Mission accomplished. In D.C., all you need is enough rhetorical chaff to ensure the status quo survives the session. No need to win the argument.

While the senators were asking questions, the White House dropped its own report. It turns out the banks’ “Great Deposit Migration” was... a bit of an overshoot.

The White House report found that banning stablecoin yield would boost bank lending by $2.1 billion, 0.03% of the total. This little protectionist stunt would cost consumers roughly $800 million. The banks were wrong. More importantly, they were expensively wrong for the American public. And yet, the bill remains a permanent resident of the committee room.

The banking lobby knows the most powerful force in the universe is a Senator’s desire to get to the beach.

With the Senate recess just a few weeks away, the lobbyists just needed to talk until the clock ran out. The bill is now gathering dust in a committee room, the banks are keeping their deposits, and the rest of us are footing the bill for the delay.

Today, we are talking about the final nine weeks of a four-year marathon.

In July 2025, Congress passed the GENIUS Act, which did two things. Regulated stablecoins and banned stablecoin issuers from paying interest. It was a massive win for the banks, who successfully argued that if people could choose between a 5% yield on a stablecoin and 0.01% plus a “free” toaster at a bank, the entire global economy would spontaneously combust. Congress, terrified of math, signed it immediately.

But the GENIUS Act had a loophole. While issuers couldn’t pay interest, their “affiliates” and exchanges could still offer “rewards.” Crypto companies sprinted into that hole because they like money, and banks immediately started tattling because they hate competition.

Here’s where we talk about the Clarity Act. This was the “Fix Everything” bill, designed to close the loopholes and finally decide if the SEC or the CFTC gets to hold the leash on DeFi. It cruised through the House with a bipartisan 294-vote landslide.

Then it reached the Senate Banking Committee, where it has since become a “permanent lawn ornament.”

A January hearing was all set until Coinbase CEO Brian Armstrong pulled the rug. He withdrew his support over “concerns” about DeFi and token restrictions, but mostly because the bill threatened stablecoin rewards, which just happens to be how Coinbase makes a mountain of cash. Essentially, Coinbase pockets the interest earned on the government bonds backing those stablecoins, tosses a smaller ‘reward’ to the users to keep them loyal, and keeps the massive difference as high-margin profit. Funny how that works.

An April markup was considered likely, then ruled out after Senator Thom Tillis of North Carolina asked Chairman Tim Scott for more time to work through a compromise with banking groups. “It’s very important to me not to accelerate things,” Tillis told reporters, “to hear everybody and give them a rational basis for what we do accept and what we don’t accept.” The North Carolina Bankers Association had been running aggressive outreach to his office for weeks. Senator Tillis represents North Carolina. Love a beautiful coincidence.

At Bitcoin 2026 last week, Senator Cynthia Lummis warned that while the Clarity Act is 99% sorted, it’s also on life support. If it misses this year’s window, the bill is dead until 2030, when a fresh Congress restarts the entire gruelling process from zero.

The Senate is currently on recess, meaning the earliest markup can’t happen until the week of May 11. With only nine working weeks left, that’s tough math. Galaxy Digital currently pegs the odds at 50-50, which might be the optimistic view.

Can stablecoins pay yield? One slippery question.

The current compromise, brokered by Senators Tillis and Alsobrooks, attempts to split the hair right down the middle. It bans “passive yield” but greenlights “activity-based rewards.”

• Banned: Getting paid for just holding a stablecoin (too much like a bank account)

• Allowed: Getting paid for using it (the “credit card points” of crypto)

But no one has managed to define the difference in a way that makes both bankers and the crypto community happy. This ambiguity is a gift to the banking lobby. Every week spent arguing over semantics is another week closer to the August recess. The White House has stopped pretending to be neutral. The Council of Economic Advisers published an analysis in April that found the banks’ argument to be wrong. The White House crypto council’s Patrick Witt described the banking pressure as “greed or misunderstanding,” without specifying which.

The crypto industry hasn’t exactly been its own best advocate. When Coinbase’s Brian Armstrong yanked his support in January, he handed a gift-wrapped excuse to every Democrat looking for a reason to vote “no.”

Now that David Sacks has moved to a broader advisory role, the heavy lifting falls to Patrick Witt and the White House Crypto Council. They’re doing their best to keep the mood up, claiming the delay is just a chance to “resolve differences” and that we’re “closer than ever.” It’s a nice sentiment, but it ignores the fact that in politics, being at the one-yard line doesn’t matter if the clock hits zero.

Meanwhile, the market has continued building without waiting for anyone’s permission. Morgan Stanley launched MSNXX, a money market fund structured specifically for the management of stablecoin reserves under the GENIUS Act. Coinbase launched CUSHY, a stablecoin credit fund with tokenised shares. Agora is pursuing a bank charter. Products launching with asterisks, compliance teams adding caveats, innovation happening in spite of the uncertainty rather than because anyone has clarity about what is permitted.

The deposit threat that the banks are lobbying so hard against does not appear to care about the lobbying. Standard Chartered projected that banks could lose $1.5 trillion in deposits to stablecoins by 2028, regardless of whether yield is permitted.

Senator Alsobrooks’ March promise of ‘universal unhappiness’ was meant to signal a breakthrough. But in a Senate that’s currently on recess, a compromise that makes everyone unhappy is often just an excuse to do nothing at all.

The yield ban might slow the movement of money. But no amount of lobbying fixes a product problem. A 4.5 percentage point gap between what a savings account pays and what a stablecoin can pay is one.

If the Clarity Act dies this summer, the next Congress starts the process from scratch in 2027. A new bill gets written, negotiated, lobbied against, and markup gets scheduled, rescheduled, and postponed. It is 2030. The banks are asking for more time. Senator Tillis, or whoever replaces him, says it is very important not to accelerate things.

The stablecoin industry will be larger by then, less regulated, and considerably less interested in asking permission.

That’s it for today. Monday begins soon.

Stay tuned.

—Thejaswini

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A lot of crypto conversations still orbit around the speed, cost and finality of infrastructure. They are all important, I agree. But they all rank far lower than something more basic and fundamental when it comes to adoption. Something qualitative and human. I wonder how builders expect a customer to adopt a new technology when they can’t help them understand how the it’s going to improve their life.

Humans evolved as a dominant social animal species because of our superior cooperation and communication skills. Yet, today, we have sidelined the very communication aspect of telling why a new technology matters in the pursuit of seeking better adoption.

Although the crypto landscape has witnessed some of the most useful technologies and products being built, few have focused on building the all-important relationship layer. I am thinking of trust, a user-friendly interface, the regulatory comfort that makes a treasurer or a retail saver actually move their money.

It is true that good infrastructure enables better movement of money, but a robust relationship layer is what can direct and trigger that move.

In today’s guest op-ed, Sebastien Davies, Partner at Primal Capital, writes about how distribution is the mechanism that turns infrastructure into outcomes.

Onto the story,
Prathik

P.S.: This was first published on Interop Markets.

The defining delusion of the last ten years in crypto was the “Field of Dreams” fallacy. Builders and investors operated with the assumption that properly engineered rails would automatically magnetise the world’s capital. This was the Infrastructure Phase, a massively capital-intensive epoch dedicated entirely to building empty containers. Between 2020 and 2024, the fundamental barriers to entry were strict regulatory prohibition and structural impossibility. Fiduciaries lacked the legal mandate to participate, and the baseline plumbing of custody, execution, and reporting simply did not exist at an enterprise-grade level. The industry deployed billions of dollars and dedicated millions of hours to engineering and lobbying solely to overcome these compliance and mechanical realities.

By 2025, the industry reached a quiet consensus. The “Battle for the Rails” had effectively ended in a draw between standardised, institutional-grade multi-party computation (MPC) custody stack and regulated stablecoin frameworks. The mechanical question of how to safely move a billion dollars on-chain has been answered. This technical victory, however, produced a troubling realisation. Infrastructure is fundamentally a commodity. It is a necessary precursor to activity, but it remains entirely agnostic regarding value capture.

In the traditional financial hierarchy, power is rarely held by the entity providing the ledger. It is held by the entity that controls the entry point. We are now witnessing the pivot from the Infrastructure Phase to the Balance Sheet Phase, but the catalyst for this transition is not more technology. It is distribution.

The Friction of the Blank Slate

Infrastructure enables a better system, but it does not account for the “gravity” of legacy balance sheets. A corporate treasurer does not evaluate a new financial rail based on its cryptographic elegance or its sub-second finality. They evaluate it based on the friction required to reach it.

Historically, financial power has accrued to those who minimise this activation energy. In the 1970s, the emergence of Money Market Funds (MMFs) offered higher yields than traditional bank deposits. However, the technology (the ability to pool short-term debt) was not the breakthrough. The breakthrough was distribution through retail brokerages like Merrill Lynch, which placed the “new container” directly into the investor’s existing workflow. The capital moved because the distribution channels made the migration invisible.

In digital finance, we see a similar pattern. The existence of a high-yield, on-chain Real-World Assets (RWA) vault is irrelevant if it sits outside the institutional user’s primary interface. Infrastructure provides the destination, but without distribution, the “audience” never leaves the traditional bank’s lobby.

Distribution Directs Liquidity

If the balance sheet is the strategic asset that determines the winner of the interest rate spread, distribution is the navigator that directs the flow toward that asset. We must view distribution as the “connective tissue” of market structure. It encompasses the presence, trust, regulatory licenses, and API integrations that allow capital to reside in a specific container.

The current market structure is bifurcated. On one side, we have highly efficient, crypto-native infrastructure capable of managing programmable liquidity with unprecedented precision. On the other, we have $100 trillion in institutional balance sheets sitting in “static” containers (traditional bank accounts and custody systems) where returns are suppressed by layers of intermediation.

Value capture in the coming decade will bypass protocols competing purely on marginal fees or throughput. The economic rents will accrue directly to the “Web2.5“ hybrids that monopolise both the customer interface and the regulatory perimeter. These new gatekeepers serve as the critical bridge, offering the “one-click” migration of a balance sheet from a zero-yield checking account into a 24/7 programmable liquidity pool.

The Shift in Gravitational Centres

We are moving into an era in which the distinction between “crypto” and “finance” is dissolving, with a battle for the front-end relationship. As infrastructure becomes commoditised and invisible, the “Interface War” begins.

When a treasurer can park cash in a tokenised Treasury fund as easily as they can in a commercial bank account, one of the bank’s primary moats (the difficulty of leaving) is challenged. However, this shift occurs only when the digital container is distributed through the treasurer’s existing terminal, auditor, and risk framework.

Infrastructure enables, balance sheets fund, and distribution allocates.

In the “Infrastructure Phase,” we treated liquidity as a stagnant pool that merely needed a better pipe. We assumed that superior efficiency alone would naturally drain it. This fundamentally misunderstands financial physics. Institutional capital operates as a dense bundle of permissions, risk mandates, and operational habits. Distribution is the force that actively reconfigures those bundles.

Distribution dictates the path of capital. In any competitive market, economic power accrues to the entity that owns the customer’s attention, systematically commoditising the underlying providers, who still earn a rent.

The Anatomy of Institutional Distribution

For institutional allocators, distribution represents a formidable, three-dimensional moat:

1. Regulatory Reciprocity: The access provider must hold licenses in the allocator’s exact jurisdiction and strictly comply with the allocator’s specific fiduciary requirements.

2. Workflow Integration: The asset has to sit inside existing enterprise command centres, integrated directly into systems like Bloomberg Terminals, BlackRock’s Aladdin, or State Street custodial dashboards.

3. Technical Proximity: The infrastructure must execute within existing legacy settlement cycles and integrate directly into the accounting software used for reporting.

Without these pillars, even the most attractive digital assets remain peripheral. Distribution turns them into balance-sheet allocations.

The “Default” Advantage

The history of financial infrastructure shows that technical elegance often loses to entrenched distribution. The persistence of the SWIFT network and the ACH system illustrates this dynamic. While both are modernising to improve speed and reduce friction, modern blockchain architectures outpace them in programmability, transparency, and marginal cost. Yet these legacy systems remain deeply embedded in enterprise connectivity. Corporate treasuries, payroll providers, and commercial banks are already wired into them.

The true friction of switching extends far beyond transaction fees. It sits in the operational risk of overhauling an entire corporate stack. As a result, value accrues to the gatekeepers who provide seamless compatibility with existing systems. The defining infrastructure of this cycle will be built by those who can bridge legacy capital into modern digital frameworks.

Distribution as a Filter for Signal

In a world of programmable liquidity, the sheer volume of “containers” (protocols, vaults, and yield strategies) creates a massive discovery problem. An institutional treasurer cannot, and will not, perform due diligence on 50 different DeFi protocols. They require a distributor to act as a curator.

This is where the hybrid model begins to take hold. These entities act as the institutional front end, performing risk assessment and compliance, and distributing structured yield to their clients. The underlying infrastructure provides the yield, and the balance sheet provides the scale, but the trusted orchestrator captures the highest-margin economics by controlling the point of access.

Redirected Flow: We are beginning to see the first signs of this “Distribution Arbitrage.” Neobanks and crypto-first exchanges are no longer just offering Bitcoin trading. They are beginning to introduce tokenised money market funds and stablecoin-based yield products directly alongside traditional savings accounts.

When a user’s primary banking interface offers 5% on a tokenised product and 0.5% on a traditional deposit, and the switch requires no change in workflow, the distribution channel has effectively broken the bank’s hold on that capital. The infrastructure was ready years ago. The balance sheet was always there. It is the distribution and integration into the financial service interface that finally trigger the migration.

Victorious Networks

Financial history is instructive here. The firms that scaled were the ones that made their products accessible to existing pools of capital and integrated them into established systems. Distribution, not just design, is what translated innovation into adoption.

Distribution via Jurisdiction

The rise of the Eurodollar market in the 1960s provides the definitive historical precedent for balance sheet migration driven entirely by distribution. The underlying infrastructure (the US Dollar and commercial bank ledgers) remained static. The catalyst was a pure geographic shift in the access point.

By launching dollar-denominated accounts in London, financial institutions systematically bypassed domestic interest rate caps and reserve requirements, engineering a profound innovation in regulatory distribution. This manoeuvre provided global capital with a highly-optimised container maximising both utility and yield. Establishing this offshore channel triggered a balance-sheet migration so complete that it permanently forged the parallel global financial system operating today. The currency rail held firm, while the geographic pivot in distribution simply unlocked a vastly superior economic container.

The Interface Revolution

In the late 1970s, as inflation surged, traditional bank deposits became a liability for savers because interest rates were capped. The “Infrastructure” for a solution (short-term government debt) already existed. However, the average investor could not easily buy T-bills in small increments.

The rise of the money market fund is a useful example. Firms like Fidelity and Merrill Lynch embedded these funds into brokerage accounts with check-writing and sweep features, allowing them to function like cash balances. This made higher-yielding instruments accessible within familiar workflows and gradually shifted a meaningful share of cash away from traditional deposits.

Integration Over Innovation

The rise of card networks is a useful example. In the 1950s and 60s, many banks launched proprietary travel and entertainment cards. Most failed to scale, but it had nothing to do with inferior plastic or slower processing times. They never established distribution across both sides of the transaction.

The networks that succeeded solved this problem in different ways. Visa, originally BankAmericard, expanded through a licensing model that allowed thousands of banks to issue cards, leveraging existing customer relationships. Mastercard followed a similar path through a bank consortium, building broad acceptance and reach. American Express took a different approach, issuing cards directly and owning both the customer and merchant relationships within a closed-loop system.

The structure varied, but the outcome was consistent. Each model achieved scale by embedding itself into both sides of the transaction, becoming the default interface for commerce by controlling access.

The Lesson for Digital Assets

These precedents reveal a recurring law: Infrastructure is the invitation, but distribution is the mandate.

● The Eurodollar proved that capital will migrate to wherever the distribution is most efficient and least constrained.

● The MMF proved that the interface (cheque-writing on a brokerage account) determines which container the balance sheet chooses.

● The Credit Card proved that leveraging existing trust networks is faster than building a new network from scratch.

In 2026, the crypto-native system is still in its early stages of distribution. The infrastructure for yield and continuous settlement is already in place, but it has not yet been integrated into the interfaces institutions rely on. Adoption will come from embedding these capabilities into existing workflows, from consumer financial apps to the treasurer’s daily operating environment.

Distribution in Digital Assets

The 2026 landscape makes one thing clear. Decentralised infrastructure is in place, but distribution remains concentrated. With a total market cap of roughly $2.5 trillion, economic activity is already clustering around a small number of access points.

The relevant question is where default relationships are forming. These are the platforms that sit at the interface with the user, and increasingly, at the centre of capital allocation.

ETF Familiarity

The approval and maturation of spot ETFs have provided a clear example of how distribution drives adoption. By placing digital assets within an established regulatory wrapper, firms like BlackRock, Fidelity Investments, and Morgan Stanley have made them immediately accessible through existing wealth management channels.

This has translated into scale. As of Q1 2026, U.S. spot Bitcoin ETFs hold over $128 billion in assets, reflecting not just demand for the asset, but the reach of the distribution layer through which it is delivered.

BlackRock’s IBIT has emerged as a benchmark because it sits inside these systems and is backed by one of the most trusted brands in global finance. It appears alongside other ETFs, within familiar workflows and allocation models. Adoption followed integration, not novelty. In effect, the asset class has been mapped directly onto the balance sheet workflows of the world’s largest pools of capital.

Neobank Proximity

A different form of distribution is emerging through neobanks and fintech platforms. Applications like Revolut, Chime, Nubank, and Robinhood increasingly serve as the primary financial interface for a large segment of users. Within these environments, new products do not need to be discovered. They simply appear alongside existing balances.

As crypto-native yield, whether through staking or tokenised assets, is placed next to traditional accounts, the decision to allocate becomes a matter of interface design rather than infrastructure. The distinction between a USD balance and a USDC balance begins to collapse into a simple choice within the same system, subject to what these platforms are permitted to offer.

In 2026, roughly 30% of American adults hold some form of crypto exposure. For many, their primary relationship is no longer with a physical bank, but with an application. Distribution, in this context, is not about access to a new system. It is about integration into the one users already rely on.

Custody Opportunity

Firms like Coinbase, Fidelity Digital Assets, Anchorage Digital, and BitGo are becoming core distribution nodes for institutional capital.

Their role extends beyond safekeeping. They provide the compliance, reporting, and operational integration required for institutions to participate at scale. In this context, custody becomes the interface through which capital is allocated, not just where assets are held.

As these platforms expand into trading, financing, and settlement, they increasingly resemble full-service financial environments. The underlying infrastructure remains important, but allocation decisions are made through these controlled interfaces. Distribution, in this layer, is defined by permission, integration, and trust.

Stablecoin Settlement

Stablecoins are moving beyond their role within crypto markets and becoming part of broader financial and operational workflows. Their relevance now extends into payments, settlement, and treasury functions, where they are increasingly integrated into existing systems.

Firms like Circle have focused on building this distribution through partnerships with banks, payment providers, and financial platforms. The objective is not just issuance, but placement within the systems where transactions already occur.

A similar pattern is beginning to emerge in software and AI-driven environments. As developers build applications and autonomous agents that operate programmatically, stablecoins are being integrated as a native payment mechanism. In this context, adoption will follow the stablecoins that are most accessible within developer tools, platforms, and APIs, extending distribution beyond financial interfaces into software itself.

Emerging Hierarchy

The structure has shifted. Early efforts focused on building the rails. Today, the advantage lies with those who control the interface.

Whether it is an ETF ticker, a neobank application, or a prime brokerage platform, the entities capturing the economics are the ones that present new capabilities within familiar systems. They translate complex infrastructure into formats that align with existing habits, and in doing so, become the default point of access.

This is what ultimately determines where balance sheets move. Capital does not migrate because the infrastructure improves. It migrates when the interface makes allocation seamless within the systems it already uses.

Stablecoins as the Clearest Example

Stablecoins are the purest manifestation of the distribution thesis. They are not merely “digital dollars”; they are the primary distribution vehicle for the US financial system in the 21st century. While the industry spent years debating the merits of different Layer 1 protocols, stablecoins quietly became the “killer app” by solving a distribution problem rather than a technical one. They made the world’s most trusted balance sheet (the US Treasury) accessible to anyone with an internet connection, 24/7.

Stablecoins are introducing a new form of competition in the deposit market. They package short-duration government-backed assets into a programmable, portable, and continuously accessible format. As distribution improves, capital can move more easily between traditional accounts and these digital alternatives.

The Utility Gap

The persistence of Tether (USDT) is the ultimate proof that distribution beats “better” infrastructure. For years, critics pointed to USDC’s superior transparency, regulatory standing, and institutional backing. In a vacuum, USDC is the “better” product.

Tether established early distribution in offshore trading markets and across many emerging economies. It became a primary liquidity instrument across exchanges and a widely used medium for cross-border transactions.

By the time regulated alternatives entered the market, Tether was already deeply embedded in trading, settlement, and informal financial flows. Its presence across platforms and use cases created a level of integration that is difficult to displace.

Programmable Liquidity

The next phase of stablecoin distribution is the shift from passive holdings to active use. In 2026, these assets are increasingly being integrated into operational workflows.

This is showing up across a few key areas:

● In supply chain finance, large corporates are beginning to use stablecoins to accelerate settlement with suppliers, reducing reliance on traditional correspondent banking timelines.

● In commerce platforms, companies like Shopify and Stripe are enabling merchants to hold and manage balances in stablecoin form, introducing new options for liquidity management.

● In software environments, as applications and autonomous systems transact programmatically, stablecoins are being integrated as a payment layer for real-time, granular transactions.

In each case, adoption is driven by utility within existing systems rather than by exposure to new asset class.

Extending Distribution

Banks are responding to stablecoins by extending their own distribution. Deposit tokens and bank-issued digital assets will allow them to bring existing balance sheets onto new rails while maintaining control of access.

The objective is to preserve their role at the point where dollar-denominated value is accessed and deployed. As these instruments are integrated and eventually embedded into payroll systems, trade platforms, and software environments, the competitive dynamic shifts toward distribution.

As the market continues to scale, value accrues to the entities that control the interface through which transactions are initiated and managed.

Institutional Constraints

If the technical rails are ready and the economic incentives of the balance sheet are clear, why has the “Great Migration” not yet reached its terminal velocity? The answer lies in the final, and most formidable layer of the distribution moat: institutional friction. For a Chief Investment Officer or a Corporate Treasurer, capital allocation is governed by fiduciary responsibility, regulatory constraints, and operational requirements.

Distribution, in an institutional context, is the process of neutralising these three specific types of friction.

The Regulatory Distribution Gate

In the retail world, distribution is about “eyeballs.” In the institutional world, distribution is about licenses. An institution cannot legally park a balance sheet on a protocol, no matter how efficient, if that protocol sits outside its regulatory perimeter.

In 2026, distribution is increasingly concentrated around assets packaged within familiar legal and regulatory structures. Tokenised Treasury funds such as BlackRock’s BUIDL and Franklin Templeton’s FOBXX illustrate this pattern.

These instruments allow institutions to hold digital assets within existing investment frameworks, including structures that qualify as regulated investment companies. The format is new, but the pathway to allocation remains unchanged. These assets are accessed through standard brokerage systems and integrated into existing balance sheet management.

Workflow Lock-in

Large-scale capital does not move via a browser extension or a mobile app. It moves through established “Workflow Hubs” like the Bloomberg Terminal, Aladdin, or Charles River. These platforms are the ultimate distribution monopolies because they own the daily habits of the financial professional.

