Over two years ago, in the first week of September 2024, I published my first piece for the Token Dispatch. Bitcoin was trading just under $60,000, and the only question our readers wanted answered was whether it would cross the $100,000-mark and how much further above the mark it could go. I spent my first few months answering such questions: technical analysis, charts, and expected price action for the upcoming weeks. In the following 12 months, BTC would go on to cross the 6-figure mark, and some more, to record an all-time high above $126,000. Today, funnily, we are still ~15% below the 6-figure mark.

Over two years after my first piece for the Token Dispatch, I can’t remember the last time I wrote an entire edition around where a cryptocurrency was headed. In the last 90 days, our top three most-read pieces are either about the agentic economy or how tokenisation will eat every asset in the world. Our most-read piece in the last three months was about how GPUs get financed. When it wasn’t about computing power, then tokenisation and the agentic economy kept our readers busy.

Today, all the stories we write are far removed from the idea of crypto being a niche industry. They instead reinforce our view of how it’s becoming fintech and the underlying rails for building a more efficient financial system.

In today’s piece, I take a step back and write this commentary to highlight some of our best pieces in the last three months to give you an overview of the spaces that we find most exciting and why we feel so.

On to the story,
Prathik

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If I were to distill the ideas from our last three months of writing to one idea, it would be that blockchains are becoming commodity infrastructure and that value is migrating to various other layers that are emerging out of the evolving financial stack. Businesses can make barely any money by moving a dollar or a share. In Making Capital Reallocate Faster, I wrote how the legacy financial industry has come under attack layer after layer from cheaper, modular alternatives, and value has kept migrating to the layer that stays scarce.

My colleagues and I kept asking this one question across all three themes we covered the most. Once the infrastructure becomes cheap, what stays scarce? Good assets, good underwriting, and trust.

Tokenisation Will Eat Everything

I find tokenisation exciting because it changes fundamentally what an asset can do. I don’t think the moat of tokenisation is in its decentralised ethos.

In Everyone Wants to Be a Neobank, I wrote on how the containerisation of cargo cut shipping costs by 97%. After the containerisation, goods moved across ships, trains and trucks without anyone touching them. A token does the same for capital. A share, a gold bar or a loan becomes a standard unit that can settle at any hour, be held in any wallet, or can serve as collateral anywhere. Tokenised stocks grew from $620 million to $3.15 billion in a year.

But there’s a lesson in the container story. When the container got commoditised, the value got captured by those who controlled volumes at the ports, shipping lines and retailers who rebuilt the supply chains around cheap freight. Tokenisation is heading the same way. Minting a token is trivial and creates little value to capture. The moat is held by those who can distribute them to capital that couldn’t reach them before.

Vaidik, my colleague, wrote Private Credit for a Dollar, our second most-read piece this quarter. The piece dives into one of the subsets of tokenised assets. For a decade, private credit’s 8% to 12% yields went to pension funds and insurers. Nobody wanted to manage thousands of $500 cheques. Vaidik likens yield-bearing stablecoins to the moment Bent and Brown opened Treasury access to ordinary savers in the 1970s. Apollo still underwrites and manages risk at an institutional scale. A tokenised fund lets deposits of any size flow into that strategy. The underwriter holds the edge here for providing something for a tokenised fund that an expert in the domain alone can.

In What Does a Token Holder Own?, our third most-read piece, I looked at Venice AI’s $65 million raise, which gave investors 8.98% equity plus a token grant. The equity holders got contracts, board rights, and a legal claim on growth. The token holders got a buyback programme that the company can stop whenever it wants.

As protocols start behaving like companies, value flows to whichever instrument carries enforceable rights. Tokens that want a share of it will have to earn one.

Stablecoins as the Enabling Layer

Moving digital dollars is close to free, and the market is worth over $300 billion. Tether cleared roughly $10 billion in profit in 2025 with about 100 employees. USDC issuer Circle’s reserve income made up 99% of its 2024 revenue.

On June 30, a consortium of 140 firms announced a rival stablecoin that promised to hand nearly all that yield back to its partners. Circle’s stock fell 16% that day, even though USDC had settled a record $1.21 trillion that month.

The biggest lesson from the instance was that investors didn’t wait for an earnings call to price the information because the revenue at stake was visible on public dashboards. Crypto maturing into fintech helps both information and capital move faster.

The Rise of the Agentic Economy

The third theme is the one I would have laughed at in 2024, and it produced our most-read piece of the quarter.

Vaidik’s Compute Capital Markets starts with a financing problem hiding inside the AI boom. Building a GPU cluster means borrowing millions of dollars against revenue you cannot lock in, because no instrument exists to hedge it. The buyers and sellers of compute are spread across the globe and most of them cannot open a US commodities account with CFTC approval. Any wallet, though, can settle a stablecoin payment and hold a tokenised compute credit. More than $120 billion in data-centre credit now sits against labs earning roughly $105 billion. Whoever makes that collateral verifiable and hedgeable will own the settlement layer for a new commodity.

Then come the agents doing the spending. What agents lack is judgment and trust. I explored this gap in The Business of Trust. Every commerce era widens the circle of strangers who transact with each other, and each produces a new middleman to vouch for them. We saw credit bureaus build the trust layer for borrowers, Visa did it for card swipes, and Amazon and Flipkart did it for anonymous sellers. Agents need the same. ERC-8004 gives every agent a permanent on-chain identity and work record, but that identity is a transferable NFT, so a clean reputation can be sold to a bad actor. Agents will need trust that gets repriced with every transaction, the way merchants’ standing is today. Payments will be cheap. Whoever becomes the Visa of agents will not be.

Across all three themes, the infrastructure is getting cheaper and the value is moving to the scarce layers. This could mean those who decide which assets are worth owning, if credit is worth extending, and whether counterparties are worth trusting. None of this depends on bitcoin setting a new high.

There are risks and caveats, though. The CLARITY Act still leaves token holders in a grey zone. The GPU debt stack may not hold if it turns out to be a bubble. And, so, we will keep writing about those too.

Stay tuned and keep reading us.

Until next time, stay curious,
Prathik

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For the longest time, stablecoins have been pitched as the fix for the slow and expensive movement of money across borders. The guaranteed way to move money cheaply and faster is by cutting out the middlemen and compressing the overall fees.

But the overall fees aren’t a single item. It’s split across a stack of several layers, where multiple players take a cut for the layer they operate in. Stablecoins are very good at gutting some of those layers. The trouble is that a cost you squeeze out of one layer rarely reaches the customer. More often, it just pools somewhere else in the stack.

So if we really want to make stablecoins the magic pill to solve money movement problems across the globe, we need to ask the right questions. And “Do stablecoins make money movement cheaper?” isn’t the right one. The better question is “What kind of stack do stablecoins make possible and where does the value accrue in this new stack?”

Jonah, an investor at Blockchain Capital, explains this in his guest essay today.

On to Jonah’s story,
Prathik

P.S. Jonah originally published this on X.

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Everyone says stablecoins are better for cross-border payments. Is it true?

Stablecoins are a great cross-border solution when you’re sending to someone who wants stablecoins. You can send them 24/7 for (basically) free, and they settle instantly.

The harder problem, and the focus of this article, is the cross-currency case. What happens when dollars go in one side and a foreign currency (e.g. pesos) comes out the other side? Most crypto talking heads would argue that stablecoins uniquely cut speed and cost here. What the bulls fail to mention is that fintechs already do this fast and cheap, without stablecoins. So what are stablecoins solving?

We’ll walk through how the legacy correspondent banking system works and how a modern fintech like Wise innovated on the model, which will help clarify what stablecoins are actually solving.

Correspondent Banking

Say Alice in the US wants to send her friend Bob in Mexico some pesos. Their banks have no presence in each other’s countries, so they can’t pay directly. Instead, their banks connect through a bigger bank (i.e., a correspondent bank). Alice’s bank keeps dollars parked at such a bank, let’s call it GlobalBank, which in turn holds pesos at a Mexican bank, which we’ll call BancoMX. When Alice pays, her bank debits her and instructs GlobalBank, which draws $100 from the dollars Alice’s bank parks there, converts at its own rate, skims the spread, and tells BancoMX to credit Bob, minus its own fee. SWIFT is used to coordinate this (and charges for the messages).

This plumbing is super expensive and slow, mainly because it is layered with rent-seekers. The average cost of using correspondent banking is ~15% (the transaction fee plus the FX spread baked into the exchange rate, measured on a typical consumer-sized remittance). Not to mention that a transfer typically takes one to five business days, since each middleman needs some time to take their requisite steps.

The Modern Fintech Fix

In 2011, two friends in London had mirrored needs: one was paid in euros but needed pounds to live there, the other was paid in pounds but owed a mortgage in euros back in Estonia. So they cut the banks out and paid each other locally, pounds into a London account, euros into an Estonian one. Neither payment ever crossed a border. Later, this system became Wise. The friends believed that you could match offsetting flows like these at scale, and it worked. Many other fintechs landed on the same idea.

Let’s run the Alice and Bob example again, but this time using a Wise-like system. Alice would pay into the fintech’s US account; the fintech would pay Bob from pesos it already holds in Mexico. Nothing crosses a border. The transfer Alice thinks she made is really the fintech paying out of one pocket and collecting into another. Because of this, the payment feels instant. The fintech takes on the balance-sheet risk of holding all that currency.

To avoid touching the legacy system, fintechs will try to “net” the transactions. For example, others might be sending pesos back the other way, too, so the fintech can cancel the two flows internally. If the currency pool gets too imbalanced, they can lean on the traditional system. Under the hood, fintechs use a patchwork of partner banks and licenses, with a local partner standing in wherever it lacks its own.

If all works properly, this beats the legacy system. Wise takes a small upfront fee, passes the real mid-market rate with no hidden spread, and still lands at a take rate of just 0.52% (it doesn’t publish an FX-only rate, so that blends in some same-currency transfers). The World Bank pegs the typical digital money-transfer service at around 3.5%, so Wise sits at the cheap end.

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The Stablecoin Sandwich

So Wise and fintechs like it are already providing a fast, cheap alternative to the legacy system for cross-border transfers. What do stablecoins do differently?

Let’s run the same scenario with stablecoins. Alice on-ramps $100 into 100 USDC. The USDC is sent via blockchain to Bob in Mexico within seconds and for less than a penny. Bob then off-ramps the USDC to his local bank, receiving pesos in the process. People call this the stablecoin sandwich.

From Alice and Bob’s perspective, it’s hard to say this is a better experience than Wise. From a cost perspective, stablecoins are not obviously cheaper. Moving the USDC is nearly free and instantaneous, and even the on-ramp leg is becoming nearly free with on-ramping fees compressing toward zero. But the off-ramp leg remains a point of friction as the local FX spread from dollars to pesos can get costly. ¹

What Stablecoins Actually Unlock: Open Competition

Building a network like Wise’s is so hard that only a handful of firms have ever pulled it off. Stablecoins knock the barrier down. You no longer need a global network to run a cross-border business, just a good on-ramp on one side and a good off-ramp on the other. You could say that stablecoins unbundle the fintech and turn what were previously closed networks into an open market. Instead of one provider capturing the whole spread on a corridor, local off-ramps can compete for each conversion, driving costs down in the long-run.

The cost savings arguably show up most in the long-tail corridors. Once you no longer need a global network, regional specialists can pick a slice of the world and own it instead of trying to cover the globe. Yellow Card does exactly that across African countries (a BCAP portfolio company).

Unbundling leads to market fragmentation, which enables the competitive dynamic that pushes costs down for consumers. You might argue that this won’t last, that there are already a ton of stablecoin orchestrators stitching these pieces together, that the largest players are integrating vertically again. Perhaps the market re-consolidates and the advantage is lost.

But an open rail is… open. It is hard to monopolisze. If a middleman starts making too much profit off the spread, a local off-ramp can undercut it. Everyone can play the game.

As the take rate gets compressed out of cross-border payments, the surplus that was once captured by rent-seeking middlemen should end up with consumers and businesses, manifesting as cheaper money movement.

If you’re a founder building around cross-border payments, we’d love to hear from you.

Appendix

¹ In terms of how much the stablecoin sandwich actually costs, I struggled to find credible numbers online. Almost everyone quotes the middle of the sandwich, the near-free cost of moving the stablecoin itself, and very few quote the all-in cost once you include the ramps. That is a tell unto itself: either the people quoting these numbers are doing so promotionally while leaving out the full cost, or they have misunderstood where the cost in these transfers actually sits. Either way, it is much of the reason this article exists. Anecdotally, from what I have heard from top cross-border executives, on major corridors like US to Mexico stablecoins are at best as good as the fintech alternatives; on long-tail corridors like US to Africa, they can be more favourable. If you have up-to-date, all-in, end-to-end numbers here, please comment. Would love to see them!

Disclaimer: The content provided herein may include information regarding past and/or present portfolio companies or investments managed by Blockchain Capital or its affiliates and are provided for illustrative purposes only. The views expressed in each blog post are the personal views of each author and do not necessarily reflect the views of Blockchain Capital and its affiliates.

P.S. This piece was first published here. For full disclosure, refer to the original piece.

P.P.S.: We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 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.

Lending is more complicated than knowing the yield you earn on a loan. Especially when money is lent at floating rates.

Lending makes credit available to those seeking capital and earns a yield for those funding it. A treasury that is borrowing at a floating rate today will have no idea what its payment obligation will be in three months. Traditional finance addressed this with derivatives around lending products that let borrowers swap floating-rate products for fixed-rate ones.

By wrapping a predictable instrument around an existing primitive like lending, a borrower can fix the cost of capital, cap the downside and budget cash flow needs accordingly.

But such a market doesn’t exist yet for DeFi lending. If DeFi lending wants to graduate from a looping casino to mainstream business adoption for funding capital needs, the industry needs a derivatives market.

In today’s guest op-ed, Mike, co-founder of Schema Research, explains how the derivatives market can help DeFi lending become usable in day-to-day finance beyond crypto-native circles.

On to Mike’ story,
Prathik

Follow Schema on X

DeFi lending made collateralised credit available on demand, but it did not make its cost predictable over time. Borrowers see today’s rate, not the cost of holding debt for 3 or 6 months. Lenders see today’s APY, not the return they will earn over the same period. DeFi has deep lending markets, but their rates still lack a liquid term market.

The Rate behind DeFi Returns

Lots of DeFi strategies earn one rate and pay another. A user supplies an asset, borrows against it and deploys the borrowed capital elsewhere. The return on equity comes from the income on the supplied asset and the destination strategy, minus interest on the debt. Once the position is opened, a floating borrowing rate keeps changing that final line.

Pooled lending markets were built for immediate access to liquidity. Deposits sit in a common reserve, borrowers enter on demand, and the rate adjusts with utilisation. Aave’s variable-rate model uses a gentler slope below optimal utilisation and a steeper slope above it. Higher rates attract liquidity and discourage new borrowing when a reserve becomes scarce. The quoted rate describes the pool now. It does not price 3 or 6 months of financing.

A fund offering a quarterly target return needs a cost of capital before choosing position size. Lenders face the same budgeting problem on the income side. They can see today’s supply rate, yet their realised return depends on the average rate over the full holding period. A term quote lets both sides decide whether the spread is wide enough before the trade starts.

The rate path in Aave shows why a spot quote cannot do that work. Across the 365 days, the Ethereum Core USDC borrow APY averaged 4.84% and ranged from 2.76% to 15.43%. During the largest event in the sample, it rose from 3.15% on March 24 to 15.43% on April 23. A strategy opened against the first number still had to fund itself at the second.

Traditional rate markets separate the loan from its financing terms. The BIS measured $7.9 trillion of average daily turnover in OTC interest-rate derivatives, with overnight index swaps accounting for $5.1 trillion. Crypto’s perpetual funding markets show how a recurring onchain cash flow can also be priced and traded on its own.

Two Rate Markets, Two Different Risk Drivers

Perpetual futures turned a recurring cash flow into a market instrument. It is the established crypto-native rate cash flow that can be separated from the underlying position and traded directly. Boros makes the future stream tradable through Yield Units (YU). A long YU pays a fixed APR and receives the funding rate of a named perp market, and a short YU receives fixed and pays funding. The position is margined and settles against the funding index without requiring the matching perpetual position.

But a perp funding rate and a lending rate are not interchangeable underlyings. Perp funding responds to positioning and the gap between a perpetual contract and its spot index. A money-market borrow rate responds to available liquidity, loan demand, utilisation and the curve chosen for that reserve. A crowded long trade can push funding higher while a stablecoin pool remains well supplied. A large stablecoin borrow can drive a pool beyond its utilisation kink even when perpetual positioning is balanced.

The two series behaved differently over the common 365-day sample. Annualised Hyperliquid ETH funding averaged 6.13% and was negative on 14.5% of days. Its 5th-to-95th percentile range was -4.39% to 10.95%. Aave USDC borrowing stayed positive, with a 3.26% to 6.57% range across the same percentiles. One-day auto-correlation was 0.661 for funding and 0.884 for lending. Their level correlation was 0.035.

Basis is also venue-specific. A Binance funding swap does not settle against Hyperliquid funding. Lending creates the same issue at pool level. Relative to Aave Core WETH, Aave Prime WETH and the sampled Morpho weETH/WETH and wstETH/WETH markets showed mean absolute gaps of 46 to 49 basis points. The gaps exceeded 100 basis points on about 9% of days, and the largest observations were above 500 basis points.

Funding derivatives price demand for perpetual leverage. It cannot lock the realised Aave borrowing cost without leaving a basis position. Lending-rate exposure therefore needs its own index, liquidity and settlement record.

Who Needs to Trade Lending Rates

The most obvious fixed-rate buyer is a leveraged borrower. A loop manager, a delta-neutral basis book, or a treasury that borrows stablecoins against ETH is short the borrowing rate: its economics deteriorate when financing rises. Paying fixed and receiving the referenced floating index changes that moving cost into a hurdle rate for the chosen term.

High rates can come from a change in the pool’s state and a change in the rate curve itself. Exploits, depegs, liquidation waves, large supplier withdrawals, reserve freezes, oracle mistakes and expiring incentives can remove available liquidity or create urgent borrowing demand.

A fund manager deciding between 2x and 8x leverage needs a financing assumption. If that assumption can change by several hundred basis points after deployment, the manager must either size conservatively, hold idle liquidity for an unwind or accept that the target return is fragile. For a strategy manager, a fixed quote preserves the financing assumption used to size the expected spread. The position still carries the protocol, collateral and market risks that caused the dislocation.

Lenders provide the opposite natural flow. Receiving fixed and paying the referenced supply index converts uncertain lending income into a term yield floor. Fund managers can then support a target return with an executed rate while continuing to disclose credit, liquidity and protocol risk.

Curators can use the same instruments across portfolios of lending venues, collateral types and incentive programs. They can adjust a financing or income component without moving every underlying loan. This is useful when principal is slow to reallocate, secondary liquidity is thin or a mandate requires the credit position to remain in a specific venue.

