are visible in real time, Time Machine leverages delayed decryption to unlock a new design space for fair and manipulation-resistant forecasting.

Each prediction or vote is encrypted and submitted onchain, with decryption triggered only after a predefined condition, such as a specific block height or timestamp is met. This approach ensures that no participant can reactively influence outcomes or copy the positions of others before reveal. As a result, Time Machine eliminates frontrunning and bandwagon biases that often plague transparent prediction markets.

Key features:

• Sealed Predictions: Participants’ responses are fully encrypted until the end of the round.

• Public Auditability: Once the decryption trigger fires, all inputs are revealed and outcomes computed in a verifiable manner.

• Custom Trigger Logic: Developers can define arbitrary conditions such as block height, oracle feed resolution, or community governance decision as triggers for decryption.

Time Machine is a strong proof-of-concept for how Fairblock’s infrastructure can empower decentralized forecasting, secure DAO voting, and future onchain governance applications where secrecy until a reveal event is crucial to system integrity.

Architecture Overview: Fairblock

The Fairblock architecture consists of several components.

1. FairyRing - Fairblock's native chain that abstracts away heavy cryptography for both native applications and those built on alternative blockchains.

2. FairyKit - Fairblock Integration packages that embed one-click confidentiality into any protocol and application.

3. Encryption SDK - Client-side software used to encrypt and submit transactions to the Destination Chain. Once the encrypter is integrated with applications (front-end or wallets), users can seamlessly encrypt their transactions. End-to-end encryption happens locally within the browser and does not rely on any third parties.

4. Destination Chain - The chain storing encrypted transactions, receiving public keys and secret keys from FairyRing, and where the transactions are executed after decryption. This can be FairyRing itself with native applications or external chains working with FairyRing.

5. Relayers - The communication services that listens and coordinates decryption requests between the FairyRing and destination chains (e.g. FairyPort, IBC, Axelar).

The core components of Fairblock define how users interact with the protocol and ensure its proper functionality. An example of the user flow of the Fairblock protocol is:

1. User submits an encryption transaction with decryption parameters

2. Transaction is transferred to the Encryption Software Development Kit (SDK), where it is securely held in the destination chain’s mempool

3. When smart contract conditions are met, relayers request decryption keys from FairyRing

4. FairyRing verifies the request, delivers the derived private key, and sends it back to the destination chain.

5. The transaction decrypts and executes, completing the state change while keeping the data safe until specified parameters.

Key Features of Fairblock

Fairblock has several key features:

Composable

With Fairblock, developers can integrate confidentiality features into the blockchains and applications they already use without switching to a new ecosystem, thereby solving further fragmentation. Using Fairblock’s FairyRing and FairyKit, they can build confidentiality-preserving smart contracts and applications in their preferred environment. This allows them to enhance security and confidentiality without compromising compatibility or requiring major architectural changes.

Chain Agnostic

FairyKit is designed to seamlessly integrate with major Layer 1 (L1) and Layer 2 (L2) blockchains. Whether a blockchain operates as a standalone L1 or scales via an L2 solution, FairyKit ensures smooth compatibility without requiring fundamental changes to its architecture. This allows projects across different ecosystems to leverage Fairblock’s encryption and decryption capabilities while maintaining interoperability and efficiency.

Performant and Scalable

Fairblock offers linear bandwidth scaling, and avoid using heavy one-size fits all cryptographic solutions. As network demand increases, performance scales efficiently without bottlenecks. Additionally, its encryption processes run in tens of milliseconds, ensuring that transactions remain fast and responsive. This combination allows users to experience seamless confidentiality protections without delays, making confidential transactions as smooth as standard ones.

Dynamic

Encryption needs vary across different applications and user needs. Fairblock’s multimodal cryptographic computation is highly customizable, allowing developers to mix and match MPC, FHE, TEE, and ZK schemes to meet specific performance and security needs.

Competitive Landscape

The race to build confidentiality-preserving infrastructure in crypto is heating up, with multiple projects tackling shared private states using different approaches. This has resulted in much excitement in the confidentiality applications and the prospect of new credible economies. Onchain confidentiality aims to achieve a shared private state amongst users. It is still in its early stages, defined by fragmented solutions and narrow use cases.

Fairblock is not a generic confidentiality project, it is an ecosystem-first, multichain infrastructure platform purpose-built to deliver real-world utility across DeFi, AI, gaming, and more. With direct integrations into partner chains and applications, Fairblock serves as the confidentiality backbone for high-performance, composable systems that demand both security and speed.

Unlike traditional privacy-focused blockchain solutions, Fairblock is a practical enabler of real-world DeFi use cases. It distinguishes itself by optimizing for performance and scalability without compromising on confidentiality. Compared to Zama, which utilizes FHE to enable computations on encrypted data, Fairblock focuses on balancing confidentiality with performance. FHE provides strong confidentiality by allowing operations on encrypted data, but heavy computation makes it unsuitable for most DeFi applications. Fairblock, on the other hand, employs Threshold Identity-Based Encryption (TIBE) while integrating MPC, ZK, and TEEs, ensuring faster, scalable, and privacy-preserving operations that are suitable for real-time blockchain applications like auctions and decentralized trading.

When comparing Fairblock with Nillion, the two share a focus on MPC but differ significantly in their approaches and use cases. Nillion provides a chain-agnostic network with an orchestration layer (Petnet) designed for general-purpose AI, DeFi, and computation applications. Its MPC protocols support secure computation and storage across multiple chains but lack native integration with DeFi use cases. In contrast, Fairblock is tailored for EVM and Cosmos chains, specifically enabling privacy for DeFi use cases like auctions, liquidations, and lending markets. By focusing on modular integration into existing blockchain ecosystems, Fairblock brings privacy-preserving infrastructure directly to DeFi, addressing the industry's specific needs for confidential trading, compliance, and real-time data protection.

Arcium, which primarily focuses on the Solana blockchain, utilizes a different encryption framework and takes a more narrow approach to confidentiality. It specializes in the Solana ecosystem, whereas Fairblock’s multichain design integrates seamlessly with both EVM and Cosmos ecosystems, providing broader support. Fairblock’s modular architecture allows it to scale across multiple chains with advanced cryptographic techniques, offering more flexible and practical confidentiality solutions for diverse blockchain applications.

Fairblock’s focus on modular integration, real-time efficiency, and compliance sets it apart as a true enabler for decentralized finance and blockchain-based applications. Whether it's for private data monetization, AI model security, leaderless auctions, or hidden-information GameFi, Fairblock delivers adaptable, scalable solutions that ensure confidentiality while maintaining the integrity and decentralization of blockchain networks.

Fairblock’s Business Model and Sustainable Revenue Flywheel

Revenue Model and Market Opportunity

Fairblock is not just building infrastructure, it's engineering a sustainable business around the incorruptible world computer. At the core of this strategy is a simple but powerful engine: confidential auctions. This single primitive unlocks high-margin revenue across multiple verticals, compounding product depth, usage, and value capture.

Below, we break down Fairblock’s monetization roadmap and run some paper napkin math to illustrate the projected potential of Fairblock’s offerings across three sequential phases.

Phase 1: Incorruptible Auction-Based Markets

One Auction Engine, Three Explosive Revenue Vehicles

Fairblock’s flagship sealed-bid auction infrastructure powers encryption-driven use cases across lending, token launches, and intent-based trading. This model drives revenue directly into Fairblock’s native chain, FairyRing, allowing rapid iteration and unlocking new design spaces in DeFi. Unlike traditional DeFi protocols that rely on fixed curves or public mempool orders, Fairblock introduces a leaderless, encrypted auction engine that unlocks fair market discovery across multiple verticals. Today’s DeFi is often constrained by toxic order flow, manipulated token launches, and inefficient lending curves. Fairblock reimagines these primitives through the lens of confidentiality and incorruptible execution engines.

A. Fixed-Rate Lending (Fairates)

What it solves: Today’s fixed-rate lending protocols (e.g., Notional, Term Finance) often struggle with bid sniping, uneven clearing, and low institutional adoption due to lack of confidentiality.

Fairblock’s edge: Every loan term is discovered through a sealed-bid auction where borrowers and lenders commit bids confidentially. Once cleared, all parties settle at a single, transparent rate, without manipulation.