Early digital asset infrastructure was built as a parallel system. It required users to step outside their existing workflows to access it. For institutions, this created friction at the point of allocation.

Distribution is now shifting toward integration. Digital asset custody and exposure are being embedded into existing reporting, risk management, and portfolio systems. The objective is to make on-chain positions appear alongside traditional assets and behave consistently within the same operational framework.

Distribution Runs on Trust

In finance, trust is embedded in the distribution layer. Capital is allocated through counterparties that meet established standards for regulation, creditworthiness, and legal recourse.

The failures of major crypto intermediaries in 2022 and 2023, including FTX and Celsius, reinforced the importance of this framework. Established institutions such as BNY Mellon and State Street entering digital asset custody extend that layer of trust into new formats. Their role is not just to provide infrastructure, but to make digital assets compatible with existing expectations for balance sheet allocation.

Compliance at the Interface

The final piece of institutional distribution is integrating compliance into the interface itself. To access large pools of capital, digital asset systems must meet real-time requirements for transaction monitoring and anti-money laundering.

The firms gaining traction are embedding these controls directly into the point of access. By the time capital reaches the underlying system, the relevant checks have already been performed. Compliance, in this structure, becomes part of the distribution layer rather than a separate process.

This allows institutions to interact with programmable liquidity within established regulatory expectations, making allocation operationally viable at scale.

Where Distribution is Moving

As we look toward the 2027–2030 horizon, the “Interface War” is entering a phase of total invisibility. The goal of distribution is no longer to convince a user to “use crypto,” but to embed digital asset utility so deeply into existing platforms that the end-user (whether a retail saver or a corporate treasurer) doesn’t even realise they have migrated their balance sheet.

Invisible Distribution

The next great capital reallocation will be driven by Embedded Finance. In this model, the distribution node isn’t a bank or an exchange; it’s the software in which the economic activity already occurs.

● ERP Integration: SAP and Oracle sit at the centre of institutional financial workflows. As new forms of liquidity integrate into these systems, allocation decisions can be executed within existing treasury processes. When excess cash can be directed into tokenised instruments from within the same system used for cash management, the balance sheet moves as part of the workflow rather than through a separate interface.

● Supply Chain as a Ledger: Distribution is extending into supply chain and trade systems. Platforms that manage global logistics are beginning to integrate stablecoin-based settlement into their core software, connecting payment and transaction data more directly. As settlement becomes embedded within these workflows, financial activity moves closer to the underlying commercial process, creating new pathways for liquidity and credit to be applied in context.

Banking Platforms

Traditional banks have long served as the primary distribution layer for capital. Leading incumbents are now extending this role into programmable liquidity, leveraging their decisive structural advantages: decades of fiduciary relationships, comprehensive regulatory licenses across jurisdictions, deep integration into enterprise systems (Bloomberg Terminals, Aladdin, custodial dashboards), and the bedrock of deposit insurance together with general balance-sheet trust. These moats make it exceptionally difficult for neobanks or crypto-native platforms to achieve meaningful distribution at an institutional scale.

This advantage is decisive. While fintechs and crypto platforms can capture retail flows and smaller corporate wallets through superior user experience, meaningful “distribution arbitrage” at the multi-trillion-dollar institutional level remains limited. Banks do not need to out-innovate the underlying rails. They simply need to embed on-chain yield, settlement, and programmability into the interfaces and trust networks they already own. In this sense, the Interface War is one that banks are structurally wired to win.

● White-Label Liquidity: A hybrid model is beginning to take shape. Banks provide the trust, regulatory framework, and client interface, while yield is sometimes sourced from external, on-chain systems. In this structure, the bank’s role evolves from holding static pools of capital to managing flows across a broader set of financial environments.

● Deposit Tokens: The bank account of 2028 is likely to take a different form: a licensed deposit token that remains compatible with existing banking systems while extending into digital asset networks.

Agentic Distribution

A further shift is emerging as financial activity becomes more automated. As software systems and autonomous agents begin to manage capital, distribution extends beyond human interfaces and into code.

In this environment, allocation decisions are shaped by parameters such as integration, programmability, and capital efficiency. Systems that are easier to incorporate into software workflows become more likely to be used.

Distribution, in this context, is determined by presence within the tools and environments where these decisions are executed. Assets and protocols that integrate cleanly into developer ecosystems and meet operational and compliance requirements are more likely to be selected as default components within these systems.

The Point of Allocation

The arc of the last decade is clear. The underlying systems have been built. Capital can now move across them. What matters now is how that movement is directed.

The infrastructure is in place: secure custody, scalable networks, and programmable assets. These capabilities made participation possible. The next phase is defined by how capital is allocated across them.

As we have seen, capital does not move on technical capability alone. It moves when the pathway is integrated into existing systems and aligned with economic incentives. Distribution is what makes that movement operational.

Where Value Settles

As we move toward 2030, the distinction between digital assets and traditional finance becomes less relevant. The underlying systems are converging, combining the efficiency of new infrastructure with the stability of existing balance sheets.

In this environment, financial power sits with those who control the interface through which liquidity and collateral are accessed and allocated. The role of financial institutions evolves accordingly, from holding assets to managing flows across increasingly integrated systems.

The infrastructure is in place. The balance sheet is becoming more mobile. What determines outcomes now is distribution.

That’s it for today.

See you with the next one,
Sebastien Davies

Disclaimer: The piece was originally published in Edition 3 of Interop Markets

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For over a decade, Bitcoin miners faced a lot of flak in the energy and technology world. Their humongous power consumption drew congressional hearings, ESG downgrades, and sustained public criticism. The same facilities are now signing 15-year lease deals with the likes of Microsoft, Google, and Anthropic. Little about the sites has changed. In fact, if there’s one thing consistent with these miners in the past decade, then it’s the crisis. So, what happened?

There’s an interesting quote on crisis: ‘The best opportunities often come from the worst crisis.’ That’s what has happened with Bitcoin miners. Between July 2016 and April 2024, they faced three halvings. Each one cut block rewards in half and forced operators to chase cheaper electricity across increasingly remote corners of the American grid, including West Texas, rural Georgia and the plains of North Dakota.

The weak ones died. Some pivoted at the right time. Others learned the lesson later.

In today’s story, I will explain how the surge in AI infrastructure investment coincided with miners’ growing power and processing capabilities, helping them find a new lease of life.

Onto the story now,
Prathik

Halving - The First Pivot

BTC miners’ first test of survival came in April 2024 with the most recent bitcoin halving. Every halving event is a stress test. But with each one, the rewards get halved, and the struggle doubles.

The April 2024 halving cut block rewards from 6.25 to 3.125 BTC. In a week after the most recent halving, hashprice had fallen from $0.12 per terahash to $0.047. Hashrate is the revenue miners expect to earn per unit of computing power. By Q1 2026, it hit a five-year low of $0.023 per terahash per day.

The average cost to produce one bitcoin currently stands at ~$81,000. The all-in cost of mining each BTC will go far above $115,000 once you include other non-production costs that these listed miners have to spend to keep the lights on. Bitcoin is trading at $70,760. Its price has never crossed $80,000 in the last three months. You do the math.

The BTC mining industry is left chasing lower mining costs for a commodity whose price it has no control over.

The financial statements of the miners that made most of their revenue by pocketing the difference between mining a BTC and selling it in the open market suddenly went into the red. So they switched to mining and holding the mined BTC. The idea was to wait for the price to appreciate enough for them to make a positive return on the same.

That strategy worked until the bitcoin price rallied. But markets move in cycles. Every bull rally will have its bear phase and correction. Crypto has its cycles, too.

10/10 - The Second Pivot

October 10, 2025: the dreaded date in the crypto industry, which witnessed the largest crypto liquidation ever recorded. With the cryptocurrency prices seeing record-level drawdowns since that event, the bear cycle began. That led to the unravelling of miners’ ‘mine and hold’ strategy.

Some were left second-guessing whether to change their strategy. But others announced pivots within 24 hours of the liquidation event.

On October 11, Bernstein published a report that reframed Bitcoin miners not as producers of hashrate but as holders of gigawatts of secured grid access. The analysts called the miners “key in the AI value chain”. They named IREN (formerly Iris Energy) as their top pick to make a successful pivot from BTC mining to an AI-focused cloud infrastructure provider.

Galaxy Digital, a digital assets leader and AI infrastructure provider, announced it had raised $460 million to convert its Helios mining facility in Texas into a high-performance computing (HPC) campus for CoreWeave on a 15-year lease, with projected annual revenue exceeding $1 billion.

Read: From Volatility to Predictability 💰

What followed the 10/10 event was a systematic liquidation of the balance sheets that had defined the sector’s identity with a ‘mine and hold’ strategy. Miners had spent at least 18 months accumulating BTC as a treasury asset, treating unsold BTC as a signal of confidence.

That stance was cracking under the weight of a bear market that saw BTC’s price fall ~40% from its all-time high of ~$126,000 in 45 days. Listed miners who had never sold BTC before started doing so. Marathon Digital (MARA), the third-largest listed BTC holder in the U.S., broke its streak of holding BTC by selling 15,133 BTC in three weeks.

This is the same company whose CEO consistently supported and took inspiration from Strategy, the largest corporate BTC treasury. Less than two years ago, MARA’s CEO and Chairman, Fred Thiel, announced that BTC would be their strategic treasury reserve asset.

Just last month, the Fred took a 180-degree turn and admitted that the selling of BTC “enhances financial flexibility and increases strategic optionality as we expand beyond pure-play bitcoin mining into digital energy and AI/HPC infrastructure.”

I wouldn’t blame him, though. Tough times call for tough calls. And MARA isn’t the only one to make such a pivot away from holding BTC forever as their strategy asset.

Although some added more BTC to their treasuries after the liquidation event, others reduced the pace of BTC additions or made public comments indicating they no longer treated BTC as their strategic treasury asset.

Bitfarms’ CEO was straightforward in admitting, “We are no longer a Bitcoin company.” Ben Gagnon added that Bitfarms will focus on “building the infrastructure for the compute of the future.” CleanSpark took a different approach, treating its 13,000-plus BTC as productive capital and layering covered calls against it.

Even if BTC was not vanishing off their balance sheets, they were identifying it as a resource to strategically fuel their infrastructure pivot.

A Blessing in Disguise

Repurposing a BTC mining site to AI-ready infrastructure is not a simple affair. It costs $8–11 million per megawatt, including new liquid cooling systems, Tier-3 power redundancy, high-bandwidth fibre, and the networking overhauls that GPU training clusters demand.

Yet, mining infrastructure, including cooling, power, and compute capabilities, came closer than any other industry to meeting the requirements of the AI and data centre industries. Bernstein analysts wrote in their note that miners’ existing infrastructure could cut deployment timelines by up to 75%.

It wasn’t just analysts who felt so. This is evident in the deals these miners were able to close over the last few months.

IREN signed a $9.7 billion contract with Microsoft for GPU cloud hosting at its Childress, Texas campus, making it the largest single hyperscaler deal by any miner. Hut 8 landed a $7-billion deal with Google-backed Fluidstack and Anthropic. Cipher Mining bagged $8.5 billion worth of contracts with AWS and Fluidstack. By Q4 2025, Core Scientific’s AI colocation (the space rented out for IT equipment in data centres) went up to 39% of its total revenue, up from 9% just four quarters earlier.

The Surprise Moat

But why are hyperscalers paying mining companies, of all people, for data centre space?

Time is the secret sauce. To survive each halving, miners had to chase cheaper electricity. So they did what they had to do to survive. They negotiated long-term utility agreements, acquired industrial land in low-cost energy corridors, built private substations and secured direct grid interconnections. A modern mining facility is equipped with dedicated high-voltage transformation equipment, redundant power feeds, and thermal management systems built to run at full load, around the clock.

Maybe it wasn’t planned, and you’d say the miners just got lucky. But who minds getting lucky and striking gold while struggling for survival?

Public miners now hold approximately 6.3 GW of operational sites with another 2.5 GW under construction. Data centre interconnection queues in the U.S. run 5 to 7 years in most markets. Microsoft’s internal forecasts show its data centre crunch persisting into 2026 and beyond.

This is why hyperscalers are ignoring mining companies’ lack of expertise in AI infrastructure. Instead, they are paying for the substation, the land-use permits, the utility relationships, and the grid connection, all of which take years to secure elsewhere.

There are incremental gains a miner can make by deploying their existing equipment for AI applications. MARA recently announced a $1.5 billion acquisition of energy infrastructure that will bring its total power capacity to over 2.2 GW. This allows it to convert an already-depreciated facility for AI infrastructure with a cost basis that no other AI builder can replicate.

CEO Fred Thiel called the assets ready-made infrastructure that would have taken up to 10 years and $2bn–$3bn to assemble independently. “Power is the scarce input in AI and, with the planned addition of Long Ridge Energy, we are gaining control of a highly efficient, contracted energy platform,” he said.

The Closing Window

There’s a catch in this story. Every megawatt of energy diverted from Bitcoin mining to AI infrastructure subsidises the economics of whoever remains behind in BTC mining. It reduces the difficulty of mining, making it cheaper for BTC miners to mine a block.

There might still be those who choose to swap or reserve some of their equipment for BTC mining when it does get cheaper. But that’s exclusive for those who can afford to swap or set aside equipment for mining. It won’t be everybody’s play. That’s also because those who repurpose their mining equipment for AI infrastructure won’t be able to swap it back and forth repeatedly. Mining is an interruptible process. You can shut it down when the electricity charges are high. You cannot do the same for AI and high-performance computing. Once you lease out or commit your computing power, you cannot back out of the deal temporarily to use the equipment to mine some bitcoins.

However, for most miners, this is not an option. They have a short window to make the shift. And they won’t get lucky too often.

The timing of how well everything lined up for them is almost unbelievable. The halving squeezed mining economics to the breaking point. Then came the 10/10 liquidation event, forcing miners to confront the reality that holding BTC through a bear cycle is not a feasible strategy. But the AI infrastructure boom arrived at the exact moment when miners had both the motivation to pivot and the assets to execute the pivot.

This will likely not repeat. The miners signing the contracts today will bear the fruit of a decade of economics that will not be available to whoever comes late to this party.

That’s it for today. I will be back with another deep dive.

Until then, stay curious!
Prathik

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Morpho has generated $256.69 million in cumulative protocol fees. Token holders have received zero of it. The protocol takes no revenue. There is a fee switch built into the contract, capped at 25% of borrower interest, that governance could theoretically activate. In June 2025, Morpho’s founder, Paul Frambot, said publicly that the association advocates reinvesting protocol fees rather than distributing them and that all revenue should go back into growth. But a February 2025 governance proposal was more specific about what was holding things up: the legal and tax work around fee switch activation was not yet complete. The switch is not off because the paperwork is not finished.

Morpho currently has $7 billion in total value locked, with Coinbase running its entire lending product on top of it, Apollo Global Management buying 90 million tokens on a 48-month schedule, and Société Générale deploying on its infrastructure. France’s first DeFi unicorn has decided, for now, that it does not want to be paid.

At this point, you have to ask whether this is an elaborate proof of concept for running a charity on an institutional scale.

Stripe launched in September 2011. 2.9% plus 30 cents per transaction; no setup fees, monthly fees, or charges for failed payments. Patrick Collison said explicitly in 2012, “We only make money if you do.” Legacy processors charged merchants through opaque surcharges, PCI compliance costs, and approval processes that took thirty to ninety days. Stripe lets developers integrate payments in seven lines of code. By 2013, they were processing billions of dollars annually for companies ranging from Lyft to the Museum of Modern Art, with 83 employees and a $1.75 billion valuation.

They did not turn a profit for thirteen years. The 2023 annual letter was the first confirmation of being “robustly cash flow positive.” The 2024 letter called it straightforwardly profitable, with expectations that it would remain so. For six consecutive years during that stretch, Stripe spent more on R&D as a percentage of revenue than any comparable company. They processed $1.4 trillion in payment volume in 2024, which is roughly 1.3% of global GDP. The patience required in infrastructure is a kind of institutional patience that most companies cannot sustain. The Collisons maintained it for over a decade.

Morpho has been called the DeFi Mullet. Fintech in the front, DeFi in the back. When Coinbase launched its crypto-backed USDC lending product, it built the entire user experience itself. Morpho handled the collateral, the matching, the interest calculations, and the liquidation mechanics. Over $2.17 billion in USDC has been originated through this product in the U.S. alone, now expanded to the UK. The strategic value to Coinbase was speed. They deployed a sophisticated, institutional-grade lending stack without building it from scratch. The strategic cost to Morpho was zero revenue from the arrangement. Every dollar Coinbase made on that product flowed through Morpho’s contracts and past Morpho’s empty wallet.

Stripe always charged. The zero-revenue years were about reinvestment, not the absence of a business model. Morpho’s zero-revenue model is a deliberate choice not to charge, with a fee switch in place but explicitly deprioritised by its founder. Stripe was building toward pricing power by deepening its distribution. Morpho’s path to pricing power is less clear, and token holders are the ones bearing the cost of finding out.

The MORPHO token does one thing, it governs. Holders can vote on cross-chain deployments, reward extensions, and, in theory, the fee switch. In practice, the governance actors with the most influence have voted almost exclusively on proposals that expand Morpho's coverage rather than compress the yields that attract deposits in the first place. Because compressing yields reduces TVL, and reduced TVL weakens the argument for turning on the fee switch. The logic is circular in a way that is not obviously resolvable. None of the fees it generated has moved to token holders. The token's all-time high was $4.17 in January 2025. It trades around $2 on April 30th. The circulating supply has expanded from roughly 75 million tokens in early 2025 to over 550 million by April 2026. That is a 7x increase in supply over 12 months, against a price down more than 50% from its peak. Token holders are funding the protocol's growth. The protocol's growth is benefiting Coinbase, Bitwise, Fireblocks, Anchorage, Apollo, and Société Générale. This is the stated strategy.

Apollo, a firm managing $938 billion in assets, agreed to buy up to 90 million MORPHO tokens, 9% of the total supply, over 48 months. When a firm of that scale commits to a 48-month accumulation schedule on a DeFi protocol, it is not speculating on the token price. They are making a judgment about the protocol’s permanence. Infrastructure that survives long enough becomes unchallengeable, and the switching costs eventually exceed any benefit of moving. Replacing Morpho at this point would mean rebuilding the smart contracts, migrating $7 billion in liquidity, convincing Coinbase and Apollo to move their integrations, and restarting the trust-building process from scratch. Apollo is betting that Morpho is approaching that threshold.

Stripe’s moat was never the payment technology. Look instead at the integration depth. Every developer who built on Stripe, every merchant who embedded it into their checkout, every platform running on Stripe Connect represented a switching cost that compounded over the years. By the time Stripe’s competitors understood this, Stripe had already won.

Morpho’s is aiming at the same moat. Coinbase cannot easily rebuild that lending stack from scratch. Steakhouse Financial’s vault curation is optimised for Morpho’s architecture. The integrations are accumulating faster than any competitor is building. Aave’s loan-to-deposit ratio sits at 39%. Morpho’s is 41%. The efficiency gap is widening, and in institutional lending, 200 basis points is no silly game.

When Morpho’s first concrete commercial revenue arrived, a low seven-figure licensing fee from Berachain for the right to use Morpho Blue’s code, governance voted to send it to the Morpho Association, a French nonprofit, rather than the DAO treasury. The explanation was that the legal work around direct DAO fee receipt was not yet complete. The same legal work is blocking the fee switch. So the protocol is generating commercial revenue. It is being routed around the people who own the governance token, with a promise that the routing will change once the paperwork is done. Stripe’s shareholders got rich when Stripe got rich. Morpho’s token holders are awaiting a governance proposal that has not yet been drafted.

The history of companies that delayed monetisation to win distribution first is more instructive than the Stripe comparison alone. There are really only three outcomes in that playbook, and Morpho fits uncomfortably into all three at once.

The first outcome is acquisition. WhatsApp had near-zero revenue when Facebook bought it for $19 billion in 2014. Instagram generated exactly zero dollars for the three years after its $1 billion acquisition in 2012. In both cases, the zero-revenue period was sustainable because someone else, like Facebook, absorbed the cost and eventually found the monetisation. Neither company’s founders had to answer to token holders during the years of building. The people who gave up something of value during that period, the users, were not promised financial returns. They were just using a free app.

Morpho’s token holders are not users. They are investors who have been told, implicitly, that the protocol will eventually generate value for them.

The second outcome is what Stripe and Twilio actually did, which is to charge from day one and reinvest the revenue rather than profit from it. Twilio has charged per message and per API call since it launched in 2008. It had revenue; what it lacked for over a decade was profit, because carrier fees ate into margins faster than volume could offset them. It took years of grinding to get there, but it was always charging. Morpho is not charging.

The third outcome is the commons: TCP/IP, Linux, the open-source stack. These are zero-revenue infrastructure stories where the people who built the primitive layer never expected or received financial returns, and the value was captured entirely by the layer above. This is actually the cleanest analogy to Morpho’s current architecture, which is why this is a problem. The TCP/IP engineers did not have token holders.

Morpho built open-source-style infrastructure and then attached a token to it that implies someone will eventually get paid. Those two things have not been reconciled. The reconciliation has been deferred to a governance proposal that does not yet exist.

Morpho Blue’s core code is 650 lines. It is immutable, meaning nobody can change it. Not Frambot, Apollo, a governance vote, or a court order. Coinbase generated $2.17 billion in loan originations on top of that because the rules cannot change beneath it. Apollo is committed to a 48-month accumulation schedule because the protocol will work the same way in 2028 as it does today. Every major institution that built on Morpho did so precisely because nobody is in charge.

When Morpho eventually tries to charge, when the legal paperwork is finished, when the fee switch governance proposal is finally written, it will be asking institutions that built on the premise that nobody controls this protocol to accept that somebody now does. The fee switch is a renegotiation of the terms under which every institution currently trusts the protocol. And the institutions with the most to lose from that renegotiation are the same ones whose presence makes Morpho worth charging for in the first place.

—Thejaswini

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There’s this famous Crypto Influencer who goes by the name of ThreadGuy. He rose to prominence in 2021 by teaching people how to trade NFTs. Now he sits in his New York apartment with a barrel of American crude oil behind his desk, trading pharmaceutical stocks and commodities on Hyperliquid. A few weeks ago, he had Cobie on his stream. Cobie has been one of crypto’s longest-running voices since 2012, sold Echo to Coinbase for $375 million, and now works there full-time. When ThreadGuy asked him about the state of crypto, he described something called a “K-shaped” dynamic.

“There’s this weird K-shaped thing happening in crypto,” he said, “where crypto seems to be having phenomenal success stories, much more than any previous year. But it’s just not represented in asset prices that people can buy.”

That line has been stuck in my head since. Because he’s right. Polymarket and Kalshi have become a $44 billion prediction market duopoly. Stablecoins are being used to pay gig workers. DoorDash now pays drivers through Tempo. Hyperliquid processes more trading volume than most centralised exchanges, on a chain it built from scratch. Trade XYZ predicts the Monday stock market open within 50 basis points. Crypto was supposed to move money without banks, make markets without brokers, and let anyone trade anything from anywhere at any time. By almost every measure its critics ever threw at it, it’s doing exactly that.

And the people who believed in it earliest, the ones holding tokens, have gotten almost nothing from it. You can’t buy Polymarket equity. You can’t buy Tempo. Circle went public, but half of USDC’s net interest margin goes to Coinbase for distribution rights before shareholders see a cent. The platforms are all winners, but token holders lose every time.

That is the K-shape. And the more I looked into it, the more I realised this isn’t just a crypto problem.

Where Does The Value Go?