Rate market makers absorb imbalances between borrowers seeking to pay fixed and lenders seeking to receive fixed. A trader may receive fixed when it expects the realised floating rate to settle below the quoted rate, or pay fixed when it expects floating rates to remain higher. Similar positions can express differences across lending venues or maturities. Market makers quote both sides, earn the bid-ask spread and hold the remaining rate exposure until offsetting flow arrives.

Maturity is the coordination problem. Campaign capital may need one month, strategy leverage a quarter and treasury allocations six or twelve months. Too many dates divide liquidity. One date forces users to roll at the wrong horizon. Initial markets need a small set of repeatable maturities that match recurring allocation cycles.

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How DeFi Currently Creates Fixed Rates

DeFi reaches a fixed rate through several different transactions. A protocol can originate a fixed-term loan, auction principal, tokenise a maturity claim or swap a floating index. Each route fixes a different cash flow and leaves a different mix of credit, collateral, margin, liquidity and roll risk.

Early request-for-loan systems struggled to match borrowers and lenders. Shared pools reduced that coordination problem and allowed entry or exit on demand. The current fixed-rate designs try to add term pricing while retaining some of the liquidity and composability created by pooled markets.

Morpho Midnight by Morpho creates isolated fixed-rate, fixed-maturity credit. Lenders buy credit units and borrowers sell debt units. Each unit maps to one loan token at maturity. Makers publish signed offers and takers execute them on-chain. Tenor (@TenorFinance) adds market and limit execution, plus renewal and migration between Midnight and variable-rate markets. Principal funds a credit position, with collateral, liquidation and bad-debt risk inside the market.

Term Finance (@term_finances) originally matched overcollateralised term repos through sealed-bid, single-price auctions. Successful borrowers and lenders clear together, collateral is locked for the repo and lenders receive Term Repo Tokens redeemable at maturity. Term V2 adds order markets and routes supporting external capital at execution, allowing maker capital to remain productive until an order fills. Maturities still divide liquidity and concentrate repayment or refinancing around term end. The August 2026 governance exploit affected Term Strategy Vaults, a separate pooled product, rather than the repo markets described here.

Maturity-token markets fix the purchase yield on a future claim. Pendle (@pendle_fi) separates a yield-bearing asset into a principal token, redeemable at maturity, and a yield token that receives yield until that date. TermMax uses a fixed-rate token and complementary X token, while a Gearing Token records collateral and debt. The underlying asset must enter the tokenised structure, and liquidity is split across assets and expiries.

Rate derivatives leave principal on its original venue. A lending-rate swap exchanges the realised supply or borrow index for a fixed stream on a matched notional. That keeps the loan or deposit inside the lending market and adds a separate margin account, settlement process and basis position. A change in loan size or maturity after entry creates a mismatch between the two legs.

Fixing the rate does not diversify the underlying credit exposure. In fixed-term credit and maturity-token structures, principal or the redemption claim remains exposed to the contracts, collateral rules and bad-debt mechanism of the platform that holds or issues it. A rate swap leaves the deposit or debt on the selected lending venue and adds a separate margined derivative exposure. The architecture determines where credit and protocol risk sits, not whether it exists.

XCCY (@xccy_finance) rate swap does not originate or pool the underlying loan principal. The user keeps the lending position at a chosen venue, the vAMM prices its rate and the Collateral Engine manages margin and settlement.

Because the swap is separate from principal, it can also be packaged with a floating-rate deposit or debt to create synthetic fixed-rate products. LockYield combines a floating deposit with the rate swap to produce a fixed-yield position, while LockBorrow combines floating debt with the opposite swap direction to produce a fixed financing cost.

Fixed-term credit sets the terms of new principal, maturity tokens fix the purchase yield of a redemption claim, and rate swaps change the rate attached to an existing deposit or debt.

XCCY as Infrastructure for Predictable Yield

XCCY (@xccy_finance) separates the lending position from the rate hedge. The APR Oracle records the cumulative supply or borrowing rate of the connected venue. The vAMM discovers the fixed rate, while the Collateral Engine acts as the exchange’s risk and settlement layer. It custodies margin, records positions, checks solvency, supports liquidations and settles matured swaps.

Direct rate LPs provide inventory by allocating margin to maturity-specific pools and quoting across a fixed-rate range. As trades move through that range, LPs accumulate the opposite fixed and floating exposure and earn trading fees. Their margin supports rate-market exposure inside the Collateral Engine, it does not fund the referenced loan.

LockBorrow pairs floating-rate debt in a supported lending market with a pay-fixed, receive-floating swap on the same notional. The floating leg offsets the referenced financing cost leaving the fixed leg as the rate applied through maturity.

LockYield supplies the asset and trades its future floating income for a fixed rate on the same notional. Direct IRS trading exposes the two legs without packaging them into a loan or deposit. Rate LPs earn trading fees for quoting ranges and carrying the other side of flow. On Ethereum, XCCY contracts are transparent upgradeable proxies so upgrade administration remains part of the protocol risk.

Case study 1: Fixed sUSDe loop

On April 19, 2026, the Aave sUSDe/USDe loop had a positive entry spread. The native sUSDe yield was 3.88% and the USDe variable borrow rate was 2.92%. At 8x gross exposure, those rates implied a 10.61% annualised return on the allocator’s equity before transaction costs. The calculation still depended on every future USDe rate update.

The position starts with $1 million of equity. Repeated borrowing and resupply build $8 million of sUSDe collateral against $7 million of USDe debt. That is an 87.5% debt ratio with a 90% maximum LTV and a 92% liquidation threshold, so the position has little room for a change in the collateral-to-debt price or additional debt accrual.

USDe borrowing moved above the asset yield within two days. The daily Aave rate reached 14.85% on April 21 and peaked at 16.76% on April 25. USDe borrow rate across the one-month cohort ending May 19 was a 7.3% realised average. Native sUSDe yield averaged 4.3% over the same case-study window.

The floating position therefore earned $28,036 on its $8 million gross sUSDe balance and paid $41,902 on $7 million of USDe debt. Net income for the 30-day period was negative $13,866. Expressed against the original $1 million of equity, that is a negative 16.87% simple annualised return.

The fixed comparison uses a 2.76% all-in borrowing rate for one month. It is a scenario input calibrated to the term curve available at entry. The asset side remains unchanged at the realised 4.26% sUSDe yield. Financing falls to $15,900, leaving $12,136 of net income, or a 14.77% simple annualised return on equity.

The financing choice changes the one-month result by $26,002. The difference equals the 4.5194 percentage-point rate gap applied to $7 million of debt for 30 days. At 8x exposure, every 100 basis-point change in the borrowing rate moves annualised equity return by seven percentage points. The loop entered with a positive spread in both versions and the floating version lost that spread when utilisation repriced USDe debt.

LockBorrow would pair the Aave debt with a swap that receives the referenced floating borrow index and pays fixed on the matched notional. When the index, term and notional line up, the variable swap receipts offset the variable interest added to the Aave debt. The allocator can then size the loop against a known financing rate before deploying capital.

Case study 2 : Locking Yield Before Rates Normalise

On August 25, 2026, Aave posted a yield of 12.57% with a utilisation rate of 99.98%. Later that same day, Aavescan’s current yield stood at 3.78% with a utilisation rate of 92.41%. Aave’s 30-day and 1-year averages were 3.55% and 3.56%, respectively. Consider a $1 million allocation for 90 days. The modeled floating path uses 12.57% for three days and 3.50% for the remaining 87. The three-day spike is an assumption as the live rate normalised faster. On a simple 90/365 basis, the path produces a 3.80% average APR and $9,376 of income.

The fixed scenario assumes that LockYield fills at 6.00%. At a 0.30% annualised opening fee, the contract reserves a $740 fee buffer and supplies and swaps $999,260. The fixed leg accrues $14,784; the fee on the traded notional is $739, and the unused $0.55 of the buffer is returned at settlement. The resulting maturity payout is $1,014,044.

Net income is $14,044, or a 5.70% simple annualised return on the original principal. That is $4,669 above the modeled floating path. Floating supply would need to average 5.70% over the full 90 days to produce the same net income after the assumed fee.

This scenario isolates rate income. The 6% quote and the three-day normalisation path are assumptions, and an executable pool quote is required before publication. The calculation excludes transaction costs and the risks of maintaining or exiting the margined swap. These include limited market depth, liquidation and index basis.

What Makes a Lending-Rate Market Work

Settlement starts with a precisely defined reference index. It must specify the venue, pool, asset and whether the swap references the supply or borrowing rate. The index must then preserve a cumulative record through maturity.

The swap should also match the notional and term of the underlying position. Repaying debt, borrowing more or withdrawing supplied capital after execution leaves part of the exposure unhedged.

Liquidity should concentrate in a few maturities that match common allocation horizons. Too many dates fragment market-maker inventory, while too few force users to roll at the wrong time. A term curve only becomes useful when borrower and lender demand returns often enough for the same maturities to be quoted repeatedly.

Fixed quotes also require capital willing to absorb rate risk. Market makers price expected floating rates, index basis, adverse selection, mark-to-market volatility and the cost of margin. Their spreads must compensate them for holding that exposure until maturity.

Before users and LPs commit capital, they must accept the smart-contract and upgrade risk of the margin and settlement layer. Market quality is then visible in executable size by maturity, spread and slippage at a stated notional basis to the referenced loan and the record of matured positions.

Conclusion

Lending-rate derivatives do not replace pooled credit but they add a tradable term price to exposures that already exist. For allocators, that changes the decision from forecasting every future rate update to comparing an upfront fixed quote with the remaining risks of the strategy.

XCCY applies that structure to Ethereum lending through separate index, execution and margin layers. Its progress can be measured through executable depth, repeat flow and settled positions across a small set of maturities.

That’s it for today.

Follow Schema on X

P.S.: This piece was first published here.

P.P.S.: We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 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.

I am convinced that we are past the crypto-as-a-product era. This shows up in what our team writes at The Token Dispatch.

Over the past 7-8 months, we’ve written extensively about stablecoins, money market funds running on the blockchain, tokenised stocks and gold, and similar topics. But when you zoom out and look at these domains, the reason we continue to write about them, and you - our readers - continue to read about them, is because they are all proven that the world has always wanted. Blockchains and crypto infrastructure have improved access and reduced the costs of moving and owning such assets.

Nobody wants to miss a good investment. Whether you are in the U.S., Japan, or Timbuktu, the globalised world has made people aspire to be part of a successful company halfway across the world. At a time like this, any technology that helps people achieve this by bringing the world closer together through access will be adopted in large numbers.

Tokenisation is a force multiplier that helps achieve this by wrapping and standardising global assets across diverse compliance rules and accessibility barriers. It can do to the financial world what containerisation did to global trade.

Aleks Larsen, a General Partner at Blockchain Capital, makes this case with far more precision in today’s guest essay, so I’ll hand it over to him.

On to Aleks’ story,
Prathik

Follow Aleks on X

Finance has a very expensive packaging problem. Every asset comes in its own wrapper and lives in its own operating system: a mortgage is a collection of contracts, PDFs, databases, and servicing relationships. A private fund interest is a subscription agreement and a row on a transfer agent’s spreadsheet. A share of stock lives across a chain of broker, custodian, and depository records. The list goes on.

When assets move between institutional boundaries, they often have to be unpacked, verified, reconciled, and repacked into the receiving institution’s systems. Much of this work exists only because institutions maintain different representations of the same underlying assets and claims. At global scale, that fragmentation places an enormous hidden cost on society: nearly $1.8 quadrillion of assets sit on the world’s balance sheets, yet much of it still can’t move freely between institutions without bespoke operational work. That friction slows the rate at which capital can be redeployed into new businesses, infrastructure, housing, and other productive uses.

Tokenisation attacks this problem at its root. A token gives an asset or financial claim a standard, machine-readable interface. Once assets can be recognised and used on shared networks, exchanges, lenders, custodians, servicers, and software applications can interact with them without rebuilding the financial stack each time. The result is a path toward capital markets that operate on common, programmable rails where assets can move, settle, and be put to work with far less friction.

The best analogy for how tokenisation will change the world is containerisation.

Containerisation Enabled the Modern Global Supply Chain

Before the 1960s, cargo moved in many different forms. Coffee travelled in sacks, machinery in crates, cotton in bales, and oil in barrels. Every commodity had its own handling requirements, so every ship had to be loaded and unloaded by hand. Skilled longshoremen developed an art around this work. They packed ships densely, distributed weight, and secured cargo so that it didn’t shift and break at sea. This craft existed purely because the system lacked a standard package. The result was that ships often spent more time sitting in port than at sea, and break-bulk cargo was repeatedly handled, counted, damaged, lost, and stolen as it moved between ships, trains, trucks, and warehouses.

In 1956, North Carolina trucking entrepreneur Malcolm McLean sailed a converted tanker, Ideal-X, from Port Newark to Houston carrying 58 detachable trailer bodies. On arrival, trucks picked them up without ever opening them. The Ideal-X loaded at $0.16 per ton, which was roughly 36 times cheaper than break-bulk. This marked the birth of the modern shipping container.

Over the next two decades, the dimensions, fittings, and load requirements of containers standardised and the entire supply chain reorganised around the box, each function now able to specialise. Ships were built with vertical cells so containers could be stacked safely. Cranes were engineered for predictable, high-speed lifts. Truck chassis and railcars were designed around matching dimensions and attachment points. And ports became enormous machines for moving standardised containers between specialised forms of transportation.

The first-order impact was a massive decrease in transport costs in both time and money. Australia–Europe transit fell from 70 days to 34 and vessel capacity quadrupled. This allowed trade to shift toward manufactured and intermediate goods, and firms began to fragment production across countries, while new logistics companies emerged to coordinate increasingly complex global networks as supply chains rapidly expanded.

The second-order impact was a massive increase in economic activity. The World Bank estimates bilateral trade between developed countries rose 1,240% in the fifteen years after both partners adopted containers. Through containerisation and the infrastructure reorganisation they enabled, global supply chains were able to scale and the world’s economic development accelerated in a big way.

Tokens are Financial Containers

A token is a container for a financial claim. Instead of cargo, it carries economic rights and operating state: who owns the asset, how it can move, what cash flows it produces, what permissions apply, and how software is allowed to interact with it.

Once an asset has a machine-readable interface, an exchange can make it tradeable, a lending market can finance it, a custodian can hold it, and a wallet can route its cash flows. Software applications can recognise the asset and apply rules to it without negotiating a new integration with every institution involved. This is what makes tokenisation different from simply digitising a document or adding another database: participants can rely on the token as the asset’s shared operating interface. This turns out to be extremely powerful when the effects compound across an ecosystem.

Stablecoins offer the clearest example of tokenisation’s potential. While international wires generally take days to move dollars through the correspondent banking system, stablecoins move around the world in seconds, at a cost of ~0. This works because stablecoins are accepted by a network of exchanges, custodians, on/off ramps, payment processors, and wallets around the world who recognise the token interface. These are the new financial ports, cranes, trucks, trains and ships, and they move value in tokens. Much of this infrastructure was originally built to support Bitcoin and Ethereum, but once it existed, stablecoins and other tokens could travel through it too. Stablecoin activity then attracted more users, liquidity, applications, and infrastructure, making the network more valuable for the tokens that followed.

The results speak for themselves: there’s $300B of stablecoins in circulation and they facilitate Visa-scale volume with roughly 10x the velocity of M1/M2. Cross-border payment costs have fallen by an order of magnitude and hundreds of millions of people around the world have gained reliable access to dollars and payment rails. The network has arguably proven its worth for dollars and delivered far better cost, speed, and reach, resulting in an order-of-magnitude increase in economic activity per dollar.

And now, that pool of highly active dollars is attracting other assets that seek stablecoin deposits. Today there’s nearly $40B, up about 10x from 2 years ago, and growth is accelerating. This is driven by Treasuries, money market funds, commodities, private credit, equities, and fund interests across hundreds of issuers.

The natural next step is for the plumbing to come onchain as well. Our portfolio company Tare, for instance, is bringing origination, servicing, and securitisation of credit assets onchain, leveraging the ability for tokens to carry with them the servicing history of all underlying loans. This allows them to replace the expensive, highly intermediated lending value chain with a thin software layer and transparent marketplace where lenders and borrowers share a universal ledger of tokenised loans. This simultaneously lowers the cost of lending while making the loans easy to integrate as collateral across the network, increasing the gravitational pull of tokenisation.

Every asset class has a similarly shaped opportunity, and as the infrastructure gets built out, the network effect grows and attracts more liquidity, users, infrastructure and applications. This will cause the growth of tokenised assets to accelerate from here.

Follow Aleks on X

Capital Markets Will Reorganise around Tokens

Just as global supply chains reorganised around the container, global capital markets will reorganise around tokens.

The shape of this is already visible in DeFi. Another of our portfolio companies, Aave, allows holders of eligible tokens to post them as collateral and draw credit from a market of lenders at floating rates. The protocol contains the rules and eligibility is determined at the asset-level. This departs from the market structure of traditional lending. To borrow against an asset today, a person or business typically begins with an institution. The institution controls access, evaluates the borrower through its own process, and offers products through its own network. The financial capability is attached to the institutional relationship.

On Aave, the gating requirement is the asset itself. Smart contracts recognise the token, apply transparent rules, and connect it to a market for capital. This inverts the relationship between asset holders and financial services: capabilities attach to the asset, not the owner’s institutional relationships.

Put differently, tokens make assets executable, like software. Once an asset exists in a form a public network can recognise, applications can compete to add capabilities to it. An exchange can make it tradeable, a lending market can finance it, a wallet can route its cash flows, etc. The issuer brings the asset onchain once rather than building a separate system for every use case.

This changes the architecture of financial institutions. Banks, brokerages and asset managers currently bundle custody, underwriting, liquidity, servicing, and compliance, and manage distribution inside closed products. Crypto networks allow those functions to separate and specialise. One institution can originate and service a loan, while others can fund it, price its risk, trade it, insure it, or build applications that use it. The asset can move between modular services through a common interface rather than being rebuilt inside each provider’s system.

The scale advantage therefore shifts from the institution to the network. In legacy finance, large institutions support more products because they can absorb the fixed cost of infrastructure for each asset and customer segment. On public crypto networks, on the other hand, much of that infrastructure is shared. New providers can reach assets, capital and users without rebuilding the ledger, exchange, custody and settlement systems, making it simultaneously an easier and cheaper stack to build on.

The network effects have already hit escape velocity with stablecoins, and as a result, capital markets will increasingly reorganise as open networks of specialised services built around tokenised assets. In this next paradigm, institutions will compete on the quality of the capital, underwriting, risk management, servicing and distribution they provide, not on their ability to own the database or control the customer’s only doorway to the market.

The World’s Balance Sheet Will Come Onchain

The most important consequence of this shift is the creation of a universal market for capital.