Where it stands: Already live on Arbitrum testnet, backed by the Arbitrum Foundation, and integrating with Hyperliquid. Fairates benefits from reduced gas costs and rapid block times, something legacy models can’t support.

Revenue opportunity: Projects like Term Finance and Notional process ~$500M in volume across dozens of markets. If even a portion of that migrates to Fairblock’s sealed auctions (augmented by liquidations and ecosystem growth), we’re looking at a conservative multi-million-dollar annual revenue stream, especially as institutional confidence rises.

B. Fair Token Launches (FairLaunch)

What it solves: Dutch auctions and first-come-first-serve IDOs frequently devolve into gas wars and last-minute frontrunning. The result? Inequitable access and massive alpha leakage.

Fairblock’s edge: FairLaunch replaces chaotic token sales with sealed-bid auctions. Participants submit bids privately, and once the auction closes, all bids are revealed and cleared at a uniform price. No sniping. No favoritism.

Where it stands: Partnered with Rova (ex-CoinList) and in active discussions with Legion and Fjord. The approach offers improved UX, better price stability, and broader participation.

Revenue opportunity: CoinList alone facilitated over $100M in token sales last year. With Fairblock targeting 10–50 launches annually in early phases, 5% of funds raised as protocol revenue yields material upside, while serving as a marketing flywheel for broader adoption.

C. Intent-Based Trading (FairIntent)

What it solves: As intent-based trading gains traction (CoWSwap, 1inch Fusion, UniswapX), current systems leave order flow exposed. This creates risk for both users and solvers, ultimately degrading price execution.

Fairblock’s edge: FairIntent encrypts both the user intent and the solver bids. Matching is done through a sealed-bid auction mechanism, where only the selected path is revealed and settled onchain.

Where it stands: Actively collaborating with CoWSwap, Squid, Anoma, and Uniswap MVPs.

Revenue opportunity: With over $50B* annual volume in intent-based flows, and Fairblock capturing even 5–10%** of that at 0.15% in protocol fees, the upside exceeds 7 figures in annualized revenue. Unlike speculative tokens or fluctuating LP fees, these flows are sticky and repeatable.

*Volumes from Cow Swap, Fusion, Uniswap X, Squid

**Cow Swap showed 132% growth in trading volume between May 1, 2024-May 1, 2025

Phase 2: Confidential and Compliant Stablecoin Transfers

Replacing Centralized Backends With Trustless Confidential Rails

Despite trillions in DeFi volume and $200B+ in stablecoin supply, most transfers still pass through centralized routing systems or leak sensitive information that makes them unsuitable for institutions. Fairblock replaces this layer with programmable confidentiality and decentralized selective disclosure.

A recent Fireblocks report surveyed 295 financial executives, 61% of whom were C-suite leaders, and confirmed what the market has long been signaling:

• 9 in 10 respondents say regulations and industry standards are accelerating stablecoin adoption

• 36% of respondents flagged improved security as the single biggest unlock for enterprise stablecoin use

This shift reflects a maturing industry: as legal clarity and AML tooling advance, risk becomes the next bottleneck. Institutions aren’t just asking if providers are secure—they want to know how protocols evolve against new threats. And here, Fairblock is well-positioned.

“Conventional security frameworks will not be sufficient to protect the next billion users as stablecoin adoption accelerates. To keep up with 24/7 cross-border flows, infrastructure must evolve at the same pace as the attack surface. Institutions are no longer asking if a provider is secure, but how that security evolves to meet emerging threats.”

By embedding decentralized confidentiality and programmable compliance at the execution layer, Fairblock delivers:

• Confidential transfers with selective disclosure

• No reliance on centralized intermediaries or private servers

• Composability across EVM chains, Solana, and institutional rails like Noble

Here, the opportunity is massive:

• DeFi: ~$3.2T in annualized trading volume, 70% still routed through centralized intermediaries.

• Stablecoins: $239B supply today, projected to hit $2T by 2028.

Let’s illustrate the Revenue Potential:

• Capturing 2% of DeFi flow at 0.15% fee = ~$67M.

• Powering 10% of stablecoin rails today = ~$36M; scaling to ~$300M+ by 2028.

Notably, the demand for confidential stablecoin rails is already proven. Projects like Railgun and Houdini Finance, despite being limited in either ecosystem reach or functionality, have generated approximately $4-6M and $1.5M respectively in revenue. These systems typically charge around a 0.1% transaction fee, a model Fairblock can replicate with greater efficiency and broader composability across chains and applications. With a more scalable infrastructure and support for institutional-grade compliance, Fairblock is well positioned to capture and expand this growing market segment.

Phase 3: Incorruptible Machines (Confidential AI Infrastructure)

As crypto-native AI agents become a reality, whether as automated traders, autonomous governance participants, or verifiable oracles, the demand for a cryptographically secure execution layer will only grow. Fairblock is positioning itself as the decentralized coprocessor for these AI systems, where confidentiality, verifiability, and composability converge.

Fairblock’s multimodal compute stack (MPC + TEE + FHE + ZK) has the potential to enable:

• Confidential AI inference

• Secure data monetization

• Strong and efficient model execution

Potential Revenue Scenario:

• DeFi trade volume: $3.2T annually.

• 86% of it is already automated via bots and agents.

• Encrypting just 2% at 0.15% = ~$83M annual protocol fee potential.

Current integrations with Phala, Ritual, and Cycles are early signals of product-market fit. As regulation and onchain AI adoption grow, Fairblock becomes the operating system for incorruptible machine logic, an entirely new frontier for both computation and monetization.

Note: The revenue figures and market sizing outlined above are forward-looking projections intended to illustrate the potential scale of Fairblock’s core products. These estimates are not financial advice and are based on current market trends, comparable protocol data, and illustrative adoption scenarios. Actual outcomes will depend on a range of factors including market dynamics, user adoption, regulatory clarity, and Fairblock’s ability to achieve strong product-market fit across its target verticals.

Tokenomics

Fairblock’s native token underpins the protocol's economic and security models. The token is designed not only to accrue value directly proportional to network adoption but also to abstract user experience complexities, driving sustainable long-term utility.

Primary Functions of Fairblock Token:

• Governance: Enables stakeholders to influence protocol evolution, security parameters, cryptographic standards, and key partnerships. Progressive decentralization ensures robust initial oversight, transitioning smoothly to broader community governance.

• Fee Mechanisms: Differentiated fee structure incentivizes native application development, with internal network applications benefiting from reduced fees compared to external integrations, enhancing network effects and platform adoption.

• Staking and Security: Node operators stake Fairblock tokens to maintain network integrity, with tiers incentivizing higher performance and advanced computational capabilities. Token-based staking aligns incentives and discourages malicious behavior through slashing mechanisms.

Fairblock implements computation fees that scale with complexity and urgency, offering premium fast-lane execution options. Fees can be paid in native tokens or stablecoins like USDC, with stablecoin transactions automatically converting to the native token, reinforcing demand.

Fairblock's meticulously structured revenue streams and robust tokenomics model position the protocol to achieve sustainable economic viability, driven by its critical role as the confidential processing layer for diverse blockchain applications and markets.

Bonus: How Confidentiality Unlocks New Design Space for Onchain Gaming

While Fairblock’s primary focus lies in transforming financial markets, stablecoin infrastructure, and AI execution, its early traction in gaming highlights the broader demand for confidentiality across Web3. These community-driven experiments demonstrate how programmable encryption can bring fairness, unpredictability, and integrity to fully onchain games.

In multiplayer betting games like blackjack and poker, Fairblock enables encrypted cards that remain hidden until specific triggers are met, such as a challenge or reveal phase. Bluff-based games like BS allow decryption only when a move is contested, while trivia and quiz games keep answers concealed until the correct guess or time-based reveal. Strategy-heavy formats like Diplomacy benefit from encrypted turn submissions that are unveiled simultaneously, preserving competitive fairness. Even games of chance such as Craps, Russian Roulette, and Spin the Wheel are reimagined using cryptographic randomness and delayed decryption to maintain suspense and fairness.

In narrative-rich settings like the Celestia-powered Murder Mystery, Fairblock enables dynamic storytelling by keeping player identities, clues, and outcomes encrypted until key moments. One standout demo, Lazy Killer, uses Fairblock’s infrastructure to keep the killer’s identity confidential until a pre-set block height. Players navigate the game without access to critical information until the reveal, adding a layer of suspense and ensuring no participant can exploit premature knowledge.