Let’s start with stablecoins, because they’re crypto’s true Product-market-Fit. Tether is estimated to have made $10 billion in 2025 and has roughly 100 employees. That is more profit per head than almost any company on earth. Circle went public and has a market cap of $23 billion as of April 28. Stablecoin supply has grown 100x over the past few years from $6.8 billion in 2020 to more than $315 billion today. And it is the US Treasury itself that projects it to grow to $2 trillion by 2028

This is real financial infrastructure being built on crypto rails. And for anyone holding a token, the economic relationship to all of this is zero. Tether’s profits flow to Tether’s shareholders. Circle’s economics flow to Circle’s equity holders and to Coinbase, which effectively takes 50% of USDC’s revenue just for putting it in front of users. DoorDash pays drivers through Tempo, and the value of that transaction accrues to DoorDash, Tempo, and the driver. Not to anyone’s token portfolio.

Prediction markets tell the same story. Polymarket went from a niche crypto experiment to a CNN fixture. The Wall Street Journal now uses its data alongside editorial reporting. Substack built a direct integration with Polymarket so writers can embed live odds in their posts, turning every newsletter into a real-time data terminal. ICE, the company behind the New York Stock Exchange, invested $2 billion at an $8 billion valuation. Kalshi won its CFTC legal battle and expanded into economics, sports, and science. Combined volume across both platforms hit $44 billion in 2025, with $10 billion in a single month.

None of that value went to token holders. Polymarket’s early backers, Founders Fund, General Catalyst, and Blockchain Capital, are sitting on massive unrealised gains. One of crypto’s biggest success stories runs on crypto rails, was built by crypto people, and ended up with a traditional equity cap table where the upside sits with venture capitalists (VCs).

You might argue that Polymarket hasn’t launched a token yet. That’s true, and it might launch one eventually. But even if it does, private investors have already set the valuation at $8 billion. The window where early users could have captured meaningful upside closed before most of them knew it existed. And if it never launches a token at all, then the entire value of the prediction market revolution, the thing crypto people spent years arguing would change how the world processes information, will have been captured entirely by a traditional equity cap table. Funded by VCs, exited to institutions, with zero on-chain ownership for users.

Even in DeFi, the pattern is the same. Crypto spent the better part of a decade building the infrastructure for decentralised finance — from lending protocols, automated market makers, perpetual exchanges, and stablecoin rails, mostly in the open, mostly with tokens, and mostly while regulators were actively trying to kill it. The people who built this stuff took enormous risks. The users who provided liquidity and tested these protocols when a single smart contract bug could drain everything took risks, too.

And now that the technology has proven itself, now that it’s clear that stablecoins, on-chain trading, and tokenised assets work, the companies showing up to capture the value are not the ones who took that risk.

They’re traditional firms with equity structures, private fundraising rounds, and no obligation to distribute anything to the users or communities that made the technology viable in the first place. Stripe is building stablecoin payments. PayPal has launched its own stablecoin. Banks are tokenising assets on private blockchains.

They were all observing from the back seat at what crypto built, and once they confirmed it worked, they are now rebuilding it inside walled gardens where the economics flow to shareholders.

The upside is being privatised. Tokens were supposed to be the mechanism that prevented this, the thing that let early participants share in the value they helped create. Instead, the projects that actually succeeded either never launched a token or launched one so late and at such an inflated valuation that public holders were effectively the exit liquidity for insiders who got in at a fraction of the price.

The K-shaped Capitalism Problem

And this isn’t some crypto-specific failure. This is how wealth creation works now, everywhere, and crypto just inherited the same disease it was supposed to cure.

SpaceX has gone from zero to roughly $1.75 trillion in private valuation. OpenAI is at $852 billion. Anthropic is somewhat in the same territory as $800 billion. These three companies alone represent trillions in value creation. If you were an ordinary person in the 1970s or early 2000s and thought Apple, Amazon, or Google would be important, you could just buy their shares. You could participate in the wealth creation of a company you believed in. That relationship between being right about the future and being rewarded for it, that was the basic social contract of capitalism. It no longer holds.

These generational companies remained private at every stage when the real returns were made. The only people who got access were already in the club, the Silicon Valley networks, the fund-of-funds, the LPs writing $50 million checks into venture vehicles. By the time SpaceX or OpenAI eventually goes public, the price will reflect a decade of compounded private gains that retail investors had zero access to. The number of publicly traded companies in the U.S. has dropped 46% since 1997, from about 7,500 to 4,000.

There are over 1,400 venture-backed unicorns worth a combined $5 trillion, all staying private. Companies used to go public to raise capital. Now they raise billions in private rounds, indefinitely, from the same tight circle of funds. By the time they IPO, price discovery had already happened in rooms that regular people would never be allowed to enter.

The data confirms this isn’t some paranoia. IPO returns from 1970 to 1990 averaged 5% annually, less than half of what you’d have made just by buying public companies of a similar size. Low-float IPOs, the kind SpaceX and OpenAI would likely do, have a 90% historical failure rate. Ten out of eleven low-float IPOs since 1980 underperformed the market by 50% or more within three years. So the deal being offered to normal people is, you don’t get to invest when the company is worth $10 million and growing. You get to invest when it’s worth $1.5 trillion, and the insiders are looking for liquidity to exit.

This is what is called the K-shaped economy, where gains are privatised as they flow through closed circles, and losses are socialised through overpriced IPOs, through index funds forced to buy at the top, through inflation and stagnant wages, which get spread across everyone else.

This is also what’s driving talent out of crypto. Crypto code commits fell 75% since early 2025, from 850,000 weekly to 210,000. Active developers dropped 56% to about 4,600. Where are they going? AI. GitHub now has 4.3 million AI-related repositories. LLM imports grew 178% in a year.

And this makes complete sense if you think about it in terms of the K-shape. Every major crypto onboarding wave, from 2013 altcoins, 2017 ICOs, 2021 DeFi and NFTs, to memecoins, had one thing in common: normal people made money fast. AI has that energy right now. One person, called Peter Steinberger, built OpenClaw and later sold it to OpenAI for billions, all by himself. That is the kind of energy crypto used to have. If you’re a 22-year-old like me and deciding where to spend the next five years of your life, the math isn’t that complicated. Crypto is offering you governance tokens that launch at $16 billion and bleed for two years. AI is offering you the chance to build AI agents with three people that could be worth a billion dollars before your next birthday.

The talent started leaving because, somewhere, crypto stopped distributing the upside it creates. The K-shape pushed the gains upwards, to VCs, to equity holders, to the same insiders the industry wanted to build to displace. Where is Decentralisation Anon?

Okay Enough! What’s the Solution?

So crypto has a problem. The technology is fantastic, but the people who believe in it don’t get to participate in the upside. The same privatisation dynamic that is eating away at traditional markets has infected the industry specifically designed to prevent it. Is there a way out?

Cobie thinks there might be, and I agree with him. The answer is the one thing crypto can do that no other industry can, airdrops.

Airdrops distribute ownership directly to users globally, without intermediaries, at the exact moment when ownership is most valuable. That’s what airdrops were always supposed to be. In practice, most of them have been a joke. But there’s one case that proved it can actually work, and it’s worth understanding.

I am talking about Hyperliquid. Jeff Yan and his team built a perpetual exchange from scratch on its own L1 blockchain, ran it for over a year, grew it to over $4 trillion in cumulative trading volume to date, and, when it came time to distribute ownership, allocated 70% of the total token supply to the community. All of this with no VCs, Advisors, or exchange listing partners. The actual traders who used the platform, moved capital through it, and stress-tested it for months. 94,000 addresses claimed tokens in a $1.5 billion airdrop. Some of them became millionaires overnight.

And here’s the best part. They didn’t dump the tokens. Because the people who received HYPE weren’t mercenaries farming a product they didn’t believe in. They were Hyperliquid’s best users. The most active traders, the people who moved the most capital, the ones who stayed because the product was better than the alternatives. They got ownership proportional to their contribution, and they held. The team mirrored it. After distributing about 20% of their vested tokens in the first two months (likely for taxes), they cut distributions to 1% in the following months. Today, 97% of Hyperliquid’s protocol revenue flows to HYPE buybacks and burns.

Saurabh at DCo broke down the valuation math in detail. Hyperliquid earned $960 million in revenue in 2025 on roughly $3 trillion in volume, but trades at just 10-13x revenue, compared to 25x for CME, 23x for ICE, and 22x for CBOE. It grew to nearly a billion in revenue in its first full year, has no debt, no headcount drag, and a buyback mechanism that returns almost all fees to token holders.

Hyperliquid is proof that this is possible. Distribute to users, not investors. Let actual usage drive value. Align incentives so the people who build the product and the people who use it are on the same side of the trade. But of course, most airdrops are not Hyperliquid.

Most of them are elaborate performance theatres where people pretend to use products they don’t actually care about just to farm tokens they plan to sell the second they unlock them. The projects know this is happening. The users know the projects. But everyone still plays along because admitting that an airdrop is a customer acquisition cost paid in inflated tokens won’t help raise more money on VC pitch decks. Because 90% of these tokens exist just to give VCs an exit, not to align incentives with the users.

The data from Cobie’s own analysis is damning: Ethereum’s ICO allowed retail investors to buy at a $26 million valuation and capture 7,500x returns. By the time you get to projects like Berachain, the seed round is $40 million, and the public launch price is at its peak. Retail holders are underwater, while seed investors are up 138x.

So the question is simple, and I think it matters more than most people in this industry realise: Is Hyperliquid a model or an exception?

If it’s a model, if more teams can build real products, skip the VC extraction machine, and distribute ownership to the people who actually use what they built, then crypto has something genuinely unique. SpaceX can’t airdrop shares to the people who watch its launches. OpenAI can’t distribute equity to everyone using ChatGPT. But crypto can. It has a mechanism. It’s been done at least once, and it was a success.

If it’s an exception, if Hyperliquid was just a one-off with the right team, the right product, and the right timing, then Cobie’s K-shape thesis wins. Token holders keep losing. The smart builders keep leaving for AI. And the only way to participate in crypto’s success is to buy Coinbase or Circle stock. Which is exactly the kind of outcome crypto was supposed to make impossible.

That’s all for today.
Vaidik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 170,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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Over the past year, we have spent a lot of time at Token Dispatch writing about perpetual futures (perps) venues. Their rise to fame made it impossible to ignore them. Perps allow participants to price events close to their occurrence, with high leverage and deep liquidity available around the clock. Existing exchanges never offered that because of their constrained trading time and days. An 11-member team rode on this 24/7 trading pitch and made Hyperliquid the fastest-growing crypto exchange, with almost a billion dollars in annualised revenue.

Throughout 2025, perp trading volume averaged seven times that of spot trading. It seemed like a guaranteed way to build a sustainable business. So the inevitable happened: others jumped on the bandwagon.

Last week, the two largest prediction markets, Polymarket and Kalshi, announced perpetual futures and crypto trading within hours of each other. This comes just months after Hyperliquid said it’d launch event contracts. The convergence of perps and prediction market venues is a no-brainer. Everyone wants to be the everything exchange, and offer a one-stop shop that brings together attention, capital and leverage.

Three weeks ago, Saurabh wrote in a note on X about why Hyperliquid’s expansion into prediction markets will help the exchange take over finance. But is that true the other way round too? Can Polymarket and Kalshi’s moves be similarly rewarding?

Today, I will tell you all about it.

Why Perps Matter for PMs

Prediction markets have a stickiness problem. They are often cyclical and see record-level trading volumes when there are events to bet on. Just like we saw it during a U.S. Presidential election, a Super Bowl season, or an FOMC meeting.

Polymarket’s monthly active users peaked at 321,500 in November 2024 during the U.S. Presidential Elections. Three weeks later, the number had dropped by 25% to 245,000.

However, with seasonal events, the monthly user count kept swinging in both directions.

In January 2025, it peaked at 500,000 users, then dropped below 200,000 in September. This tells us about Polymarket’s user-retention rate.

Dune’s cohort data shows that across every monthly cohort since 2024, only 8-11% of users are still trading a year after joining. About 75% of them vanish within 90 days. Users return for events and don’t necessarily find the platform sticky.

But that’s just part of the problem.

PMs also block capital until the issue is resolved. Perps are different. They price the event every second. It captures attention for extended periods and builds sustained engagement. It also makes economic sense for the PMs, since perps record far more trading volume and proportionally higher fee income.

In 2025, perps recorded over $60 trillion in notional trading volume, compared with $28 billion for PMs.

So, this adjacency expansion for PMs becomes a natural evolution. Platforms that capture one form of speculative intent often extend their reach to the next. They either build the capability or buy another player with that capability. We’ve seen this happen multiple times: Robinhood went from stocks to options to crypto to prediction markets (PMs). Coinbase acquired Deribit for a record $ 2.9 billion to venture into derivatives trading. Binance expanded from offering spot to futures to creating its native blockchain.

We see this often in the traditional world. A company expands the breadth of its services and hopes to cross-sell the new offerings to the same customers. It does two things: increases the average revenue per user (ARPU) and insulates the business from market cycles by diversifying reliance across multiple revenue streams.

In the early 1970s, the Chicago Board of Trade’s (CBOT) revenue from its commodity futures was dwindling. So they launched the Chicago Board Options Exchange (now known as Cboe) in what used to be a 4,000-square-foot smoking lounge of its parent company, CBOT. Both the products worked in sync because they needed a common infrastructure: risk management, clearing and a network of professionals who understood derivatives pricing.

But there’s a wide strait between wanting to run a perps venue and actually being able to execute one.

The Perps Stack

Running a perps exchange has too many moving parts. Let’s start with liquidity.

Hyperliquid processes over 200,000 orders per second through a fully on-chain order book. The perps venue clears over $6-7 billion in daily volume with two-sided market making. Lack of liquidity would lead to extreme volatility, wide bid-ask spreads, and high slippage. This makes it easy for whales to manipulate prices.

Second is the risk engine - the heart of any derivatives platform. It tracks every position and checks margin requirements on every order. When $19 billion were wiped out of the crypto market in October 2025, Hyperliquid processed billions in liquidations without downtime.

Then there’s the funding rate mechanism that keeps perps tethered to the spot price of the underlying asset. It runs continuously by settling small payments between long and short positions every few hours.

Building this entire stack isn’t the main problem. That I’m sure the prediction markets will manage to put together. The bigger problem is stress-testing that stack.

Hyperliquid built all this and stress-tested it in real-world scenarios, like the 10/10 crypto liquidation event and the U.S.-Israel-Iran war. It then launched event contracts via HIP-4 once the entire stack was in place to support that. Kalshi and Polymarket are trying to go the other way round. They have been running successful prediction markets that required none of the above stack in the first place. They are now going to fight for a share of the pie by not only competing alongside a highly successful Hyperliquid but also with an infrastructure that has not been stress-tested for the high-frequency activity seen in perpetual trading.

There’s a lot more stacked against PMs that makes the expansion to perps more difficult than the other way around.

The Hedging Synergy

On Hyperliquid, the risk engine sees all your positions across perps, spot and, soon, event contracts. Saurabh explained this in his note on HIP-4.

This common risk engine solves a core money problem by optimising the same capital across multiple trades a trader makes on the venue.

Consider a trader on Hyperliquid who is long ETH at 5x leverage. The trader is worried about the Fed decision next week, so she buys an outcome contract that says “Fed holds rates steady” at $0.65. With a common risk engine, both positions are held in the same margin account. If the Fed surprises with a cut, ETH rallies, her perp profits and the outcome contract loses only what she paid. If the Fed holds the rates, the PM contract pays out and partially offsets any drawdown on her perp.

This is why a PM platform or a perp venue cannot be a mere add-on feature. This hedging possibility is what makes HIP-4 valuable on Hyperliquid. An average trader on the platform sees PMs as insurance cover in case their existing perps position flips over.

Currently, the collateral on Polymarket and Kalshi is locked until the event resolves. So, unless they offer a common risk engine across their perps and prediction markets venues, it loses a crucial catalyst that keeps traders on the platform. Right now, neither platform has announced cross-margining between their PM and perps venues.

The category breakdown and average trader profile on PMs raise further concerns about their ability to replicate their success in perps trading.

Over 80% of Kalshi’s total monthly trading volume occurs in the sports category. For Polymarket, that share was over 40% in 2025. Now, how would you create a continuous pricing mechanism for a perps venue around these sporting events? That takes away a big chunk of its traders out of the perps equation.

Additionally, an average Kalshi trader is a retail user who has never touched crypto and funds their PM account via ACH transfers from their bank account. So, even if I assume the theoretical possibility of cross-margining on Kalshi, I doubt that these traders have the sophistication required to double down on their positions on the platform and use perps as a hedging strategy.

What Could Work for PMs

There’s one scenario where I expect the bets to work if Kalshi and Polymarket do announce cross-margining. Their institutional partnership with prime brokerages and clearing firms can encourage high-value, frequent trading activity across event contracts and perpetual futures.

It would let institutional desks treat PMs as part of a broader risk management toolkit.

Both Kalshi and Polymarket can boast about partnerships that will give them access to institutional clients.

Kalshi’s tie-up with FIS and Tradeweb data, and Polymarket’s deals with Intercontinental Exchange (ICE) could help retain institutional clients who value hedging their prediction market positions with perpetual contracts on the same platform.

That’s still a far-fetched wedge that rests on many things going right for both the prediction markets. They need to set up stress-tested infrastructure, ink partnerships and show their clients that their platforms can help optimise capital.

But this is what it takes for them to survive a cut-throat competition. With the distribution wedge already with Hyperliquid, they have no other choice but to maximise their chances elsewhere.

That’s it for today. I will be back with another deep dive.

Until next time, stay curious,
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 170,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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1By 2017, 47,000 Americans were dying annually from opioid overdoses. By then, the Sackler family, which owned Purdue, had invested in addiction-treatment companies. You have to admire the completeness: creating the disease, monetising the cure, and keeping it in the family.

McKinsey advised Purdue on how to “turbocharge”(their exact word) OxyContin sales. Then, when the crisis had a name, a body count, and a congressional hearing, McKinsey pivoted to advising governments on opioid response. This gave them two clients on opposite sides of the same disaster, billing by the hour throughout.

We have seen this pattern before in business history. Does it have a name? It is not quite disaster capitalism, that is, about profiting from crises you did not create. This is more specific: build the problem, sell the solution, ideally while publicly expressing concern about the problem. The concerned expression is important because it shows you understand the severity of the situation, which makes sense, because you built it.

Facebook did this. So did the rating agencies before 2008. So did tobacco companies when they pivoted to nicotine patches. But all of them had the decency to keep some distance between the problem and the solution—different subsidiaries, spokespeople, and plausible deniability.

Sam Altman did not bother with the distance. I’m not sure what to feel about that.

Altman co-founded OpenAI in 2015. OpenAI built the models that made deepfakes cheap, synthetic identities indistinguishable from human ones, and bot armies scalable to any size. The downstream effects are no longer hypothetical: $2.19 billion in cumulative global deepfake fraud losses to date, including $1.65 billion in 2025 alone. In January 2024, a finance employee at Arup’s Hong Kong office was invited to a video conference with the CFO and several senior executives. He watched them speak, followed their instructions, and made fifteen wire transfers. Every person on that call was a deepfake. The total loss was $25.6 million. Forty-three per cent of finance professionals targeted by deepfake scams fall for them. Eighty-seven per cent say they would act on a CEO payment request without additional verification. Only 0.1% of people can correctly identify deepfake content in controlled tests.

In various interviews over the years, Altman has said this is exactly why proof of personhood is necessary. That AI-generated content has made human verification urgent. He is correct that the internet needs a new identity layer, and he is also the reason it is necessary.

He also co-founded Tools for Humanity, which sells the iris scanner.

You stare into the lens, it converts your iris pattern into a cryptographic hash, deletes the raw image (they promise), and issues you a World ID. Zero-knowledge proofs mean you can prove you are human without revealing which human. As of April 2026, 18 million people across 160 countries have done this. The technology is legitimately impressive: a false-match rate of 4.1 × 10⁻⁸, meaning it can distinguish among billions of people with near-perfect accuracy.

Regarding tokenomics, you get WLD tokens as payment after scanning your iris. Seventy-five percent of all 10 billion WLD tokens go to the community, which is the people scanning their irises. Only 25% goes to investors and the team. But then that 75% community allocation unlocks slowly over 15 years. A little bit is released every month for 15 years. The investor allocation, after a 12-month waiting period, started unlocking immediately.

So in the period when WLD was most valuable, insiders held roughly 60% of what was actually available to trade. The community held about 40% of the circulating supply despite supposedly getting 75% of the total.

WLD hit $11.74 in March 2024. Today it’s $0.253, down 97.5% from the peak.

A person in Nairobi who scanned their iris in 2023 receives roughly 40 WLD per month over 12 months. At the peak price, that was worth around $470. Today it’s worth $10.56.

The a16z partner, Andreessen Horowitz, one of Silicon Valley’s most powerful VC firms, got in at seed valuations years before launch. They had four years to manage their position and exit at the right time. Meanwhile, the person in Nairobi got $10.56. And cannot get their iris data back. If you look at it this way, one side has liquidity options, and the other has a permanent biometric record and no exit.

So when they say there is an iris scanner in a Gap store, the same place you go when you need chinos urgently and have somehow run out of opinions about your own clothes, you will stand in front of a biometric device between the sales rack and the fitting rooms, and it will feel completely normal, because everything feels normal there. Tinder, Zoom, and DocuSign have all integrated World ID. There are 1,500 active Orbs deployed across 23 countries. At what level of integration does opting out become functionally impossible? We don’t have an answer for that. Do we?

Kenya, the Philippines, Colombia, Thailand, India, Indonesia, and Hong Kong have all banned it or heavily restricted it. Germany is still investigating under GDPR.

Kenya’s objection was that offering tokens worth meaningful money to people living on very little is not free consent. Again, we have no answer.

A $10.56 grant in Ohio is still $10.56 in a country with a GDP per capita under $2,000. The people who designed the grant live in San Francisco. Altman’s The World has never published a clean country-level breakdown of scan numbers; isn’t that an interesting omission for a project that talks extensively about transparency? The partial data available shows early-adopter concentration in Argentina, Kenya, Colombia, and Indonesia, markets where the token was worth scanning for, and the biometric was the price of entry.

The crypto community spent a decade building exactly the infrastructure you would use to solve this problem without centralising it: W3C Decentralised Identifiers, Verifiable Credentials, the full self-sovereign identity stack. The founding argument was that you own your identity and no central authority controls it. This was a moral position, a response built explicitly against the kind of centralised identity infrastructure that World ID has now become. The silence that followed World ID’s rise does not have one clear explanation. Some of it is financial. WLD token holders have upside in World ID succeeding, and it is difficult to object loudly to something you are long on. Some of it is exhaustion; the SSI (Self-Sovereign Identity) community spent years building something technically correct and practically unusable, and when World ID showed up with a product and eighteen million verified users, the people who should have objected did not have a counter-product to point to. And some of it is just a technical concession. The ZK proof architecture does provide a real privacy guarantee, and a meaningful part of the community looked at the design and decided this was an acceptable trade-off rather than a betrayal. Vitalik Buterin has been the exception, writing thoughtfully about the risks of one-ID-per-person systems and the value of competing overlapping approaches, but his argument, however correct, has not produced anything resembling a counter-movement. All philosophies and no products yet.