Today, capital markets are bottlenecked by institutions. Most people and businesses don’t access capital markets directly; they access the narrow menu offered by the institutions willing and able to serve them. Those institutions determine which customers, geographies, asset classes, and transaction sizes they support.

Investors face the same constraint in reverse. They don’t have access to the world’s assets. They have access to the assets that institutions have chosen to underwrite, package, integrate, and distribute.

As a result, much of the world’s economic value is unreachable by today’s capital markets. Small receivables, local infrastructure, private businesses, emerging-market credit, and unconventional cash flows may be economically valuable, yet too fragmented, unfamiliar, or geographically distant to justify the expensive machinery that the legacy system required to finance them. The opportunity may be attractive and the capital may exist, but the network between them doesn’t.

Tokenisation changes this by giving assets a standard interface through which they can become discoverable on global rails. As the financial system reorganises around this new interface, the cost of participation drops dramatically for everyone. Finance becomes a native capability of software, which increases the capacity of the application layer to specialise within niche asset classes and geographies and lets markets reach where they couldn’t before. Business applications that didn’t traditionally have access to sophisticated financial services will be able to easily incorporate payments, working capital financing, collateral management, and treasury functions within their stack, allowing many of today’s idle or “dark” assets to connect to onchain capital markets. To be clear, tokenisation won’t magically make unfinanceable assets financeable, but it will eventually allow many quality assets, that are structurally disconnected today, to participate in markets.

And AI will amplify this shift by helping with operational requirements. AI agents will help evaluate assets, price risk, allocate capital, manage collateral, and settle transactions across this global, efficient, machine-readable market, thus further lowering the costs of delivering financial services. Through a combination of AI and crypto rails, markets that are currently bespoke and episodic stand to become continuous, global, and increasingly automatic, creating far more opportunities around the world and unshackling capital from institutional silos.

Capital allocation is one of civilisation’s most powerful steering mechanisms. It determines which businesses expand, which technologies reach scale, which homes and factories get built, and which regions develop. Assets that are too small, local, bespoke, or operationally expensive for today’s financial system can become worth underwriting when the cost of reaching, financing, and servicing them collapses.

This was the deeper consequence of containerisation. The container made entirely new patterns of trade and production economically possible. Goods could be produced where they were cheapest, assembled somewhere else, and sold around the world because the cost of coordinating the network had collapsed.

Tokens can do the same for capital. Over the next few decades, the world’s balance sheet will be able to evolve from a collection of isolated records into a market that software can navigate, allowing capital to flow according to merit, instead of simply who controls the pipes. If stablecoins are any indication, this could enable the world’s capital markets to grow immensely and reach into places they’ve never been able to before.

That’s it for today.

Follow Aleks on X

P.S.: This piece was first published here.

P.P.S.: We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 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.

Money that’s not liquid enough is as good as a souvenir. Electricity stored at the grid is of no use if it can’t be circulated.

Often when I read about the new kinds of money humans innovate, the first question I ask is: “Does this circulate enough to ensure it satisfies the basic utilities of money?” It must have a store of value, should compound, and be freely movable to allow transfer of value.

Money sitting in a bank account doesn’t benefit the masses - be it dollars or stablecoins. Liquidity that’s disconnected from its points of application is exclusionary by nature and defeats the fundamental idea of money.

Circulation is key for any kind of money to be adopted by the masses, and credit is the perfect tool to help circulate money. Credit does to money what gridlines do to electricity. What makes the dollar the dollar is that an entire credit apparatus sits behind it, including banks, underwriters, and guarantee structures, that takes idle liquidity and routes it toward people who can put it to work.

Stablecoins have solved the movement problem, but it’s not yet a complete form of money.

Criptolawyer/Ana Ojeda, who leads institutional business development at Blend, writes in today’s guest essay about the role credit needs to play to make stablecoins support financial inclusion.

On to Ana’s story,
Prathik

Follow Ana on X

Crypto Investing On Autopilot

You’ve probably had your brain fried tracking 50 things for the few tokens you handpicked from a pile of random. Crypto is not a joke. If anything, it is a sad place that robs people off their money. Glider builds on the opposite philosophy. It helps you pick a basket of assets and programmatically rotate it based on conditions you define.

• One basket holds the Mag7 at equal weight.

• Another mirrors congressional stock trades, the Pelosi Tracker.

• Everything stays non-custodial. No gas fees either.

Today’s issue was sponsored by them, and it comes in the week they launched ATPs with Bitwise. More on their release on Bloomberg here. Explore how Glider combines RWAs and programmatic finance directly in its product here.

Let your strategy run itself at Glider.fi.

We have built faster settlement, cheaper cross-border payments, dollar-denominated accounts, crypto cards and increasingly efficient fiat ramps. These are meaningful achievements, particularly in emerging markets, where accessing a stable currency can already transform someone’s financial life.

But we have mostly solved how money moves. We have not solved who gets access to capital.

A worker may now receive part of her salary in USDC. A merchant can accept stablecoins from customers. A small business can preserve its treasury in dollars instead of watching its local currency depreciate. Then the merchant needs financing to purchase inventory, the worker needs to cover an emergency, or the business needs thirty days of working capital.

At that moment, the supposedly new financial system usually offers one answer: bring us more collateral than you want to borrow.

That is excellent collateralised liquidity. It is not financial inclusion.

I come from Venezuela, so I have never thought about stablecoins as an abstract crypto product. I think about them as protection against devaluation, access to dollars and a way for people and businesses to operate when the traditional financial system cannot serve them effectively.

But protection is only one part of finance. People do not only need a place to store money. They need to receive it, move it, earn on it and sometimes borrow against their future income rather than their existing wealth.

My thesis is that stablecoins will reach their full potential in emerging markets only when they become connected to a credible credit layer: one that combines productive accounts, local underwriting, regulated lending, business operating data, guarantees and institutional capital.

1. Overcollateralised lending solved a crypto problem

Overcollateralised lending was a rational response to the conditions in which DeFi emerged.

An open protocol does not automatically know a borrower’s identity, income, employment, repayment history or legal enforceability. Requiring liquid collateral made it possible to lend without relying on a conventional credit relationship.

That model works well for traders, market makers and treasury managers. It can be transparent, automated and highly efficient.

But someone who needs $100 because they do not have $100 cannot deposit $150 to borrow it. A small merchant seeking working capital usually cannot lock up more liquid capital than the inventory it wants to purchase. The people most likely to benefit from credit are precisely those least likely to possess excess collateral.

Real credit requires something more difficult than a liquidation engine. It requires an informed judgment about someone’s ability and willingness to repay.

That judgment may use payroll history, merchant sales, recurring cash flows, employment records, transaction behaviour, previous repayments, invoices or commercial relationships. It also requires servicing, collections, consumer protection and a party capable of absorbing losses when the judgment is wrong.

A blockchain can make the infrastructure surrounding credit more transparent and efficient. It cannot make credit risk disappear.

This is the central limitation of the stablecoin economy as it exists today. We have created global liquidity without creating a sufficiently sophisticated system for allocating that liquidity to the people and businesses that need it.

The encouraging part is that the different components of that system are beginning to appear.

2. Cashea: underwriting through local behaviour

One of the most instructive examples is Cashea in Venezuela.

Cashea is not a crypto lending protocol and does not offer unrestricted cash loans. It provides purchase lines that consumers can use across a broad merchant network. Users make an initial payment and complete the remainder in instalments.

What makes the model relevant is not simply the buy-now-pay-later format. It is the information generated through the commercial relationship.

Cashea can observe where someone shops, how regularly they repay and how their behaviour changes over time. Responsible repayment can increase purchase capacity, while the merchant network gives every credit decision a specific economic purpose.

This is often more useful for local underwriting than knowing that a wallet temporarily holds a token worth 150% of the requested loan.

Cashea demonstrates that creditworthiness can be constructed through real commercial activity, even where traditional credit bureaus and banking relationships remain incomplete. The merchant network is not merely a distribution channel. It becomes part of the underwriting system.

Its exact model will not be portable to every country because credit is deeply local. But the underlying principle matters: emerging-market underwriting will increasingly depend on real economic behaviour rather than crypto collateral alone.

The next challenge is connecting that local information with properly licensed lenders and deeper pools of capital.

3. Portola: distributing credit without turning every wallet into a bank

This is why one of the products that interests me most right now is Portola.

I recently met one of Portola’s founders in person, and the conversation stayed with me because the company is addressing one of the most complicated questions in embedded finance: how can a wallet or fintech offer credit without pretending that it should become a bank?

Portola connects platforms with regulated lenders while allowing the platform to preserve its user experience. The licensed lender still performs the underwriting, funds the loan and bears the credit risk. Portola coordinates the infrastructure around applications, offers, compliance, settlement and servicing.

That separation is important.

A wallet should not need to become a licensed lender simply because its users need credit. A fintech should not be forced to build loan origination, servicing and collections infrastructure from zero. At the same time, embedding a credit button cannot make regulatory responsibilities or credit losses disappear.

Portola’s current architecture is not yet a complete answer to emerging-market microcredit. Local markets may require alternative data, domestic lenders, local consumer-credit licenses and very different servicing practices.

But its model provides a compelling blueprint: keep the experience embedded while preserving clear responsibility for underwriting, compliance, capital and losses.

The next step is connecting that architecture with richer local signals. Payroll history, merchant revenue, recurring payments and transaction behaviour could help a regulated lender evaluate a borrower who may not have a conventional credit file.

Cashea demonstrates how local credit information can emerge. Portola shows how that information could eventually connect with regulated lending capacity.

4. Flex: credit inside the operating life of a business

For businesses, the same thesis appears in a different form.

A company does not experience payments, treasury, accounting and credit as separate theoretical markets. It experiences them as one continuous operating problem.

Revenue arrives today. Payroll is due tomorrow. A supplier needs to be paid in another country. Inventory must be purchased before the next sales cycle. An invoice will settle in thirty days, but the company needs liquidity now.

This is why Flex is another product that interests me. Flex combines business banking, stablecoins, payments, cards and private-credit products within one financial environment. Its financing products include working capital, revenue-based financing, business lines of credit and accounts-payable and accounts-receivable financing.

When revenue, supplier payments, receivables and expenses live inside the same platform, the financing experience can be connected with how the business actually operates.

Instead of asking a company to present a static snapshot of itself, the platform can understand the timing and movement of its cash flows. That context is particularly valuable for businesses whose financial reality does not fit neatly inside a conventional credit application.

Flex is not an emerging-market microcredit platform, and product availability depends on geography and eligibility. But it demonstrates what the eventual product should feel like: financing embedded into the financial operations of a business rather than offered as a disconnected loan.

Stablecoins make this model even more relevant for internationally operating companies. A business may receive revenue in one jurisdiction, pay suppliers in another and run payroll somewhere else. Stablecoin settlement can reduce the friction involved in moving that money, while embedded credit can finance the gaps between those activities.

Portola shows how platforms can connect with lenders. Flex demonstrates how credit can live inside the actual financial life of a business.

Follow Ana on X

5. Cap: separating the borrower from the risk capital

The next question is who absorbs the risk when the borrower cannot provide sufficient collateral personally.

This is where Cap becomes relevant.

Cap separates its system into depositors, borrowers and external underwriters. Rather than requiring the borrower to provide all the collateral personally, another participant can supply financial coverage and earn a premium for assuming that risk.

In one example, Flow Traders accessed stablecoin credit through Cap, while Lombard BTC holders supplied the coverage.

This is institutional credit, not emerging-market microfinance. But the structural idea is powerful: the borrower and the provider of risk capital do not need to be the same person.

Applied to emerging markets, a similar guarantee layer could eventually involve local financial institutions, insurers, development-finance organisations, employers, merchant networks or specialised first-loss funds.

A merchant network might guarantee part of a credit portfolio because the financing increases sales. An employer might support salary-linked advances. An institutional investor might fund a first-loss pool because the transaction data is transparent and the risk-adjusted return is attractive.

This is not about making collateral disappear. Credit risk must sit somewhere.

The objective is to move the obligation to provide coverage away from the individual borrower and toward professional parties capable of evaluating, pricing and absorbing risk.

Cap’s architecture helps demonstrate how that separation could work.

6. Blend: making the underlying account productive

At Blend, where I lead institutional business development, I work on another part of this emerging system: the productive account and capital-access layer.

Before a fintech, payroll platform, wallet, or neobank can offer sophisticated savings or credit products, it needs infrastructure that keeps each user’s balance separate, applies controls, produces audit trails and connects the account with appropriate financial strategies.

Blend provides non-custodial, per-user accounts that let platforms offer governed access to stablecoin yield and institutional financial products under their own brands.

We are not trying to replace the local lender or decide whether someone in Brazil, Mexico or Venezuela should receive a loan. That decision belongs to properly licensed institutions with relevant local information.

Our role is to make the underlying account productive, controlled and interoperable.

The more I work on Blend, the more convinced I become that yield is not the endpoint. It is the first productive use of a digital financial account.

A stablecoin balance should not remain idle while the user waits to spend it. It should be able to access an appropriate return, remain available for payments and eventually become part of a broader financial relationship.

That relationship could include payroll, treasury, merchant payments and, once the necessary underwriting and lending infrastructure exists, credit.

This is why we are interested in working with teams across the entire emerging stack. Blend does not need to become Cashea, Portola, Flex or Cap. The more valuable opportunity is to make these specialised layers interoperable.

The future financial system will not be built by one company rebuilding accounts, payments, underwriting, guarantees, servicing and capital allocation internally. It will be built by connecting the best infrastructure at each layer while presenting the user with one coherent product.

7. How the pieces fit together

Once these components are placed together, the shape of the future product becomes much clearer.

A person receives salary or business revenue in a stablecoin account. They use that same account to pay merchants, suppliers, employees or contractors. The portion they do not need immediately can access an appropriate return rather than remaining idle.

Over time, the account develops a financial history based on verified income, transactions, commercial activity and repayment behaviour. That information can be evaluated by a local underwriting system and routed to a properly licensed lender.

A guarantee provider or first-loss investor can cover the portion of the risk the borrower cannot collateralise. Repayment can then be coordinated with future cash flows.

For a business, the same account could receive customer payments, run payroll, manage supplier bills, allocate treasury and provide working capital based on actual operating performance.

The user should not need to understand which company provides the account, which lender funds the loan, which underwriter supplies the guarantee or which infrastructure executes settlement.

They should experience one coherent financial relationship.

Cashea represents the local behavioural context. Portola represents the connection with regulated lenders. Flex represents the business operating and working-capital experience. Cap represents guarantees and professional risk capital. Blend represents the productive account and access infrastructure underneath the user’s balance.

These companies do not compete to solve the same problem. They are potential components of the same architecture.

8. Credit remains local, even when capital becomes global

None of this eliminates regulation.

Credit is considerably more local than stablecoin settlement. Consumer disclosures, affordability assessments, interest-rate restrictions, data privacy, licensing, collections and credit reporting vary significantly by jurisdiction.

Stablecoins may reduce settlement costs, but they do not remove these obligations. If anything, embedded credit makes it more important to define responsibilities clearly.

The account provider does not need to become the lender. The lender does not need to build its own stablecoin infrastructure. The distribution platform should not silently assume the underwriting risk. The guarantor does not need to control the user relationship.

But every participant must know what it is responsible for.

This is why I do not believe there will be one global smart contract offering identical unsecured loans everywhere. Capital may become increasingly global, but underwriting, regulation and enforcement will remain connected to local realities.

A lender in Colombia may understand merchant cash flows that a global protocol cannot evaluate. A payroll company in Mexico may understand income stability better than a foreign credit bureau. A merchant network in Venezuela may observe repayment behaviour invisible to the traditional banking system.

The opportunity is to connect global stablecoin liquidity with institutions that understand these local realities.

9. The real stablecoin killer product

The stablecoin killer product for emerging markets is not simply a better way to buy USDC.

It is a financial operating account where a user can receive money, preserve its value, spend it, generate an appropriate return and gradually access credit based on real economic activity.

Giving someone access to digital dollars protects their purchasing power. Giving them a card makes those dollars spendable. Giving them yield makes their savings productive. Responsible access to credit lets them build.

It allows a merchant to purchase inventory before demand arrives. It allows a contractor to buy better equipment. It allows a family to manage an emergency without selling productive assets. It allows a business to convert future cash flows into working capital.

This is why I no longer believe the central question is whether stablecoins can replace bank transfers.

The real question is whether stablecoin infrastructure can support a complete financial relationship.

My thesis is that it can, but only if we stop treating payments, yield, underwriting, guarantees and credit distribution as separate industries.

The next financial system will be assembled from complementary layers: local behavioural data, regulated lenders, embedded business finance, institutional risk capital, productive accounts and compliant settlement infrastructure.

Payments made money move. Yield makes it productive. Credit is what can turn it into an economy.

That’s it for today.

Follow Ana on X

P.S.: This piece was originally published here.

P.P.S.: We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 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.

Whatever crypto forum I’ve been in lately, the consensus seems to be that the party is over. They all talk about how prices are down, founders can’t raise, and that the industry has slipped below AI, defence and space.

That is why the essay our colleagues at Decentralised.co — Saurabh Deshpande, Joel John and Siddharth — wrote felt like a breath of fresh air. They argue that adoption has never been higher, highlight the verticals that have found durable revenue despite market downturns, talk about the state of regulation, and more.

The best bets are made when everyone else loses hope. That’s precisely why this one is an important read.

Onto the essay,
Prathik

History is a cruel poet with a passion for rhyming.

In 2002, one of the columns the New York Times published argued that Herman Miller was a beaten-down stock, owing to the post dot-com bubble days. It suggested that when venture dollars flowed back, it made sense to buy the best hardware. And with it, the stock would appreciate in price.

The metaverse, sadly, has no Herman Miller to bid on.

You could, however, bid on the plot next to Snoop Dogg’s in The Sandbox. In 2021, at its peak, its price was $450k. It lists for a little over $1k today. Or a 99.8% drawdown.

We have been writing about crypto for a little over a decade. For the first time in quite a while, we feel the energy shifting. We internalise it by saying different things

• Crypto is fintech now

• It’s just infrastructure, in the backend

• The tokens need revenue! And then the revenue needs to buy the tokens back!

• Maybe it was just a bubble?

How can an industry be at the peak of adoption, and also be in a trough of depression when it comes to price?

In 2022, being in crypto may have raised eyebrows because of the fraud in it. In 2026, it would raise alarm out of concern.

Our About page, written a long time ago, clarifies that we write for founders. In pursuit of that mission, this piece is all we could gather about what’s happened to crypto venture as a construct, where it’s headed next, and the parallels in history for the current glut.

Let’s dig in.

Before we start, acknowledgements to all who contributed to the conversations that helped shape the article, and to those who gave feedback. Full list of contributors in the original piece published here.

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Seeding Liquidity

By many measures, crypto is having a landmark year. Institutions hold over $175B of crypto through exchange-traded products. On-chain companies earned $11B in fees over the trailing twelve months. The GENIUS Act is about to wipe out a decade of regulatory limbo. Exits have reached a record, with $8.6B of M&A and eleven IPOs.