These examples are not just novelties. They show how confidentiality can unlock new design spaces in Web3, even outside of finance. While gaming is not Fairblock’s core focus, its organic adoption in this vertical validates the need for incorruptible infrastructure wherever fairness, unpredictability, and trust are essential.

Lingering Thoughts

As we’ve seen, the earliest and most enduring promise of crypto has always been simple: peer-to-peer transactions that are permissionless, decentralized, and open to all. Yet ironically, this core use case remains incomplete. Why? While transparency ensures verifiability, it simultaneously prevents true usability at scale. Whether it’s two individuals transacting, a protocol launching a token, or a game executing logic, the lack of onchain confidentiality has created real constraints.

Today, most onchain transactions are not only public but pre-public. Every bid, trade, or move is visible before it executes, exposing users to frontrunning, value extraction, and exploitation. This dynamic turns the mempool into a battleground, not a marketplace. As a result, crypto has largely been limited to speculation, meme coins, and basic swaps while the intended real-world use cases remain on the sidelines.

Fairblock closes this gap not by simply adding a confidentiality "patch," but by reimagining the infrastructure required for secure, performant, and trustless applications to operate at scale. Its architecture, built around leaderless, rapid auctions and programmable encryption, enables something we’ve long been missing: fairness by design.

Whether it's lending markets that discover rates dynamically through sealed-bid auctions, token launches without sniping and manipulation, or generalized intents that allow users to safely express preferences without leaking alpha, Fairblock’s technology allows applications to work as they were always intended to. Not by hiding data for secrecy’s sake, but by preserving integrity until the right moment. This unlocks everything from confidential AI inference to unruggable multiplayer games, and institutional-grade onchain participation.

At its core, Fairblock is building the world’s Incorruptible Computer, an engine for powering incorruptible markets and machines. It detoxifies Web3 by turning every onchain interaction, whether a trade, AI action, or game logic, into a fair, trustless, and confidential experience. Institutional flows, retail trades, and autonomous agents can operate without leaking alpha, enabling new forms of coordination and value exchange.

The FairyRing chain and FairyKit toolkit are not black-box privacy tools. They are open, modular systems designed to be used by real developers building real applications. HTTPS didn’t change what websites were;it enabled them to be trustworthy. Similarly, Fairblock enables credible economies to emerge onchain with confidentiality built in, not bolted on.

To understand what this shift could mean, it’s helpful to zoom out. In 1900, New York’s Fifth Avenue was filled with horses and carriages. Horses had worked fine for centuries, and most people didn’t feel limited by them. But by 1913, that same street was full of cars, with only a single horse remaining. The change was subtle at first, then happened “slowly, then all at once.” Cars didn’t just offer more speed; they were cleaner, safer, more efficient, and easier to scale. Confidentiality wasn’t the reason people adopted cars, but it was an inherent advantage once they got there.

In the same way, Fairblock is ushering in the shift from horse-drawn infrastructure. Slow, exposed, inefficient blockchains, towards modern, programmable systems built for fairness, compliance, and performance. Confidential computing isn’t the end goal, it’s the engine under the hood that makes all of this possible.

The recent $2.5 million in funding secured will enable Fairblock to expand its research capacity, develop new prototypes, and roll out user-friendly tools that ensure safer transactions across various blockchain platforms. More so, Fairblock has been supported by credible organizations that boost its credibility, with backers including Anagram, Axelar, Robot Ventures, Arbitrium Foundation, and Reverie, among others.

As regulatory clarity emerges, the blockchain industry stands at the cusp of a new era. Confidentiality will no longer be a barrier to adoption but a cornerstone of innovation, ensuring that onchain confidentiality is preserved while meeting regulatory demands. Fairblock’s commitment to this vision ensures that the future of blockchain is not only secure and decentralized but also inclusive and compliant, unlocking new possibilities for DeFi, AI, gaming, healthcare, and beyond.

Fairblock is building the next default: fast, secure, compliant infrastructure that scales trustless coordination beyond what transparent ledgers ever could. The world didn’t replace horses overnight. But once it saw what was possible, there was no going back. With Fairblock, crypto is about to have its automobile moment.

References

Fairblock Official Documentation: https://docs.fairblock.network/docs/welcome/Vision

0xFairblock: https://website-0xfairblock.vercel.app/applications

Fairblock Network: https://www.fairblock.network

Crypto's New Whitespace: WTF is MPC, FHE, and TEE? By @millianstx. https://x.com/milianstx/status/1873767492767654359

Nillion: https://nillion.com/

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer.This post has been created in collaboration with the Fairblock team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research

A Primer on Restaking, Liquid Restaking

The adoption of Proof-of-Stake (PoS) has surged as blockchains seek more efficient and secure consensus. Ethereum’s long-awaited transition to PoS in 2022 was a milestone, slashing the network’s energy use by an estimated 99.95%. Even before this merge, major networks like Solana, Cardano, and Algorand had embraced their own variations of PoS for its eco-friendliness and security, signaling an industry-wide shift away from energy-intensive mining.

Solana’s blend of Proof-of-Stake (PoS) and Proof-of-History (PoH) consensus mechanisms illustrates how staking can secure a network while supporting lightning-fast transactions and minimal fees. In essence, holders delegate their SOL tokens to trusted validators, much like casting votes in a democratic election. Validators, in turn, ensure smooth network operations and earn rewards from transaction fees and protocol inflation.

Yet, even as staking became a popular and effective method of securing blockchain networks, it introduced a notable drawback: locked capital. Assets staked in traditional PoS are immobilized, limiting their broader economic utility and capital efficiency. Recognizing this inefficiency, many teams began exploring ways to leverage staked capital more effectively, enabling it not just to secure one blockchain but to extend its security benefits across multiple networks.

The rise of modular infrastructure and services has given way to a proliferation of new app-specific blockchains, which have historically struggled with bootstrapping their validator networks due to a lack of activity and, therefore, economic incentives for participation. As such, new mechanisms were formed to extend the utility of staked assets to help secure and bootstrap new blockchains. This is what has come to be known as restaking.

The Restaking History

Restaking extends the utility of staked assets by allowing them to simultaneously secure multiple protocols, and as a concept, it has been tested and implemented in app-specific blockchains such as Polkadot, Cosmos, and Avalanche, where security is a primary concern. A handful of different ecosystems and blockchains have implemented some form of shared security over time. The individual details may vary, but the core concept still remains the same: a larger protocol enables smaller protocols by allowing such protocols to tap into their existing vast pool of economic and computational resources to aid in their initial growth while improving capital efficiency and returns to the larger protocols.

In the Polkadot network, validators stake DOT to participate in the relay chain, which is then used to provide security to Parachains. On the Avalanche network, validators securing the C-chain (the primary hub of economic activity) can participate in subnets, which connect to a dynamic set of validators working together to secure or achieve consensus on the state of a set of chains. Subnets secure many chains on the network, but each chain is validated by one Subnet.

Cosmos, on the other hand, implements Replicated Security (formerly Interchain Security), where the top 95% of the Cosmos Hub’s validator set is replicated across “consumer chains.” These validators must run nodes for every consumer chain, effectively sharing the slashing risk and infrastructure burden. ATOM stakers can choose to opt-out of this cross-chain exposure by delegating outside the top 95%, albeit at the cost of reduced yield.

However, it was EigenLayer that truly popularized “restaking” as a standalone paradigm in 2023 on Ethereum and beyond​. EigenLayer allowed Ethereum stakers to opt-in and re-stake their ETH or liquid staking tokens to secure new modules (initially dubbed Actively Validated Services, or AVSs), thereby extending Ethereum’s security to new use-cases without launching new blockchains. This innovation opened eyes to the untapped potential of leveraging existing stake for additional utility. Yet, EigenLayer’s early design also highlighted some inefficiencies, from single-asset limitations to reliance on off-chain reward distribution processes, largely due to Ethereum’s technical constraints.

Jito Restaking on Solana

On the other hand, Solana’s implementation of the Restaking concept launched on July 25th, 2024, when the Jito Foundation released their code for the Jito (Re)staking protocol, a hybrid multi-asset staking protocol on Solana enabling any new network or application to bootstrap their economic security.