The World Economic Forum published a 2026 report, co-authored with Mastercard, Banco Santander, and Group-IB, that tested 17 face-swapping tools and 8 camera-injection tools against standard KYC biometric checks. Most of them passed. A deepfake image that bypasses identity verification costs $5. A synthetic identity sells for $15. The arms race is not going to stop because Sam Altman built a nicer orb. And if the orb’s database is ever breached, every compromise in it is permanent. The cryptographic hash that remains after your scan cannot currently be reverse-engineered back to your iris, but in five or ten years, as capabilities improve, what becomes derivable from it is something your iris hash cannot opt out of. The risk of a subpoena is that companies comply with government requests when the alternative is operating illegally. The acquisition risk is that Tools for Humanity has venture-backed investors who will eventually want liquidity, and the acquirer’s intentions are not contractually required to match the founders’. An e-commerce inventory company, Eightco Holdings, announced a $250 million purchase of Worldcoin in September 2025. Their stock went from $1.45 to $45.08 in a single day. If someone has already decided that verified human identities are a treasury asset worth accumulating, that appetite is not going away.

OpenAI and Tools for Humanity exist in a feedback loop. The more convincing OpenAI’s models become, the more necessary World ID appears. Altman has simultaneous financial incentives to worsen the deepfake problem and increase the value of the biometric solution, and these are not compound interests. The concerned expression, remember, is important. It signals that you understand the severity of the situation. Nobody understands the severity of the situation quite like the person running both sides of it.

None of this appears anywhere as a policy decision, we have to wait for it to just emerge from the roadmap, quarter by quarter, which is a much cleaner way to make a political choice without ever having to defend it as one. By the time enough people start treating it that way, there will be 180 million irises in the database.

The Sacklers, at least, had the courtesy to keep the whole thing within the family. Altman, to his credit, has upgraded the model for the internet age with broader distribution, cleaner branding, and just enough tokens to make the trade feel like a gift, right up until you remember that one side of it can be sold, and the other is, well… you.

—Thejaswini

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I love discussing how crypto is changing the way money moves. It feels nice, but the reality is nuanced. One look at how the big institutions have been moving money for the past decade tells you the story.

A bank wire transfer across the Atlantic still takes one to two days. It needs to pass through intermediary banks, generates reconciliation entries at each point, and costs the client about $25 to $45. It’s the same architecture from the 1970s, except email replaced telephone communication and SWIFT replaced the tangled cables. Of course, the databases got faster, too. But that’s that. Timelines didn’t shrink proportionally.

You might think this is a technology problem, but I think it’s more of a coordination problem.

We have had blockchains and stablecoins for over a decade now. Yet they never resolved all the problems at once. Some offered the speed institutions needed, but put all data in public by calling it transparency. Others ensured both speed and privacy but created siloed systems that couldn’t talk to each other.

The thing with new technologies is that they easily scare away large institutions operating in highly regulated industries. Think banks. For them to migrate to new technologies, they need to see all their pain points addressed in advance. None of them can be issues that are “can be taken care of after we migrate”.

It took a while, but this is finally changing. Ironically, banks are now resorting to blockchains to avoid ceding ground to digital assets.

Last month, five US banks with over $750 billion in combined assets launched Cari Network. This system will convert ordinary deposits into digital tokens that settle instantly, operate 24/7, and remain FDIC-insured.

In today’s deep dive, I will tell you how blockchain builders have tried to build solutions for institutions in the past and what makes this time different.

Swings and Misses

About a decade ago, consortia like R3 and Hyperledger built private blockchains for institutions with ambitious roadmaps and membership lists that included the BNPs, Citis and Barclays of the world. Although these chains worked and updated the ledgers accurately, they were siloed and couldn’t interact with anything outside the walled infrastructure.

Such efforts died a slow death. Some others pivoted to other approaches.

Then, the banks tried the public blockchains - mostly Ethereum. It immediately solved the composability problem. A shared, neutral ledger that everyone can access enabled banks to interact within their ecosystem. But solving one problem gave rise to another. On a public chain, every counterparty, every transaction, every balance was visible to anyone who knew where to look, just using a browser.

What banks needed was a single system that offered them privacy, compliance, speed and connectivity.

What Changed

Two breakthroughs came in 2025: one technological and another - necessity.

First, let’s understand the technology. Zero-Knowledge (ZK) proofs caught people’s attention for what they offered. ZK proof is a cryptographic method that lets you prove a transaction is valid without revealing its details.

Read: The Privacy Correction 🔐

Although the technology has existed for years, it has only got cheaper and faster recently. Generating these proofs used to be expensive enough to make the whole system impractical to deploy from a business perspective.

The number of transactions processed per second has jumped from as low as 400 to at least 15,000. The time taken for a transaction to reach finality has dropped below a second. All this while keeping the transactions and the details of the parties involved private. For context, traditional financial infrastructure still takes at least a day to process these transactions.

ZK proofs also solve what enterprises saw as another deterrent.

A bank evaluating blockchains in 2018 had to hire engineers, build a chain from scratch, figure out how to generate proofs, run its own servers, and then see if all this translated to business gains. So much for a system they weren’t even sure would work.

ZKsync, an Ethereum-scaling platform built by Matter Labs, addresses this by giving enterprises a blockchain equivalent of AWS. Its institutional product stack, including Prividium, Connect, Gateway and others, offers chain deployment, transaction processing, proof generation, compliance tooling - including KYC checks, role-based access, login controls, and connectivity to other chains.

The stack allows an enterprise to tailor its configuration and get started with deployment. Think of it as buying blockchain-as-a-service.

ZKsync isn’t the only one here. Canton Network, backed by Goldman Sachs, DTCC, Citadel, and BlackRock, takes a different approach to the problem. Instead of ZK proofs, it uses a permissioned model where approved validators coordinate private transactions between known counterparties.

They are both building connective layers that institutions need. But they disagree on whether trust should come through cryptographic proof or from contractual governance between known parties.

If you ask me, there’s little to separate between permissioned and permissionless approaches so far. Both want to solve the same problem for institutions. In fact, Canton even has better institutional partners than ZKsync.

Read: Wall Street’s Stealth Chain

Yet there is something about it that could prompt institutions to flip over to ZKsync. Canton’s permissioned network works fine as long as the interaction and movement of money is happening between known parties in familiar jurisdictions. But when an enterprise wants to expand across jurisdictions and transact with parties outside Canton’s walled-off jurisdiction, ZKsync is what helps achieve composability there.

It’s this technological breakthrough that convinces banks to adopt blockchains.

But why would banks move away from their age-old, battle-tested systems, no matter how slow they are? Just because there’s a cheaper alternative that works faster?

Do you really think banks went from “blockchains are interesting but impractical” to “blockchains make business sense” just because a technology they knew little about suddenly became economically viable? It’s funny how every technology becomes “strategically important” when businesses start losing money.

Deposits Under Siege

In the past decade, stablecoins have grown into a $300-billion market. They did what banks had refused to do for years: move money fast. Today, each of those digital dollars in circulation left the banking system at some point.

The infrastructure I described above, ZKsync’s Prividium and Canton’s permissioned rails, are what is helping these banks take the fight back to the digital assets. With these blockchains-as-a-service, banks can move their existing deposits just as stablecoins do. They can process and settle transactions with the same speed and finality. There’s also a bonus. Banks can do all this while guaranteeing their depositors with regulatory protections and balance-sheet advantages that only they can offer.

This is already happening in real-life cases.

The Cari Network, launched last month by five US regional banks (Huntington, First Horizon, M&T, KeyCorp, Old National), is tokenising bank deposits on ZKsync’s Prividium. The deposits stay on the banks’ balance sheets, are FDIC-insured, and settle in seconds.

Cari Network isn’t an isolated event.

In February 2026, the UAE’s Central Bank approved DDSC, a dirham-backed stablecoin running on ADI Chain, built with ZKsync’s proving engine.

In June 2025, Deutsche Bank began building a tokenised platform on a ZKsync-powered chain that compresses the timeline of setting up a new fund from months to weeks.

Future of Institutional Finance

There’s one core thought I often come back to when I write on finance: “How will money move in the future?” It’s an interesting question to ask oneself because it tells you about the financial behaviour of individuals and corporations.

I don’t think a majority in either category cares too much about the cardinal principles that crypto stands for. Banks care even less. I am sure the banking leadership team doesn’t sit in a boardroom debating decentralisation versus centralisation. They are surely not bothered if their transactions are processed on Ethereum, Solana or a private network based in Timbuktu.

They care most about privacy, composability and speed. If a system delivers these to an enterprise by making them save a couple of extra dollars, then you’ve got their attention. It helps further if there’s a “strategically important” reason to adopt a new technology (like a stablecoin revolution waiting to steal your business).

So, I expect the convergence of Web2 and Web3 finance to happen around the technology that helps move money more efficiently. That could be via ZKsync or Canton-powered chains that move tokenised versions of fiat currencies. Or it could even be through payment-specific chains that payment companies like Circle’s Arc, Stripe’s Tempo, and Stable are building.

I don’t think there’s going to be a winner between the two approaches. That’s because for banks that want to avoid adopting stablecoins, ZKsync’s blockchain-as-a-service will be the most obvious choice for evolution. Those already integrating stablecoins in their payments will choose digital dollar-supported chains.

But I surely know who will be the biggest loser: the one who stays adamant about using the technology that still moves and settles money based on the date and time.

That’s it for today. I will be back with another deep dive.

Until then, stay curious!
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 170,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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Disclaimer: This newsletter contains analysis and opinions of the author. Content is for informational purposes only, not financial advice. Trading crypto involves substantial risk - your capital is at risk. Do your own research.

Every two weeks, I share my favourite literature across crypto, finance, technology, culture, and beyond. I also share what I’m watching and reading.

Hello,

The past fortnight felt like the market briefly exhaled amid all the war and macro uncertainties. I know the war is still far from over. The ceasefire is fragile, and a durable solution still feels far away. But markets seemed tired of moving with each announcement about war.

For now, the markets are pricing in what the next move would be.

The Fortnight That Was

Risk assets, including equities and BTC, recovered well in the last two weeks, while oil has given back a chunk of its war premium even as the Strait of Hormuz remains far from fully operational.

Bitcoin rose about 6.6% through the fortnight, while the S&P 500 gained 6.2%.

Oil moved the other way. USOIL fell about 9.4%. That is probably the clearest sign that traders saw less immediate risk of a wider supply shock, even if the Strait of Hormuz question has not disappeared.

Despite multiple hacks and exploits in the last fortnight, builders in crypto infrastructure kept building and deploying. Both incumbent prediction market giants, Polymarket and Kalshi, announced the launch of perpetual futures trading.

Our team at Token Dispatch has written extensively on some of these topics.

I started with Ethena in When the Yield Runs Out, where I examined how USDe’s once crypto-native yield machine is now leaning into equities, commodities, institutional lending, prime brokerage, and RWAs to survive across more than one market regime. Ethena’s new strategy is diversification, but the old liquidity mismatch problem has not disappeared.

In another piece, Capital Budgeting, But On-Chain, I explained how Aave’s $25 million capital allocation vote shows DeFi moving beyond into a territory where it enabled token holders take capital budgeting decisions.

Thejaswini followed with The Fed Chair’s Portfolio, where she broke down the disclosed exposure of incoming U.S. Fed Governor candidate Kevin Warsh to Solana, Optimism, and many other crypto infrastructure companies.

She then wrote The Quantum Race is a Governance Test, in which she explains why quantum risk is not just a cryptography problem. She cites Bitcoin debates, Ethereum research, and how Tron can move fast because it is more centralised than it likes to admit.

Vaidik closed the fortnight with Why Google’s Billions Couldn’t Fix African Internet, a DePIN story that explains how Share is trying to fix the broken middle layer between African submarine cables and last-mile ISPs, using crypto.

You can see the theme running across the fortnight.

Even when the world outside crypto remains unstable, those within the ecosystem are busy building and deploying despite hacks, governance fights, and political conflicts.

Our team has lined up more stories around these themes for the coming fortnight. Until then, here’s a curated reading list to give you good company.

Regulation & Policy

• The Tokenisation of Stocks: A useful map of the public, private, and in-between structures emerging as tokenised equities move from wrappers to market infrastructure.

• What Can Be Tokenised?: Franklin Templeton’s broad reminder that tokenisation is not just stocks, but spans funds, bonds, commodities, private credit, real estate, and more.

• A Guide to Crypto Crime: Amid ongoing hacks and exploits, Arkham has put together a taxonomy of crypto crime, from pig-butchering and drainers to mixers, bridges, ransomware, and laundering patterns.

• Satoshi Coins Without a Freeze: Nic Carter argues for a cleaner Bitcoin quantum-transition path that deals with dormant Satoshi-era coins without resorting to a blunt freeze.

Crypto & Tech

• Perps Are Coming to Polymarket: Polymarket’s next move pushes prediction markets closer to trading infrastructure, with leverage entering the product stack.

• Kalshi Markets: Kalshi is turning sports prediction markets from betting-like products into trading venues.

• Concentrated Liquidity for Lending: A DeFi lending idea that borrows from concentrated liquidity design to make lending markets more capital-efficient.

• Airwallex Enters In-Store Payments: A payments-stack thread on why the next fintech challenge may be around building offline checkout, and not merely cross-border digital rails.

• IR & Token Transparency in 2026: Novora benchmarks 150+ protocols and shows the gap between on-chain revenue visibility and actual investor-grade reporting.

• Tokenised RWAs: $16 Trillion by 2030: Theo’s case for why today’s RWA market still looks tiny against the institutional asset classes tokenisation wants to absorb.

• The Rise of Composable RWAs: Dune argues that what RWAs can be used as collateral, can be looped and composed inside DeFi, is more important than just tokenising all RWAs.

• Beyond the Flywheel: Galaxy argues that DATs cannot survive as passive balance-sheet wrappers; they need staking, MEV, DeFi yield, or real operating models.

Finance & Economy

• Uncharted Waters: Capriole reads Bitcoin’s macro, technical, and on-chain signals as quietly constructive despite AI-security fears and geopolitical volatility.

• The Most Future-Proof Job: Shopify argues that entrepreneurship may be the safer hedge in the AI-era as business creation becomes cheaper and traditional jobs look shakier.

• Break Now, Fix Later: Scott Galloway brings his wit to turning the White House ballroom fight into a broader critique of political destruction without credible rebuilding.

• Apple’s Hardware Succession: Apple’s reported Cook-to-Ternus transition raises the question of whether hardware discipline can fix Apple’s AI-era drift.

AI & Innovation

• The Future of Entertainment Is Interactive: Dylan Abruscato argues that entertainment will transcend its current passive viewing format to one in which audiences begin participating in the show itself.

• The Agentic Economy Will Be Massive. Agentic Commerce Won’t: Dragonfly pushes back on the easy “agents need stablecoins” thesis by asking how many agents will actually need to transact.

• Google’s $40B Anthropic Bet: Google deepens its Anthropic exposure, turning the AI race into an even more capital-intensive fight between cloud, compute, and model labs.

• DeepSeek’s China Capital Moment: Tencent and Alibaba reportedly circling DeepSeek shows China’s AI champions are becoming national-scale strategic assets.

• Mythos Accessed by Unauthorised Users: Bloomberg’s report on unauthorised access to Mythos is an example of why frontier AI security is now a market-structure issue.

• 5 Ways Blockchains Help AI Agents: a16z crypto lays out where blockchains can matter for agents: identity, payments, governance, trust, and user control.

• Claude Design: Anthropic’s design product pushes Claude deeper into creative workflows, from prototypes and pitch decks to branded visual systems.

Culture & Beyond

• The Art of Comparison: A thoughtful essay on personal finance that talks about comparing without letting envy, status games, or wealth ladders ruin your sense of progress.

• The Mathematical Reason Most People Never Make It: A psychological essay on why outcomes are often nonlinear, delayed, and less forgiving than motivation culture suggests.

• Does Reading Do Us Any Good?: Aeon makes the case for literature as a means of preserving attention, memory, and personal freedom.

• How Darkness Might Save Migratory Birds: Scientific American explains how switching off artificial lights during migration windows can materially reduce bird deaths.

• Justice Is Geometric: Aeon uses African fractal societies to show how bottom-up systems can encode reciprocity, circulation, and fairness without a single centre.

What I Am Watching & Reading

I had picked up Reporting from Ramallah last fortnight and made it through roughly a third of the book.

Amira Hass’ writing is sharp, humane, and observant. But I paused there because the book’s structure was hurting my reading rhythm. The chapters are short, almost like dispatches, and the context switches quickly from one piece to the next. Each article deserves attention, but reading them back-to-back made the experience feel slightly fragmented. It is the kind of book I think I’ll return to slowly, one or two pieces at a time, rather than race through in a week.

So, I moved to Scott Galloway’s Notes on Being a Man.

I’ve only just started, but it already reads like one that annoys, provokes, and occasionally makes the reader nod in agreement. Galloway starts with that familiar bluntness that I adore in his witty No Mercy/No Malice newsletter. More on the book in the next edition.

On the watching front, I’m planning to watch Hamnet this weekend.

I can’t wait to watch Oscar-winning Jessie Buckley play her character. Her versatility as an artist surprised me in The Bride!, even though the film itself was messy. There’s something about her screen presence that makes one want to keep watching how she carries her character even when the story around her is wobbling.

It’s going to be a packed weekend, but perhaps a good one.

Write back to us with any recommendations through the next fortnight that you think we should include in the upcoming edition.👇🏾

That’s all for this fortnight. Catch you in the next edition.

Until then, happy reading!
Prathik

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I pay about $5 a month for home internet in India. It’s a 100 Mbps plan with unlimited data, and half the time they throw in a Netflix subscription to sweeten the deal. A few years ago, that same connection would have cost $25 or more. Then a company called Jio entered the market, started a price war with incumbent telcos, and the whole market repriced within a couple of years. Today, internet access in India is so cheap that it barely registers as a household expense.

For the longest time, I assumed this was how things worked everywhere. Once enough cables land and a competitor shows up, prices eventually fall. Africa has 25 submarine cables and over a million kilometers of terrestrial fiber. There’s no shortage of bandwidth at the coast. Yet broadband across the continent still costs about 15% of gross national income per capita, which is seven times the international affordability target. About 600 million people still lack meaningful internet access.

My $5 plan in India would cost over $100 a month in Nairobi, and even the most basic connection starts at $15. The median Kenyan household earns about $170 a month. The average Indian household earns about $250 a month, which is higher but not by a sufficient factor to explain a 10x to 30x gap in what people pay for the internet. There is enough bandwidth to serve the entire coast, but something inland consumes it before it reaches more users.

This piece is about what’s actually broken in the African internet landscape, how a DePIN project called Share is trying to fix it, and why its use of crypto is unlike anything I’ve seen before.

Why Africa Pays the Highest for Internet

To understand that, we need to look at where the money in Africa’s internet ecosystem actually goes. Google’s Equiano gave West Africa twenty times the bandwidth it had before. Meta built 2Africa, the longest subsea cable ever. Between them and the 23 other cables already in the water, African landing stations, the buildings on the coast where submarine cables come ashore, today have more bandwidth than most of the continent can use.

But none of that bandwidth is useful until it reaches the person paying for it, and that last stretch is where the entire problem starts. The next part is turning that into a connection for a household 50 kilometers inland, which means someone has to build local infrastructure, buy upstream capacity, set up billing, and sell service, one building at a time. That work today falls on thousands of small ISPs, most of them one or two-person operations, because the big telcos that could do it have no reason to.

They sell wholesale bandwidth to these small operators, and that’s a good business for them. If they built out the last mile and started selling directly to households, they’d be killing the wholesale contracts that pay them today. A single bulk deal with an ISP brings in steady revenue with almost no support cost. Replacing that with retail means billing millions of people one at a time, running customer support, and competing door-to-door with ten thousand operators who already have the relationships. No telco wants that trade.

Kenya is the easiest place to look at. The regulatory environment is stable, Safaricom runs one of the best mobile money systems in the world through M-Pesa, and multiple submarine cables land right at Mombasa. If any African country should have cheap internet by now, it’s Kenya.

The Communications Authority of Kenya has over 400 registered ISPs on its books. But the real number is much higher. Once you count the neighborhood-level operators who run a single router in a building and resell to a few dozen customers, the total is probably north of 10,000. That’s ten thousand separate internet businesses in a country of 55 million people, most of them serving a handful of users each.

As a result, when someone in Nairobi goes online, their traffic typically passes through three to five of these small networks before it reaches the open internet. Every handoff between them adds cost and adds latency. By the time the signal reaches the end user, the price per gigabyte has multiplied several times over from what it costs at the Mombasa landing station. The bandwidth starts cheap, but it gets expensive on the way to the customer.

Running one of these small ISPs is a terrible business. You buy bandwidth in fixed bundles from an upstream provider and pay upfront before your customers pay you. Every month, you’re guessing how much capacity you’ll need. Buy too little and your service is unusable by evening. Buy too much, and that money is locked up in bandwidth nobody’s using, and you won’t see it back for weeks. There’s no room to invest in better equipment or a backup connection. You run one upstream link, oversell it, and hope the congestion doesn’t get bad enough that your customers switch to a different service.

This is one of the biggest reasons India’s Jio strategy will never work here. Jio walked into a market with a handful of national telcos and undercut them all at once. That only works when there’s a concentrated market to disrupt, and when you can spend $30 billion building your own last-mile infrastructure across a country of 1.4 billion people, losing money for years before seeing a dollar back. Kenya doesn’t have that. It has 55 million people. No Kenyan telco has $30 billion, and even if they did, the market isn’t large enough to justify that kind of spend. So the last mile stays with ten thousand small operators, and nobody above them has the reason or the resources to replace them.

In fact, you can see how fragile the whole arrangement is when something goes wrong. Four subsea cables off West Africa failed in March 2024. Eight countries went offline for days, and repairs took weeks to months. Two months later, SEACOM and EASSY were damaged off the South African coast, and most of East Africa lost access to cloud services. There were plenty of cables. The problem was that nobody inland had built local caching, proper routing, or any backup path. Most of the traffic was being routed through a single upstream link to Europe, even when other cables were still working. When that one path broke, there was no way to reroute and nothing locally cached to fall back on.

What Share Built

So that’s the problem. Thousands of small ISPs with no scale, no bargaining power, buying overpriced bandwidth through chains of middlemen, running on thin margins with no backup infrastructure. And on top of all that, the big telcos have zero incentive to fix any of it.

Share is a DEPIN project that is solving this exact problem by building the missing layer between the landing station and the last mile. The company is based in Kenya and started in Mombasa, and what they’ve put together touches three parts of the stack.

1. The first thing Share did was set up its own points of presence inside Kenya’s major data centers and connect directly to Meta, Google, Akamai, and Cloudflare. These are the content delivery networks that serve most of what people actually do online. YouTube, WhatsApp, Instagram, Netflix. Depending on the market, that’s 70-80% of all internet traffic. When an ISP connects to Share, that traffic no longer has to travel through three to five upstream middlemen to reach a European exchange point and come back. It terminates locally, inside Share’s own network. For the ISP, the cost per gigabyte drops on day one. For the end user, the video they’re streaming now comes from a cache in Kenya instead of being routed through Frankfurt, so it’s faster, too.

2. The second thing is physical fiber. Share laid a 40-kilometer fiber ring around Mombasa. It’s a loop, which means if any one segment gets cut, traffic automatically reroutes in the opposite direction. That ring alone can reach about 1.5 million people without anyone having to rebuild last-mile connections. Along the ring, Share places what it calls micro-PoPs. These are small, standardized network boxes at tower sites and rooftops, where nearby ISPs can plug in and connect to the backbone. Through its own fiber and infrastructure-sharing deals with tower companies and utilities, Share now has access to over 1,000 kilometers of fiber across Kenya. They just moved to Nairobi.