These are fertile grounds for the “seed” of venture capital to flourish. But speak to any founder raising in these markets, and they’d quickly clarify that liquidity is nowhere to be found. According to Galaxy Research, only eight new venture funds formed last quarter - the fewest since 2020. Quarterly deployment is down to $4B. While the institutions are validating the asset class, VC dollars that create new categories and assets are yet to catch up.

The amount of money allocated towards private market technology investments was a fraction of what it is today. So there used to be a pecking order. In 1980, Sequoia had to sell its stake in Apple for $6M in order to produce a 40x return for their backers. They did not have the liberty of being a permanent hold vehicle back then because the asset class was considered risky. Almost like rich-people memecoins.

That lack of belief has played out multiple times over the years. In 1987, the NYT suggested that Pizza shops are a possible competitor for tech investment dollars to flow towards.

Can’t Eat Pizza Tokens

Crypto’s coming of age happened at the end of decades of tech evolution - with VCs wondering whether they should invest in pizzas and the asset class itself refusing to die.

The ICO collapsed capital formation and listing into a single event. Selling an idea to the crowd worked as capital formation at first. Ethereum raised $18M this way in 2014 and built all the infrastructure that all EVM chains still rely on. But the crowd eventually got sold just ideas and tokens.

In June of 2017, ICOs raised more than venture capital funding for the industry for the first time. By December, it was established that ICOs would kill venture capital.

More than half of the projects were dead within 120 days of their sale, and academic work on the period puts the share of outright scams near 80%. But we did not have a mechanism of pricing seed-stage companies with no revenue without guardrails in place.

Crypto-VC as a construct emerged in this hubris. Instead of raising directly from a market, teams would raise from a curated subset of capital allocators with a promise of a future token. A SAFE with a token warrant attached became an insulated, sane version of raising. For VCs, it meant public market liquidity for ventures that struggled to prove private market valuations.

A fund that entered at seed and saw the token list at 4x was made whole by its first 25% unlock. This meant that incentives were aligned with the pace of liquidity rather than the value a project might accrue.

Investors realised that they could exit before they had to be right. Early token listings took care of funds, and tokens became the primary product. Instead of being a mechanism to fund continued development, tokens were about paying out the cap table.

Mistakes Were Made

Tokens of all kinds, whether governance or utility, failed for one of the two or both of the following reasons:

1. The business model was flawed or non-existent

2. Unlike equity, tokens were not a legal claim on the business

Crypto learned painfully that just because you have a token does not automatically mean you have staying power. With crypto projects, the activity was rarely linked to increased revenue for the business. When X-to-earn emissions ended, the users left with them, because the product had no demand of its own.

Unlike a token, equity is a legal claim on the business. Shareholders can take the board to court when the board’s actions warrant it. Friend.tech’s protocol earned tens of millions in fees while its token captured none of it, because holders had no claim on those economics. So when a token trades below an equity multiple, the discount is often correct.

Tokens have undergone two shifts in response to this.

1. Some have begun swapping back to equity when the market has clearly mispriced what a business is worth. Across Protocol is one instance of this.

2. The breakout winners for 2026 have all tied their revenue back to the token in some form.

Peter from 1kx had a beautiful way of framing this in our conversations.

“Apps didn’t fail because they used token incentives. They failed because they weren’t good businesses. Crypto’s main sin was that there wasn’t enough durable innovation resulting in profitable businesses or protocols with durable product-market fit.” — Peter Pan, Research Partner at 1k(x)

The days of easy liquidity are gone, what is left, then?

Proof Of Work

Three verticals have found durable product-market fit.

Stablecoins

Supply exceeds $300B, annual transfer volume reached $46T, and roughly $9T of that remains after stripping the bots. This vertical belongs to growth equity, corporate acquirers, and bank strategy teams now. The seed window has now moved up a layer, to the companies built on top of the dollars. Bridge went on to get acquired by Stripe within three years.

Prediction markets

ICE committed up to $2B to Polymarket. Robinhood made event contracts its eleventh $100M+ business line. Susquehanna is building a prediction markets business. When the world’s biggest exchange operator, its biggest retail brokerage, and one of its biggest quant firms all arrive within twelve months, price discovery on the vertical itself is complete.

Perp exchanges

Hyperliquid handles roughly 44% of on-chain perp volume and out-earned most public exchanges last year with a team of eleven.

These three categories work the way they do because crypto’s architecture enables things that aren’t possible without it. Dollars settle around the clock without a correspondent bank. Exchanges hold client assets in custody that the client can verify. And the revenue is no longer bought. The largest applications cut token incentives from $2.8B to under $0.1B, and fees still kept growing.

The clearest sign that the categories are real is outsiders trying to build into crypto. The tokens and the businesses have parted ways. A decade of overbuilt infrastructure is finally paying out at the application layer, the same sequence the internet followed when overbuilt fiber made the application era possible.

The next verticals to reach fit will most likely form adjacent to the established ones, because a vertical at scale creates demand at its edges. Stablecoins need credit, brokerage, and treasury products built around the dollars they hold. The job of an early-stage fund deploying in 2026 and 2027 is to back these adjacent verticals before their fit becomes visible in usage and revenue data.

But what makes a vintage in 2026 distinctly different from that of 2020 or 2018? A huge part of that conversation is how regulations and mindsets around the industry have evolved.

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Make Crypto Great Again

GENIUS was signed in July 2025. CLARITY cleared the House with both parties aboard, and the FDIC is writing rules for bank-issued stablecoins. Clearer regulations impact crypto ventures in three direct ways.

1. For a decade, every American crypto term sheet priced in some probability that the government would simply end the company, the way it nearly ended the exchanges in 2023. With US policies shifting in favour, the additional risk premium goes away.

2. The second impact is on founders. According to Electric Capital, the absence of clear US policy may have caused the share of US crypto developers to decline by roughly half over the past decade, to under 20%. The same adverse selection that shaped the ICO market in 2017 shaped the founder market for a decade, and it has now reversed in the industry’s favour.

3. The third change is on the cost of doing business. Compliance cost is the last durable moat in fintech. Licensed crypto rails are machines for collapsing compliance cost, which means crypto companies are being handed the same moat.

Capital usually follows Washington with a lag. When the US led with enforcement, capital formation moved offshore and quality deteriorated. Wherever a founder incorporates now, some rulebook applies.

Mixing Revenue

We discussed how the lack of revenue led to the forced listing earlier in this piece. That has now changed.

Speculation remains the core of that revenue, and crypto’s first fee-paying product was always going to be its casino. But there are degrees to the speculation. At one end, Axiom’s fees track memecoin volumes and fall with them.

At the other end, Hyperliquid (roughly $843M last year with eleven people) and Aave earn from trading and lending that persist through cycles. Phantom did roughly $325M in 2025 from swap fees on seventeen million monthly users. Ten years of crypto produced one Circle. The last two years produced candidates at every degree of the spectrum.

By late 2025, applications in DeFi and financial services earned 73% of all onchain fees while blockchains earned 12%, yet blockchains held 91% of protocol market capitalisation and the applications roughly 6%. The market is still pricing the infrastructure of the last cycle over the businesses of this one.

Delphi built a portfolio of the ten largest revenue-generating tokens, weighted by revenue, and tracked it from January 2025 to May 2026. It returned 30.6% while BTC fell 17%, ETH fell 35%, and SOL fell 58%. Tokens with real cash flows beat everything else in the asset class, including the majors. Picking businesses over stories now works in the liquid market, and it works earlier in the private one.

Every asset class that moves on-chain creates a generation of companies around it, and stablecoins have established that template. We think something similar is underway for two more asset classes: Treasuries and equities. Tokenised stocks moved ~$23B last month and are growing ~100% month over month.

The most valuable companies stay private longer, so investors locked inside them sell stakes to one another as they wait. Retail cannot buy these companies at all until they list. Tokenised equities are the venue where that locked supply and that excluded demand eventually meet.

Banks cannot innovate at the speed their customers move, so fintech built neobanks. Neobanks reached the limits of the rails they rented, so builders made crypto neobanks. Now come crypto-native banks with actual charters (Erebor received its national bank charter this year) and FX settlement built on stablecoins. Each generation moved one layer deeper into the stack.

Crypto has stopped being a parallel financial system waiting for permission. It is becoming the plumbing of the existing one, and the venture opportunity of the next cycle is in that merger.

The dollar migration is already far enough along that its users stop noticing blockchain elements. The crypto part turned invisible, and the companies that made it invisible earned the fees. Equities and Treasuries are at the start of the same curve. Stocks on-chain need brokerages, collateralised lending, and market makers. Tokenised Treasuries need custody and distribution. Almost none of those companies exist yet, and companies like that get built at seed.

Taking The Right Exit, After Tokens

Blockspace is now oversupplied and earns little, while fees have shifted to applications. Apps are where the value has been accruing, and they are usually starved of capital.

Experts think in an industry’s constraints; outsiders arrive with a problem and treat the industry as a tool. A decade of overinvestment in blockspace and tooling ended that. And with it, the “shape” of what we considered “crypto founders” has shifted too.

• Hyperliquid was built by Jeff Yan, a high-frequency trader from Hudson River Trading who wanted a better derivatives exchange

• Circle was built by Jeremy Allaire, who had taken two internet software companies public before he touched a blockchain.

• Erebor, the first bank to receive a national crypto charter, was founded by Palmer Luckey, the defence-tech entrepreneur behind Anduril.

Each of them came to crypto with a business that needed rails rather than a belief in search of a use, found the rails ready, and built on them. Operators now look to blockchains as just infrastructure. Like Linux. Or databases.

This is where venture capital as a construct becomes relevant.

For a decade, the token was the only exit, so every company was forced toward a listing whether its business was ready or not. The token remains an exit, and a stronger one now that buybacks and revenue make it a claim on the business.

It is no longer the only one.

M&A set a record at $8.6B. IPOs work again. A crypto company built today can be acquired, go public, or return cash through its token. No earlier point in the industry’s history offered all three.

A longer path to liquidity restores the incentive to build businesses worth holding. The easy route, launch a token and get immediate liquidity, is closed. This is probably the best thing to happen to innovation in crypto. Founders now differentiate on product and adoption rather than on marketing and emissions.

One place we are seeing liquidity is through secondary buy-outs. Brooke Pollack from Hutt Capital used to invest directly into VCs in the past. Currently the fund is looking towards secondaries.

At least three quarters of our capital now is buying out LPs in existing funds…so we are now a secondaries fund, though still making some primary allocations within this strategy

This does not mean tokens as a construct are dead. Some of the best names in crypto are powered by tokens. But it does mean the age of easy money from tokens is behind us.

How Do Investors Adapt?

Deep in the bear market of 2003, Fred Wilson from AVC wrote this note on How Much is Too Much.

There were too many VCs getting funded that weren’t actually VCs. […] If all of that money went only into the hands of experienced VCs, a lot less of it would have been invested, and a lot less of it would have been lost.

Years later, he wrote a potential fix for the situation. It was to go deep where others went wide. There were no more “internet investors”. Just like perhaps today there are no longer “crypto investors”.

Crypto’s broad arc from here on is defined by the same three themes. A requirement to look beyond the insularity of what made the industry, going deeper towards sectoral expertise and expanding hold times from a few years to almost a decade.

The founders have already adapted. The token-as-the-product playbook is finished. As founders change, investors have to change with them. When a company takes five or more years to exit, the investor has to be right about the business, and being right about a business starts with understanding the market in which it operates.

Investors’ edge will likely come from specialising in a vertical. Cambridge Associates found that US venture and growth equity funds across the 2001 to 2010 vintages returned 2.2x gross on invested capital with a 23.2% gross IRR for sector specialists, versus 1.9x and 17.5% for generalists. Crypto venture never had to learn that discipline because the token listing paid for the lack of it.

Capital for growth-stage crypto companies has probably never been as available. What the market is missing is investors willing to write the first cheque with an informed view. Seed managers who have done the work become the layer that finds companies for all of that downstream capital.

If the setup is this favourable, why are only a few funding it?

Several of the largest crypto-native firms have raised non-crypto funds or expanded their mandates beyond the asset class, and roughly half of the troubled mid-sized crypto funds are expected to liquidate or convert by 2028.

The early stage itself remains open, and its round sizes fit small, specialised funds. Fewer than 20 firms actively write pre-seed and seed cheques. A fund usually deploys over the three years after it closes, so very few investors will actually compete for the seed rounds of 2027 and 2028.

Which leads us back to that ancient question markets have been asking since the time Sequoia invested in Apple - Is venture capital in the sector dead? Yet?

The reality is far from it. The best funds tend to be the ones that double down when it gets scariest. Collaborative Fund defined the economic reason for this in simple terms a decade back.

Markets reward being right in non-consensus bets.

Crypto today is a non-consensus bet today.

“The best entry points never feel like consensus. That understanding has anchored our conviction in digital assets from the start, knowing this is a story that plays out over decades, not cycles. Today, capital continues to consolidate and adoption is reaching record levels across use cases with proven PMF. We believe digital assets will redefine financial markets and networks, and we are excited to lean in during the time when others are looking elsewhere.” — Kinjal Shah, General Partner at Blockchain Capital

Patient Capital

The arc of crypto rhymes with that of the internet. It started out with an ideological pool of users, who gathered without a business model. It then transitioned towards nation-states and governments embracing it, while the initial euphoria of investment dollars and novelty faded out. People often draw parallels with crypto and the dot-com bubble.

But perhaps, we are closer to 2009 than we are to 1999.

The infrastructure is mature, the regulations are clear, the use cases are here, and sentiment is at one of its worst. Deep in the weeds of that crisis, a handful of firms generated some of the best DPIs that the venture asset class has created. History, as we’ve seen repeatedly, is a cruel poet with a passion for rhyming.

Here’s why: The founder pool has thinned to its strongest cohort. As the opportunists left, the average teams left with them, and a small group of exceptional teams remains with very few venture dollars competing for them. LPs no longer need convincing that crypto matters. What they are choosing now is the manager.

When the money does return, it may not even be called crypto investing. Nobody calls themselves an internet investor anymore. Crypto is on the same path through finance. The companies in this piece will increasingly be called brokerages, payment companies, and banks, and the funds that back them will simply be called venture funds.

Finance is absorbing crypto one function at a time. AI collapsed the cost of intelligence. Stablecoins are now collapsing the cost of trust the same way.

Every migrated function needs a generation of products built around it, and small teams build those products on a few cheques. The capital required to start a company is falling as AI writes more of the software, which shifts the scarce inputs to judgment, distribution, and patience, at exactly the moment the field emptied.

What this new age needs is what VCs have always provided when sentiment is low. In 2009, Fred Wilson defined it as Slow Capital. In 2026, Will Mandis calls it Patient capital. In this new age of generative slop and hyper-financialisation, the ability to sit out patiently for the right opportunity and the “taste” to identify what is right is where all of the value in the next crop of venture funds emerging within crypto lies.

Doubling down,
Saurabh Deshpande

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P.S.: This piece is an abridged version. The original piece was published here.

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Every once in a while, someone notices that some currency is paying 8% while the dollar is paying 4%, and asks why not just hold the 8% one and pocket the difference. It sounds like free money, or arbitrage - if you will. That’s how carry trade works. But it isn’t that simple. Although the spread appears practical to pocket, the financing cost, including borrowing, hedging, and moving such money, may often eat away at the spread. Mature players in the markets have known this for a century. The high yield and the cost of using it move together in a way that almost entirely closes the gap you were chasing.

The same logic is being applied to tokenised Treasury bills in DeFi right now. People question why the funds are sitting passively in DeFi and not being used to earn more yield. There’s a fundamental problem in that thinking.

The market often focuses on the 3% to 4% yield sitting safely on-chain. But a yield does not become useful simply because it has been tokenised. To loop the asset, a trader must borrow stablecoins against it. And often, the cost of that borrowing can be equal to or higher than the Treasury yield itself. Once the spread turns negative, leverage works against you. The asset may still be a perfectly sensible investment to hold, but there is little economic reason to use it inside DeFi.

That’s why $35 billion can sit on-chain doing nothing, and yet none of it is a missed opportunity.

Alessandro, who builds the tooling to move assets into these on-chain vaults at Li.Fi, sees this problem from the inside. In today’s guest essay, he takes the idle-90% figure everyone obsesses over and walks through the actual economics around why the money sitting still isn’t stuck or locked out.

On to Alessandro’s story,
Prathik

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BlackRock’s BUIDL holds $2.6 billion of tokenised Treasuries. Less than 1% of it sits anywhere DeFi can touch. Zoom out and the picture is the same. Tokenised real-world assets crossed $35 billion in July, an all-time high, while open DeFi lending puts to work about $2.5 billion. Treasuries and money market funds, the largest slice of the market, show 5.5% utilisation. Tokenised stocks are at 2.9%. The rest sits in wallets doing nothing.

Measured by issuance, institutions have arrived. Larry Fink has told investors that every stock, bond and fund can eventually live on-chain, and BlackRock, J.P. Morgan, Apollo, Janus Henderson and Circle all have tokenised funds on public networks today.

However, measured by usage, almost nothing has happened.

Ask why, and you get the same three answers: KYC restrictions, slow redemptions, transfer-restricted tokens. While true, they describe the fence, not the reason so few people try to climb it. The better answer might be an economic one. For tokenised Treasuries in particular, there is currently no levered trade. The 90% that sits idle is not stuck. It is capital making the rational choice. And part of the answer, which we will get to, is that the yield already reaches users through a different door.

The Carry Theorem

Start with what DeFi would actually do with a yield-bearing token. The native move is looping. You deposit the asset as collateral, borrow stablecoins against it, buy more of the asset, and repeat. At every given LTV, the maximum exposure is 1/(1-LTV), so an 80% LTV gives you 5x. This is how stETH grew, how Ethena’s USDe reached more than $14 billion at its peak, and how every yield asset in DeFi gets levered.

The net return of a loop is the asset yield plus (leverage minus one) times the spread between asset yield and borrow cost. In plain terms, leverage multiplies the spread, not the yield. Everything depends on that spread.

Here is the problem for Treasuries. A tokenised T-bill pays the “risk-free” dollar rate. The person lending you stablecoins on Aave can reach that yield too, directly by clearing the token issuer’s KYC, or through a plain brokerage account. So, they face a choice: lend stables to you, or buy T-bills. Reaching the bill has a cost: clearing KYC, minimums, moving money off-chain, so lenders will tolerate earning a bit less than the bill. However, when Aave’s lending rate sinks too far below the bill, they pick the bill and stop adding deposits. The borrowing rate is always higher than what lenders earn, because the protocol takes a spread in between. Put those two together, and the bill yield ends up inside Aave’s spread: above what lenders earn, below what you pay to finance it by borrowing stables.

Your financing cost ends up above the bill rate for long stretches. The asset you want to loop pays that same bill rate. You are financing the risk-free rate at the risk-free rate, or worse.