Jito (Re)staking is Solana’s first-class framework for restaking, developed by Jito as a next-gen infrastructure for multi-asset staking. In simple terms, Jito’s protocol lets users stake (or re-stake) any supported asset on Solana to secure additional networks or services – called Node Consensus Networks (NCNs) – while maintaining liquidity via tokenized receipts. The protocol consists of two primary components: the Vault Program, enabling the creation and management of staked assets, and the (Re)staking Program, coordinating activities and incentives among network participants. Together, these two core programs offer developers a modular, scalable framework for streamlining staking mechanisms for any SPL asset, marking the first protocol of its kind on Solana.

Vault Program

This on-chain program manages the creation and operation of Vault Receipt Tokens (VRTs) – Solana’s flavor of liquid restaking tokens​. A vault in Jito can accept any SPL asset (SOL, staked SOL like JitoSOL, or even a project’s own token) as its underlying stake​. When you deposit tokens, the Vault program stakes them (either directly or via existing liquid staking tokens) and mints you VRTs in return. These VRTs act as liquid representatives of your staked position – you can hold or use them in DeFi while the vault deploys the underlying stake to earn rewards. The Vault Program handles all the heavy lifting: minting and burning VRTs, delegating the staked assets to chosen operators, and even enforcing any slashing conditions specified by the network you’re restaking to​.

By design, the Jito vault supports only one asset per vault and can delegate only to node operators, not directly to NCNs. To overcome this limitation and enable multi-asset support, Fragmetric developed a wrapper program that extends the vault’s functionality. This enhancement allows for the creation of a unified restaking experience with customizable delegation strategies. For example, a vault’s stake could be spread across several node operators or even indirectly support multiple NCNs, all governed by a DAO or automated protocol logic.

Restaking Program

If vaults are where users interact (depositing tokens and receiving VRTs), the Restaking Program is the coordination layer that ties everything together​. It’s responsible for registering and managing the Node Consensus Networks (NCNs) themselves, as well as the node operators who run them. An NCN in Jito’s framework is analogous to EigenLayer’s AVS – it’s any service or protocol (on-chain or off-chain) that wants to leverage Solana’s staked assets for security. The Restaking Program lets these NCNs define their parameters: which vault(s) (i.e. which tokens) they accept, what the reward structure is, and what conditions would trigger slashing of misbehaving operators​. This program implements opt-in/opt-out logic so that vaults or operators can choose which NCNs to participate in, and it handles the distribution of rewards and slashing penalties on-chain. In other words, when an NCN pays out rewards, the Restaking Program makes sure those rewards get routed to the right vaults and thus to VRT holders and operators; if a node operator violates the rules, the program can slash their staked assets per the NCN’s policy. All of this happens transparently on Solana’s ledger.

How do these pieces work together?

Let’s say a new protocol (call it ORCL, an oracle network) wants to use Jito (Re)staking. ORCL would register as an NCN via the Restaking Program, perhaps specifying that it will accept restaked $ORCL tokens (its governance token) and JitoSOL as collateral from stakers. Jito’s Vault Program can then set up vaults for ORCL – one for $ORCL deposits and another for JitoSOL. When users stake into these vaults, they receive, for example, rORCL and rJitoSOL VRTs (liquid tokens representing their stake in ORCL’s security)​. The vaults delegate the actual $ORCL and JitoSOL stake to ORCL’s node operators.

Now, ORCL’s oracle nodes are economically secured by two assets. If they try to feed wrong data, they can be slashed in either or both vaults. While the enforcement of such slashing policies, such as penalizing a portion of the operator’s rORCL and rJitoSOL stake, is defined by ORCL’s predefined rules, it is not natively handled by the Jito Restaking Program. Instead, these rules must be implemented and enforced externally through custom program logic. Meanwhile, as ORCL’s oracles perform their duties correctly, they might reward participants, including stakers, with freshly minted $ORCL tokens or fee income. These rewards are then tracked and distributed through ORCL’s own reward mechanisms, not the Jito Restaking Program, to the appropriate rORCL holders.

All the while, users who hold rORCL or rJitoSOL can use these tokens elsewhere or redeem them, just like any liquid staking token. In this way, Jito (Re)staking acts as a one-stop, on-chain platform for launching and managing a restaking-enabled service on Solana – you get secure staking, customizable slashing, and liquid tokens without building a thing from scratch​.

Why Restaking Matters for Solana’s Future

Enhanced Economic Security

By allowing multiple forms of collateral to secure a service, Jito (Re)staking drastically increases the cost to attack that service. A malicious actor would need to compromise or acquire a significant amount of each staked asset to do harm, and even then they’d be financially punished via slashing. This creates a kind of insurance mechanism for Solana’s dapps and off-chain integrations​. While Solana’s base layer historically leaned on high performance over heavy slashing, adding restaking gives the network strong crypto-economic guarantees where needed. Critical services (like price oracles, bridges, DePIN, or upcoming Layer-2 networks) can bootstrap security from Solana’s existing stake pool, aligning their fate with the network’s economic security. This in turn hardens the overall resilience of the ecosystem – fewer single points of failure and a higher cost to subvert core services.

MEV and Fee Distribution

Solana’s design already reduces Maximal Extractable Value (MEV) compared to some chains, but Jito is at the forefront of making sure the MEV that does exist benefits the community. In fact, the first live NCN on Jito Restaking is the Jito Tip Router​. This service captures a portion of transaction fees (tips) from Solana’s MEV ecosystem and redistributes it to restakers and validators.

For example, 0.15% of all Solana tip revenue is directed to SOL restakers, and another 0.15% to Jito’s own JTO token restakers, via the Tip Router vaults​. By restaking, regular SOL stakers can earn a share of MEV income that normally only validators or block producers would get.

Originally, MEV tips were intended for stakers. What’s important is that restaking has removed the single point of control and decentralized this distribution process. Prior to the introduction of Tip Router, Jito managed the entire flow of MEV rewards internally. By delegating this responsibility to a restaking-enabled mechanism, the system now ensures a more transparent and inclusive reward path, bringing MEV income to a broader set of network participants.

This fair distribution of MEV rewards strengthens incentives for staking and keeps Solana’s validator set economically healthy. It’s a prime case of how restaking can channel new value streams (like MEV or fee rebates) back to the people securing the network.

Network Scaling and Utility

Restaking opens the door for Solana to scale horizontally in terms of services. Instead of every new decentralized service launching its own token and validator set (which dilutes security and is a huge engineering lift), they can plug into Jito (Re)staking as an NCN. This is much like an “AWS for decentralized trust” – a project can quickly borrow Solana’s security by creating a vault and defining slashing rules, and voila: they have a decentralized trust layer for their app. This could accelerate innovation by lowering the barrier to launch new protocols that require trustless execution or consensus. For Solana, this means more useful services running on or adjacent to the network without compromising security. It’s a path to scale out functionality (similar to Ethereum’s rollup ecosystem) while keeping the economic security tightly coupled with the Solana ecosystem, rather than fragmenting it.

Capital Efficiency & Staker Rewards

From a staker’s perspective, restaking makes Solana staking much more attractive. It layers additional rewards on top of base staking yield, essentially stacking yields from multiple sources​.Instead of just earning ~7% APY from SOL inflation, a Solana staker might earn that plus oracle service fees, plus MEV tips, etc., all through one integrated platform. And they can do this while keeping their stake liquid via VRTs. This boosts participation (why sit idle with just one yield source when you can earn more, albeit with higher risk due to potential slashing?), which in turn increases the total stake securing Solana. More stake means higher economic security for the L1 as well.

Additionally, by making stake more productive, features like restaking could allow Solana to adjust its inflation down in the future without hurting validator economics, as suggested by some ecosystem researchers​. In essence, restaking aligns incentives so that what’s good for the individual staker (maximizing returns) is also good for the network (strengthening security and utility).

Restaking, Liquid Restaking, & Liquid Restaking Tokens (LRTs)

Restaking allows assets staked to secure one network to be reused across multiple services, significantly boosting capital efficiency and economic security. However, just like traditional staking, restaking usually requires assets to remain locked, limiting their liquidity and usability elsewhere. This immobilization of capital reduces potential returns and restricts users' flexibility. To solve this challenge, Liquid Restaking Tokens (LRTs) were introduced, unlocking new possibilities for restakers by providing a liquid representation of their underlying restaking positions.