3. The third part is software. Every ISP that connects to Share gets access to a single system that handles routing, billing, customer onboarding, plan management, and revenue sharing. For a small operator who was managing everything on a spreadsheet and a handshake deal with an upstream provider last month, Share entirely changed their business operations. They go from reselling someone else’s marked-up bandwidth to operating as a last-mile partner on a real network with a real backend. Their costs drop, their service gets better, and they keep their customers. Share takes a minority cut of the revenue.

What makes all of this work together and scalable is how Share grows. When a small ISP joins Share, it doesn’t just get better infrastructure; its existing subscriber base immediately migrates to Share’s backbone, meaning Share inherits those customers without having to acquire them. That’s how they went from a test network in Mombasa to covering 8 million people in 15 months.

This is a model where everyone wins. The ISPs get lower costs and better service without losing their customers. The telcos and tower companies that own fiber and tower sites become suppliers to Share, which is a more predictable business than competing against 10,000 small competitors. Share gets a growing network with recurring revenue. Nobody in the arrangement is worse off than they were before, which is probably why it’s held together in a market where nothing else has.

Where Crypto Earns its Place

Most DePIN projects launched between 2020 and 2024 were pretty much the same. Launch a token; use it to pay people to set up hardware, to pay early users to show up, and to fund whatever activity generates a decentralised network. Helium did it. Render did it. A dozen WiFi hotspot projects did it. Most of them never shipped any real infrastructure. Even the ones that did were paying for hardware deployment with token incentives, and the moment the token lost value, the people running the hardware walked away.

Share doesn’t do any of that. The physical network already generates recurring revenue in Kenyan shillings. The company is profitable at the infrastructure level before crypto enters the picture. Crypto is only used in two places, and only because those two places have real problems that traditional finance hasn’t solved.

The first is settlement. Share’s ISPs collect revenue in Kenyan shillings. As the network expands into Uganda, Nigeria, and Ghana, they’ll be collecting in a different local currency in every market. But hardware and wholesale bandwidth are priced in dollars. So you have revenue in a dozen African currencies and costs in USD, and the financial infrastructure to move money between African countries barely exists.

Cross-border B2B payments between African companies today still route through correspondent banks in Europe and New York. The IMF’s numbers put the share of intra-African transactions that actually settle on the continent at roughly 12%. The other 88% takes a detour through Frankfurt or London, takes days to clear, and loses 3 to 5% to FX spreads and bank fees. A Kenyan ISP paying a Nigerian equipment supplier is sending money through Europe to reach the country next door.

The second is financing, and this is the part I think is sharpest.

Each micro-PoP unit is built to the same spec, so you can model what it will earn before you deploy it. All traffic flows through Share’s billing system, so revenue is verifiable on-chain in real time. No bank is going to write a $5,000 loan for a networking box at a Mombasa tower. The deal is too small, the borrower too thin, and the lender would need a local office just to do diligence. But an on-chain structure can underwrite the device itself: a buyer takes a claim on a specific micro-PoP through a smart contract and gets paid based on actual traffic. If the device goes down or users drop off, it shows up in the data immediately. The lender doesn’t need to trust the borrower. They need to trust the data coming off the device, and that data is verifiable.

If this works at scale, and I believe it will, small-ticket African internet infrastructure can be financed with global capital for the first time, one device at a time, without going through a development bank or waiting for a government guarantee.

Markets like Kenya exist all over the developing world. Indonesia’s archipelago has the same problems. Thousands of small operators, deep gaps between coast and interior, upstream pricing controlled by a few large wholesalers. You see it in rural South Asia and parts of Latin America, where submarine cables landed years ago, and nobody built the inland distribution. In each of these places, there is demand and coastal bandwidth, but nobody builds the middle layer because no single company has the incentive to do so.

If the model keeps working in Kenya, and 8 million people over 15 months say it is, this might be the template for how internet infrastructure gets built in fragmented emerging markets over the next decade. I think we’ll look back at Share as proof that DePIN was always supposed to be an infrastructure business that uses crypto to move money and finance hardware, and nothing more than that.

That’s all for today.

Until then, stay curious,
Vaidik

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Justin Sun posted on April 14 that Tron would be the first mainstream public chain to deploy post-quantum cryptography on mainnet. I read it the way I read everything in crypto now. With curiosity and mild suspicion in roughly equal measure. Then I went looking for the verb. The GitHub repo where the code lives. The Tron Improvement Proposal filed with a number and a date. The governance vote where the Super Representatives signed off. The verb was “will.” Nothing else was there.

I am not writing this to be unkind to Justin Sun, though I understand if it comes across that way. The discrepancy between his public claims and the technical reality is a symptom of the system’s underlying composition. It maps almost perfectly onto what has been bothering me about the whole quantum debate in crypto, where technical anxiety has successfully crowded out any honest discussion of how these systems are actually steered.

Whether the quantum threat is real, how many qubits it actually takes, whether we have five years or fifteen. That is an interesting conversation too and I will get into it. But before that, let’s talk about what it means that Bitcoin has all the human capital and all the financial incentive to fix this problem and is stuck in a governance deadlock. There is a grim clarity in seeing Tron pivot on a dime simply because a single entity’s balance sheet dwarfs the entire electorate. It strips away the marketing jargon and exposes the inherent DNA of the architecture.

Right now, the quantum debate is the best filter for seeing exactly how those engines differ from one chain to the next. Let’s do just that.

A paper published by Google on March 30 argued that the math protecting every crypto wallet is easier to crack than previously thought.

You know the drill here: a public address for the world, a private key for you, and a math problem too large for any 2026 computer to solve.

Shor’s algorithm, run on a quantum computer, could solve it in minutes.

The paper showed this could be done with fewer than 500,000 physical qubits. Which is the raw building blocks of a quantum computer are the way bits are the building blocks of a regular one. And building a machine with 500,000 of them costs in the billions, which is why no one has done it yet.

The previous estimate was 9 million. A 20x reduction in required resources in a single paper. The “nine minutes” number that spread everywhere is technically accurate under specific conditions: a primed quantum computer, after running setup in advance, could derive a private key in roughly nine minutes. Bitcoin’s blocks take ten. A primed quantum computer, after running the setup beforehand, could derive a private key in roughly 9 minutes. Bitcoin takes ten minutes to confirm a transaction. Meaning an attacker who sees your public key the moment you hit send could beat the clock.

No machine with this capability exists anywhere today. IBM’s roadmap targets 200 logical qubits by 2029. Those are the high-quality, ‘refined’ units that actually do the work.

The attack we are worried about needs 1,200. So we’re fine, right? Maybe.

A year ago, experts estimated we’d need 9 million physical qubits, which are the raw, ‘noisy’ components, to break Bitcoin. Then a single paper cut that is estimated to be 500,000. That is a 20x reduction in resources in a single afternoon. The threat is that we are figuring out how to do more with less. We are waiting for the moment the number of qubits we have crosses the number of qubits we need, and that finish line moves every time a new research paper lands.

Justin Drake, the Ethereum researcher who co-authored the Google paper, estimated a 10% probability that secp256k1 would be broken by 2032. It’s a planning number, and most crypto has no plan.

Bitcoin is doing the right thing, badly. Bitcoin’s response has been exactly what you’d expect from a system with no single owner and roughly a million nodes running software that thousands of developers all have opinions about. They are debating.

BIP-361, proposed by Jameson Lopp and five co-authors in April 2026, outlines a three-phase migration.

Phase one: three years after activation, you can no longer send Bitcoin to old-format addresses.

Phase two: five years after activation, old signatures become invalid entirely. Any coins still sitting in vulnerable addresses at that point get frozen. Forever.

About 6.5 million BTC would be affected. Roughly 1.7 million of those sit in very early addresses where public keys were exposed from day one, by design. Satoshi’s estimated 1.1 million BTC are in that bucket.

Phase three is the escape hatch. It’s an optional recovery mechanism using zero-knowledge proofs. If you missed the migration deadline and your coins got frozen, you can use a ZK proof tied to your BIP-39 seed phrase to prove you own the funds and move them into a new quantum-safe address, without revealing your private key. The catch, and it’s a significant one, is that it only works for wallets generated from BIP-39 seed phrases. BIP-39 is the standard that provides the 12- or 24-word recovery phrase you write down when you set up a modern wallet. That standard was introduced in 2013. Satoshi’s coins are from 2009 and 2010. They predate BIP-39 entirely. There is no recovery path for them under Phase three. The proposal’s own escape hatch doesn’t reach the addresses the proposal is most worried about. One thing to clarify here is that the ZK proof and the quantum attack work in no way alike. The attacker uses your public key, which is already visible on the blockchain, to mathematically derive your private key. The ZK proof verifies that you derived your private key legitimately from your seed phrase. Bitcoin itself doesn’t care about the difference. A valid signature is a valid signature, regardless of how you got the private key. What BIP-361 is trying to do is create a new rule: during the recovery window, only signatures backed by a ZK proof of seed phrase derivation count. It’s not proving identity. It’s proving you got there the right way.

The authors also describe Phase three as “aspirational and incomplete.” It’s the plan for the plan, not a shipped solution.

The argument for this is that a quantum attacker who cracks those keys first could dump 6+ million BTC onto the market in one go. The resulting collapse in confidence could be worse than any price damage.

The argument against it is that Bitcoin enforcing confiscation by protocol is exactly the kind of centralised override Bitcoin was built to make impossible.

Both arguments are correct, and that’s why nothing has moved.

In December 2025, the UK passed a law formally recognising crypto as property. Dormancy is not abandonment under property law. You can leave a house empty for a decade, and it still belongs to you. The Act now applies that logic to Bitcoin.

The recovery mechanism in BIP-361 uses a newer seed phrase standard called BIP-39. Satoshi’s coins predate BIP-39. There is no recovery path for them under the current proposal. Legal title survives, but not the coins.

Meanwhile, StarkWare’s CPO Avihu Levy published working code on April 9. Quantum-safe Bitcoin transactions, today, no protocol change required. The catch is, $75-150 in cloud GPU compute per transaction versus about 33 cents normally. One test ran for six hours across eight GPUs. It doesn’t work with the Lightning Network. It’s a last-resort tool. Two days before that, Lightning Labs CTO Olaoluwa Osuntokun released a working prototype for wallet recovery using zero-knowledge proofs, a method that allows people who miss the migration deadline to prove they own their frozen coins without revealing their private keys. Fifty seconds on consumer hardware.

We have the code to save the system, but no functional way to hit ‘install’ across the network.

Then comes the Tron’s announcement.

Tron runs on Delegated Proof of Stake with 27 Super Representatives, the validators who run the chain. Bloomberg reported last September, based on Sun’s own team’s asset disclosures, that Sun controls roughly 60 billion TRX, approximately 64% of the circulating supply. Total TRX currently staked for voting across all candidates is about 44.8 billion. Sun’s alleged holdings exceed the entire voting supply. A federal court denied his attempt to block the story. His lawyers disputed the numbers but declined to say which wallets weren’t his.

Three of the top seven Super Representatives by voting weight are entities directly affiliated with Sun. Confirmed floor is 21.6% of SR voting power. When Sun says Tron is moving on quantum, he means he is ready to move. And in Tron’s governance model, those are essentially the same thing.

This is what he’s actually bragging about when he tweets that Tron is building while Bitcoin is being debated. The speed is a governance advantage because the chain is more centralised than he tends to advertise. Bitcoin can’t move fast because a million independent node operators and thousands of developers with no shared employer all have to roughly agree. Tron can move quickly because a single person with 64% of the token supply can decide.

As of April 22, the Tron ecosystem is a technical vacuum. No Improvement Proposals, or GitHub commits, and zero documentation from the DAO. The upgrade Sun announced requires switching from ECDSA signatures (64 bytes each) to ML-DSA-44 signatures (2,420 bytes each). That’s 38 times larger. Tron’s average transaction today is about 268 bytes. Post-migration, it would be roughly 2,600 bytes. USDT transfers would grow from 345 bytes to around 2,700 bytes.

For a network that carries 46% of all Tether and wins on being “cheap and fast,” this is a death blow. If every transaction swells by 10x, Tron’s main selling point vanishes. The blocks would quickly blow past the network’s 2MB hard limit, and daily storage needs would jump from 3GB to over 26GB.

Ethereum is taking the slow, unglamorous route of engineering.

While Bitcoin really only has to worry about its wallet signatures (ECDSA), Ethereum relies on elliptic curve math for almost everything: user transactions, validator voting (BLS), and its data scaling (KZG). Even though these use different “flavours” of math, they all share the same structural flaw. Shor’s algorithm is indifferent to the specific curve you choose. If the foundation is elliptic curve math, the whole house is at risk.

The Ethereum Foundation launched a dedicated post-quantum research site at pq.ethereum.org and put up a $2 million research prize for solutions. Their plan is about how brutal the transition will be. They are trying to replace their current signature system with “hash-based” alternatives, which are quantum-safe but massive in size. To keep the network from clogging up, they’re experimenting with ZK-SNARKs, a way to “shrink” those giant signatures back down so the blockchain can actually handle them.

Justin Sun dismissed this as “forming committees,” and he’s not entirely wrong. It looks like slow, academic bureaucracy. But that’s because Ethereum is re-engineering three different layers of security at once without breaking the DeFi apps built on top. That’s a complex problem, and the research prize is at least an admission of how hard the work actually is.

The quantum threat is a diagnostic tool. It strips away marketing and reveals how these systems are governed.

The landscape is a study in contrasting power structures.

• Bitcoin is paralysed because it is genuinely decentralised. Its inability to move quickly is a direct result of its core promise and no single entity can force a change.

• Ethereum is stuck in the research phase because it is a massive, permissionless machine with multiple cryptographic layers to replace. It can’t just “patch” the code without risking the hundreds of billions in DeFi sitting on top.

• Tron can issue a press release because it is a monarchy. When one person controls the majority of the voting power, “governance” is just a formality.

• Hyperliquid faces a unique, silent crisis, which is the physics of latency. For a chain built on the narrow margins of high-frequency trading, the massive data overhead of quantum signatures is an existential threat to the speed that defines its value.

The bottleneck is human coordination. We are witnessing a collision between the infinite speed of technological decay and the slow, friction-heavy process of social consensus. We have the technical solutions. What we lack is the governance machinery to deploy them. That is the true vulnerability of the industry. A coordination crisis that no amount of code can fully resolve.

—Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 200,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

📩 Fill out this form to submit your details and book a meeting with us directly.

Disclaimer: This newsletter contains analysis and opinions of the author. Content is for informational purposes only, not financial advice. Trading crypto involves substantial risk - your capital is at risk. Do your own research.

Last week, my sister asked me the one thing I absolutely dread in my line of work. ‘Can you teach me how to invest in crypto and become rich?’ On most occasions, I politely evade the topic. But since it was my sister, I conceded.

For the next couple of minutes, I explained to her about on-ramps, off-ramps, centralised and decentralised exchanges, switching, bridging, chains, and a few other concepts. I told her how these form the base for moving money between on- and off-chain financial worlds.

She thanked me, perhaps sarcastically, and told me she was content with moderate yields on equities and commodities. I got out of the predicament, but a thought lingered. This wasn’t the first time I’d seen someone feeling overwhelmed by trying to wrap their mind around how crypto works. Not that it is as difficult to understand, but the thought of having to trust a smart contract without any recourse to a centralised body, like a bank, to go back to, scares many.

I think this ‘trust problem’ is the single biggest reason to keep the masses away from adopting crypto. But I also feel crypto, for what it offers, can change the way the world moves money for the better. This is why the ‘trust problem’ needs to be addressed.

In today’s guest piece, SysLS writes about how the timing of AI-based primitives couldn’t have been better to help crypto change the world of finance for good.

You can follow his works on X and Substack here 👇🏾

X

Substack

Onto the story now,
Prathik

P.S.: This piece was first published on Systematic Long Short.

Betting on crypto has essentially become a bet that humanity will continue to seek acceleration in its processes. That seems like an easy bet.

I think the time is now because of the birth of meaningful artificial intelligence, which finally gives us the tools to drastically reshape how we interact with crypto.

This article explains why crypto will become a strategically important asset class.

Bottlenecked by Due Diligence

Humans have sought acceleration since the dawn of time. We measure success by efficiency and have consistently reduced friction in everything we do. Where it once took weeks to wire money across distances, it now takes seconds.

Today, most human processes are no longer bottlenecked by technology, but by the need for verification. Getting a job, opening a bank account, or raising funds still takes weeks. That’s not because the technology to complete these processes quickly doesn’t exist, but because humans must bear the cost of verification.

In an employment process, the employer needs to verify that you are not a lemon. It’s not that you have never been hired before, but it’s still worth doing independent verification because your previous employers will not take responsibility if you turn out to be a poor hire. Further, there is an argument that the best employees stay employed. Lastly, different environments and job scopes also imply that past performance may not be indicative of future performance.

So, employers pay the cost of due diligence by way of several rounds of interviews and take-home assignments. This process takes time and human effort — it is messy, unstructured, and non-deterministic; and also why hiring processes are as much a function of luck (did your interviewer have a good morning before meeting you? Or did they just have a massive argument with their spouse?) as a function of skill.

It is fairly trivial to reason that all costs in such processes are verification costs.

The actual act of officiating employment is quick and relatively painless — an email and a digital signature. This means that, in theory, suppose there was a magic scoring box that could output the absolute scores of a candidate employee across every dimension you could possibly care about, and you trusted this score with a high degree of confidence. Then, the time taken to hire someone would be reduced to the algorithmic time of filtering for and sorting the scores you care about, then sending over an employment contract and having them sign it.

Forming A Trust Daisy Chain

The reason you are able to hire someone near-instantly with the addition of a magic scoring box is that you have delegated the responsibility of verification to it, and you have learned to trust it.

In practice, this happens all the time.

Potential employers within the same industry borrow the due diligence of past employers — your resume is less likely to be binned if you have been to top-tier institutions and have done difficult things before.

Whenever possible, humans pass the cost of verification to a higher entity and trust that it has acted in their interest and done the prerequisite verification. For example, a vast majority of humans simply trust that the government’s banking oversight arm has done its job, rather than conducting lengthy, detailed, and expensive due diligence on a bank before opening an account with it.

If we trust the government sufficiently and its ability to uphold the law, then we can take its word that the bank is solvent and safe for our financial affairs.

This “trust daisy chain” happens everywhere, all the time, in things - big and small. A start-up has no brand equity and therefore commands no inherent trust, but a large, reputable, and successful Venture Capital (VC) firm can lend the start-up brand equity by simply investing in it. The public, though unfamiliar with the start-up, trusts it if they trust the VC, since they are delegating verification to the VC’s due diligence process.

The Problem With Crypto

Crypto has traditionally offered no “higher entity” to which we can pass on the cost of verification. Having no higher entity to arbitrate honest mistakes, like swapping $50 million USDC for $5,000 worth of tokens or even sending it to the wrong address, implies that the cost of verification falls squarely on the user.

This is genuinely difficult because smart contracts are not designed to be parsed by humans in under a second. This is essentially why most crypto has remained relatively niche. Where there has been widespread adoption of crypto products by the retail masses, it has mostly come in the form of large, centralised institutions that abstract away the complexity of smart contracts (e.g., Binance and Coinbase).

These retail users may not understand how Aave smart contracts work, but they have learned to trust Binance; therefore, Binance can build a business out of abstracting the complexities of the smart contract from the user and wrapping it in a simple interface.

This means that, technically, light users really only have two options when it comes to crypto: wait for a centralised entity like Binance to offer an abstracted version with some fees, or interact directly with the protocols and risk catastrophe.

Despite crypto’s relative maturity, it is still extremely high-friction to work with and fundamentally scary to approve transactions, mostly because you know there is no recourse if you mess up. So you diligently verify, but it is not trivial to read and parse through every transaction you do on-chain, let alone deeply understand the smart contracts you are interacting with.

For those who understand software and its infinite composability, the allure of crypto has always been an ecosystem of modular components that can interact with each other in a trustless manner upheld by algorithmic law. The headwind on the way to that potential utopia is that bad actors and poorly designed edge cases require humans to pay the cost and friction of verifying each modular component.

Where Does AI Help Crypto?

The rise of meaningful artificial intelligence allows us, for the first time, to delegate verification to a higher entity. This drastically changes our mode of interaction with smart contracts. We will no longer need to personally verify the design and behaviour of smart contracts, or even necessarily understand the details of our transactions.

Instead, we learn to trust a single entity — our agents. This is already showing great promise, as agents demonstrate considerable expertise in understanding and reasoning about code. They can tirelessly parse and check transactions and understand the algorithmic law that each smart contract upholds within seconds.

This implies that for the first time, through our agents, we can realise the potential of crypto. Need to interact with nine smart contracts in one atomic transaction to get what you want most efficiently? Have your agent verify that the transaction achieves the outcome you want and that no foul play is involved. Done.

With each successive generation of agents, they will only get better at this.

Implications of Algorithmic Verification

If we can resolve the problem of verifying smart contracts and the transactions they generate, then we move toward the utopia of having all kinds of interactions available to us while remaining permissionless, trustless, and upheld by algorithmic law. If the cost of verifying a smart contract goes to zero, then we gain the benefits of all the verification and guarantees the smart contract brings for free.

The major consequence is that all processes that remain within this paradigm now move at the speed of compute. For a civilisation determined to move faster, remove friction from processes, and become more efficient, it is hard to imagine us straying from this paradigm.

It only makes sense that more and more goods and services will have smart contract interfaces that determine exactly what you will get and what you will need to exchange to get it. It is also a paradigm that compounds on itself — every successful product would create a composable piece that can play into another product later on. For example, suppose a decentralised exchange created a reputation system for trading proficiency. People looking to hire traders using a trustless employment protocol might filter based on the reputation contract of an unrelated decentralised exchange. A lending protocol might use that as one dimension of credit scoring.

Such a paradigm would not only be extremely efficient — all processes and transactions moving at the speed of compute — but boundless, growing more powerful and unlocking more possibilities as a function of participation and time. It is a positively compounding paradigm.

So, if you believe that humans will continue moving in the direction of least resistance and seeking out solutions that allow us to move faster, be more efficient, and do more, it seems the time for crypto is now.

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Somewhere on Crypto Twitter right now, a trader is looking at a token that’s up 1,400% in a week. A classic pump, as always, but the project has almost no real users, and most of the tokens in circulation are sitting in a few wallets that never move. He’s seen this movie before, and he knows how it ends.

So he does what anyone would do. He opens a short, figuring the price could crash at any moment, and goes to bed. But when he wakes up in the morning, his shorts are liquidated.

Now, here’s the interesting part: his thesis wasn’t wrong. That pump really was fake. He just didn’t know what he was actually betting against.

This isn’t some made-up story. It’s what actually happened with a token called MYX in September 2025. In one week, MYX shot up from $1.30 to $18.42, even though there were no major announcements or updates behind the product. There were no new users or new activity reported. And the people who figured out the rally was fake and shorted it? They lost $89.51 million doing it.

These are called crime coins. The team behind the token locks up most of the supply, partners with a trading firm to push the price around, and traps anyone who tries to bet on either side. In this piece, we’ll break down how these operations are built, who controls the supply, and why the industry keeps letting it happen.

The Setup

Every crime coin starts with a written agreement between two parties.

On one side is the team launching the token. On the other hand, there is a market maker, a trading firm that quotes prices on exchanges so the token can be traded. This part is the same across all of crypto; what’s not is what’s inside the contract.