In 2024, Aave v3 USDC borrow averaged 9.0% while bills paid about 5.3%. In 2025, it averaged 5.8%. As of mid-July 2026, borrow sits at 3.97% against a tokenised Treasury average yield of 3.46%. Run the loop today at 80% LTV and five turns: 3.46 + 4 x (3.46 - 3.97) = 1.42%. Leverage points down. You would earn less than if you simply held the token unlevered.

The squeeze is not airtight. As I write, Aave’s USDT borrow rate sits a few tenths below the bill yield, and roughly $700 million of leverage against RWAs has formed across Aave, Morpho and Kamino. But look at when the spread flips. On-chain dollar rates only fall below TradFi rates when demand for crypto leverage collapses, so T-bill collateral activates exactly when nobody wants dollar leverage. And a spread hovering around zero, after collateral haircuts, fees and rate volatility, still never pays at size.

An economic working paper (Barbon, Barthélemy and Nguyen, 2023) found that stablecoin lending rates are driven mostly by crypto demand and reconnect with conventional rates only slowly, through exactly this arbitrage channel. That slow reconnection is the 2024 premium visible in the numbers above. A Keyrock and Securitize report showed that tokenised Treasuries out-yielded DeFi’s benchmark stablecoin lending rate on 98% of days in Q1 2026, with 3.6 times lower volatility. Read that carefully. The tokenised T-bill is a better hold than lending on Aave, and simultaneously impossible to lever. The bill pays more than Aave lenders earn and less than Aave borrowers pay.

So the idle 90% is not waiting for a better front-end. It is capital declining a negative-carry trade.

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Utilisation Follows Carry

If the carry explanation is right, it should predict which RWA categories DeFi actually uses. It does, across all three classes.

Private credit yielded 8 to 10% when most of these integrations were built, so the deployed capital arrived when the carry was wide.

The obvious objection is that carry and transferability are tangled together, since the high-yield categories happen to be freely transferable tokens while Treasuries are dominated by whitelisted ones. Class-level data can’t separate the two. But specific comparisons where transferability and carry pull in opposite directions can separate them, and those comparisons are unambiguous. Apollo’s ACRED is a transfer-restricted, qualified-purchaser-only tokenised credit fund yielding around 8 to 9%. It became the flagship DeFi looping asset last year. Securitize built a wrapper, Gauntlet automated the strategy, and KYC-gated markets appeared on EVM chains and Solana to capture the spread. Meanwhile, Ondo’s USDY is freely transferable, holds over a billion dollars, and has essentially no leverage footprint. Its carry is negative.

Restrictions are a fixed cost. Wide carry pays the cost. Negative carry means there is nothing to pay for it with. Better access tooling shrinks the cost, but it cannot create the spread.

Three honesty notes on Table 1. First, the 46.7% private credit figure excludes Figure’s HELOC book, which is tokenised but not composable; include it and utilisation drops to roughly 12 to 15%. Second, the largest ‘private credit’ asset in DeFi is Maple’s syrupUSDC, which is overcollateralised lending to crypto trading firms, so real-world credit in DeFi is smaller than the headline suggests. Third, the reinsurance row is two protocols designed as DeFi-native products from day one, so read it as directional rather than statistical. One scoping choice matters too: these figures count open, permissionless lending. Looser definitions that count every RWA deposit in any DeFi protocol reach about $7.4 billion for Q2 2026, up 200% in a year. The level moves with the definition. The ranking across asset classes, which is where the story lives, does not.

TradFi Leverages the Same Asset at 50x

Here is where it gets interesting, because the no-demand story is wrong in one specific way. Demand for levered Treasuries is enormous. It just lives somewhere else with better infrastructure.

In TradFi, fixed income stands on three legs: financing, rate hedging, and shorting. DeFi has built none of the three in a permissionless form.

Financing. The US repo market carries ~$12.6 trillion in outstanding exposure. Overnight general collateral repo currently runs 16 to 29 basis points below the 3-month bill yield, so right now TradFi finances the risk-free asset below its own yield. That sign flips with the rate cycle. The structural part never flips: financing in trillion-dollar size, at haircuts near zero, meaning lenders demand almost no extra collateral at a rate that hugs the risk-free curve. DeFi has never had any of the three. That deep, nearly frictionless financing is why the basis trade can exist at all. Hedge funds ran about $830 billion of these positions as of late 2025, long cash Treasuries against short futures, at 50 to 70x leverage on the futures leg. DeFi’s version of financing is variable-rate, priced above the asset yield, and capped at 80 to 92% LTV for a maximum of 5 to 12.5x, with no term structure and no netting.

Rate hedging. Outstanding interest-rate derivatives total roughly $668 trillion. This is the machinery that lets insurers and pensions run fixed-income books against liabilities that stretch decades longer than the bonds they can buy. UK pension funds at their peak hedged about £1.4 trillion of liabilities through strategies built on swaps and repo. DeFi’s equivalent is a graveyard. Element, Yield Protocol, Sense and Voltz were a whole generation of on-chain rate protocols, and all of them wound down. Pendle survives, and its Boros venue for funding-rate swaps has done $18 billion of cumulative notional in eleven months. That is real, but still tiny. Boros open interest peaked above $250 million, about 0.17% of the roughly $150 billion sitting in funding-rate positions on an average day.

Shorting. Securities lending has $4.4 trillion on loan and produced a record $9.1 billion of revenue in the first half of 2026. There is no native on-chain mechanism to short a tokenised Treasury. No shorting means no two-sided price discovery and no special markets.

Duration. The quieter structural problem sits underneath all three. DeFi liquidation assumes the collateral can be sold within the same transaction. Tokenised funds redeem on a cycle; next-day for some funds, quarterly for others. A liquidator who seizes that collateral cannot sell it in the same transaction. Someone has to hold the position until the fund pays out. You can see protocols contorting around this in production. Every serious RWA collateral listing since 2025 has gone to isolated or permissioned venues rather than the main pools. Morpho hosts them in isolated markets, Kamino keeps them in their own markets, and Aave built Horizon as a separate permissioned instance rather than touching its core pool.

Strip those legs from any market, and the rational position size is close to zero. TradFi investors are not avoiding on-chain Treasuries out of ignorance. They are declining to trade a market with no repo desk, no swaps desk and no borrow desk.

The On-chain Buyer and Cashflows

Even if the infrastructure existed, there would still be a demand-side problem, because the people actually trading on-chain today don’t want cashflows. They want price exposure with leverage.

The cleanest evidence is tokenised stocks. Spot tokenised equities on Solana, which handles 96% of all tokenised stock trading, did $4.84 billion in volume in the entire second quarter of 2026. Hyperliquid’s HIP-3 builder markets, led by equity perps, did about $62 billion in May alone, and by mid-July, these markets were roughly half of all Hyperliquid perp volume, up from 2% in January. That is a revealed preference of nearly 40 to 1 for synthetic price exposure over owning the tokenised asset.

Dividends make the same point from the other side. None of the three major tokenised stock issuers pays dividends out as an on-chain cashflow. xStocks rebases dividends into your balance, Ondo Global Markets accrues total return into the token, and Robinhood’s stock tokens are legally derivatives, with the newer on-chain versions structured as debt instruments that pay dividends as dollar credits inside the app. There is nothing for a DeFi protocol to compose with. The main lending venue that accepts tokenised stocks as collateral, Kamino on Solana, holds roughly $20 to 30 million against them. All tokenised stocks in DeFi combined: $78 million.

Pendle, DeFi’s fixed-rate venue, tells the same story with better resolution. The rails demonstrably work. Pendle settled $58 billion in fixed yield in 2025, and its principal tokens are functionally zero-coupon bonds. But the demand that used those rails was leveraged, points-subsidised Ethena carry, and when that spread died in September 2025, Pendle’s TVL fell 92%. RWA underlyings on Pendle reached all of $151 million, about half a percentage point of the tokenised RWA market. Tokenised Treasuries don’t need a yield-splitting venue. They need a repo market that doesn’t exist.

The Yield Travels Better than the Asset

There is one more twist, and it changes how you should read the headline number. The T-bill yield is already flowing through DeFi at scale. It just doesn’t travel as the fund token.

Look at who actually holds the big tokenised Treasury products. Ethena’s USDtb stablecoin keeps over 90% of its reserves in BUIDL. Ondo’s OUSG is reported to be BUIDL’s largest single holder. Sky (ex-MakerDAO) allocated $2.4 billion of its reserves into BUIDL, Superstate and Centrifuge funds through Spark, and another billion into Janus Henderson’s CLO fund through Grove. Circle’s USYC, which overtook BUIDL in March 2026 and now sits at $2.9 billion as the largest tokenised Treasury product, grew almost entirely as off-exchange margin collateral for Binance institutional clients. BUIDL itself has been accepted as derivatives margin on Crypto.com and Deribit since June 2025.

Every name on the above list is a wrapper, a DAO treasury, or an exchange collateral program. Not one of those use cases shows up as “DeFi utilisation” of the underlying token, yet the yield reaches users anyway, through sUSDS ($6.2 billion earning the Sky Savings Rate), through USDY at around 3.5%, and through USDtb rewards. Yield-bearing stablecoins drove more than half of net stablecoin supply growth in Q1 2026.

This is not a failure mode. It is how money has always been structured. Reserves sit still while claims on them circulate. Tokenised Treasuries are settling into the same role: the reserve layer of onchain dollars, and reserve layers do not circulate. This means utilisation was partly the wrong metric from the start. It measures whether the base moves, when the whole point of a base is to hold still while everything above it moves.

Regulation is actively pumping this structure. The GENIUS Act, signed in July 2025, prohibits payment stablecoin issuers from paying any form of yield. So the issuer cannot legally pay the T-bill return to its own holders, and the yield migrates into wrapper tokens and rewards paid through affiliated entities. Regulators are already moving on the rewards door (the OCC proposed in February 2026 to treat coordinated issuer and affiliate rewards as presumptively prohibited), which leaves the wrapper as the structure regulation durably favours. The 90% idle statistic partly measures the wrong layer: the yield composes, the asset doesn’t.

Will It Change?

Two sides are attacking this problem from opposite directions, and they disagree about what the problem is.

The first one is institutional and rebuilds the missing TradFi legs on-chain in permissioned form. Tokenised repo already runs at scale inside banks. Broadridge’s distributed ledger repo platform processed $7.5 trillion in June 2026 alone, and JPMorgan’s Kinexys has processed roughly $3 trillion of tokenised repo since its 2020 launch. Closer to DeFi, Grove’s Basin facility offers up to $1 billion a day of instant liquidity against BUIDL and JTRSY redemptions, which is functionally an on-demand repo window for tokenised funds. Aave’s Horizon holds about $255 million. On the access side, before capital enters the vault, Plume’s Nest Vaults fold compliance into the transaction itself: instead of a static wallet allowlist, a signed policy attestation checks eligibility the moment capital enters a vault, making compliant access far easier to compose with. The infrastructure is arriving. It is arriving wearing a suit.

The second side is crypto-native and changes what gets tokenised. A tokenised T-bill is a worse product than the brokerage version, as it delivers the same asset with extra smart contract risk, extra fees, and a buyer base that never asked for it. Plume’s answer is not to choose between the two sides, but to connect them: use institutional-grade vault and compliance infrastructure to distribute assets whose yield can actually clear the on-chain financing floor. nOPAL is the clearest example. The Plume Nest vault behind it allocates mainly to BlackOpal’s short-dated credit-card receivables strategy, holding roughly $35 million in TVL at an 11.8% trailing 30-day APY. The Morpho market financing nOPAL collateral currently charges roughly 4.9%, with about $814,000 borrowed against it, a positive gross carry before fees, haircuts and rate movement. Plume, in other words, is side-one plumbing carrying a side-two asset. While both agree the trade has to earn a positive spread, Plume’s model is to work both sides of it.

My watchlist for when the idle 90% actually moves:

1. Someone offers permissionless dollar financing below the bill yield against tokenised Treasury collateral. That is the on-chain repo moment.

2. A liquid rate market emerges for RWA yields, so duration can be hedged rather than just held.

3. A borrowing mechanism makes tokenised funds shortable.

4. Async redemption standards make slow-to-redeem collateral possible to liquidate without 35-day windows.

Financing has permissioned prototypes today, and the async-redemption standard is live and spreading. Rates and shorting have nothing yet.

At LI.FI I work on Composer, the orchestration engine behind our DeFi and RWA Earn product. It handles the access leg of this problem, moving any token on any chain into a KYC-gated RWA vault in one flow with the compliance check included. Access decides who can get into an asset. Carry decides whether there is a trade once they hold it, and no router can fix a negative spread.

Today’s idle 90% is a carry problem. But look at what happened with ACRED. The moment carry turned wide, wrappers, KYC’d markets and automated strategies showed up within months, because restrictions are a fixed cost and someone has to make that cost cheap to pay. When the spread flips, the binding constraint moves back to access.

The tokenisation thesis was that putting assets on-chain makes them more useful. For an investor with a brokerage account, that is not yet true, because a tokenised T-bill today carries more risk than its off-chain twin and can do less. The capital that already lives on-chain has voted for a different form. Until the financing floor breaks, the rational trade is the one the market is already making. Hold the wrapper, collect the yield, and leave the fund token alone.

And the issuers seem to know this. The fees earned on a few billion dollars of assets barely register at firms that manage trillions. So, these funds are loss leaders, sold rather than bought. It’s the price of being in position before the market structure arrives. Today’s demand was never the point.

That’s all for today.

Until next time,
Alessandro

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P.S.: This piece was originally published here.

P.P.S.: We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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The first thing I got reminded of when I read today’s guest essay for the first time was what insurance reveals about human behaviour. Ask most people what insurance they have, and chances are high you might hear phone insurance. Not health cover or a safety net to cover income for dependents. But phone insurance was offered at checkout for a few hundred rupees, and opting for it required the least decision-making.

This reveals how we make decisions around risk. Nobody sat down, listed what could go wrong, ranked it by damage, and decided that their phone screen should be at the top of the list. The risk coverage was offered by default, and the protection got shaped around what was easy to sell. Little did the risk exposure itself matter.

Yet, it works. The claim goes through, the screen gets replaced, and the coverage provides for what it promised.

This brings me to today’s piece.

Lauris works on how states of the world become financial markets — event contracts, derivatives, corporate risk, and the legal and market structure sitting between them.

In today’s piece, Lauris argues that a hedge isn’t a thing to buy at random. It should be a curated relationship built around an exposure you’ve already identified. If you get that order backwards, then you will walk away holding something that pays out on a proposition while your actual problem persists unattended.

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On to Lauris’ story,
Prathik

Anyone can hedge a trade. That does not mean anyone can sell a company “a hedge.”

For a trader, any position that reduces risk elsewhere on the book can reasonably be called a hedge, whether the trader sits on a macro desk, trades crypto, or uses a retail brokerage account.

Buy an election contract against a portfolio exposed to the result and you have hedged the portfolio. The fit may be rough and the protection partial. That still counts as a hedge.

Corporate finance uses the same word for a more demanding relationship. The exposure comes first: a cash flow, liability, or operating risk. The instrument has to match the amount, tenor, and risk factor. Whatever it misses is basis risk. Credit, collateral, documentation, and accounting sit around the trade because the relationship, not merely the payoff, is the product.

A swap, forward, option, or event contract can hedge one account and express a view in another. Its economic function depends on the exposure elsewhere on the holder’s balance sheet. For a company, the relevant questions are which exposure the instrument offsets, in what amount, and for how long.

Both meanings of the word are valid. The category error is treating them as interchangeable.

I have now been in several conversations where experience in a quantitative-trading or market-making seat was assumed to carry over to structuring. The confusion is especially common in tech, where a quant background carries a broad competence halo, often deserved: traders have built excellent exchanges, and many are formidable operators.

But technical horsepower does not make role-specific knowledge portable. Quantitative trading and market making are organised around price, information, and inventory. Structuring a product or a swap that may serve as a hedge is a service operation around a client’s exposure. Not isomorphic.

Prediction markets operators, when thinking about this, make that error frequently. Their natural unit is the contract: list an event, attract liquidity, and find the flow. The same order works poorly for corporate risk transfer, where the exposure comes first. Yet the habit has spread outward from trading and crypto: take the available yes-or-no contracts, map them onto a company’s problems, route the orders to an exchange, and call the portfolio a hedge.

IFRS 9 makes the distinction operational. It asks for a hedged item, a hedging instrument, the risk being hedged, a risk-management objective, and an economic relationship between them [1]. The CFTC starts from the same premise when testing swaps used to hedge physical positions: the risk must arise from an asset, liability, service, or physical commercial activity [2].

What Event Contracts Add

The financial case for prediction markets begins with state-contingent claims. Arrow and Debreu supplied the framework: markets become more complete as they can name, price, and transfer claims on more future states [18]. An event contract can pay if a tariff passes, a merger closes, a drug is approved, or a carbon auction clears above a specified level.

Traditional markets often reach those states only through a proxy, if they price them at all. An event market can create the first observable market price for a state that previously lived inside research notes, scenario models, or bilateral conversations.

For price-threshold claims on the same underlying, the connection to traditional derivatives is exact in the limit: the price of a digital payoff is the negative slope of the call-price curve with respect to strike and can be approximated by an increasingly tight vertical spread [19]. The event contract isolates the terminal state. Before expiry, the options route can carry volatility and mark-to-market exposure, as well as dealer intermediation and replication friction that the buyer did not want.

That identity has limits. “The S&P closes above 7,000” can be mapped onto the S&P option surface. “The Fed cuts in March” or “a tariff passes” remains a legitimate state claim without being the strike derivative of one call curve, and its market price differs from a literal physical probability because it also reflects risk preferences, collateral, liquidity, access, and settlement rules.

The state claim is an input to the company’s cash-flow problem. Prediction markets can manufacture the missing claim, but they cannot manufacture the relationship between that claim and a company’s balance sheet.

Where Event Contracts Matter

Two variables determine where event contracts matter financially:

• Whether a traditional derivative or credible replication already exists.

• Whether a company or investor materially holds the underlying risk.

When material risk already has a derivative, the event contract is a substitute. It has to offer a better fit or a lower all-in cost. The structural cost embedded in the traditional route must exceed the event market’s spread, depth, collateral, and impact costs. This is the replication-cost wedge.

The largest long-run opportunity lies in material risks with no existing derivatives. Here the event contract may be the first instrument, making shutdowns, policy decisions, regulatory milestones, weather outcomes, and corporate events observable and transferable for the first time.

Where a derivative exists but no relevant user materially holds the exposure, there is little unmet risk-transfer demand. Another binary may still be a useful trading product. With neither condition, the market belongs to forecasting, entertainment, or general price discovery rather than corporate hedging infrastructure.

Tradability tells you that a proposition can be priced. Materiality tells you whether anyone has a risk worth transferring.

A contract-first funnel skips the second test: it starts with available listings and searches for companies that can be associated with them.