State of Liquid Restaking

The total amount of liquid restaked assets locked in the general liquid restaking market is $11.746b. Ether.Fi, a decentralized and non-custodial staking protocol on Ethereum, has a higher percentage of assets in the pool with a TVL of $6.543b, followed by other protocols such as Kelp, Renzo, and EigenPie, amongst others.

At the time of writing, there is $229.64m worth of actively liquid restaked assets on Solana, with Fragmetric leading the way with over 60% share of the restaked pool. Renzo, Kyros, etc account for the remaining significant portion of the pool, with Meta Pool mpSOL accounting for the least thus far.

Solana optimizes for fast execution speeds and low transaction costs at high volumes. Solana ultimately seeks to maximize performance for developers and users by fully leveraging available hardware performance capabilities, such that hardware is, in fact, the only restriction on network performance long-term. As the second-largest chain by TVL, the restaking landscape on Solana is well-poised for growth and transformation in the near to long-term future. One team seeking to propel the concept of Restaking on Solana is Fragmetric, as they are a project building the architecture needed to aggregate different staking technologies on the Solana network.

Restaking Approach: Solana vs. Ethereum

Fragmetric’s approach deliberately avoids the pitfalls observed in early Ethereum restaking. One major inefficiency in EigenLayer’s initial design was the use of single-token vaults and off-chain accounting. For instance, distributing rewards that come in many forms (imagine an oracle service paying out in its own token, plus perhaps USDC fees) is tricky on Ethereum. In practice, some Ethereum restaking projects resorted to simplifications like converting all rewards into a single token (e.g. swapping various tokens into ETH) before distributing, a clunky solution that dumps reward tokens on the market and loses granularity​.

Even EigenLayer’s core system leans on off-chain processes: a “trusted” component computes each participant’s earnings off-chain and posts a Merkle root to Ethereum, which users then claim against​. This off-chain reward distribution adds latency and opacity (participants have to trust the off-chain computation) and is necessary largely because doing complex, frequent calculations on Ethereum would be prohibitively expensive in gas. Similarly, Ethereum’s limited throughput makes real-time, multi-asset tracking difficult, so much so that EigenLayer is developing a separate data availability layer (EigenDA) to scale its capabilities​.

Furthermore, liquid restaking protocols built on EigenLayer (e.g., Ether.fi, Swell, Renzo) experience operational friction when redistributing rewards to token holders. High Ethereum gas fees pose a challenge, making frequent or automated reward distributions economically infeasible. This often results in protocols indirectly passing gas costs onto users through manual claim processes or accumulating rewards to periodically adjust exchange rates, inadvertently creating selling pressure on AVS tokens. Solutions like King Protocol, which consolidates multiple AVS tokens into a single vault-backed token (KING), partially mitigate this but introduce new complexities, including smart contract risks, token peg stability issues, and reduced flexibility for individual reward token management.

Solana, by contrast, was built for high-frequency, on-chain computation with low fees, and Jito (Re)staking capitalizes on that. All reward distribution in Jito’s framework can happen on-chain, transparently. Because Solana can handle many transactions quickly, Fragmetric’s vaults are able to track and distribute multiple types of rewards in real time. In fact, Solana’s token infrastructure has unique features that Fragmetric leverages for it’s solana-native liquid restaking protocol. One example is the use of token extensions like the transfer hook, a program that allows attaching custom logic whenever a token is transferred. Fragmetric uses this to precisely track how long each user has held their VRT so that Node Consensus Network rewards (which might accrue over time in various tokens) can be split fairly​. Every time someone moves their restaking token, the transfer hook updates an on-chain record of their “time-weighted” balance. Achieving this level of precision simply isn’t feasible on Ethereum’s ERC-20 standard without expensive workarounds.

Solana’s distinct edge stems from its recent advancements with Token-2022, the new SPL token standard that introduces sophisticated token extensions. Token-2022 maintains backward compatibility while unlocking advanced functionalities like transfer fees, transfer hooks, interest-bearing tokens, and confidential transfers via zero-knowledge proofs. These extensions offer developers an unprecedented level of customization in defining economic and operational behavior for tokens, something significantly constrained by the conventional ERC-20 standard on Ethereum.

This foundational difference in token standards brings us directly to Fragmetric. Built on Solana and integrating deeply with the Jito restaking ecosystem, Fragmetric harnesses the Token-2022 extensions to deliver uniquely tailored value accrual mechanisms. Specifically, Fragmetric issues fragSOL tokens representing deposited SOL or JitoSOL. Unlike traditional liquid staking tokens, fragSOL is a yield-bearing asset designed explicitly around Token-2022’s transfer hook functionality.

Solana’s Token-2022 standard extends the traditional SPL token model by enabling modular token functionalities without requiring separate vaults or permissioned contract layers. This is achieved through Token Extensions, a set of programmable token behaviors that allow for fine-grained control over transfers, fees, governance, and yield mechanisms.

Fragmetric fully embraces Token-2022 to create a yield-bearing, composable, and capital-efficient liquid restaking token (fragSOL) with native programmability. Unlike Ethereum’s vault-based restaking model, which requires off-chain governance and custom implementations to manage fee-sharing, Fragmetric uses on-chain, protocol-native extensions such as:

Transfer Hooks: Enables precise, programmatic control over token transfers. Fragmetric leverages this extension to track time-weighted holdings of fragSOL, enabling accurate and fair reward distribution directly on-chain. This eliminates the need for off-chain computation or vault-based reward systems.

Note: While Token-2022 supports other extensions like interest-bearing tokens and confidential transfers, Fragmetric currently utilizes only the transfer hook to implement its reward and accounting logic.

Fragmetric capitalizes on Transfer Hook extensions to ensure specific economic incentives and slashing conditions are programmatically enforced during token transfers. This sophisticated token design drastically reduces operational complexity and overhead associated with reward distribution—a challenge particularly acute in Ethereum’s ERC-20 and liquid restaking ecosystems.

In the subsequent section, we will explore Fragmetric’s technical architecture, focusing on how it distributes rewards. We will also examine how Fragmetric leverages the Token-2022 to dynamically accrue yield directly within fragSOL tokens, transparently reflecting accumulated staking and restaking rewards.

What is Fragmetric?

Fragmetric is a native liquid restaking protocol on Solana that aims to enhance Solana’s current ecosystem security as well as increase its economic potential. Through liquid restaking, the total amount of staked SOL across nodes increases, which helps to strengthen the network and improves security. Furthermore, the Token2022 extension improves capital efficiency by enabling users to restake without compromising capital efficiency.

Fragmetric is focused on building the infrastructure that is responsible for aggregating all restaking protocols on Solana. This raises an important question: What are the key objectives of Fragmetric that support its vision? The key objectives of Fragmetric are:

• Establishing a Secure LRTs Standard: Fragmetric develops and maintains standards for LRTs to ensure accurate reward distribution. This allows such LRTs to be used as restaked assets across various protocols, improving capital efficiency—users earn restaking rewards and additional revenue at the same time.

• Delegate Deposits to Secure Restaking Protocols: Fragmetric is developing a governance-based Risk Management Committee to verify the profitability and secureness of Node Consensus Networks (NCNs) and Autonomous Verifiable Services (AVSs).

• Contribute to the growth of Solana with SANG: Through Fragmetric restaking, users become SANG—Solana Network Guard—guardians who secure Solana’s ecosystem.

With a seamless restaking experience made possible by Fragmetric, which has several features that aid in achieving economic security for Solana. This brings up the question: how does this procedure of restaking using Fragmetric work? The next section goes over how that is feasible.

The Fragmetric Restaking Process

The Fragmetric restaking process begins with the following steps:

• Deposits: When a user first deposits SOL, LSTs, or other SPL Tokens into Fragmetric, they receive an equivalent amount of $fragmetric asset (e.g., fragSOL).

• Normalized Token Program: This fragmetric program maintains precise conversion ratios between deposited assets and minted $fragmetric assets. Users’ combined deposits (SOL, LSTs, and other tokens) form a unified basket that Fragmetric allocates across various restaking protocols and NCN/AVS.

• Rewards Distribution: Assets are delegated to partner validators who secure NCN/AVS networks. The earnings from these delegations are distributed to $fragmetric asset holders. Fragmetric serves as both a portfolio manager and a liquidity layer between users and restaking protocols.