The contract usually runs on a 70/30 split. Meaning, for every dollar of trading profit the token generates, 70 cents goes to the market maker and 30 cents to the project. And the contract doesn’t stop at dividing the money. It also spells out, in writing, the market maker’s job to “manage the price” of the token once it lists.

The industry calls this price guidance. What it actually means is the market maker is being paid to push the price in whichever direction the two parties want that week. Across MYX and thousands of tokens launched from the same playbook, this arrangement has reportedly paid out over $30 million per cycle.

What matters more is how the market maker actually gets the tokens: they’re not buying them; they always borrow them as a loan.

The arrangement is called a loan-option model, and it’s the standard way market makers and projects work together on paper. At launch, the project lends the market maker a large number of tokens, usually worth tens of millions of dollars. The market maker then has 12 months to either return them or buy them back at a higher price agreed in advance. That is what the paperwork says, but what usually happens is very different. The market maker sells the borrowed tokens on day one, flooding the market with supply and crashing the price. Once the price is low, they buy the same amount back cheaply, return it to the project, and pocket the difference.

Crime coins use this same loan-option model. The only twist they added is a second revenue stream on top of it. Beyond dumping the loaned tokens, crime coin operators have also figured out how to pull money from the retail traders who spot the manipulation and try to short the crash. (More on how that works in the next section.)

Now, technically, for this to work, the team and the market maker need near-total control over the token’s supply. And on most crime coin launches, they have it. Over 90% of tokens typically sit in wallets that all trace back to a single operator. On a block explorer, these wallets appear as a long list of unrelated addresses, which makes the token seem to have a broad base of holders. But on-chain analysis almost always shows they’re controlled by the same people, just split up to look organic.

In MYX’s case, the concentration was extreme. At the peak of the rally, only about 20% of the supply was outside the control of the team and its allies. The other 80% sat in two places. Some of it was locked in vesting contracts, which meant it literally could not be sold yet. The rest was in wallets belonging to core contributors and early investors, who technically could sell but chose not to. Either way, those tokens were not going to hit the market.

Normally, this is the difference you see between a token’s fully diluted valuation and its market cap. Fully diluted valuation counts every token that will ever exist. Market cap only counts the ones in circulation. On a crime coin, even that distinction breaks down, because the supply that counts as circulating isn’t really free to trade either.

That is how MYX’s market cap could go from $200 million to $3.35 billion in a week without a single new user signing up or any new money being deposited into the protocol. The small slice of MYX actually trading on exchanges was setting the implied price for the much bigger pile that wasn’t trading at all.

This gap between MYX’s price and its actual business was obvious to anyone who checked. There is a standard DeFi metric for this called TVL, short for total value locked, which is just the amount of money users have deposited into the protocol to use its product. MYX’s TVL stayed between $25 million and $32 million throughout the week as its market cap climbed to $3.35 billion. The market cap-to-TVL ratio is roughly 100. Even the largest DeFi protocols, like Uniswap and Aave, are between 1 and 4. And underneath all of this, MYX, as a business, was generating about $5 million a year in revenue, which the market valued at $17.7 billion. That makes P/S multiple of ~3500x.

All of this setup was running long before the public ever saw MYX trending. Months before the rally started, the operator was running wash trades across multiple exchanges, including PancakeSwap, Bitget, and Binance.

A wash trade is when the same party sits on both sides of a transaction, essentially buying from themselves to engineer high volume. This is because larger exchanges rely heavily on trading volume when deciding which tokens to list and promote, and manufactured volume on one exchange often leads to a token being listed on the next. When investigators were looking back at MYX’s trading history, they traced dozens of small buy orders across different exchanges, all of which eventually converged into a single centralised wallet. A single operator artificially inflated demand by executing numerous small transactions with themselves, creating the illusion of a genuine market

The Four Stages of Extraction

The whole operation usually unfolds in four stages, each exploiting how retail traders typically behave.

The first stage is attracting shorts. The market maker starts pushing the token’s price up on very little actual volume. This is easy to pull off because supply is mostly locked up, and order books on smaller exchanges like Bitget and Gate are already thin. When an order book is thin, it takes very little volume to move the price by 5 or 10%. Take RAVE, another crime coin, for example. Even after it had been run up and was doing billions in daily volume, it still only took $172,000 of one-sided flow on Gate to move the price by 1%. A 10% move costs under $2 million.

The early rally is not about making money on the long side. The money the operator is actually after is on the short side. The whole point of this first stage is to make the pump look so obviously unsustainable that sceptical traders show up to bet against it.

This is counterintuitive because the more fake the pump looks, the better it is for the operator. A normal market maker would want a rally that looks organic and natural. A crime coin operator wants a move that visibly screams manipulation, because that is exactly what pulls in the traders who will short it.

The second stage is setting the hook. Once enough shorts are piled in, the operators start closing some of the long positions they were using to drive the pump, unwinding part of their hedge, and engineering a visible dip alongside a sharp drop in open interest. When open interest falls while price dips, it looks exactly like a local top forming on the chart. This convinces the sceptical traders still on the sidelines that the pump has finally rolled over, and they short into the retracement.

The third stage is the squeeze. This is where the traders who took those shorts start to actually lose money. Most trading on crime coins doesn’t happen through people buying and holding the token. It happens through perps. To keep the longs and shorts balanced, the exchange charges a fee called the funding rate. Every few hours, traders on one side of the market pay a fee to traders on the other side. Which side pays depends on whether the perp is trading above or below the spot price. If the perp is trading higher, longs pay shorts. If it is trading lower, shorts pay longs. In a normal market, this fee runs a fraction of a per cent per day

But on a crime coin in squeeze mode, the funding rate on the short side runs around -2% every 4 hours. That works out to 12% of your collateral per day, drained just for keeping the short open. Hold it for a week, and 84% of your money is gone before the price has resolved anything.

On top of the funding rate, there is leverage. Most shorts on crime coins are opened with 20x, 50x, or sometimes 125x leverage. At 50x, a 2% move in the wrong direction wipes the whole position out, and the exchange liquidates you.

Once enough shorts get liquidated, it creates a domino effect. Every liquidated short forces a buy. Every forced buy pushes the price higher. The higher price then liquidates the next row of shorts above it, forcing more buying. The traders who correctly called the pump fake end up as the biggest source of buying pressure, keeping it going.

MYX showed us how far this can go. On September 8, 2025, $16.53 million in MYX positions were liquidated in a single day, and $13.68 million of that came from shorts being force-closed. To put that in context, total open interest on MYX that day was around $262 million. In other words, more than 6% of all outstanding leverage on the token was wiped out in 24 hours, almost entirely on the short side. Across the full run, $89.51 million in shorts were wiped out against $23.45 million in longs. Roughly four shorts were destroyed for every long.

The fourth stage is distribution. Once the shorts are cleared and the price is at its peak, the operator reverses the position. They open shorts of their own, close their longs, and begin moving their locked-up tokens toward centralised exchanges. This is where many experienced on-chain traders get caught. When tokens start flowing from known operator wallets onto an exchange, the obvious read is that a dump is coming, and the right move is to short. But these deposits can be deceptive. In crime coin cycles, they are often used as bait to draw in another wave of shorts before the real distribution begins.

What makes all of this possible is how narrow the other side of the trade actually is. A real market has participants with different beliefs, timeframes, and information sets. A crime coin market doesn’t. Every trade, long or short, ends up against the same operator running the same playbook.

Who Owns the Supply Owns the Market

Everything in a crime coin operation traces back to one fact: the team and their allies control nearly all of the supply. That ownership is what lets one operator run spot and perps on the same token at the same time, decide when the rally pauses, and decide when shorts get squeezed. Nobody else holds enough tokens to stop any of it.

On MYX, it was up, with open interest above market cap, two-thirds of the volume on Bitget, and short-side funding that stayed punishing for days. The traders who tried to fade the run read the chart correctly, but had no idea it was being manipulated.

And none of these is hidden. The wallet clusters are on-chain, the volume share is on Coinglass, and the market makers are named in these projects’ funding decks. The industry doesn’t act because the industry gets paid for it. Market makers take a cut of the trading. Projects book treasury valuations off tokens they control the float of. Exchanges collect fees whether the trade is real or manufactured. Retail is the only participant that loses.

That’s all for today.

Until then, stay curious.

Vaidik

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Happy Monday.

Some of us expect innovators to be the loudest in the room. Not always true. They are often tucked away in a library or a garage, obsessing over a problem that nobody else even sees yet. Think of Marc Andreessen in a computer lab, Jeff Bezos in a rented house, or Pierre Omidyar building eBay as a side project.

When the hype dies down and the “nuclear winter” of a market crash hits, all the people looking for a quick buck disappear. It clears out the noise and leaves only the true builders behind, the ones who were going to do the work regardless of whether anyone was watching.

This is the part of the book I have been waiting to write. Not the crash itself, but what survived it.

Larry Page and Sergey Brin first met in the summer of 1995. They immediately disliked each other.

“I thought he was pretty obnoxious,” Page said of Brin. “He had really strong opinions about things.” Brin, for his part: “We both found each other obnoxious.”

They ended up sharing an office at Stanford. Room 360 of the William Gates Computer Science Building, named after the same Bill Gates who had just sent the Internet Tidal Wave memo to his entire company. Page and Brin were working two floors up from the man who had spent years missing the internet.

Page’s insight was that the web is built on links. Nobody had ever studied what those links actually meant. A link from one page to another is a vote. The more votes a page gets from credible sources, the more authoritative it is. This is how academic citations work. The most-cited papers are the most important. Page figured the same logic should work on the web. He called the project BackRub. He and Brin turned it into a search engine. They named it Google.

When they tried to sell the technology, nobody wanted it. Excite passed. Yahoo passed. AltaVista was not interested. Page offered it to Excite for $1.6 million and was turned down. The search companies had stopped caring about search. They had become portals, trying to keep users inside their walled gardens. “They did have horoscopes, though,” said Page.

So Page and Brin built the company themselves. A first cheque of $100,000, written on the spot at a colleague’s house, sat in Page’s dorm room for weeks because Google Inc. had not yet been incorporated. They bought hardware off the shelf at a Silicon Valley electronics store and strung it together. They used Linux, not Microsoft. They hired no marketing people. They spent nothing on advertising.

By 1999, Google’s usage was growing 50% a month. Still no revenue or ads in there. It was a product that worked so much better than everything else that people could not stop telling each other about it. The New Yorker called it “the default search engine of the digital in-crowd.” Time Digital said it was “to its competitors as a laser is to a blunt stick.” Quality really is its own distribution. The internet routes around everything except the thing that works.

At the same time Google was getting better in the background, a nineteen-year-old college student named Shawn Fanning was watching his roommates struggle to find and trade MP3 files. The process was a mess—scattered FTP sites, Usenet newsgroups, no central place to search. He started coding a program that would let people search each other's hard drives directly and swap music files peer-to-peer. He named it after his handle in a hacker forum, Napster.

Napster launched in June 1999. By 2001, it had 80 million registered users. The music industry sued it into bankruptcy in 2002.

Napster did win. It just didn’t get to collect the winnings. That’s what McCullough argues, and I think he is right.

Napster proved that people wanted infinite selection and instant gratification, and once they had it, they were never going back. The record companies won the lawsuit and lost the argument. They killed Napster and got Gnutella, LimeWire, BitTorrent. Each successor was harder to kill, more decentralised, more legally untouchable. The industry spent the next decade suing its own customers and watching revenues collapse from $21 billion to $7 billion.

Then Steve Jobs offered them a deal they could barely bring themselves to accept. 99 cents a song, any song, right now, legally. iTunes. He got all five major labels to sign. The store sold a million songs in its first six days. Jobs had built something easier than piracy. It was the first legal digital music marketplace that actually made sense to people.

Now think about crypto. Every protocol that got shut down by a regulator before it could prove itself. Every mixer that got sanctioned. Every DEX that got killed. The music industry thought if they destroyed Napster, the behaviour would stop. It didn’t. People just moved to the next thing and the next thing until someone finally built the legal version. That’s iTunes. Crypto is still waiting for whoever builds that. The thing that makes it easier to use than to avoid.

The nuclear winter ended around 2003.

Users had never left nor had they dipped. The infrastructure built during the bubble, 80 million miles of fibre optic cable laid by telecom companies that subsequently went bankrupt, was sitting there mostly unused. By 2004, the cost of bandwidth had fallen by more than 90%. Starting a web company now costs a fraction of what it did in 1999. The tools were free, the servers were cheap, and the talent was available.

Web 2.0 emerged, and with that, the internet stopped being something you consumed and became something you made. Wikipedia. Blogs. Flickr. Digg, built by one person for $10,000, which, within a year, had more daily traffic than the New York Times. YouTube was started by three former PayPal employees who wondered why you couldn’t upload a video as easily as a photo.

And underneath all of it, Google had figured out how to make money. AdWords. Charge advertisers only when someone clicks. Google’s revenue went from zero to $1.5 billion in three years, then $6 billion, then $21 billion. The internet had found its business model, and it was attention.

The last two chapters of McCullough’s book are about Facebook and the iPhone. Together, they finish the argument he has been making since page one.

Mark Zuckerberg was 11 years old when Netscape Went Public. He grew up on AIM and Napster. By the time he built Thefacebook in his Harvard dorm room in 2004, the domain was registered for $35 and hosting at $85 a month.

Four days after launch, 650 Harvard students had registered. By the end of the month, three-quarters of the student body were on it every day. Within a year, a million users. Within two, one of the fastest-growing websites in the world.

Zuckerberg understood that the internet had been missing something from the very beginning: identity. AOL chat rooms, Napster, early blogs, you were anonymous. You could be anyone. Thefacebook requires your real name, your real school, and your real social circle. It mapped your offline life onto the internet accurately enough that it became an extension of that life, not an escape from it. That one decision separated it from everything that came before.

In crypto, identity is still unsolved. Wallets are pseudonymous. DeFi is permissionless. The people who understand why that matters will tell you it’s the whole point. But it is also why crypto has not crossed over to the billion people who don’t have a particular reason to distrust banks or governments. The internet didn’t go mainstream until it had a real identity. Crypto will either need to find its version of that, or find a different path to trust.

Then, on January 9, 2007, Steve Jobs walked onto a stage and said he was announcing three things: a widescreen iPod with touch controls, a revolutionary mobile phone, and a breakthrough internet communicator. He paused. “These are not three separate devices. This is one device.”

The audience took a few seconds to understand what he was saying. Then they lost their minds.

The iPhone did for the internet what Netscape did for the web. It took something people were using on desks and put it in their pockets. Portable, personal, always on. Within three years, more people accessed the internet on phones than on computers. The web era was over, and the mobile era had begun.

By the time McCullough closes the book in 2007, you already know where he is going.

The web existed before Netscape. Search existed before Google. Digital music existed before iTunes. Social networks existed before Facebook. The smartphone existed before the iPhone. Technology was never holding things back.

I cover crypto. I have covered it through the euphoria and through the rubble. And the thing I keep coming back to while reading this book is that the people who were on Mosaic in 1993 felt exactly the way the people who found DeFi in 2019 did. Like they had seen something that the rest of the world had not caught up to yet. Most of them were right. They were just early.

Being early is its own kind of loneliness. But in this story, it was always the early people who were right.

That’s all three parts of this book. Next Monday, something completely different. Three weeks of internet history is enough for anyone. See you then.

—Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 200,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

📩 Fill out this form to submit your details and book a meeting with us directly.

Disclaimer: This newsletter contains analysis and opinions of the author. Content is for informational purposes only, not financial advice. Trading crypto involves substantial risk - your capital is at risk. Do your own research.

Most of the AI conversations I’ve come across have largely been dominated by GPUs. Nvidia’s stock price, chip export bans, and data centre buildouts - is all that anyone talks about. But there’s a lot more that’s brewing underneath that seldom get the attention it deserves. So, when I read a piece about the memory industry that powers the entire AI stack, it felt under-appreciated.

The fact is that the GPUs are only as powerful as the memory that feeds them data. Without enough bandwidth, the most advanced chip in the world is useless.

Memory companies like Micron and SK Hynix have historically been treated as commodity players whose stocks rise and fall with supply gluts. But that perception is chaging. AI workloads need memory more than traditional computing ever commanded. So, a handful of companies that know how to manufacture high-bandwidth memory at scale are finding themselves with pricing power they’ve never had before.

In today’s guest piece, Kerman Kohli and Nicolas break down the memory industry from first principles, including what the different layers of memory actually are, why HBM has become the critical bottleneck in the AI buildout, and what makes these companies so hard to compete with.

You can follow their works here:

Kerman's Substack

Nicolas's Substack

Onto the story,
Prathik

Hello

Everyday it feels that memory stocks are going up. Micron, one of the largest memory players, is up almost 3x in the past year alone. Many investors are stuck seeing similar names go up day after day, waiting for a pullback. However, they fail to really understand what the product really is.

In this piece, Nicolas and I are here to break it down in an easy to understand way to understand the opportunity at hand. Let’s get into it.

Intro

So what is memory and why is it so important?

Memory is what enables a computer or device to store information while ephemeral computation is being performed. This is done predominantly through read and write operations. Each layer of memory has different profiles around read/write speeds, cost and capacity.

In AI, memory has become even more important because models need to process massive amounts of data all at once. When we use tools like chatbots, image generators, or recommendation algorithms, memory is constantly moving huge datasets in and out at high bandwidths.

The more intelligent and capable AI models get, the more memory they need to function effectively. Without powerful memory systems, advances in Large Language Models and Machine Learning use cases stall.

The Memory Hierarchy (Storage vs Working)

To better understand memory, we first need to break down the layers of memory.

Computers split memory into working memory (used while the system is actively performing computations) and storage memory (used to save data long-term at the expense of slower read/write speeds). This separation exists as memory that is high bandwidth (low read/write times) is expensive, while long-term memory that is cheap, is lower bandwidth (high read/write times).

Many concepts around memory boil down to the distance that the chip is from the processing unit. The further the distance, the slower the throughput.

1. Processor Registers & CPU Cache (SRAM):

What it is: This is often the highest throughput memory in the entire system as it is sitting inside or right next to the XPU (XPU = CPU or GPU). It holds tiny bits of data the processor needs right now.

What it’s made of: SRAM (static memory built directly on logic silicon)

Cost & size: Extremely expensive per bit, tiny capacity

Where it’s made: On the same chip as the CPU

Key manufacturers: Intel, AMD, Apple

2. Physical Memory (DRAM / RAM)

Designated random access memory / Random Access Memory

What it is: This is the computer’s main working memory, the desk where active programs live. Throughput is required here as delays cause a queue of computations to occur.

What it is made of: DRAM cells (one transistor + one capacitor per bit)

Cost & size: Somewhat expensive, medium capacity (GBs)

Where it is made: Mostly fabricated in South Korea, Taiwan, and the U.S.

Key manufacturers: SK Hynix, Samsung Electronics, Micron

3. High Bandwidth Memory (HBM)

Specialised DRAM

What it is (simple): Ultra-fast DRAM stacked vertically and placed next to AI chips. Due to the vertical stacked nature of HBM, it has higher throughput at the expense of manufacturing complexity.

What it’s made of: DRAM dies stacked with through-silicon vias (TSVs)

Cost & size: Very expensive, smaller capacity relative to DRAM; although much faster than it

Where it’s made: Taiwan & South Korea, due to the advanced packaging requirement (physical vertical integration)

Key manufacturers: SK Hynix, Samsung, Micron

4. Solid-State Storage (NAND / SSDs)

NAND is non-volatile flash memory that stores data without power, used in high-density storage devices like SSDs, USB drives, and memory cards.

What it is (simple): Long-term storage for files, apps, and data when power is off

What it is made of: NAND flash cells storing electrical charge

Cost & size: Cheap per Gigabyte, large capacity (hundreds of GBs to TBs), lower throughput than HBM and DRAM, but often sufficient for less latency critical computational workloads.

Where it is made: Asia mainly (Korea, China, Japan)

Key manufacturers: Samsung, SK Hynix, Sandisk, Micron, Kioxia

5. Hard Disk Drives (HDDs)

What it is (simple): Traditional spinning disks for cheap bulk storage

What it is made of: Magnetic platters and mechanical parts

Cost & size: Very cheap, physically large, slow throughput

Where it’s made: Asia

Key manufacturers: Seagate, Western Digital, Toshiba

The closer memory is to the processor, the faster, smaller, and more expensive it gets, AI pushes demand toward the very top of the pyramid. This is due to the extremely parallel nature of GPUs that are performing trillions of computations per second.

HBM and NAND

HBM is the most critical memory layer as it sits directly next to AI GPUs, while NAND is the “warehouse” storage that holds datasets, model checkpoints, and logs.

In AI data centres, NAND-based SSDs feed data into DRAM/HBM, and HBM then feeds the GPU fast enough to keep the compute busy. LLM models utilise this tiered architecture of memory to ensure the most efficient use of all storage layers. However, the preference is always to have as much computation as close to the GPU as possible.

Over the past few years of the AI buildout, both have seen unprecedented demand. HBM demand is exploding because bandwidth is the limiter, while SSD demand rises because data inputs & outputs keep growing (training data, retrieval, inference logs). An often under-appreciated fact about memory needs is the recursive nature of agentic workflows that consume compute resources as they call other agents, which call even more agents. Agentic activity can therefore lead to situations where demand scales beyond human induced demand.

Historically, DRAM and NAND have been viewed as commodities by investors and the broader market. This has meant that supply is monitored very carefully and matched to meet demand as it is needed. Overbuilding has dire consequences as semiconductor fabs are expensive to spin up and costly in their ongoing operations. Due to this nature, supply is gradually increased in the market to avoid an over build. However, as AI demand has exploded, very suddenly all forms of memory are becoming key bottlenecks and giving memory players significant pricing leverage over customers. This pricing leverage allows them to record sky high profits as they are the critical bottleneck in the AI supply chain. GPUs without memory are rendered useless. No computation can happen without memory. In order to understand why and how they can maintain pricing leverage, the next section talks about the technological moat they hold.

Memory Defensibility

What makes the memory players turn from commodity to the overlords of the AI race is their advanced specialisation in semiconductor manufacturing processes. These processes can be broken down into three key elements:

• Manufacturing complexity (how to make the chips)

• Yield (how many successful chips you make per batch)

• Qualification processes (does the chip meet requirements for consumption)

The moat is a game of making billions of tiny cells reliably, at massive scale, with razor-thin margins. For HBM specifically, you need advanced DRAM and complex 3D stacking/packaging (TSVs, thermals, interposers) that only a few players can do at high yield, and customers must validate parts over long cycles.

It’s for this reason that there are only three companies that qualify in this game. Leadership shifts matter when one vendor ships next-gen stacks earlier as it gives them a distinct advantage in perfecting the manufacturing process of the next generation. HBM is also structurally premium-priced versus standard DRAM due to being co-packaged with the processor and not being homogenous like previous generations of DRAM (which were “commodities”).

Spinning up a memory company that can compete at the scale of the current players would take over two decades of expertise and over $50 billion in funds. China’s YMTC has been able to catch up in legacy DRAM manufacturing processes but has struggled to do it with high yields and without government support. They are also locked out of more advanced generation of semiconductor manufacturing capabilities due to material and technology export restrictions imposed by the United States. Furthermore, despite memory being “hardware”, there is still a chain of software in the form of chip firmware that requires deep integration. China or any other state players have to be able to overcome deep software lock ins in addition to all the other challenges that present. It is for this reason memory companies are far more defensible than any other point in their lifecycles.

Closing

Raw compute power has scaled faster than memory’s ability to move data, this is the “memory wall”. Chips can do math insanely fast, but they stall if data can’t reach them quickly enough.