The Wrong Side of the Desk

A trading mindset starts with a payoff and looks for flow. A structuring mindset starts with a balance sheet and builds the trade.

In fixed income, currencies, and commodities, a corporate client arrives with existing exposures such as floating-rate debt or currency mismatch; fuel bills, inventory, or planned bond issues; and acquisition financing, for an abstract object called a hedge.

It identifies the risk component, chooses the instrument, and sets the amount and tenor. Then it measures the residual basis and fits the result into the client’s documentation, credit arrangements, and accounting policy.

ISDA organises the derivatives market in that order: user, underlying risk, instrument [3]. HSBC’s Autohedge does the same, taking exposures, hedge policy, and risk preferences as inputs before calculating trades [4]. The instrument may be legally separate; economically, the hedge is the relationship around it.

In FICC, a dealer may answer a request for quote with a firm principal-risk price [5]. That is execution: it prices the defined instrument without deciding what exposure the client has or whether the instrument offsets it. Adding a Request-for-Quote (RFQ) to a mismatched contract gives the mismatch a price.

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What Goes Wrong When the Contract Comes First

Consider an importer worried about a possible tariff. Its loss depends on shipment volumes, timing, inventory, pass-through to customers, currency movements, and the company’s ability to find substitute suppliers. An event contract might instead pay one dollar if a public tariff crosses a threshold before a fixed date. The proposition is clean; the fit to the importer’s cash flow is not.

That mismatch is basis risk. A structurer decides which part of the exposure can be transferred and which part the client will retain. An intermediary that starts from the contract does the opposite. It finds a public binary that resembles the client’s problem and treats the resemblance as the hedge. After the trade, the treasurer owns the binary and still owns most of the original risk.

Tailoring the contract creates a second problem. Robert Bartlett and Maureen O’Hara studied 41.6 million Kalshi trades. In their framework, there are no liquidity traders in the Glosten-Milgrom sense because the instrument does not routinely serve the hedging and portfolio-rebalancing role that generates that flow in mature markets [6]. Individuals can still use a contract defensively.

Credit markets show how institutions buy state-contingent risk. In Significant Risk Transfer (SRT), a bank retains its loans and buys first-loss protection through an investor-funded credit-linked note; the BIS counted roughly €800 billion of protected loan pools at the end of 2024 [12]. This is a bank-capital precedent, not a recommendation that corporates buy credit-linked notes. It shows why institutions hold the risk inside a funded instrument with a coupon, documentation, loss allocation, and a mandate line.

The paper also finds higher informed price impact in single-name markets than in broad macro markets [6]. A tariff decision is usually exogenous to an ordinary importer, so its order carries little information. A market maker can welcome the flow, but the public tariff line is only loosely connected to the importer’s loss.

Tighten the contract around a merger, a drug trial, a plant, or another company-specific outcome and the basis improves. The company may now know more than the person quoting it, so the market maker widens, cuts size, or walks away. The welcome trade is the one with the largest basis. The close-fitting trade is the one the market is least willing to warehouse.

A filled contract still leaves the company to explain the objective, ratio, basis, valuation, and financial-statement treatment. A dashboard does not establish that relationship, and an RFQ cannot make an audit committee accept it.

The business model is squeezed from both sides. If the exposure is small and already matches a listed contract, the client can trade it directly. If it is large or bespoke, the client needs structuring and capital. A company that only routes the listed contract adds no capacity. A company that designs the payoff, arranges documentation, and commits or sources capital has become a broker, insurer, or FICC structurer rather than a new category.

The Wrapper Changes the Market

WeatherBill launched a self-serve weather-derivatives platform in 2007. The thesis was that exposed businesses would buy protection. They did not. The company narrowed to farmers, moved onto insurance paper and outbound distribution, and became The Climate Corporation [7].

Event contracts found native demand elsewhere: CME’s FanDuel platform launched in December 2025 and reported 100 million contracts in about eight weeks [8].

Weather went to insurance; sports found retail distribution. The standalone corporate hedge sold through a software funnel remains the missing case.

Where The Risk Sits

Once the exposure is defined, the remaining problem is risk-bearing capacity.

In practice, event risk reaches a balance sheet through three routes. Risk never appears, and has to be transferred somewhere.

1. Trade it directly when the exposure already fits

A Manhattan bar covered a free-drinks-if-the-Knicks-win promotion with roughly $5,000 of event contracts; the liability and contract resolved on the same game [9]. If settlement fits, the same route can handle larger exposures.

A contract tied to a California solar-credit tax rebate reportedly transferred about $600,000 through a displayed, centrally cleared market [10]. Company size is not the dividing line; fit and book capacity are.

I am working with some of the extremely talented folks at @kalshi, especially with @0x_ultra, on providing a showcase for this, as part of the Builder’s Program. It will be released shortly.

2. Fund a pool when the risk is actually poolable

A parimutuel market divides a committed pool among the winners, capping aggregate liability at the capital already present; Goldman Sachs and Deutsche Bank used the mechanism for economic derivatives from 2002, with non-farm-payroll auctions averaging about $9 million before moving to CME in 2005 [11]. The BIS still questioned whether genuine hedging demand would balance sophisticated informed traders [11].

Pooling works only when losses differ, opposite exposures exist, or outside capital is paid to participate. When every participant loses together, the pool simply produces a queue of claimants that someone with capital must stand behind: insurance, reinsurance, or a warehouse again.

I know really smart people like @AadvikVashist are working on this.

3. Put the event inside an instrument institutions already own

In April, Marex issued up to $10 million of a structured note to one Swiss institutional client, paying 7% if Nvidia remains the world’s largest company after a year; the client owns a Marex obligation, and Marex uses event contracts to run the replication [13]. The institution bought a security with an issuer, documentation, and a mandate line, while the event contract stayed on the dealer’s side of the trade. Before the trade reached the client, Marex had turned the event contract into FICC.

Software has a role after the exposure is defined. It can test traditional instruments, identify a coverage gap, and compare an event claim on basis, cost, execution, collateral, law, mandate, accounting, and residual risk. A useful product would encode that process and allow the answer to be no trade.

The Regulatory Blast Radius

A bad corporate-hedging pitch would normally be a problem for its buyers. The damage is wider here because event markets are still fighting over the role they are allowed to play in the financial system. Commercial use does not determine CFTC jurisdiction over an event contract.

The Crypto Council for Innovation grounds that jurisdiction in the Commodity Exchange Act’s swap definition, federal preemption, and the CFTC’s exclusive authority over derivatives markets [14], and an event contract does not stop being federally regulated merely because the person buying it is speculating.

The public-interest case extends beyond corporate hedging. Regulated event markets can name previously unpriced states, produce public prices, and create transparent, collateralised claims with defined settlement rules. Those are serious financial functions even before a treasurer trades one. But hedging remains central to the policy case.

The CFTC describes prediction markets as tools for forecasting, planning, hedging, and speculation. In February 2026, the Commission defended its jurisdiction against state gambling regulators by pointing to business hedging, portfolio management, and information about future outcomes [15]. CCI draws the distinction from gambling through many-to-many execution, transparent pricing, defined settlement benchmarks, surveillance, customer protections, and market integrity [14].

In March, the CFTC reminded prediction-market exchanges of their obligations under the Commodity Exchange Act and Core Principle 3. Its June proposal addresses event-contract design, public-interest review, and responsible innovation [16].

A serious corporate hedge failure would follow a simple pattern. A business is told that a binary position offsets an operating risk, so it posts cash. The contract may expire worthless even though the company loses money, because volumes, timing, or pass-through were never in the payoff; before settlement, its mark may move through earnings while the original exposure remains, leaving the company with a proposition rather than protection.

Regulators have seen the payoff shape before. The CFTC and SEC issued a joint alert after complaints about binary-options platforms refusing withdrawals, identity theft, and software manipulation that generated losses [17]. The regulated event venues discussed here are not those fraudulent brokers: their markets are supervised, transparent, and centrally cleared, but that history raises the cost of mislabeling a trade as protection.

Opponents of federal prediction markets will not write a seminar about basis risk. They will say a company was sold a bet under a derivatives label.

An intermediary that sells a mismatched binary as corporate protection will hand state gambling regulators the industry’s strongest argument against federally-regulated prediction markets.

If you got to the end, truly appreciate you for reading this.

That’s all for today.

Until next time,
Lauris

Follow Lauris on X

References

[1] IFRS Foundation, IFRS 9 Financial Instruments, paragraph 6.4.1 and related hedge-accounting provisions. [PDF]

[2] Commodity Futures Trading Commission, discussion of swaps used to hedge physical positions, 83 FR 7927 (2018). [Final rule]

[3] International Swaps and Derivatives Association, *Evolution of OTC Derivatives Markets Since the Financial Crisis* (January 2021), table 1. [PDF]

[4] HSBC, [Autohedge] and [FX Overlay for Corporate Clients].

[5] Citi, Markets & Banking Execution Policy — FICC. [PDF]

[6] Robert Bartlett and Maureen O’Hara, *Adverse Selection in Prediction Markets: Evidence from Kalshi*, Stanford Law and Economics Olin Working Paper No. 615 (April 16, 2026). [SSRN]

[7] OpinionX, *Deconstructing WeatherBill’s $930M Startup Pivot*. [Article]

[8] FanDuel and CME Group, [FanDuel Predicts launch release]; CME Group, [100 million event contracts announcement].

[9] Catherina Gioino, *This NYC Bar Just Covered Everyone’s Tabs Because the Knicks Won, and Used Kalshi to Hedge Their Bets*, *Fortune* (June 3, 2026). [Article]

[10] John Lothian News, *Kalshi Eyes Institutional Scale as Prediction Markets Prove Fed-Grade Forecasting Value* (July 9, 2026). [Article]

[11] Blaise Gadanecz, Richhild Moessner and Christian Upper, *Economic Derivatives*, BIS Quarterly Review (March 2007). [PDF]

[12] Prashant Babu, Michael Chui and Costas Stephanou, *The Rise and Risks of Synthetic Risk Transfers*, BIS Quarterly Review (March 2026). [Article]

[13] Finance Magnates, *Marex Launches Structured Note Linked to Prediction Market Outcome* (April 2026). [Article]

[14] Crypto Council for Innovation, response to the CFTC prediction-markets advance notice of proposed rulemaking (April 2026). [PDF]

[15] Commodity Futures Trading Commission, [*Understanding Prediction Markets and Event Contracts*] and [press release 9183-26].

[16] Commodity Futures Trading Commission, [prediction-markets advisory] (March 12, 2026) and [*Prediction Markets; Public Interest Determinations*].

[17] Commodity Futures Trading Commission and Securities and Exchange Commission, *Binary Options and Fraud*. [Investor alert]

[18] Kenneth J. Arrow, *The Role of Securities in the Optimal Allocation of Risk-bearing*, *Review of Economic Studies* 31(2) (1964), [DOI]; Gérard Debreu, *Theory of Value: An Axiomatic Analysis of Economic Equilibrium* (1959), [PDF].

[19] Douglas T. Breeden and Robert H. Litzenberger, *Prices of State-Contingent Claims Implicit in Option Prices*, *Journal of Business* 51(4) (1978). [JSTOR]

We will be back with another guest essay soon.

P.S. This piece was originally published here.

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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.

Two decades ago, I used to accompany my mom to the vegetable market. I’d try to help by picking a few tomatoes and some onions. They never made it to the final basket. She would quietly set aside my picks and choose her own. Each one that made the cut was pressed, turned over, held up against some standard only she could see.

For most people, grocery shopping is a chore. For the chef in my mom, it was a high-precision job. The right tomatoes meant the curry would taste the way she intended. Choosing wasn’t merely a step before cooking. It was where the cooking began.

That’s the thing about shopping, it’s not one activity. Some of it we’d delegate to anyone who’d take it. Some of it we won’t hand over even to our own kids. And any attempt to delegate how we buy things has to start by understanding which is which, not by assuming that automated robots are the default solution to everything.

In today’s guest op-ed, Anderl takes apart the “give the agent a wallet” thesis and shows why paying was never the hard part of shopping.

On to Anderl’s story,
Prathik

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There is a line going around the AI and crypto world right now: give an agent a wallet, let it shop for you. The ultimate use case. It sounds clean, it sounds like the future, and it still doesn’t feel quite right. The mismatch is worth unpacking because it shows that the line confuses the hard part of shopping with the easy part and then puts the easy part at the centre.

Let’s start with shopping itself, not with payment.

Shopping Splits into Two Things

Shopping is really two activities that today only look fused. One is search, the other is judgment. Search (gathering, filtering, comparing, pre-sorting) is mechanical and delegable, almost entirely. Judgment (is it any good, does it suit me, do I trust the seller) is the part that a human is attached to.

It is measurable that search is already migrating. Adobe Analytics reported a roughly 4,700% rise in traffic from generative AI referrals to US retail sites between July 2024 and July 2025. The wallet thesis now quietly assumes the agent takes on both search and judgment. This is where the sleight of hand sits. One can be handed off, the other only under conditions, and sometimes not at all.

Judgment Splits Again

The interesting move is that judgment isn’t monolithic either. It breaks into two parts. One is evaluation, which involves checking options against a utility function. The other is authorship, where you set that utility function in the first place: which dimensions count, how they are weighted, which values bind, and what “good” ends up meaning.

Evaluation automates. Authorship doesn’t. And it doesn’t sit as a single gate up front that you pass through once. It runs as a thin thread through every layer. Conformance is about which specifications matter to you. Quality is about whether a broken zipper is a deal-breaker or beside the point. And the choice of merchant turns on which values bind you. Every layer does (human criterion × agent evaluation). In every case, what automation eats is the evaluation half. What’s left is the authorial half.

This has a consequence that everyone underestimates who thinks the human writes one spec sheet and walks away. You can’t. It’s one of the most robust findings in decision research. Since Paul Slovic’s work on decision-making under risk, we speak of constructed preferences. The American professor and psychologist found that we don’t carry a finished liking inside us that we simply retrieve; we form it in the act of choosing (Slovic 1995; Lichtenstein & Slovic 2006). The evidence is preference reversals. Normatively equivalent elicitation methods like choosing versus pricing produce systematically different rankings, violating the basic axiom of rational choice. Ariely, Loewenstein and Prelec (2003), in their work on coherent arbitrariness, showed that even an arbitrary number (the last digits of a Social Security Number) anchors willingness to pay for familiar products, and that the effect disappears neither with experience nor through market forces. The “stable preferences” picture is largely an illusion of order.

The realistic interface is therefore not the spec sheet but an iterative one. The agent surfaces, at each layer, the decision a criterion needs (”you said durable, at what premium does durability stop being worth it?”), and the human writes exactly that.

The Real Axis: Chore or Pleasure

Now, the distinction that orders everything. We like to talk about “standardised” versus “individual” products, as if it were about specifiability. It isn’t. The real axis is different. On one side, choosing is a pure chore. On the other hand, choosing is part of the point.

For a commodity like printer paper, batteries or the recurring reorder, the act of choosing has zero consumption value. Nobody enjoys deciding between two identical toner cartridges. That’s why commodities are the natural home of full delegation: nothing is lost when the agent silently reorders the right thing.

With pleasure goods, it’s the reverse. The wine, the piece of furniture, the coat, and the book. Here, choosing is part of the good itself. Whoever hands off the judgment not only saves effort but also destroys part of what they wanted to consume. And that holds even if the follow-up questions were free. You wouldn’t want to delegate it even at zero friction.

With pleasure goods, the agent therefore doesn’t withdraw but should switch roles. From buyer to scout. It still does the search, the shortlisting, the spec matching, the merchant verification, and the pulling of recurring weaknesses out of a thousand reviews. It narrows 200 to 5. And then it stops. The human savours the final choice.

The Friction Trilemma

“But the agent could just ask for my criteria.” It could, and that’s exactly the annoying variant that people resort to doing themselves. Worse, interrogating degrades the choice. Wilson and Schooler (1991) had participants rate jams. Those asked to analyse the reasons for their feeling beforehand ended up with rankings that matched expert judgment less well than the control group’s. In a follow-up study, people who spelt out their reasons chose different posters and were less happy with them weeks later. Articulating directs attention to what is easy to put into words, not to what actually carries the preference. Taste is recognised, not verbalised.

So there is a trilemma:

Interrogate. Faithful to your current self, but high friction, and it can even worsen the choice.

Infer from the past. Low friction, but it locks you into your past self and shuts down discovery. You only get more of what you already want.

Judge yourself. Full autonomy, full labour.

There is a fourth mode that avoids the bad corners: recognition instead of interrogation. “Show me three, I’ll point to one.” Low friction and accurate at once, because it doesn’t require naming criteria in the abstract that you often can’t name. Here, the most famous study on choice overload helps too. Iyengar and Lepper (2000) sold jam at a tasting stand. With six varieties, far more people bought than with 24. Still, the effect is disputed, and honesty requires saying so. A meta-analysis by Scheibehenne and colleagues (2010) found, averaged over many studies, almost no general choice-overload effect. A second one across 99 studies (Chernev, Böckenholt & Goodman, 2015) showed that it appears only under certain conditions, such as high complexity, a hard decision, and an unclear preference. Telling is the original authors’ self-criticism. Perhaps, faced with 24 varieties, customers simply didn’t have enough time to “determine their preferences”. The line where autonomy actually lives is the one between “the agent applies my criteria” and “the agent invents them from my history”.

Back to the Wallet

With all that behind us, we can finally name why the wallet line sounds off. It throws together three things that are separable: who decides, who executes and who holds the funds. “Give the agent a wallet” answers only the third, and that only matters if the first falls to the agent, too.

Three cases. Human decides and pays themselves. Here, the agent doesn’t pay but remains a scout. In the second, the human decides and delegates execution (”yes, buy that one”). Now, the agent runs the checkout, but it needs no custody of money, only a narrow, revocable authorisation for exactly this approved purchase. Only in the third case, when the agent decides and pays autonomously, with no human at checkout, does the wallet itself become load-bearing.

And here it gets interesting because the payments industry built exactly this separation in 2025. Not custody, but authorisation. The Agentic Commerce Protocol, developed by OpenAI and Stripe, issues a “Shared Payment Token” bound to a single merchant, with a fixed amount, that is time-limited and single-use. Mastercard’s Agent Pay (April 2025) issues “Agentic Tokens” scoped to a specific agent, a merchant scope and a consent policy. The agent never sees the raw card number. Google’s Agent Payments Protocol (AP2, September 2025) even cleanly separates an Intent Mandate (what the human wants) from a Cart Mandate (what the agent proposes to buy), both as signed verifiable credentials, which is exactly the authorship-versus-evaluation axis cast in cryptography. Visa’s Trusted Agent Protocol (October 2025) takes the same route. None of these approaches hands the agent funds. All of them hand agents a narrowly bounded authority. The industry independently confirms the article’s thesis.