Protocol Architecture

FRAG-22 is a novel token standard developed by Fragmetric that extends the capabilities of Solana’s Token-2022 program, particularly its transfer hook functionality. Originally designed to support Fragmetric’s liquid restaking infrastructure, FRAG-22 has evolved into a composable asset framework enabling unified management of multiple yield sources under a single token architecture.

At its core, FRAG-22 allows for multi-asset deposits, normalizing various input tokens (e.g., SOL, JitoSOL, mSOL) into a single fungible representation. This simplifies user experience while enhancing liquidity efficiency across DeFi protocols. Each FRAG-22 token captures not only the underlying asset value but also an on-chain record of contribution over time, used to calculate real-time, precise reward entitlements for each holder.

Unlike conventional token standards which rely on off-chain reward logic or inflexible vault systems, FRAG-22 embeds all reward accounting directly on-chain through programmable transfer logic. This allows FRAG-22 tokens to dynamically integrate with lending markets, liquidity pools, structured products, and yield strategies, all while maintaining transparent and predictable reward distribution.

As Solana’s DeFi ecosystem evolves, FRAG-22 sets a new bar for capital efficiency and composability, offering developers and users a powerful primitive to unlock more sophisticated and modular yield-generating applications.

To provide the infrastructure needed to aggregate all restaking protocols on Solana, the Fragmetric comprises the Fund, Operator, Reward, and Normalized Token Pool.

Fund, Operator, and Reward

In the Fragmetric ecosystem, the seamless management of user assets and precise distribution of staking rewards form the cornerstone of its value proposition. Three core modules—Fund, Operator, and Reward—coordinate closely to deliver an intuitive, secure, and transparent user experience. Let’s explore how these modules interact, followed by a simplified working example to illustrate exactly how rewards are managed and distributed within Fragmetric.

The Fund is the foundational module responsible for managing user deposits and withdrawals. Users deposit assets such as SOL or supported Liquid Staking Tokens (LSTs), including JitoSOL, mSOL, BNSOL, and bbSOL, directly into the Fund. Upon deposit, the Fund mints an equivalent value of fragSOL tokens for users. The quantity of fragSOL minted is dynamically determined using accurate, real-time price data sourced from the Normalized Token Pool (further detailed in subsequent sections).

The following diagram demonstrates the interaction between users, the $fragSOL Fund, and the Oracle systems:

1. Deposits: Users deposit SOL or supported LSTs (including JitoSOL, mSOL, BNSOL, bbSOL) into the Fund.

2. Minting fragSOL: Upon receiving a deposit, the Fund mints fragSOL for the user. The number of tokens minted is determined by the current price data, reflecting the combined value of the underlying assets.

3. Withdrawals: Users may request withdrawals, prompting the Fund to burn the corresponding fragSOL and reserve the equivalent amount of SOL for them to claim. These withdrawal requests are processed on a periodic schedule.

4. Pricing fragSOL: The price of fragSOL is dynamically calculated based on the overall value of the LSTs managed by the Fund, ensuring that the tokens accurately represent each user’s share of the pooled assets.

The Operator comes next in line as it manages staking, restaking, withdrawal operations, and the execution of restaking strategies. It handles all asset flows from the fund and rewards that configure the investment strategy through integration with various staking and restaking protocols. The operator in Protocol is responsible for managing asset movements according to dynamically changing configurations. These configurations are adjusted based on withdrawal requests and the governance-driven restaking portfolio.

The Operator ensures the reconciliation of asset amounts between the fund, reserved fund, restaking protocols, and staking protocols. Its tasks include setting target amounts for the reserved fund account (to handle withdrawal requests) and determining unstaking and restaking amounts to maintain these targets. It configures investment allocations and delegates funds to the NCN Node operator based on the latest configurations.

The Reward Module is the core system of Fragmetric, designed to accurately distribute various rewards earned through restaking. By leveraging Solana's Token Program 2022—transfer hook, the program detects changes in the amount of fragSOL in user wallets, allowing the system’s internal logic to function seamlessly on-chain. Since all reward-related data is stored on-chain, users can easily predict the rewards they are entitled to receive. In traditional restaking systems, especially when using LRT, the user’s restaking reward tends to be diluted within the pool, making accurate participation calculations difficult. Fragmetric has overcome this issue by utilizing an Only Possible On Solana solution that ensures precise calculation of the user’s reward and equitable reward distribution.

Normalized Token Pool

Although Fragmetric operates similarly to other liquid staking protocols on Solana by exchanging SOL and LSTs for fragSOL and nSOL, it takes a unique approach by supporting restaking across multiple LSTs through the Normalized Token Program.

Other LSTs like jitoSOL, mSOL, and bnSOL all hold different values due to varying differences in staking yield, protocol fees, and reward distributions. This further pushes the need to issue fragSOL at an equivalent rate before minting. To tackle this, the Oracle-based approach was employed, where oracle systems like Switchboard and Pyth Network ensure that fragSOL is priced. Conversely, despite the Oracle-based approach offering higher security and serving as a reliable fallback system, their involvement in this process is unreliable as they provide price feeds that reflect the broader market conditions for these LSTs and are further used to determine the price of fragSOL. At the time of writing, Fragmetric's Normalized Token Pool holds over 760K+ LSTs, including more than 750K JitoSOL.

Fragmetric brings a new approach as it directly taps into their respective stake pool, giving access to the on-chain state data like the current value of staked assets, rewards distributed, or performance metrics. By using all the on-chain data, the system accurately accesses the value of SOL-based LSTs and calculates the fragSOL issuance rate. This provides users with a reliable and transparent pricing mechanism.

Competitive Landscape

Fragmetric enters a competitive landscape that includes crypto-restaking upstarts. Key players include crypto-native platforms like Renzo and Kyros.

Fragmetric’s edge lies in its token mechanics, multi-asset staking approach, and real-time on-chain pricing. Unlike Renzo and Kyros, Fragmetric goes all-in on Token-2022, letting rewards accrue natively and cutting out the manual vault swaps. While Renzo and Kyros keep it simple with single-asset restaking, Fragmetric aggregates multiple LSTs—JitoSOL, mSOL, BNSOL, and bbSOL—into fragSOL, making it more capital-efficient, which is possible via its Normalized Token Program. Then there’s pricing, for eg, Renzo rely on its oracles, which can lag behind real staking rewards. Fragmetric pulls data straight from Solana’s stake pools, meaning more accurate pricing and smoother reward distribution. The result? A smarter, more composable restaking model that actually fits the way Solana works.

Tokenomics

fragSOL and fragJTO

fragSOL is Solana’s first Liquid Restaking Token (LRT) and the culmination of Fragmetric’s technological innovation. By restaking SOL or other Liquid Staking Tokens (JitoSOL, mSOL, BNSOL, or bbSOL) through the Fragmetric protocol, you receive fragSOL, which represents your restaked SOL or LST and entitles you to:

1. Solana Staking Yield: Fragmetric collaborates with top-tier yield-focused protocols, aiming to deliver competitive returns to fragSOL holders.

2. MEV Rewards: All SOL and LSTs deposited into Fragmetric are used to secure TipRouter, the first Node Consensus Network (NCN) under Jito Restaking. MEV tips generated from these activities are distributed to fragSOL holders, boosting overall APY.

3. Additional Restaking Revenue: With integrations on the Jito (Re)staking Protocol, including collaborations with NCNs, Fragmetric efficiently redistributes extra restaking rewards to fragSOL holders.

fragJTO is the first non-SOL Liquid Restaking Token (LRT) introduced by Fragmetric. By staking JTO, the governance token of the Jito Protocol, you receive fragJTO, which represents your staked JTO and entitles you to:

1. MEV Rewards: The JTO is utilized to secure TipRouter, a core component of the MEV ecosystem on Solana. The MEV tips generated here are shared with fragJTO holders, enhancing overall APY.

2. Additional Restaking Revenue: With integrations on the Jito (Re)staking Protocol, including collaborations with NCNs, Fragmetric efficiently redistributes extra restaking rewards to fragJTO holders.

wfragSOL and wfragJTO

wfragJTO are wrapped versions of fragJTO, designed for broader DeFi compatibility. Some DeFi protocols do not support Token Extension (also known as Token-2022), an advanced token program on the Solana blockchain. To address this, wfragSOL and wfragJTO were created as DeFi-compatible versions of fragSOL and fragJTO, with the wrapped tokens pegged to their respective tokens.