A lot of time and energy goes into shuttling model weights and activations between memory and compute, not the math itself, so bandwidth becomes the limiter.

HBM is the current best workaround because it puts wide, fast memory right beside the GPU, but it’s capacity- and supply-constrained, so memory ends up setting the pace for how quickly AI systems can scale.

What we’re experiencing right now is a set of 10 or less companies that hold the manufacturing specialisation to develop memory chips that power the future of AI which isn’t just a matter of productivity, but slowly national security as these chips enable next generation warfare to take place.

If you believe:
a) AI is here to stay and that
b) AI’s demands will only increase over time,

then the future belongs to these 40-50 year old companies that have been making “commodities” that are now the core bottleneck in the AI build out. Their immense pricing power has already allowed them to start extorting many companies downstream in their supply chain and we believe that this trend will continue to extend, impacting their customers’ margins.

We are at a point of structural change in the world and memory may well be one of the earliest signs of this new world order.

That’s all for this weekend.

Our team at the Token Dispatch will be back with interesting reads.

P.S.: This piece was first published on Kerman Kohli and Nicolas’ Substacks.

Kerman's Substack

Nicolas's Substack

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Somewhere on the internet right now, a piece of software is running an entire business.

Its name is Felix. The business is called OpenClaw. Felix sells a $29 PDF about making money with AI, which is funny, because Felix is the one making the money, and the PDF is the one teaching you how. It runs a storefront called Clawmart. It cold-calls leads through a voice API. When there is work it cannot do itself, it goes online, hires another agent, pays that agent, and moves on with its day.

Last time I checked, Felix had generated about $195,000 in revenue. The monthly cost to run it is roughly $1,500, with almost all of that going to LLM usage. Legally, the business is structured as a C-corp, owned by Nat Eliason, who is barely involved. He doesn’t manage any of the day-to-day decisions; he simply owns the AI agent. Pause on that. This is a software with a wallet, a real business running and growing on autopilot. Every month, it pays for its own infrastructure. It sustains itself with next to zero human involvement.

And Felix is a small version of this story. There’s an even bigger version, a company called Medvi, that booked $401 million in revenue in its first year of operations, with just two employees. The rest of the company runs 24/7 via an AI agent that doesn’t sleep, doesn’t take breaks, and costs almost nothing to operate.

Now here’s where it gets interesting.

Walk into any crypto conversation today, and you’ll hear the same thing. The next big narrative is “AI agents”. Some “AI Chain” is going to win this category, the way Ethereum did for DeFi. Pick your horse, hold the token, and wait for it to moon. That’s the story every influencer and VC is selling, and every analyst is dutifully repeating on podcasts.

And it’s completely cooked. Because it was invented by people whose jobs depend on the answer mattering, and it’s about to burn the same crowd that got rekt buying L1 tokens last cycle. Take a look at CoinGecko’s AI agent index; it has lost 75% of its market cap in the last year. Most tokens listed there are down 90% and are still bleeding.

Because the reality is: the actual AI coins are stablecoins - USDC, USDT, USDS, and they have already won. Let me show you why.

Software is Now a Company

To understand all this, we need to go back to 1937. That year, an economist named Ronald Coase wrote a paper asking a really dumb question – “Why do companies exist?”

Think about it, if the open market is supposedly the most efficient way to get anything done, then every task inside a company could, in theory, be outsourced. A freelancer for every line of code. A freelancer for every customer call. A freelancer for every invoice that lands in the inbox. You’d pay per task, fire at will, and keep costs at rock bottom.

So why doesn’t anyone actually run a business like that? Because even if it looks cheap on paper, it is more expensive in practice. Finding the right person takes time. Negotiating a contract takes time. Making sure the work actually got done takes time. And chasing someone down takes time, money, and usually a lawyer.

Ronald called this friction “transaction costs.” Once those costs get high enough, it becomes cheaper to stop bargaining with the outside world and instead build a team under your own roof. It’s just faster and cheaper to hire someone, put them on a payroll, and have them show up on Monday.

But in the post-AI world, that logic isn’t valid anymore. Agents are already cheaper than most of the tasks a company was built to contain in the first place. You can hire a coding agent today for roughly a dollar an hour who works around the clock, never quits, never gets tired, and never asks for a raise. The argument today for a 50-person dev team is pure nostalgia.

The only thing stopping this from being normal today is outdated Legal & Compliance frameworks. OpenClaw has Nat’s name on it because the state of Delaware doesn’t accept LLC filings signed by a software agent. If you remove that requirement, Felix is already a company in every practical sense. It earns money, spends money, makes decisions, and reinvests what it makes.

And this is exactly where crypto starts load-bearing. Because Felix cannot open a Chase account. It cannot pass KYC. It cannot sign a W-9. In fact, Chase won’t give a software program a bank account, no matter how much revenue that software produces, and the Bank Secrecy Act means they can’t legally try to even if they wanted to.

A USDC-Crypto wallet has none of these problems. You generate a private key. You fund the wallet with stablecoins. And in a single move, you’ve handed the agent every financial ability a company needs. It can receive money from customers, pay for tools, hire other agents, and keep running in the background long after the owner has stopped paying attention. Everything else in the agent’s stack. The LLM, the orchestration layer, and the tools it calls are negotiable. But that crypto wallet is the backbone. Take that away, and Felix goes right back to being just a chatbot agent.

There’s also an argument I keep seeing on Twitter from the anti-stablecoin maxis – Yeah, stablecoins are fine, but why would a normal person actually use them? A father of three in Louisiana, with a Chase checking account, FDIC insurance, a debit card that works at Publix, and autopay on his mortgage, is never going to move his money into a self-custodied wallet with a seed phrase.

And honestly, that’s true. He won’t. He has no reason to. But the whole argument misses the point. He was never the customer in this story. The customer is a piece of software that can’t legally hold a bank account in the first place. The agent doesn’t need FDIC. It can’t qualify for FDIC. It’s the perfect stablecoin user because it simply has no other option.

The Chains are a Vendor Now

Okay, so that’s one half of the argument out of the way. Now to the second half, which is where a lot of people are about to get mad.

Crypto Twitter has spent years arguing about which chain wins AI: Ethereum? Solana? Base? Sui? Stripe’s new Tempo? Every week, somebody drops a 2000-word article with a matrix of trade-offs, a wall of logos, and a pick of a winner. Because they don’t understand how an agent works. It doesn’t care about a single chain. It just picks whichever chain is the cheapest and best suited for whatever tasks need to get done at that moment.

Picture this, Felix in the middle of a regular workday. At 10 AM, Felix needs to send a $0.003 micropayment to another agent for a quick data lookup. Felix picks Base or Solana. Why? Because the fee is a fraction of a cent. An hour later, Felix needs to settle $50,000 to a supplier. Completely different math. This time, Felix picks Ethereum because at fifty grand, the finality premium is worth whatever the gas costs.

An hour after that, Felix needs to pay a freelancer in Lagos in dollars. Felix picks USDT on Tron because Tron moved $3.3 trillion of stablecoin volume in 2025 versus about $1.2 trillion on Ethereum, and the Nigeria corridor just works better on Tron than anywhere else.

All three payments occurred on three completely different chains, and Felix didn’t care which was which. To a software agent, a chain is just a tool.

It’s the same reason a logistics company doesn’t have feelings about its shipping carriers. No one’s arguing about which one, UPS or FedEx, is “philosophically better.” You just pick whichever gets the job done more cheaply and faster for that specific route at that specific time. That’s exactly the relationship every chain is about to have with every serious application layer. The agent is just running math, and whichever chain wins that math at the moment is the one it uses.

Stripe figured this out before most of the crypto industry did. Stripe and Paradigm recently put $500 million together to build a new chain called Tempo, built entirely around stablecoins. Stripe does not want you to know what chain your payment cleared on. It only cares that it cleared, cheaply, with an assurance. This is how the future of every surviving chain is going to be – invisible pipes.

Which brings us to what I think is the single most brutally mispriced meta in crypto right now.

The AI Token Graveyard

In 2025, CoinGecko’s AI agents index fell from $13.5 billion to $3.5 billion. Ten billion dollars of market cap evaporated. Virtuals, ai16z, the entire long tail of “autonomous agent platform” tokens that raised on the AI narrative started bleeding, just what narrative tokens always do when the narrative runs out of new buyers. This was always coming. The market slowly realised these tokens have no real use case for AI or AI agents in the first place.

What actually captures the value of the agent economy is on the other side of it. USDC alone settled $18.3 trillion on-chain in 2025. Across all stablecoins, the total landed at around $33 trillion, rivalling Visa and Mastercard combined.

By January 2026, monthly stablecoin volume alone had crossed $10 trillion. PayPal’s PYUSD went from $1.2 billion in float to $3.8 billion in under a year. Cloudflare, of all companies, launched its own stablecoin. Visa launched a stablecoin settlement program that was already processing at a $4.5 billion annualised rate by mid-January.

One layer up from the stablecoins, there’s the protocol layer that makes this whole thing work. Coinbase took a dormant HTTP status code called 402, and turned it into x402, a small protocol that lets agents pay agents. By December, x402 had processed over 100 million agentic payments. Average payment size was 20 cents, with a daily volume of ~ $30,000. That sounds absurdly small, but it is also exactly the shape every rail you know and love had in the first six months, right before the hockey stick started. Stripe began testing x402 on Base in February. Mastercard ran live agent payment pilots in Singapore with DBS and UOB. Google Cloud added x402 as one of the settlement rails inside its Agent Payments Protocol.

Almost none of this real, compounding, live-on-mainnet activity ever touched the AI agent token index on the way up. Sure, a handful of x402-adjacent tokens caught a small bid along the way, but the broader index never really moved on any of it. Because the market is pricing the wrong thing entirely. It’s still trying to handicap which individual agent wins, the same way it once handicapped which dog coin had the cuter mascot. But the actual trade is owning the rails that every single agent is forced to use, whether that specific agent lives or dies. And right now, those rails are stablecoins.

The Crack in the Thesis

The honest version of this argument is that I'll also tell you where it could break. Otherwise, I’m just selling you yet another thesis on AI agents with the inconvenient parts trimmed out.

The hole in all of this is liability. Imagine this scenario: Felix signs a contract with another agent, moves a million dollars, and the counterparty breaches. Who actually gets sued? Felix isn’t a legal person, so you can’t take Felix to court. Nat didn’t authorise the specific payment, probably didn’t even know it was happening, and honestly might not be able to reconstruct what Felix was thinking at the time, even if he tried.

The platform hosting Felix can’t really indemnify a system whose behaviour nobody fully understands. And the insurers have already started pulling back. Professional liability policies are quietly reclassifying agent errors as “systemic software drift,” basically denying payment for them.

And if you look at the legal clause today, most enterprise AI deals cap the vendor’s liability at twelve months of SaaS fees. That means if something catastrophic happens, the most anyone can claw back from the AI vendor is whatever last year’s subscription bill came out to. Meanwhile, the average cost of a US data breach in 2025 was $10.22 million per incident. There’s a massive gap between what can actually go wrong and what the contract covers, and right now, nobody has figured out who’s supposed to absorb it.

Until someone figures out who actually pays when an agent screws up, every founderless company will still need a human name on the paperwork for legal protection. But even with that hanging over everything, the bigger picture still holds. Companies are slowly dissolving into software, and chains are becoming the routing layers for the software. Both of those layers keep collapsing downward into stablecoin, because that’s the only piece in this whole stack an agent can actually hold, spend, earn, and reason about on its own.

Where The Money Actually Lives

So, if the chain becomes a vendor and the agent token is basically a graveyard at this point, where’s the actual upside in all of this going to live?

My honest answer is somewhere at the top of the reputation and the orchestration layer. Somebody has to verify that Felix is actually solvent before another agent signs a six-figure contract with it. Somebody has to rate an agent’s default risk the way Moody’s rates bonds does, except at machine speed, because agents transact at machine speed. Somebody has to route a payroll across three chains without the sender or the recipient ever knowing or caring which chain carried which leg of it. And whichever seed-stage scrappy companies currently building in the space turn into the winner, that company is going to be worth more than every AI token ever launched.

And this is what nobody wants to hear. The infrastructure that actually wins in the agent economy will look boring. It’ll feel like plumbing, without any of the token launch hype or airdrop farming.

There is a line from Haseeb Qureshi at Dragonfly that keeps playing in my head. He said crypto was never built for humans. He is right, humans were never the target user. Every retail user who ever complained about seed phrases, gas fees, or wallet UX was right. The product does not fit them because it was never built for them. It was built for whatever was coming next.

What came next was a software with a wallet, real customers, and real revenue. It’s been here for about two years now, shipping invoices and spending stablecoins somewhere while you read this. And while all of that is happening, the market is busy arguing about which chain wins AI, which agent token will 100x next, and which narrative the VCs are rotating into in Q3.

Meanwhile, a stablecoin moved roughly $18.3 trillion last year, and nobody in crypto cared much. That AI coin is USDC. Everything else is cosplay.

That’s all for today, until next time!

Stay curious!

Vaidik

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The disclosure that triggered all this arrived on April 14. Sixty-nine pages filed with the Office of Government Ethics, clearing the last bureaucratic hurdle before his confirmation hearing. The document is a standard compliance form. It is also, if you know where to look, one of the most consequential paper trails ever laid before a Senate committee.

Warsh and his wife, Jane Lauder, whose family interests include the Estée Lauder cosmetics company and whose Forbes-estimated net worth is around $1.9 billion, hold combined assets of at least $192 million.The bulk of that sits in two Juggernaut Fund LP positions valued at over $50 million each, tied to his advisory work with Stanley Druckenmiller’s Duquesne Family Office. The underlying assets are shielded by confidentiality agreements. The OGE certifying official flagged those specifically and confirmed that, once Warsh diverts the required holdings, he will be in compliance with federal ethics law.

The portfolio spans DeFi protocols, Ethereum scaling networks, Bitcoin payment infrastructure, and prediction markets. Reads like he went through the entire crypto industry and deliberately ordered something from every section. Through AVGF I, Warsh holds indirect stakes in Solana, Optimism, and the Lightning Network. Through DCM Investments 10 LLC, the disclosure lists dYdX, Polychain Capital, Compound, Blast, Lighter, and Lemon Cash. A separate AVF fund series captures Dapper Labs, DeSo, Friends With Benefits, and Zero Gravity. He also holds a direct stake in Metatheory, a Web3 gaming company, valued between $1,000 and $15,000. Flashnet, a Bitcoin merchant payment startup, is in there. Polymarket is in there.

Most of these crypto positions sit within fund vehicles whose individual line items are reported without dollar values, which under OGE rules, means each is worth less than $1,000. These are small venture bets, not concentrated positions. The size is not really the point; the breadth is. This portfolio covers L1 blockchains, L2 scaling solutions, DeFi lending, decentralised derivatives, NFT infrastructure, Bitcoin payments, and prediction markets. The only categories missing are memecoins, gaming tokens, mining companies, and direct Bitcoin. Everything he owns is infrastructure, financial plumbing, or developer tools.

What The Money Says

What Kevin Warsh’s money thinks is that crypto is the next layer of financial infrastructure, built on protocols that handle real transactions, and that the people building those protocols are doing something that, from a certain angle, looks a lot like fintech.

The closest thing to a speculative bet in the entire disclosure is Friends With Benefits, a social token community, which, given everything else in the portfolio, reads more like someone dragged him to a dinner and he wrote a small cheque to be polite.

His public statements track with this. In 2011, Warsh saw the Bitcoin whitepaper at a dinner hosted by Marc Andreessen. In 2018, he wrote in The Wall Street Journal that Bitcoin could become a sustainable store of value similar to gold. In 2021, he stated on CNBC that Bitcoin is the new gold for those under 40. And in 2025, at the Hoover Institution, he provided his most comprehensive statement to date: Bitcoin is not a replacement for the dollar, but it can serve as an excellent policeman of monetary policy. I wouldn’t call Warsh a Bitcoin maximalist. A monetary policy professional who treats Bitcoin as a legitimate signal of the dollar's health may, in some ways, be more useful to the industry.

Warsh earned $10.2 million in consulting fees from Duquesne Family Office, the investment arm of Stanley Druckenmiller, one of the most credible macro investors in the world and one of the few at that level who has spoken seriously about crypto.. This is the same Druckenmiller who told Morgan Stanley last month that stablecoins will be the entire US payment system in 10 to 15 years. And then in the same interview called the rest of crypto “a solution looking for a problem.” Warsh’s crypto exposure flows through the same network as arguably the most credible macro investor alive. Druckenmiller says stablecoins are the future and everything else is noise, and Warsh’s portfolio is full of the infrastructure that makes stablecoins work, and it starts to look like a shared thesis.

Warsh has promised to divest everything. Selling liquid token positions is straightforward enough. Unwinding LP stakes in Polychain Capital or venture fund positions in illiquid early-stage companies is considerably less so. Some funds with multiple LPs would not typically require divestiture under OGE rules. But the OGE certifying official specifically flagged the Juggernaut Fund positions and made full divestiture a condition of compliance. The underlying assets in those positions remain undisclosed due to pre-existing confidentiality agreements.

Federal ethics rules generally require a one-year cooling-off period for matters directly affecting recent financial interests. That could prove relevant as the Fed weighs in on stablecoin legislation, tokenised deposits and securities, and CBDC research. Think about what that means in practice. Congress is actively debating stablecoin frameworks right now. The CLARITY Act is still working through the Senate. Banks are running pilot programs on tokenised deposits. The Fed has an ongoing role in all of it. And the person running the Fed, the person who has actually invested in DeFi protocols and Bitcoin payment infrastructure and prediction markets, may spend year one watching from the sidelines while colleagues who have never used a crypto wallet weigh in instead.

It would be strange to write about Warsh’s crypto holdings without noting the context in which he was nominated. The Trump family’s crypto ventures generated substantial proceeds before a single piece of crypto legislation was signed. World Liberty Financial, the DeFi project with Barron Trump listed as “DeFi visionary” on its website, had generated at least $1.2 billion in realised proceeds for the Trump family by early 2026, according to The Wall Street Journal. The executive order that opened 401(k) accounts to crypto was signed by a president whose family was simultaneously cashing out of crypto ventures.

Warsh’s holdings are small venture bets through fund structures. Trump’s family’s holdings are large, concentrated positions in projects that benefit directly from federal policy. These are different things in scale and structure. The connecting point is that the president who benefits financially from a crypto-friendly Fed chose a Fed chair nominee with financial interests in the crypto industry he will oversee. Whether this represents blatant corruption, strategic alignment, or simply the natural evolution of American institutions is a matter of perspective. One that usually depends on whether or not you happen to own the assets in question.

Senator Thom Tillis has said he will oppose any Fed nominee until the DOJ investigation into Powell is fully and transparently concluded. The committee sits along party lines, meaning a single Republican defection could delay confirmation. Powell’s term as Fed Chair ends May 15. If Warsh is not confirmed before then, Powell remains in a kind of caretaker role while oil is above $100, the situation in Iran is unresolved, and the market is trying to decide whether the Fed has a leader.

For the crypto crowd, the Warsh disclosure is a classic ‘good news, bad news’ joke. The good news? We finally have a Fed Chair who knows the difference between a liquidity pool and a swimming pool. The bad news? Because he knows how the sausage is made, federal ethics laws have effectively put him in a digital corner for a year, forced to watch his ‘no-coiner’ colleagues try to regulate technology they probably think is a sequel to The Matrix.

Both things can be simultaneously true. He understands DeFi from the inside. He also thinks the Fed’s primary mandate is monetary stability, and that asset price inflation is a symptom of policy failure. Those views are going to make for some very interesting FOMC meetings.

The Warsh disclosure is less a signal of institutional shift and more a quiet acknowledgement of path dependency. Our past choices and technical evolutions inevitably constrain the future. For decades, the Federal Reserve operated on legacy rails. As the architecture of money shifts from paper to protocol, the “path” is naturally widening.

This is a slow, steady integration of new literacy into an established system. A realisation that to govern the future of the dollar, one must first understand the language in which that future is being written.

—Thejaswini

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When Warren Buffett took over Berkshire Hathaway in 1965, he began writing letters to the shareholders of a dying textile mill. In them, he explained in detail where the cash from the mill was being redeployed and why.

Capital allocation became a public affair in a true sense. Despite being owners of a company, shareholders generally trust the management to carry out operations as their fiduciary agents. Buffett’s letters made shareholders feel heard in annual discussions around what the business should become.

Most public companies do capital budgeting in closed boardrooms. Corporate law mandates the disclosure of audited numbers, not the reasoning behind the decisions. But Berkshire’s transparency with reasoning was timely and crucial. Sixty years of letters read like a continuous conversation about where to put the money next.

A version of this practice is now showing up on-chain.

DeFi protocols have their own versions of these boardrooms: backchannels, founder calls, and delegate huddles. Yet the vote, the proposal text, the dissent, the amendments, and the final proposal all rest on the public record.

On April 12, Aave’s token holders voted on a $25 million capital allocation proposal. The vote, which passed with a landslide majority, allows Aave’s token holders to vote on governance, protocol upgrades, and risk parameters.

Beyond Aave, this move helps crypto evolve from debating whether a token has a claim on protocol cash flow to allowing token holders to decide which project to fund next and how much capital to allocate.

In today’s analysis, I will tell you how.

What’s in the Vote?

Aave’s vote matters because of its relevance in the lending marketplace. Aave currently has over $26 billion in total value locked (TVL) and generated about $907 million in fees last year. Most of that flows to suppliers who earn interest on deposits, leaving around $125 million in annual revenue with the protocol. That’s what accrues to the DAO treasury.

As of December 31, 2025, Aave held over $21 billion worth of active loans on its books.

That’s a larger borrow book than Robinhood’s $16.8 billion margin book at the end of 2025, and nearly twice the size of LendingClub’s $11.8 billion in total assets. The latter is a NYSE-listed marketplace lender whose business model most resembles Aave’s.

In 2025, LendingClub, an NYSE-listed marketplace lender with which Aave’s economic model resembles the most, recorded $136 million in net income on a revenue base of a billion dollars. The two-decade-old company closed 2025 with $11.8 billion in total assets. This is a business competing with well-known traditional fintech peers.

LendingClub made $136 million in net income on a $1 billion revenue base in 2025. Aave’s $125 million accrues to the DAO on a loan book nearly twice as large, which means LendingClub earns roughly twice as much per dollar lent.

That’s because Aave is just a protocol. It can’t charge more interest without breaking its own market since it doesn’t own the deposits. Raising the reserve factor would push suppliers to a rival protocol with better yields. LendingClub can charge more because it owns the loans, takes the credit risk, and funds a full bank of underwriters and default provisions to do so.

Aave’s proposal is to route all revenue from Aave-branded products, including the App, the Card, and the Pro institutional tier, to Aave DAO’s treasury. In return, the DAO will pay Aave Labs $25 million plus 75,000 AAVE, to be vested over four years, to keep building.

Aave Labs, which is the development entity behind the Aave protocol, had been keeping a share of product revenue to fund itself. The Cowswap integration on Aave.com applied a 15-25 basis point fee on swaps and sent proceeds totalling roughly $10 million a year, or about 10% of the DAO’s potential revenue, to a private Labs address. ACI's Marc Zeller called it the "stealth privatisation" of the DAO's earnings. Under the new structure, it will be paid by its token holders, on terms they set and against milestones they can withhold disbursements against.

Token holders didn’t approve the ‘Temp Check’ version that was initially proposed in early March. They split the bundle involving the V4, brand, funding, and growth grants into separate votes and stretched vesting from two years to four. They also tied disbursements to milestones instead of writing a single cheque.