So where does the agent-owned wallet become native? Precisely in the commodity tier and in the machine room. The genuinely strong crypto cases don’t lie in consumer shopping. The x402 protocol, backed by Coinbase and Cloudflare, fills exactly the gap that card rails don’t close: fully autonomous agent-to-agent payments with no human in the loop, billed per API call, around the clock. Over 100 million transactions in the first months. Mastercard, in turn, announced “Agent Pay for Machines” for high-frequency, low-latency micro-payments between machines. That is infrastructure for a machine economy, not shopping. So the thesis isn’t wrong. But its importance is inverted. The wallet of its own becomes load-bearing where the goods are most interchangeable and the amount per transaction is often the smallest.

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Where the Wallet Really Belongs

This explains, in passing, why crypto has shifted its centre of gravity over the past two years, away from the consumer-driven culture of liberation, toward rails for companies: stablecoin settlement, tokenisation, and institutional infrastructure. That isn’t a turn away from agent shopping but is a homecoming to the one tier where the funds-holding wallet actually fits. Corporations are its anchor tenants for structural reasons. A procurement department is the institutionalisation of “choosing is a pure chore”. Its purpose is to replace taste and gut feeling with specification, price, reliability and contract terms. Procurement is agentic shopping, carried out by rule-following humans. So the agent wallet teaches corporations no new behaviour, and instead automates one they perfected long ago.

You can see it in an entire industry layer built for exactly this. Many companies have outsourced the purchasing of C-items, such as office supplies, to specialised platforms, including Mercateo and Amazon Business. Their promise is not the lowest price. It’s the lowest effort: one catalogue instead of twenty suppliers, one invoice instead of many. The company accepts a slightly higher unit price and saves the process costs, which for C-items often exceed the value of the goods themselves. The agent attacks exactly this logic. It pushes the ordering effort toward zero, while simultaneously searching the fragmented market nobody had time for before. What remains for the platforms is only conformance: approved suppliers, a clean invoice, protection against counterfeits. This verification, further down, turns out to be the real bottleneck.

The recommendation is therefore more precise than “wallets for firms instead of consumers”. It reads: ‘match the instrument to the tier’. The autonomous, custodial wallet (the agent decides, holds, pays) has its market in the procurement of standard goods and in machine-to-machine payments. The buying there is high-volume and repetitive, driven by specifications, so largely B2B and M2M. The consumer side, by contrast, wants authorisation instead of custody, meaning a narrowly bounded, single-use token on existing rails, only when the human approves a purchase. “Give the agent a wallet”, as a consumer headline, therefore, aims at the weakest slice. The product’s actual bridgehead is the machine room of companies.

A caveat that stays honest. Corporations don’t buy only commodities either. They have their own non-delegable tier, just one driven by consequence rather than pleasure: the choice of a law firm, an acquisition target, or a strategic supplier. There, the human stays, and the autonomous wallet has as little business as it does with the consumer’s wine.

The rule is domain-independent. The wallet rides the commodity layer wherever it sits, and that layer is simply the largest and densest inside companies.

What the Bottleneck Really Is

Paying was never the bottleneck. Moving money is solved, several times over. The two real bottlenecks are different.

One is trust in the inputs. Automating judgment is only a gain if the data ground holds. If it doesn’t, a judging agent is worse than a sceptical human. It launders manipulated signals at machine speed. That this ground is shaky today is official. The US trade regulator, the FTC, issued a rule in 2024 (effective 21 October 2024) banning fake reviews, noted that such fakes are widespread, and explicitly named generative AI as a tool that eases their mass production. By late 2025, the first warning letters followed, with penalties of around $51,744 per violation. Add the physical counterpart. Per OECD/EUIPO (2025), global trade in fakes amounted to roughly $467 billion in 2021, or 2.3% of world trade, and in the EU to 4.7% of imports, with clothing, shoes and handbags at the top. Pleasure goods, of all things. Anchored product identity, verifiable reviews, a verifier independent of the seller, and provenance records at the unit level (as the EU anti-counterfeiting directive and the US DSCSA have long required per pack in pharma) are the precondition that tips judgment automation from dangerous to useful.

The other bottleneck is authorship. Everything downstream of a defined wanting becomes increasingly automatable. Defining the wanting stays human, not out of nostalgia but structurally. To automate it, the agent would have to want for you, and a wanting fabricated for you is not yours.

Give the agent a wallet, and you’ve solved the easiest part. The interesting part is making judgment partly and safely automatable, and leaving the human where they belong: as the author of the criteria and as the one who savours the final choice.

For Anyone Building Marketplaces

One consequence to close, for pleasure goods. Once agents commoditise sourcing, the goods are findable everywhere. Then your product is no longer the goods. Choosing is. Make yourself maximally legible to the scout agent (verifiable provenance, clean data) so it routes the human to you. And on top of that, occupy the one thing automation can’t take: the discovery that widens taste rather than confirming it, and the pleasure of the final decision. Hand sourcing to the agents. Build your business around the better choices.

That’s all for today.

Until next time,
Anderl

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We will be back with another guest essay soon.

P.S. This piece was originally published here.

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“The world’s $2.5 trillion trade financing gap is not a credit problem. It is a cost-of-underwriting and information-asymmetry problem.”

Hello,

Banks are great at lending money to people who don’t need it as badly as others.

That’s not foul play, but business economics. Checking whether someone is trustworthy costs roughly the same in money and effort whether they’re shipping $40,000 worth of cashews or $40 million of semiconductors. Both still have to follow similar paperwork and compliance procedures. The transactions will have to go through the same network of three to six banks. This gives banks the benefit of achieving economies of scale if they prefer higher-order values to small-value ones.

This is the single most important incentive for banks to turn down small exporters that are yet to build their credibility. That’s probably why small and medium exporters have a 50% rejection rate, roughly seven times that of multinationals.

The asymmetry in the trade financing gap is not only based on enterprise size but also on geography. Asia-Pacific alone accounts for 40% of the world’s $2.5 trillion trade financing gap.

The system is broken for a specific set of people in certain areas. When exclusion structurally affects certain stakeholders, it makes it easier to map the problem and find a solution.

In global trade financing, one of the biggest problems is duplication. Every trade forces four or five institutions to verify the same facts, like who shipped what, to whom, against which invoice. Each institution records these details in its respective systems and charges for doing so. The cost of trust is incurred repeatedly, making it more difficult for small and medium exporters to absorb it.

Blockchains can solve this by moving stablecoins across a shared ledger in minutes, rather than routing fiat currency through multiple correspondent banks over three to five days. This is where blockchains can improve inclusion in trade financing by specifically making the entire proposition viable for global traders who are often ignored at the periphery.

Today’s guest essay is an investment thesis from 1kx, an investment firm that focuses on decentralised finance and stablecoin payments. In this thesis, Nichanan Kesonpat - Research Principal at 1kx - maps out how on-chain trade financing unfolds layer by layer and where value accrues for the industry to capture.

Read 1kx's Thesis

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P.S. This thesis was originally published by 1kx here.

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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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.

For a long time, since OpenAI made large language models a common household phenomenon, AI felt like a new toy in the town to play around with. It was only after the new models began consuming significantly more energy and resources that the reality of AI hitting its token limits dawned on many.

Even in our editorial calls and conversations on AI forums, every new model always prompted people to utilise it to its full potential and compare the results. Most of those who debated AI models paid less attention to how each marginal improvement in the thinking model increased unit cost disproportionately, often for similar or only slightly better output.

Better thinking models meant you exhausted your daily token limit faster, with fewer queries and prompts. This changed the user behaviour almost immediately. Even after I started hitting my daily limits more often, I still used AI, but I became more careful about choosing models based on the task I wanted to carry out.

That extra decision annoys users, but it begs a much larger question. The trillions of dollars of capex flowing into the AI industry, the series of data centres being set up, and the investments flowing into the portfolios of companies operating in compute suggest that demand for intelligence is infinite. But most people and companies don’t repeatedly buy capacity for intelligence unless they have a clear task to achieve - like write faster, code better, sell more, support customers, cut costs, or make decisions.

This is why presuming the demand for intelligence is near-infinite is a flawed assumption.

In today’s piece, Kevin Simback, COO at Delphi Labs, explains this clearly. Demand for intelligence may be enormous, but it is not infinite at every price. It becomes near-infinite only when the cost of useful work falls below the point where buyers can justify it.

On to Kevin’s story,
Prathik

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I keep coming back to one assumption that only some people are starting to say out loud.

The entire buildout - the data centres, the chips, the trillion-plus dollars of committed capital, and the large, growing share of the stock market now riding on it - rests on a single belief. That the demand for intelligence is infinite.

It isn’t. Demand for intelligence is near-infinite at a price. That qualifier is the difference maker.

In the first half of this year, we’ve mostly ignored price and just devoured intelligence. You can see it clearly in Anthropic’s revenue numbers.

We’ve even coined a new term, tokenmaxxing, to gloat about our unbounded consumption.

For most companies, this just isn’t sustainable. Companies need to find ROI, otherwise they will absolutely throttle demand.

In the first half of this year, we got away with convincing the CFO to loosen the purse strings, but the second half will be about tightening them back up.

This article double clicks on this thesis, what to do about it, and what happens if we get this wrong.

The Demand Curve Nobody Drew

“Near-infinite demand” and “infinite demand” may sound like the same thing. They are actually two completely different economies.

Infinite demand is a vertical line. It says: people will buy intelligence at whatever price is offered, so the only thing that matters is making more of it.

Just keep scaling the supply and the demand is already there waiting. Most of the capital being deployed today behaves as if this is true.

How many people have you heard say, “If Anthropic had more compute, they could be doing $100B ARR?”

Near-infinite demand is a downward-sloping line with a hard floor underneath it. It says: demand for intelligence explodes, but only once the price drops below the level where the buyer earns a return on it.

Above the ROI line, demand is thin and more volatile. Below it, demand is effectively bottomless. The floor is ROI, and where a company sits relative to it decides everything.

This shouldn’t be considered crazy talk, yet many speak of the demand curve as vertical.

The actual buyers are telling us exactly where the line is. An MIT study last year found that only about 5% of enterprise generative-AI pilots produced a rapid, measurable financial impact. The rest stalled.

A large cross-country survey of executives this year found that nearly 90% of firms reported no measurable effect from AI on productivity or headcount over three years.

The typical response is to call this a “skill issue” and just assume companies will get there.

That may be true; perhaps we just need more Anthropic and OpenAI Forward Deployed Engineer (FDEs) embedded within enterprises to make ROI a reality. That seems to be what the new “deploy cos” are betting on.

But it’s hard to assume both things can be true at once - either the demand for intelligence is infinite (in which case, why do the labs need deploycos?) OR demand is near-infinite but only under the right conditions.

Tokenmaxxing: Optimising against Your Own Customer

Here is where it gets a bit self-inflicted.

The reflex across the industry is to reach for the biggest model and the deepest reasoning setting for every task.

If you leave this up to users, that’s exactly what you’ll get - they’ll just run everything through Opus 4.8/GPT-5.5 because, why not?

But this behaviour is what will quietly kill ROI.

The newest reasoning models make it so easy to burn tokens. They generate long internal chains of thought before they answer, and you pay for every one of those hidden tokens.

A request that returns a few hundred visible words can burn many thousands of billed tokens under the hood, which means the real cost per useful answer can run an order of magnitude higher than the sticker suggests.

In one controlled evaluation, a top reasoning model cost roughly 14x more than a cheaper general model on the same task and produced four times as many failures. Maximum intelligence, applied indiscriminately, made the result both more expensive and worse.

The deeper problem is an incentive mismatch - the seller is paid by the token, but the buyer is paid by the outcome.

Tokenmaxxing maximises the first and starves the second. It pushes the price of every decision up the demand curve, away from the floor where demand actually lives, at the exact moment the whole system needs to drive price down through it.

You cannot fill near-infinite demand by selling the most expensive version of the product to everyone. You fill it by getting the price of a good-enough answer under the ROI line and letting volume do the rest.

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Why “But Costs Always Fall” Isn’t the Rescue

I know you want to say “Jevons paradox” right now, but that’s not going to let you off the hook.

First, the cost that has to fall is the cost per useful outcome, not the cost per token. Those two can be related, but not if we don’t address the behaviour mentioned above.

If per-token prices drop while tokenmaxxing pushes token consumption per-task up even faster, the cost of getting a real job done doesn’t fall at all. It could even rise.

Falling unit prices and rising bills coexist comfortably, and I hear reports of teams living that contradiction right now.

Second, the savings have to actually reach the buyer. Efficiency captured entirely by the seller, or burned on more reasoning that the task never needed, never crosses the ROI line where the demand is.

If you lower the cost of tokens for a model, but the reasoning and tool calling under the hood drives up the number of tokens consumed, it’s not clear that the outcome itself is getting cheaper.

This is a markets problem, not a procurement problem

If this were only about enterprise software disappointing, it wouldn’t be that big of a deal. But we’ve now put most of the global economy’s chips in the “AI trade” basket, so any slip-up here and the implications get big.

The major US hyperscalers will spend more than $1 trillion in 2025-26. That spend is underwritten by an expectation of future revenue that depends on enterprise adoption, which in turn is dependent on ROI.

The gap between capex and revenue is being bridged with financing, some of which looks quite circular. I’m not ready to raise any alarm bells yet, but we need to connect all this back to the demand curve.

In the simplest view, if demand turns out not to be infinite, but is actually near-infinite and gated by ROI that hasn’t yet materialised, then the big risk is that the revenue that justifies the capex doesn’t arrive on schedule.

The financing loop can’t keep substituting for real cash flow forever. A technology this leveraged and this concentrated doesn’t get to underdeliver without major economic reverberations.

Said bluntly, if ROI doesn’t show up at scale, the AI trade begins to unwind, and a reflexive, circular, concentrated structure unwinds the way it was built: fast, and on everyone at once.

That is the worst-case scenario. I’m not saying that is the most likely, but it is a scenario that starts with enterprises becoming unhappy about their pilots and throttling demand.

“ROI takes time” - yes, and that is exactly the danger

The most serious objection to the ROI issue is one of timing.

Every general-purpose technology lags, and the payoff can be real. But it might not be on the same quarterly clock that drives the markets.

The mismatch is between a payoff that runs on the slow clock of organisational redesign and a financing structure that runs on the fast clock of capital markets.

The technology can be a genuine multi-decade transformation, and the trade financing can break in the meantime. Both can happen at once.

That is why time is the scarce input here, and why we need to get moving on it.

What ROI Already Looks Like

The picture is not entirely bleak, and that’s not what I want as the takeaway either.

Some enterprises are getting real, measured returns right now. But, in many cases, it is not the teams chasing the smartest possible model on the broadest possible mandate.

It is the teams running narrow, bounded, repeatable workloads, with the right-sized model for the job and an actual measurement of the outcome.

The ROI is happening in companies that deploy intelligent routing - sending easy queries to cheap models and escalating only the hard ones.

The ROI is happening in teams that swap a frontier model for a small, fine-tuned one on high-volume structured tasks.

The ROI is occurring in enterprises that employ prompt and context-trimming tools to mitigate against users sending everything to the most expensive, highest-reasoning model.

The ROI is happening in projects that build custom agent harnesses that manage tool access, memory, permissions, workflow state, evals, logging, human approvals, fallbacks, and cost controls.

None of these are examples of weaker AI. They are examples of the same outcome delivered below the ROI line instead of above it.

The early winners, those finding ROI, obey one discipline: intelligence per dollar, not intelligence at any price.

The Reframe

The entire premise of tokenmaxxing measures the wrong number.

The number that decides whether this entire AI trade is sustainable is the outcome per dollar.

Useful work delivered, divided by what it cost to deliver. That ratio is what moves a use case from above the ROI line to below it, and the volume of demand waiting below that line is the only thing large enough to justify what’s already been spent.

So the urgent work, the genuinely urgent work, is driving cost-per-outcome down hard enough and fast enough to pull the mass of stranded use cases under the line, and getting those methods adopted before the markets run out of patience.

The primary levers already exist:

• Route every task to the cheapest model that can actually do it, instead of defaulting to the most expensive one.

• Trim context and reasoning to what the task needs, rather than dumping everything into the biggest model on the highest setting.

• Wrap agents in harnesses that control tools, memory, evals, approvals, and budgets, so reliability comes from the system and not from brute-force model size.

• Fine-tune smaller open-weight models on proprietary data for the high-volume, repeatable workloads that don’t need a frontier brain.

Each of these is a way of doing the same job for less, which is the only thing that grows near-infinite demand into the real thing.

The optimistic reading of all of this is that the demand really is there. It is enormous, it is waiting, and it is gated by a single solvable variable - ROI. We just need to collectively make it happen soon.

P.S. This piece was originally published here.

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We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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.

I’ve been reading David Graeber’s ‘Debt’ lately, and it opens by exploring how old and how human debt actually is. It explained debt as a relationship that assumed a future for both lender and borrower. The tab remained open throughout the loan tenure, assuming both parties would be around.

But the modern version of debt has drifted far away from that. On-chain lending makes the contrast look more ridiculous: a loan can be closed out without the borrower’s fault. If your collateral dips and a predetermined price level is crossed, the position is liquidated at a penalty. Ironically, this could happen just a day before the collateral recovers and rises well above the loan level. For being briefly underwater, the borrower is assumed to be insolvent and penalised.

The crypto advocate in me expects blockchains to do much better. In its current form, liquidation defeats the purpose of debt, which was to keep a relationship open through uncertainty rather than slam it shut at the first sign of trouble.

So what would it look like to build a loan that keeps faith with the borrower instead of betting against them? A loan that bends when things get hard rather than breaking?

In today’s guest essay, Jayesh argues that the answer has been sitting in traditional finance for 40 years. He takes a technique called Constant Proportion Portfolio Insurance (CPPI), the same idea behind capital-protected products banks have sold for decades, and maps it onto on-chain lending to produce a loan that defends its own collateral.

It is an honest piece because it offers a solution while remaining clear-eyed about where it can still fail.

On to Jayesh’s story,
Prathik

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Most on-chain loans die in the same dull and predictable way, where the collateral falls, a threshold is crossed, a liquidator closes the position at a penalty, and the borrower is left holding the loss on an asset that very often recovers a week later. We have spent five years making the price of credit smarter, and almost none of that energy on the thing that actually wipes people out: the liquidation itself.

I want to propose a different loan structure that borrows a 40-year-old idea from traditional finance called Constant Proportion Portfolio Insurance (CPPI). It produces a loan that defends its own collateral instead of waiting to be liquidated. Curve and f(x) have already shown the instinct works on-chain. What nobody has done yet is name the mechanism for what it is, drive it off the right variable, and hand the borrower the dial that decides its risk.

Let me walk through where lending has been, what this primitive is, the math that makes it work, where it can fail, and how a protocol could actually build it.