Partners

Switchboard is launching its external Jito Node Consensus Network (NCN) on Solana, in collaboration with Fragmetric, its exclusive Vault Receipt Token (VRT) provider. This partnership enhances the security, efficiency, and decentralization of Switchboard’s oracle services. Fragmetric enables users to restake SOL, JitoSOL, and other LSTs, earning additional rewards on top of existing restaking and MEV yields.

Switchboard’s decision to exclusively partner with Fragmetric for its Jito Node Consensus Network (NCN) restaking launch is rooted in the protocol’s deep technical alignment with Solana’s architecture and its unique ability to optimize staking capital across assets. While Switchboard could have opted for generalized VRT solutions, Fragmetric offers a specialized, purpose-built infrastructure that addresses the specific demands of decentralized oracle networks.

Fragmetric’s Restaking Vaults are engineered to support a diverse basket of LSTs such as JitoSOL, mSOL, and bSOL, and seamlessly convert them into normalized restaking collateral via its proprietary Normalized Token Program. This system ensures efficient and fair restaking value attribution. This is critical for oracle security where precision in capital allocation and slashing guarantees are non-negotiable.

More importantly, Fragmetric’s integration with Token-2022 and use of transfer hooks allows Switchboard to track user-level contributions with time-weighted precision, all on-chain. This real-time reward infrastructure drastically reduces the operational overhead associated with reward tracking and MEV distribution. These are two of the most critical value flows for oracle participants.

Unlike general-purpose liquid restaking solutions that are built for simplicity or cross-chain compatibility, Fragmetric is Solana-native by design. This enables it to plug directly into Switchboard’s high-throughput oracle layer and deliver capital-efficient security without compromising decentralization or composability. The collaboration ensures that restakers can participate with minimal friction, while Switchboard gains robust security guarantees, transparent economic alignment, and a predictable pathway for incentivizing reliable oracle node operators.

By leveraging Fragmetric’s advanced infrastructure, Switchboard becomes the first external NCN on Jito Restaking to implement decentralized and economically driven oracle security. This sets a new standard for how oracle networks are bootstrapped in the Solana ecosystem.

Protocol Usecases

Let’s explore some of the use cases of Fragmetric:

Liquidity Provision on Orca

Fragmetric integrates with Orca’s Concentrated Liquidity Automated Market Maker (CLAMM) to enhance capital efficiency for liquidity providers. By allocating liquidity within specific price ranges, providers can maximize yield compared to traditional AMMs. Users supplying liquidity to pools such as wfragSOL-JitoSOL or wfragJTO-JTO earn a share of trading fees, ensuring continuous rewards from asset swaps. These fees are distributed proportionally among liquidity providers.

Automated Liquidity Vaults on Kamino

Kamino's liquidity vaults are an automated liquidity solution that allows users to earn yields on their crypto assets by providing liquidity to concentrated liquidity market makers (CLMMs).

Fragmetric utilizes Kamino’s automated liquidity vaults to simplify liquidity provision in concentrated liquidity market makers (CLMMs). These vaults deploy liquidity into Orca pools, allowing users to deposit assets like wfragSOL or wfragJTO while simultaneously earning fragAsset-based yield and trading fees. This automation optimizes capital deployment and enhances returns for liquidity providers.

Yield Trading on Exponent

Exponent is a yield exchange protocol on Solana for fixed-rate and leveraged yield farming. Users can exchange their productive yield assets (e.g., Jito’s VRTs, lending positions, yield-bearing tokens) for a fixed return or amplified exposure to their yield.

Fragmetric enables users to participate in yield trading through Exponent. Users can trade yield-bearing assets (e.g., Jito’s VRTs) for fixed returns or amplified exposure to yield fluctuations. High-risk, high-return traders can maximize F Point exposure by taking a long position on yield through YT-fragAsset, while liquidity providers earn staking rewards, swap fees, fixed yield, and additional point boosts.

Yield Trading on RateX

RateX Protocol is a decentralized exchange (DEX) built on Solana, specializing in leveraged yield trading. On RateX, users can trade synthetic Yield Tokens (YT) of various yield-bearing assets (YBA) with leverage, benefiting from yield movements in a capital-efficient way.

By integrating with RateX, Fragmetric leverages yield trading on synthetic Yield Tokens (YT) of various yield-bearing assets. Users can capitalize on yield fluctuations in a capital-efficient manner while also accessing fixed-yield and liquidity farming options. Those seeking high-risk, high-reward strategies can increase F Point exposure, while liquidity providers benefit from staking rewards, swap fees, and special point incentives.

Whitelisting and Liquidity Expansion

While DeFi liquidity pools and vaults are permissionless, Fragmetric initially whitelists only Orca and Kamino, ensuring F Points rewards remain exclusive to these platforms. As liquidity deepens and stabilizes, Fragmetric plans to expand its whitelist, increasing accessibility and optimizing incentives across additional DeFi protocols.

Wrapping Up

Fragmetric effectively addresses significant challenges within the Ethereum-based restaking ecosystem by leveraging Solana's powerful Token-2022 extension. This innovative extension enables Fragmetric to seamlessly perform all reward calculations directly on-chain, storing reward-related data transparently and ensuring accurate, predictable reward distributions. Users benefit from this design as it provides clear, verifiable reward tracking and allows them to directly claim their rewards on-chain, enhancing both transparency and user confidence.

With the Token-2022 extension, Fragmetric can dynamically calculate rewards based entirely on on-chain contribution data, simplifying the tracking process and ensuring equitable distribution without off-chain intermediaries. This streamlined approach significantly reduces operational complexities and the selling pressure commonly observed in Ethereum's liquid restaking protocols, resulting in a healthier overall ecosystem.

Fragmetric currently maintains a robust Total Value Locked (TVL) exceeding $210 million and continues to experience rapid growth. As its ecosystem expands, it has attracted notable protocol partnerships, including Switchboard Oracle and Sonic’s HyperGrid, further validating the effectiveness and appeal of its reward distribution model.

As Solana's restaking landscape continues to evolve and expand, Fragmetric is well-positioned to become the industry standard, setting a benchmark in transparency, operational efficiency, and user experience. If this trajectory continues, Fragmetric could redefine the liquid restaking market, making it more predictable, secure, and beneficial for both users and participating protocols alike.

References

1. Fragmetric Documentation

   1. Official Fragmetric Docs

2. Shoal Research Articles on Restaking and Yield Strategies

   1. Jito Restaking Strategies: Optimizing Yield within the Solana Ecosystem

   2. The Rise of Restaking on Solana — A Jito Deep Dive

   3. Liquid Restaking Tokens: A New Frontier for Yield

3. EigenLayer Documentation

   1. Official EigenLayer Docs

4. Solana Token-2022 Resources

   1. Solana Token-2022 Program Overview

   2. What Are Token Extensions? By Helius Lab

Not financial or tax advice. The purpose of this post is purely educational and should not be considered as investment advice, legal advice, a request to buy or sell any assets, or a suggestion to make any financial decisions. It is not a substitute for tax advice. Please consult with your accountant and conduct your own research.

Disclosures. All posts are the author's own, not the views of their employer. This post has been created in collaboration with the Fragmetric team. At Shoal Research, we aim to ensure all content is objective and independent. Our internal review processes uphold the highest standards of integrity, and all potential conflicts of interest are disclosed and rigorously managed to maintain the credibility and impartiality of our research.

Introduction – The Foundation of Productive Capital

Investors have long debated the merits of productive versus non-productive assets. Historically, productive assets—such as equities, real estate, and business ownership—have consistently outperformed inflation, while non-productive assets—such as gold—have primarily served as stores of value.

As the restaking narrative began unfolding last year, it became important to revisit the concept through first principles thinking. This approach underscores the fundamental shift toward maximizing capital productivity and efficiency by unlocking new utility and yield-generation opportunities.

The fundamental distinction lies in the ability of productive assets to generate returns independent of price speculation. Productive assets create value through dividends, rental income, interest payments, or intrinsic business growth. Non-productive assets, on the other hand, rely on market demand to appreciate in value.