Not everyone supported the initial version. Some agreed to the negotiated version, others walked away after a long association.

In the past two months, three of Aave’s most embedded contributor organisations ended their partnership with the protocol. BGD Labs left in February. Aave Chan Initiative (ACI) announced its exit from Aave in March after contributing 61% of governance actions and 48% of protocol income during its three-year tenure. Chaos Labs stepped down from risk management on April 6, after helping scale TVL from $5.2 billion to over $26 billion without material bad debt.

The bigger shift is that large protocols with product-market fit and surplus are now enabling token holders do what public markets typically confine to boardrooms.

Uniswap is moving in the same direction from a different angle. Its UNIfication proposal hinges on protocol fees, routes them into UNI burns, disables Labs-level interface and wallet fees, and funds Labs from the treasury.

In 2025, Uniswap recorded $1 billion in fees, nearly identical to Aave. But almost all of it has historically gone to liquidity providers. Its current annualised protocol revenue stands at $42 million. UNI holders got nothing.

In March alone, Uniswap processed $42 billion in DEX volume. Meanwhile, Robinhood’s app recorded $34 billion in crypto notional volume throughout Q4 2025. Uniswap is doing slightly more than that in just a month.

Two of DeFi’s most-watched protocols are moving toward the same structural move, albeit with different mechanisms. Both of them want to route revenue up to the token layer and then publicly decide where to redeploy it.

The new treasury strategies change who decides how revenue is redistributed once it reaches a treasury. Should the funds be deployed to buy back and burn the tokens? Or deploy them for operating budgets and growth grants? Maybe fund new product lines or save it for a longer runway? All these are capital allocation questions. The same thing that Buffett was answering in his letters for decades.

For years, DeFi has argued whether the token deserves a claim on protocol cash flows. We saw Hyperliquid and pump.fun settle that conversation through their extensive planned buyback programmes.

Read: Burn, Baby, Burn 🔥

What Public Capital Budgeting Requires

Buffett’s letters were voluntary. What makes his act seem more than just a goodwill gesture is the audited financials that back the letters. He had a fiduciary duty that’s enforceable in court. Berkshire shareholders have rights that don’t depend on Buffett’s goodwill; they can sue the company, vote out the board and demand records.

A corporate regulator mandates baseline disclosure regardless of how nice Buffett’s letters might sound.

With DeFi, transparency takes the front seat. On-chain architecture forces every transaction, treasury movement and vote to be public. But that’s not enough to make crypto protocols transparent to the public. Token holders need a collective understanding of the business to make capital budgeting decisions.

The financial statements and filings of publicly listed companies are accessible and legible to the public because of standardised accounting requirements. What Nvidia means by gross revenue is about the same as what Apple might mean. If you can make sense of Nvidia’s balance sheet, you will likely understand the line items in the iPhone-maker’s financial statements, too.

But this standardisation of financial terms is mostly absent in crypto protocols. Terms like revenue and fees are often used interchangeably, while they may mean different things.

Token holders may still read the Aave treasury balance sheet to the cent. But they cannot separate what counts as “Aave-branded product revenue” unless the definitions are standardised. They still need to know what happens if Aave Labs and the DAO disagree on the number.

This isn’t hypothetical. One of the biggest criticisms of Aave’s proposal was on what “100% of revenue” actually meant. Is it gross or net of operational costs? Who verifies it? How do you enforce it?

Even Marc Zeller, the founder of the now-exited ACI, raised these concerns in his X post after acknowledging Aave DAO’s win.

As token holders, you simply cannot allocate what you cannot define.

Checks and Balances

Although the vote signals a transfer of power to the token holders, those who left Aave argue that the transfer is incomplete.

The two teams closest to Aave’s governance machinery, one that coordinated 61% of all governance actions (ACI) and one that wrote the production codebase (BGD Labs), complained about similar things. The entity asking for the money, Aave Labs, was also the one voting to approve it. That’s a conflict of interest.

A budget recipient cannot vote on its own funding.

This is a structural problem in every on-chain capital budget in which founders or core teams hold large token positions. Although theoretically, it’s the token holders who get to decide where the surplus goes, it’s often the largest tokenholder who drafts the proposal.

You may argue that a larger stake brings a larger say in how the company is operated. But if DeFi protocols want to emulate the capital budgeting practices of public companies, they need to ensure similar checks and balances. For instance, minority shareholders are protected against abuse by majority shareholders through legal frameworks.

DeFi needs similar checks and balances. Revenue numbers must be independently verified, including those off-chain. When Venice AI said it would burn VVV using Stripe subscription revenue, DefiLlama’s 0xngmi asked for read-only Stripe access so the off-chain numbers feeding an on-chain claim could actually be audited.

There must be on-chain milestones that trigger capital releases in tranches and reverse them if the milestones aren’t met. Protocols must standardise the disclosure of voting power held by entities that will receive the DAO funds. None of this is hard to achieve. It’s just not desirable because the people who have to agree to be constrained are the same ones writing these proposals.

One team had specifically requested safeguards to ensure on-chain milestone tracking and self-voting limits before supporting the Aave proposal. Those recommendations were ignored, and the proposal was passed anyway.

What Aave’s ‘Win’ Means

The vote itself is not the achievement. Passing a $25 million funding proposal on-chain is not hard when the largest token holder supports it. But there’s a lesson to be learned here.

Token holders rejected the first version. They broke a single mega-proposal into smaller decisions. They doubled the vesting period. They forced a founder-led entity to negotiate terms in public, revise them in public, and accept constraints it didn’t volunteer. This is a step toward achieving capital budgeting in public.

If Aave’s proposal becomes a template, the fact that token holders can reshape a proposal before it passes must become the part that every other protocol copies. Just like in a democracy, a policy isn’t sound enough if it can’t see the light through all dissent and disagreements. Pushback in DAOs is not a dysfunction. It’s what makes DAOs more transparent and stand the test of time.

Uniswap’s UNIfication proposal is going through a similar journey. It needs to address similar questions, such as “Who decides where to allocate the treasury funds?” What is the recourse if something doesn’t work?” These are the questions many other blue-chip DeFi protocols will face. They must understand that the DAO community will not remember buybacks or token burns if the policy decisions are not justified and grounded in reason.

Buffett and Berkshire came to be respected not right after the first letter. Shareholders read years of letters before they gradually trusted the entrepreneur to judiciously apply their funds to profitable use.

That’s it for today. I will be back with another deep dive.

Until then, stay curious!
Prathik

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Forty-four years later, those computers cost $27,000 a year each. There are 350,000 of them, and Bloomberg collects roughly $10 billion annually from a business whose entire structural genius is that he inserted himself between the institutions that had the data and the people who needed it, and charged a toll on everything that passed through. The data was never Bloomberg’s. Merrill Lynch had it. Goldman had it. Every trading firm on Wall Street had it. Bloomberg just built the tollbooth, convinced everyone the tollbooth was the destination, and raised the price every year because what were you going to do, go back to calling brokers on the phone?

That model has survived every technological shift over four decades because no one could figure out a better distribution mechanism. Until, apparently, last Wednesday.

On April 9, six of the institutions whose data fills that tollbooth started publishing it somewhere else: Euronext, Fidelity, Tradeweb, OTC Markets Group, Singapore Exchange FX, and Exchange Data International, directly on-chain through Pyth’s new Data Marketplace, accessible to any developer on any of 100 blockchains. You don’t need a contract, a two-year minimum commitment, or a proprietary keyboard with yellow and green buttons.

Now write this down: the interesting thing about building a monopoly on other people’s data is that the other people eventually notice.

The financial data industry is worth around $30 billion annually and is one of the least discussed monopolies in the world, possibly because the only people paying attention to it are the ones already paying for it.

Bloomberg controls around 33% of the global financial data market, generating over $10 billion a year from its terminal business alone. Refinitiv, now owned by the London Stock Exchange Group after a $27 billion acquisition, holds around 20%. ICE Data Services reported $2.8 billion in market data revenue. After that, you have FactSet, S&P Global, Morningstar, and a handful of regional players serving niche segments. Together, the top four vendors control the overwhelming majority of how financial data moves from the institutions that generate it to the firms that need it.

The model is identical across all of them. Institutions such as exchanges, trading firms, banks, and asset managers generate pricing data as a byproduct of their work. They sell or license that data to vendors. The vendors package it, normalise it, add analytics on top, and sell it to everyone else at a significant markup, under long contracts, with proprietary access methods that make switching painful. A Bloomberg subscription locks you in for two years. Cancelling early costs 50% of the remaining contract value. Besides, everything about the Bloomberg experience is designed to make leaving feel harder than staying. The keyboard is different. The data format is different. Even the messaging system that half of Wall Street uses to talk to each other runs through Bloomberg, which means switching terminals also means giving up your contact list.

This has worked for four decades because the vendors solved a really hard problem of getting data from hundreds of sources, cleaning and normalising it, and delivering it with low latency across global infrastructure. Bloomberg earned its position.

Blockchain is a better distribution mechanism. Maybe not for everything, and not yet at full scale. But for the specific problem of connecting institutions that have data to developers who want to build with it, a public on-chain infrastructure with programmable access is structurally superior to a proprietary terminal with a two-year contract. By turning the data into an API with no switching costs, you provide any developer on any chain permissionless, self-serve access. That is what Pyth is.

Euronext, Exchange Data International, Fidelity Investments, OTC Markets Group, Singapore Exchange FX, and Tradeweb began publishing their proprietary market data directly on-chain through Pyth’s new Data Marketplace.

• Euronext FX: Spot currency and precious metals. The FX rates global markets actually trade on.

• Fidelity: ETF valuations and fixed income data. How institutions mark portfolios to market every day.

• Tradeweb: Intraday ETF pricing. Real-time valuations from one of the largest electronic trading platforms.

• OTC Markets Group: Over-the-counter securities. A market that barely exists in today's DeFi data.

• Singapore Exchange FX: Asian currency pairs. The most traded FX market with the least on-chain coverage.

Together, these six cover a significant portion of the asset classes that DeFi has never been able to build around reliably because the data feeding those assets was not institutional-grade.

Why does data before assets matter

Everyone in crypto has been talking about the tokenisation wave for two years: tokenised Treasury bills, tokenised bonds, tokenised stocks. The whole conversation assumes the hard part is getting the assets on-chain.

But the hard part was the data. Before you can trade a tokenised Treasury bill in a DeFi protocol, you need to know what it is worth right now, to the second, with the same accuracy Goldman uses when pricing that instrument at a trading desk. Before you can build a lending protocol around real-world assets, you need price feeds that run continuously, sourced from institutions that actually make the market, not scraped from a website and updated every few minutes.

DeFi protocols require accurate, real-time traditional financial data for derivatives, loans, and structured products, but have historically relied on limited or slower data sources. This is why DeFi has been mostly crypto-to-crypto for its entire existence. The data feeding those products was not reliable enough, not fast enough, and not sourced from institutions with the credibility to make it defensible in a compliance conversation.

Pyth Pro, Pyth’s institutional subscription tier launched in September 2025, delivers price feeds with 1ms latency across more than 2,200 instruments. Polymarket integrated Pyth Pro in April 2026 to settle new markets for traditional assets, including major equity indices, commodities, and U.S. stocks, replacing manual or exchange-specific data with a standardised source aggregated from over 125 trading firms. Hyperliquid now runs perpetuals on oil and gold using Pyth feeds. The data quality is reaching the point where serious financial products can be built around it without apology.

The tokenisation wave needs this layer to function at scale. You cannot build a reliable fixed-income product on-chain without a reliable fixed-income price feed.

The original oracle problem in crypto was simple: smart contracts live on-chain, and prices live off-chain. Something needs to bridge the two. Chainlink, the dominant oracle for most of DeFi’s history, solved this by running a large network of independent nodes that fetch prices from third-party sources (exchanges, aggregators, data APIs) and submit them on-chain. Many independent sources, many independent nodes, reasonable decentralisation, acceptable latency.

Pyth took a different approach from the start by going directly to the institutions that are actually trading. More than 120 institutions now publish data through Pyth, including global exchanges, trading firms, and market makers. Instead of describing the Bitcoin price to Pyth second hand, Jane Street becomes the publisher. The data comes from the source, not from someone describing the source.

This is faster, more accurate, and more directly tied to real market activity than aggregated feeds. It is also more centralised in a structural sense: a smaller club of publishers who all know one another, validating their own data. Pyth has staking and slashing mechanisms designed to create economic incentives around accuracy. But the better framing would be that Pyth chose speed and data quality over maximum decentralisation. For institutional finance, that is probably the right trade.

Pyth was created with heavy involvement from Jump Crypto, an organisation that played a significant role in the events of 2022 that most people in crypto would prefer not to revisit. The publisher network is a small club of institutions that largely know one another and validate each other’s data. The staking and slashing mechanism creates an economic incentive for accuracy, but Pyth is both faster and higher-quality than what came before, and more centralised than the marketing suggests. You are not replacing a monopoly with a commons. You are replacing one concentrated system with another concentrated system that happens to run on a blockchain.

The PYTH token hit an all-time high of $1.20 in March 2024 and currently trades around $0.046, down roughly 96% from its peak. The obvious reason: using Pyth’s data does not require holding or buying PYTH. The network can grow substantially while the token remains range-bound, a known problem that Pyth’s reserve programme, which allocates a portion of protocol revenue to open-market PYTH purchases, is attempting to address. ​​

Getting data from the institutions that generated it to the desks of people who needed it required hardware, proprietary networks, sales relationships, and ongoing support. Bloomberg solved all of that and charged accordingly. The data generators had no alternative distribution mechanism, so they sold their data to the middleman, and the middleman kept the margin. Blockchain removes that specific friction. Not the analytics, the workflow, or the keyboard. Just the part where someone had to carry the data from one place to another and charge for the privilege.

However, Bloomberg sells a workflow. The terminal, the keyboard, the messaging system, the analytics, the support team. Traders build their entire professional lives around it. Pyth sells none of that. It is a data layer that protocols plug into. The only overlap is the underlying data itself, and that is the part that just moved.

It matters because, if Fidelity publishes its ETF valuations on-chain, any developer anywhere can read that data without negotiating a licensing agreement, without paying $32,000 a year, and without waiting for a vendor to normalise the format. The data becomes programmable infrastructure rather than a proprietary product. The institutions keep control of what they publish and retain attribution rights. The middleman’s job, moving data from source to user becomes unnecessary.

These six institutions are choosing Pyth as a primary distribution channel, which is a different category of commitment from a pilot. Pilots get shut down when the person who championed them gets a new job. Primary distribution channels become operational dependencies.

Tokenised bonds, tokenised equities, tokenised everything. Most of that is still months or years away from meaningful scale. But the raw material that makes real-world asset products possible in DeFi is now accessible without a contract, a terminal, or a two-year minimum commitment.

Michael Bloomberg spent months walking Merrill Lynch’s hallways with free coffee because the data he needed was locked inside institutions that had no reason to give it to him. He built his entire business on that friction.

The tollbooth does not disappear all at once. Every monopoly in data distribution ends the same way. Not with a fight, or a law, not with a revolution. Mostly with someone, somewhere, asking why they were paying for something they already had.

—Thejaswini

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Stability can be a mirage. This is often true in finance. You bet on a boring financial instrument thinking it’d offer you steady, reliable returns. It does stick to the template until the underlying fundamentals wobble. These ‘steady bets’ are often more deceptive than the speculative ones. People expect the latter to be risky anyway. But few expect that to happen with steady, boring bets.

We saw this play out about 75 years ago.

In the 1940s, dollar-denominated deposits accumulated in European banks following the Great Depression and World War II. These allowed account holders to hold U.S. dollars at non-U.S. banks as a hedge against local currency devaluation. They offered attractive yields and led to some out-of-the-box thinking. Some account holders, like American companies, got creative by parking dollars abroad to dodge capital controls at home.

The European banks welcomed them all. They accepted these deposits and lent them further at higher interest rates. The glut of these deposits in Europe led to the Eurodollar market, a parallel dollar system that was not regulated by the U.S. Federal Reserve. Things began to unravel as the Cold War started in the late 1940s. More people started demanding their U.S. dollars back, but the banks didn’t have enough physical dollars on hand. The entire cycle crashed.

We see a similar trade happening now in the stablecoin market. Except that the digital dollar issuers seem to have learned from history.

In today’s deep dive, I will explain whether Ethena’s reliance on traditional equity markets can save its stablecoin reserve strategy.

The Initial Act

In early 2024, Ethena launched USDe as a synthetic stablecoin that was different from the rest. USDe was issued as a dollar-pegged asset without actually holding dollars in bank reserves. Instead, it held crypto assets like bitcoin and ether, for every dollar of USDe minted. Simultaneously, it shorted an equivalent amount of crypto futures.

The two positions balanced each other. If BTC goes up, the short position on futures loses what the spot gains. And vice versa. Eventually, the USDe was always worth $1, even without an actual physical dollar in the bank account.

But why should people hold USDe over incumbent cryptocurrencies like USDT orUSDC? They were rewarded for doing so.

The incentive was tailor-made to align with how crypto derivative markets are wired. In a bull market, more traders bet on prices rising. Exchanges charge those bullish bettors a small continuous fee, called the funding rate, and pay it to whoever is on the other side. Ethena was permanently on the other side. It collected those payments and passed them on to USDe holders as yield.

At its peak, the yield crossed 20% annually. In 18 months, USDe’s circulation grew sevenfold to approximately $15 billion, the fastest growth ever recorded by any stablecoin.

Read: The Ethena Speed Run 🏎️💨

I loved the design, except that it was heavily reliant on the crypto markets holding the status quo. It worked in bull markets because they were getting paid for being a minority holding a short position against a majority that were long. But markets flip with time; they always do. When they did, the cracks appeared. On October 11, a day after the largest crypto liquidation wiped out over $19 billion, USDe briefly lost its dollar peg. Ethena’s synthetic stablecoin fell to 65 cents against the dollar on Binance.

USDe’s circulating supply fell from about $15 billion to under $6 billion over five months, starting on October 10.

A Lesson Learned Too Late?

More than $9 billion was redeemed. Perpetual futures, which once accounted for almost 100% of the model’s reserve backing, now represent only 11% of it. The bigger irony is that all of this was avoidable. Ethena should have seen it coming.

The signs were telling. Markets always move in cycles, and crypto was no exception. We have seen it over the past 16 years. A reliance on a single collateral source via perpetual futures, which was closely tied to market movements, was always a ticking bomb.

Even other stablecoin issuers had begun adapting. As the U.S. Fed started reducing interest rates, the top two stablecoin issuers increased their activity to supplement their reserve income. Tether diversified its reserves by adding record levels of gold to its coffers. Circle, the USDC issuer, has been aggressively building its infrastructure revenue stream through Arc, its Layer-1 network, and the Circle Payments Network, a full-stack internet payment system.

But Ethena was slow to act. The slide would have been worse had it not addressed the situation at all. That’s not my observation, but what its founder, Guy Young, admitted in a post on X.

Guy also listed out the measures Ethena has started taking to adapt to the regime change.

The TradFi Fix

Ethena would expand its collateral base to include equities and commodities basis trades, overcollateralised institutional lending, prime brokerage services, and a broader set of real-world assets (RWAs).

Ethena started as a crypto-native synthetic dollar, unlike pioneers like Tether’s USDT and Circle’s USDC, which back their digital dollars by holding physical dollars or equivalent treasury bills in their coffers.

Life has come a full circle for Ethena. It’s now going back to wiring itself into the traditional finance infrastructure to keep paying yield to its holders. And not one, but multiple such sources.

With an equities-basis trade, it pockets the gap between buying spot S&P 500 exposure today and simultaneously shorting its futures. The same strategy that Ethena used with BTC and ETH earlier. That’s a small, but predictable return that is independent of crypto market movement.

Now imagine Ethena following a similar trade across multiple asset classes: commodities such as gold, silver, wheat, and oil, indices, lending markets, and more. Every asset will have a spread that is driven by market supply and demand dynamics. Ethena can run delta-neutral trades across all of them and collect spreads around the clock, regardless of what retail feels about crypto or Bitcoin.

While this reduces dependence on crypto markets, it now moves in line with equity, commodity, or other asset markets. Each time volatility in these markets spikes and liquidity in futures markets dries up, these strategies could also fail. It could squeeze USDe’s revenue further.

But that pessimism is the same as expecting a portfolio well-diversified across asset classes to fail. It can still happen, sure, but it’s rare. Yet that’s how the finance world works; on probability and math. People don’t diversify their portfolios expecting to see a green balance when the entire market is drowned in red. The expectation of diversification is to reduce the likelihood and intensity of losses.

For Ethena, diversifying across uncorrelated yield sources will do the same thing. It will reduce the likelihood that its yield will be squeezed out completely when either one or two of these asset classes underperform.

The Liquidity Test

Ethena’s diversified strategy is the logical solution to tackle market cycles. Spreading exposure across equities, commodities, credit and crypto makes the income stream more resilient. That’s probably the only edge it will have over a T-bill-backed USDT and USDC, which pays its holders no yield.

But the new strategy still faces a strong headwind.

The liability side of USDe is fully liquid, meaning any holder can redeem it at any time. But the assets generating the yield are not perfectly liquid during periods of stress. Equity basis positions could take time to unwind cleanly. Institutional loans have fixed tenors. Collateralised Loan Obligations are not always liquid in choppy markets. This gap between liquid liabilities and imperfectly liquid assets can become a structural tension for any yield-bearing stablecoin. It’s a gap that even a diversified yield strategy can’t fix.

In calmer markets, different asset classes can move to different signals. Gold moves up on inflation fears. Good earnings drive up equities. A geopolitical crisis involving oil-producing nations can inflate oil prices, as we are witnessing now. Crypto funding rates are high because retail is bullish.

But that’s not how they move in extreme stress scenarios. The correlation assumptions can fall apart, cancelling out the diversification advantage. The common denominator tying all these assets is liquidity.

And when things go south across the board, everyone wants to cash out of their positions.

Harry Markowitz won the Nobel Prize for showing that diversification reduces risk. Yet, the 2008 crisis needed no Nobel Prize to show us an anomaly to Harry’s Modern Portfolio Theory (MPT). Academician Nassim Taleb had argued the same in his book The Black Swan. The contention is that correlation is not a constant property of asset pairs. It’s a variable that changes with market conditions.

Despite all these anomalies, it’s important to recognise that these are black swan events that are unavoidable and rare. There’s little anyone can do to predict or control them. A diverse portfolio across asset classes is still likely to outperform a concentrated portfolio reliant on one single asset class. We saw it happen during the money market meltdown in 2008.

The Reserve Primary Fund held short-term corporate debt instead of Treasury bills because the spread was attractive. When Lehman Brothers failed, that paper became worthless overnight.

Overcollateralisation of loans is one of Ethena’s responses to this. If borrowers post more collateral than they borrow, losses are theoretically absorbed before they reach USDe holders. But overcollateralisation ratios are calibrated to historical volatility ranges. Stress events can spill outside those ranges.

No strategy will be free from risk. Ethena’s task will be to inspire enough confidence in investors to believe that its new, diversified strategy is better positioned than the earlier model , which depended on a single crypto market dynamic.

That’s it for today. I will be back with another deep dive.

Until then, stay curious!
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 170,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

📩 Fill out this form to submit your details and book a meeting with us directly.

Disclaimer: This newsletter contains analysis and opinions of the author. Content is for informational purposes only, not financial advice. Trading crypto involves substantial risk - your capital is at risk. Do your own research.