Many parallels have been drawn to analyze the impact of productive versus non-productive assets. Let’s take a look at one such example from a Blockworks article, which goes back to ancient Roman times to examine how productive and non-productive assets behave in traditional markets.

A Roman centurion in the time of Augustus earned about 38.58 ounces of gold per year—an amount that, when converted to today’s gold prices, is equivalent to $86,342. If you look closely, this mirrors the salary of a modern U.S. Army captain (around $85,000). This eerie precision highlights gold’s ability to hedge against inflation but also underscores its limitations—it merely preserves value rather than growing it. By contrast, productive assets like equities have historically compounded wealth, turning modest investments into massive fortunes over time.

Had Roman equities existed in 24 BCE and generated even a modest 1% return above inflation annually, an investor’s portfolio would be worth over a trillion dollars today after almost 2,000 years. This illustrates the power of compounding—the driving force behind why equities, real estate, and income-generating businesses vastly outperform non-productive assets over long time horizons.

Yield in productive assets stems from multiple mechanisms:

1. Income Generation – Rental yields, dividends, staking rewards (in case of crypto).

2. Capital Appreciation – Business expansion, property value growth.

3. Leveraged Reinvestment – Using returns to generate further gains, such as reinvesting dividends or compounding staking rewards.

Non-productive assets like gold do not generate wealth; they only preserve purchasing power. Scarcity alone does not ensure growth, making productive assets structurally superior for long-term wealth creation.

Blockchains aren’t companies in the traditional sense. Protocols like Ethereum are decentralized rule sets encoded in software and executed by independent operators in a permissionless manner. They function as distributed networks, not corporate entities, and typically don’t meet the legal definition of a common enterprise.

The native assets of most blockchains (e.g., BTC, ETH) don’t grant governance rights, distinguishing them from traditional equity-based financial structures. Unlike businesses, these networks don’t generate revenue or incur losses—they exist as neutral infrastructure.

Concepts like “income” and “expenses” imply a counterpart, but blockchains lack a central entity to accumulate or distribute value. Defining “network profit” for Bitcoin or Ethereum is as impractical as measuring the profitability of the internet—value flows through them but isn’t captured by a single party.

When discussing productive assets in crypto, the key question is: How can capital be made productive in a secure (or relatively secure) way? In other words, what mechanisms exist beyond price speculation to generate yield?

Ethereum has long stood apart as the only truly productive asset among major blockchains. One of the reasons is its functioning fee market and deflationary burn mechanism, introduced with EIP-1559. Under this model, Ethereum’s transaction fees consist of a base fee (burned) and a priority tip (paid to validators). This ensures that ETH holders benefit from network activity through fee burns, which reduce the total ETH supply, creating deflationary pressure. Additionally, staked ETH earns rewards for securing the network, making ETH both a yield-bearing and deflationary asset—a unique combination absent in other blockchain ecosystems.

Beyond this, several other mechanisms exist in crypto for making capital more productive, ranging from traditional DeFi strategies to emerging restaking models. These include:

• Lending/ Borrowing protocols like Aave and Compound, where users supply assets to liquidity pools and earn yield from borrowers.

• Proof-of-Stake (PoS) staking rewards, where validators secure blockchain networks and earn rewards in return.

• Restaking is an advanced evolution of PoS, allowing assets to secure multiple layers and applications, unlocking yield opportunities beyond the base network.

• Yield farming and liquidity provisioning, where users supply assets to decentralized exchanges (DEXs) and automated market makers (AMMs) like Uniswap or Curve, earning rewards through trading fees and incentives.

• Emerging Structured DeFi products, including automated vaults, leveraged staking, and cross-chain liquidity strategies, further optimizing yield generation.

While multiple yield-generation strategies exist, Ethereum's fee burn mechanism, combined with staking rewards and emerging structured DeFi products, makes ETH the (only) truly productive native asset in crypto today.

In this report, we revisit first principles thinking about making capital more efficient in a secure and (relatively) simple way.

• We will build a strong foundation by exploring productive asset classes and what makes crypto assets productive.

• We will trace the evolution of crypto asset productivity, using Ethereum as a case study, delving into the rise of DeFi, on-chain AI agents, and their role in reshaping DeFi 2.0.

• We will explore (re)staking, critical risks, and LRTs, specifically examining YieldNest as an enabler of efficient capital deployment with secure, risk-adjusted yields.

• Finally, we will dive into the architecture of YieldNest, its MAX LRTs, and how it seamlessly integrates the best of both DeFi and restaking to unlock new capital efficiencies for users.

Productivity of crypto assets

Cryptocurrencies, particularly Bitcoin and Ethereum, have sparked renewed debate over this dichotomy of productive vs non-productive asset classes. Bitcoin, much like gold, has long been considered a non-productive asset, appreciated for its scarcity and decentralized nature but lacking inherent yield-generation mechanisms. However, with the advent of Bitcoin Layer 2 solutions and (re)staking protocols like Babylon, DeFi on Bitcoin is expanding. BTC, as an asset class, is gradually shifting towards productivity, offering users the ability to earn yield rather than relying solely on price appreciation.

The total value locked (TVL) on Bitcoin has increased significantly, rising from $100 million in January 2023 to approximately $5.7 billion today (peaking at $7 billion in December 2024) —representing more than a 60x increase in just two years.

Ethereum, on the other hand, has significantly evolved (and continues to evolve) as a productive asset class, primarily due to its programmable smart contract functionality. Unlike Bitcoin, which was designed primarily as a decentralized store of value, Ethereum introduced the concept of programmable money, enabling Decentralized Finance (DeFi), a sector that has redefined traditional financial services by removing intermediaries and enabling trustless, permissionless finance.

Source: DeFi 201 by Amadeo Brands - 2020

Ethereum’s smart contracts allow developers to create self-executing agreements that operate without the need for a centralized authority. This innovation laid the foundation for decentralized applications (dApps) that replicate and enhance traditional financial services, including lending, borrowing, trading, and asset management. The launch of MakerDAO in 2017, a decentralized lending platform that introduced an algorithmic stablecoin (DAI) was a pivotal moment in DeFi’s growth.

Since then, Ethereum has powered some of the largest DeFi protocols, including:

• Decentralized Lending Platforms (e.g., Aave, Compound) – Allow users to lend and borrow crypto assets without intermediaries, earning interest in a trustless manner.

• Decentralized Exchanges (DEXs) (e.g., Uniswap, SushiSwap) – Facilitate peer-to-peer trading through automated market makers (AMMs), eliminating reliance on centralized exchanges.

• Yield Aggregators (e.g., Yearn Finance) – Optimize returns by moving user funds between the highest-yielding DeFi strategies.

• Synthetic Assets & Tokenization (e.g., Synthetix) – Enable the creation of blockchain-based representations of real-world assets, allowing global access to financial markets and the creation of decentralized stablecoins.

DeFi’s core services can be broken down into three categories: stablecoins, liquidity systems, and lending markets. It's that simple, and everything around is just a combination of these categories.

DeFi applications and protocols currently have around $150 billion in total value locked (TVL) across hundreds of chains, with Ethereum alone accounting for over $100 billion+. This explosion of financial innovation has transformed Ethereum into the backbone of DeFi and the main settlement layer for on-chain finance.

DeFi has not only enhanced Ethereum’s utility but has also established ETH as a highly productive asset, fundamentally differentiating it from traditional non-productive stores of value like gold.

For example, users can take their ETH and deposit it into Aave’s Wrapped Ether pool, earning a stable ~2% APY today.

Ethereum’s transition to Proof of Stake (PoS) with the Merge marked a fundamental shift in how the network secures itself, giving ETH holders an additional yield for securing the network with their capital in terms of staking rewards. Since the introduction of the Beacon Chain in December 2020, Ethereum staking has grown rapidly, with around 34M ETH now staked, accounting for over 27% of the total ETH supply (120.4M). This staking ratio doubled post-Shapella but has since stabilized as demand cooled.

To participate in staking, validators must deposit 32 ETH as collateral into the beacon chain to spin up a validator node and perform certain duties for securely running the network. And this staking of ETH generates yield from two primary sources:

• Consensus Layer Rewards: Validators earn ETH for attesting and proposing blocks, funded through newly issued ETH (network inflation).

• Execution Layer Rewards: Includes priority fees and Maximal Extractable Value (MEV), which fluctuate based on network demand.