Innovation of On-chain Lending

The first generation of on-chain lending was variable rate, and it still anchors the entire category. Compound v2 went live in May 2019. ETHLend, which launched in 2017, rebranded as Aave in 2018, and moved to a pooled liquidity model with Aave V1 in early 2020. Both set the borrowing rate in the same way: algorithmically based on utilisation. When more of the pool is borrowed, the interest rate rises along the interest rate curve, attracting additional supply and discouraging new borrowing. The rate floats for everyone in the pool and updates every block as borrowing demand shifts.

This pooled variable model won decisively and still anchors the largest part of the market today. Aave alone holds around $13 billion in deposits in mid 2026, and Compound, now on its isolated market design called Comet, sits a little above $1 billion. It is worth noting how hard it has been to improve the rate side, even for the established ones. Aave shipped a stable rate borrowing option years ago, then disabled new stable rate borrows in November 2023 after a vulnerability was found in the stable rate logic, and fully deprecated it through governance in 2024. The dominant on-chain lending experience today remains variable rate, and that has barely changed in years.

The current frontier is fixed-rate lending, and this is where most of the recent design talent has gone. Pendle is the clearest example and the category leader, holding around $1.3 billion in mid 2026. It works by splitting a yield-bearing asset into a Principal Token and a Yield Token. The Principal Token behaves like a zero-coupon bond: you buy it at a discount and redeem it at face value at maturity, locking in a fixed yield. In May 2026, Morpho published the Midnight whitepaper, a fixed-rate, fixed-maturity protocol in which lending and borrowing are expressed as the trading of credit and debt units whose payoff is analogous to that of zero-coupon obligations, with offers that do not lock up capital until settlement. Midnight is freshly released and open-sourced, and it squarely signals that serious builders are pointing at the rate.

So it is worth looking at the two axes of innovation side by side. On the axis of what credit costs, we have moved from variable to fixed and built a genuine toolkit along the way. On the axis of what happens when the collateral falls, almost nothing has changed. Since the overwhelming majority of on-chain debt is still protected by hard liquidation, a single line change in price could get your position closed and penalised. This neglected second axis is the gap I care about.

Exceptions that Already Point the Way

Two protocols deserve full credit because they addressed this exact problem before anyone else.

Curve’s crvUSD introduced soft liquidation through a mechanism called LLAMMA - the Lending Liquidating AMM Algorithm. Instead of one liquidation price, your collateral is spread across a set of discrete price bands, anywhere from four to 50 of them, and each band acts as its own small liquidation zone. As the price falls through a band, that band’s collateral is progressively sold into crvUSD. As the price recovers within a band, the crvUSD buys back the collateral. Curve calls this de-liquidation. Arbitrageurs do the actual trading because LLAMMA quotes prices offset from the oracle, making rebalancing against external markets profitable. The position is never slammed shut at a single threshold, and its exposure is instead continuously shifted between the volatile asset and the stablecoin. crvUSD has run real borrowing through this design.

f(x) Protocol does something structurally similar with its Liquidation Brake. When a leveraged position approaches its liquidation price, the protocol burns part of the position’s debt, sells some backing collateral, and pulls the leverage ratio back down. This reduces risk while keeping the user’s directional exposure, rather than closing them out. External keepers monitor positions and trigger the sale when needed. The protocol holds roughly $90 million in mid 2026.

Irrespective of how they describe themselves, both are simply doing portfolio insurance on collateral. They de-risk into a decline and re-risk into a recovery. That is the exact reflex of a strategy traditional finance formalised in the 1980s, and once you see the connection, a cleaner and more general version of the loan falls out.

What is CPPI?

Constant Proportion Portfolio Insurance (CPPI) was developed by Perold in 1986, extended to equities by Black and Jones in 1987, and formalised by Black and Perold in 1992. The idea is simple and is built to protect a floor.

You define a floor, which is the value your portfolio must not fall below. You measure the cushion, which is how far you currently sit above that floor.

Cushion = Portfolio Value - Floor.

You then hold an amount of the risky asset equal to a multiplier times that cushion, and you park the rest in a safe asset.

risky exposure = m × (portfolio value - floor)

The strategy’s behaviour follows from that single line, and it plays out intuitively as markets move. When the cushion is fat, you hold a lot of the risky asset. As losses eat into the cushion, your risky exposure mechanically shrinks toward zero, which moves you into the safe asset before you can breach the floor. When the cushion rebuilds, you lean back into the risky asset. The strategy sells into weakness and buys into strength.

Mapping that same structure onto a loan makes the correspondence exact. The floor sits just above the debt, at the debt plus a small gap margin I size later. That’s because the collateral must never actually fall below the debt, which, if it does, you have a bad debt and a liquidation. The cushion is your collateral value minus the floor, and acts as the safety buffer that every borrower watches. The risky asset is the volatile collateral, ETH or a BTC wrapper or SOL, and the safe asset is a stablecoin. A CPPI loan holds its collateral as a managed basket of the two, and it rebalances that basket as the cushion breathes. As your collateral falls toward the floor, the basket steps out of the volatile asset and into the stablecoin, defending it. As the cushion grows, it steps back into the volatile asset, handing you the upside. What changes is that the ordinary dips that would liquidate a normal position no longer kill this one because it de-risks instead of crossing a single line. The loan fails only if you stop paying interest, miss maturity, or the market gaps so violently that the basket cannot rebalance in time, which is the gap risk at the heart of this design.

The Math and the Numbers

Take the simplest possible example, where your collateral is worth $100, and the loan is $70. Set the gap margin aside for this first pass, so the floor sits at the debt of $70. That makes the cushion $30. Suppose you want to start with your position fully in the volatile asset, so all $100 sits in BTC at origination. That choice sets your multiplier (m).

Risky exposure = m × cushion; so, $100 = m × $30, which means m ≈ 3.33.

In the real design, the floor level sits slightly above the debt. That shrinks the cushion and increases the multiplier required for the same starting exposure. However, the mechanism is identical.

That multiplier is not a cosmetic setting but the entire risk profile of the loan, and it is governed by the well-known gap-risk property of CPPI. It does two jobs at once. First, it sets how much of your collateral starts in the volatile asset, which is m times the cushion, capped at the whole position. Second, it sets the size of the gap you can survive between rebalances, which is 1/m. With m ≈ 3.33, that gap is 30%. So the loan defends its floor against any drop up to 30% that happens gradually enough to rebalance, and it breaks the floor on a gap larger than that before the basket can react. A more conservative borrower who picks m = 2 starts with only 60% in the volatile asset, since exposure is 2 × $30 = $60, allocating the other $40 in stablecoins from the start. This move buys a wider 50% gap tolerance in exchange for upside. An aggressive borrower who picks m = 5 also starts fully in BTC, since 5 × $30 = $150 is capped at the $100 they hold, but the higher multiplier de-risks the basket faster once price slips and tightens its gap tolerance to 1/m, or 20%. This is the primary dial. It is a single, legible number that lets a borrower choose how much downside protection they are buying and how much upside they are forgoing. Later, the floor gives a second dial, for how large a safe reserve to keep. Currently, no on-chain lending protocol exposes either of these.

This is also where I refuse to oversell the idea, because CPPI has a famous and instructive failure mode. The portfolio insurance strategies blamed for amplifying the Black Monday crash on October 19, 1987, were the synthetic put and dynamic hedging programs that sold index futures into the decline, creating a feedback loop. Reputable postmortems, including the Brady Commission, treat portfolio insurance as having amplified the crash rather than caused it.On the day of the crash, portfolio insurers accounted for roughly 40% of all non-market-maker futures sell-off. Strictly speaking, CPPI was not the named culprit, since the 1987 programmes were the option-replication variety. But CPPI shares the same de-risk-into-declines reflex and the same gap risk. The lesson is not that the mechanism is broken. It is that gap risk is real, that you must set the multiplier with respect to how violently the asset can move between rebalances, and that you cannot rebalance through a true discontinuity.

This is live, working machinery in traditional finance

Far from dying in 1987, CPPI is alive and widely used, serving as a standard engine behind capital-protected products. Those products are a large corner of traditional finance, with structured-product sales hitting a record near $1.4 trillion in 2024 by Structured Retail Products’ count. CPPI issuance specifically was making a comeback that year, which makes it a current practice rather than a museum piece.

What matters to us is that traditional finance has spent decades learning to tame CPPI’s gap risk by keeping the multiplier conservative, charging a premium to guarantee the floor, overlaying options on the underlying, and rebalancing often enough to keep the gaps small. That is exactly the toolkit a protocol would inherit, and it is the same gap risk that showed up on-chain, in public, in the protocol that pioneered soft liquidation.

What the Honest Version Costs

If I am proposing this, I owe you the downsides in full, because they are not hypothetical, and there are three of them:

1. The bleed comes from trading the basket back and forth. Continuously selling the volatile asset on the way down and buying it back on the way up is, mechanically, selling low and buying high, and it erodes value whenever the price chops sideways inside the rebalancing range. Curve documents this directly for crvUSD and is admirably blunt that the loss is hard to quantify because it depends on the number of bands, the speed of the move, and how deep the collateral’s liquidity is. Their own worked example, for a position that spent more than half its life in soft liquidation, shows a loss of 6.37%, and they note that the erosion accrues both on the way down and on the way back up while you are in range. A CPPI loan inherits exactly this convexity cost. You are, in effect, short volatility, and you pay for the floor protection in whipsaw.

2. Forgone upside follows because a position that de-risks near the bottom and only partially rebuys on the recovery has sold its dip. The borrower trades the chance to ride a full rebound for the certainty of not being liquidated. That is a real trade, and some borrowers will not want it. The sharpest version of this cost is cash lock, the point where the basket has fully de-risked into USDC, and the cushion is zero. So, even a sharp rally cannot rebuild the position, and its upside is frozen. In a perpetual fund, that state is close to permanent, but in a loan, it is an exit ramp rather than a trap. That’s because the borrower can top up collateral to inject fresh cushion and re-risk, or simply sit safely in USDC just above the debt and repay at maturity. A borrower who wants more room can set a higher floor up front, so the locked position parks well above the debt, a choice I come to later.

3. Gap risk is the one that matters most, and it is the most concrete of the three. Soft liquidation is a buffer, not a guarantee, and crvUSD still maintains a hard liquidation backstop that closes the position if health reaches zero. We saw that on October 10, 2025, when the crash produced roughly $19 billion of liquidations across crypto in a single day, the largest on record. Curve’s CRV long LlamaLend market could not rebalance quickly enough through that gap, leaving it with about $700,000 of bad debt, backed at roughly 70% of stated value. Later, a market-based recovery was proposed in April 2026. This is not a reason to abandon the design, but rather proof that any honest CPPI loan must ship with a hard liquidation backstop and a funded reserve to cover the gap that the math cannot rebalance.

Why this belongs on-chain, and why it can bring fresh capital

The core of the argument turns on shape rather than magnitude, because a CPPI loan does not promise to be safer than holding the bare asset. What it changes instead is the way the risk is shaped. It converts a discontinuous, all-at-once liquidation cliff that’s complete with a penalty, into a continuous, smaller, and more predictable cost that you can see coming and price in advance.

That shape is precisely what risk-averse capital is built to buy. The entire capital-protected product industry exists because a very large pool of money will accept lower expected upside in exchange for a defended floor and no sudden holes. On-chain lending has, so far, offered that pool almost nothing except a liquidation cliff, whereas a loan whose collateral defends its own floor is the on-chain expression of a product structure that this capital already understands and already allocates more than $1 trillion a year toward.

It also widens what you can safely borrow against, because volatile collateral is dangerous in a hard liquidation world where a single wick is fatal. Whereas in a CPPI world, the collateral self-hedges as it approaches the floor, making a long tail of volatile assets more usable as collateral at a given level of lender safety, as long as the multiplier is set with respect to how violently each asset can gap.

How a protocol could actually build this

I would build it as an isolated lending market, in the spirit of the minimal, immutable market designs the category has converged on, with four pieces.

One choice sits above those four pieces: the interest rate. CPPI is independent of how the rate is set, so it can sit on top of either a variable rate or a fixed one, and a variable-rate version is entirely valid and worth building. The version I am proposing pairs it with a fixed rate and a fixed maturity because that completes the one quadrant nobody has built: a loan that is both fixed rate and self-defending. It most closely mirrors the capital-protected products from which this idea borrows.

1. The collateral is held as a CPPI-managed basket of the volatile asset and a stablecoin, not as pure volatile collateral. The borrower picks the multiplier at origination, which is the product. A conservative borrower picks a low multiplier and starts with more stablecoin and a wider gap tolerance. An aggressive borrower picks a high multiplier, keeping more upside and more gap risk.

2. A rebalancing engine moves the basket as the cushion changes. You can do this the LLAMMA way, by quoting prices that pay arbitrageurs to rebalance for you, or with an explicit keeper network executing through a batch auction or a CoW-style settlement to blunt MEV and slippage, priced off a robust, manipulation-resistant oracle. Rebalance frequency is a real parameter, since rebalancing more often reduces gap risk but raises the whipsaw bleed, and that tradeoff should be tuned per collateral asset.

3. You set the floor above the debt rather than on it, and you keep a hard liquidation backstop in the band between them. The floor sits higher because the rebalancing swaps are neither instant nor free. So, a slippage or a fast move can carry the collateral through the floor before the basket has finished converting to USDC, and if the floor were the debt itself, that overshoot would land below the debt and create bad debt directly. By placing the floor at the debt plus a modest margin, the basket is fully de-risked into USDC by the time collateral reaches that floor, and the space down to the debt acts as a protective band.

4. That band is also where the liquidator incentive lives, because if collateral pierces the floor despite the de-risking, the hard backstop fires inside the band while the collateral still exceeds the debt. So, a liquidator closes the position and takes a bonus from the remaining margin. The lender is still made whole, and bad debt only occurs if price gaps clean through the band below the debt. The margin should be modest, since a wide one de-risks too early and bleeds more upside, so its size is tuned to expected slippage and how violently the asset can gap. You also fund a reserve for residual bad debt from a spread charged to borrowers, which is the on-chain analogue of the gap risk premium that a structured products desk already charges for guaranteeing a floor.

5. You grow it the way this kind of risk shape wants to grow. Start with blue-chip collateral and conservative multipliers, where gap risk is smallest, and the floor is most defensible. Target the capital that wants yield without a liquidation cliff, which is treasuries, DAOs, and the more conservative end of allocators. Let curators build vaults on top that package multiplier and collateral policies tailored to risk appetites. The TVL story here is not degens chasing a number, but patient capital that was never going to accept a liquidation cliff in the first place, finally being offered a shape it recognises.

TL;DR

• On-chain lending is still mostly hard-liquidated, so one sharp wick can close a position that would have recovered days later.

• CPPI lending holds your collateral as a basket of the volatile asset and a stablecoin, and de-risks it toward a floor as your cushion shrinks, so the loan glides down instead of crossing a single liquidation line.

• The borrower’s dial is the multiplier (m), where risky exposure = m × cushion and 1/m is the gap it can survive. So, a higher m keeps more exposure and less gap protection, and vice versa.

• Curve’s crvUSD and f(x) already proved that soft liquidation works on-chain, and CPPI is a 40-year-old technique in traditional finance. So, the new part is naming it, driving it off the cushion, and handing the borrower the dial.

• It is not free, since it carries whipsaw bleed, forgone upside, and gap risk that still needs a hard-liquidation backstop, so it reshapes risk rather than removing it.

• That smoother shape, a glide instead of a cliff, is exactly what conservative capital already buys in traditional finance, which is the real case for bringing fresh capital on-chain.

If you build in this direction, or you think the gap risk kills it, I want to hear the argument. This is a proposal, not a product, and it gets better by being attacked.

Disclaimer: This piece was first published here.

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For almost nine months of 2025, every Monday on Token Dispatch opened with our Bitcoin macro edition. Price, on-chain flows, ETF prints, what the miners were doing and where the industry was in the crypto cycle. It seemed like the most obvious thing to write about. If you were a crypto publication, you would lead with Bitcoin, just as a market wrap leads with the index.

Then, one Monday, we shipped without it. We have now gone almost nine months without a Bitcoin macro column. I didn’t think much of it at the time. But now, when I look back, it helps me acknowledge how far the industry we operate in has come.

For most of crypto’s life, people cared about just one question: what is Bitcoin doing? It made sense then. But crypto has outgrown its connection to BTC price charts and ETF flows. When I hear crypto today, I think of how it is building a better financial world, running stable businesses on existing or new infrastructure, or turning into a new version of fintech that improves the way we transact or move money.

Many great innovations have taken this road in the past. The smartphone went through this when it stopped being measured against the phone call. The internet did too. That’s how crypto is graduating, too.

We have been writing a lot lately at Token Dispatch about the convergence of crypto and fintech. So, we deemed it timely to publish this guest op-ed, in which Nikshep argues that crypto no longer needs Bitcoin. He explains why Bitcoin lost two jobs this year and why it bodes well for the larger industry. Nikshep invests and builds at the intersection of AI and crypto. He has previously worked with Kalshi and Coinbase.

On to Nikshep’s story,
Prathik

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AI took its job as the market’s risk trade. The dollar took its job as crypto’s money. And the thing quietly tying a fragmenting, multi-chain economy together isn’t Bitcoin anymore. This is the most bullish thing that’s happened to crypto in years — and almost nobody is framing it right.

Bitcoin broke below $70,000 this week, down roughly 45% from its October high, and the timeline filled with tombstones. Record ETF outflows. The longest redemption streak since the funds launched. “Digital gold” that somehow forgot how to be gold while actual gold ripped 89% and blew past $5,000.

They’re mourning the wrong thing.

Because while Bitcoin bled, an on-chain exchange most people have never opened did more trading volume last year than Coinbase. A prediction market hit a $20 billion valuation, printing a $365 million fee run-rate. A privacy coin everyone wrote off ran 70% in a week — going up while Bitcoin went sideways. And a network you’ve probably underrated made it possible to move value privately across any chain without having to buy its token.

Crypto isn’t dying with Bitcoin. Crypto stopped needing Bitcoin.

That sentence sounds bearish. It’s the opposite. What’s actually happening is a coming-of-age: crypto is graduating from a single-asset momentum casino, where every coin was just leveraged Bitcoin-beta, into a real, dollar-denominated economy where businesses live or die by their own fundamentals, and where a new connective layer, not Bitcoin, holds the whole thing together.

Bitcoin lost two jobs this year. Here’s who took them, and here’s what’s being built in the space it left behind.

Job one: AI ate the risk capital

Bitcoin has no cash flow — no earnings, no dividend, no coupon. Its price is almost purely a function of how much money is hunting for risk and how willing people are to take it. That makes it a liquidity sponge: it swells when money is loose, gets wrung out when money is tight. In 2026, one rival is wringing it dry.

AI infrastructure spending is heading toward $700–830 billion this year, about half of the entire US investment-grade bond market, on its way to $7 trillion by 2030. It’s roughly 5% of US GDP and has recently added more to American growth than consumer spending. Nvidia alone is ~8% of the S&P 500. This isn’t a sector competing for capital. It’s a gravity well bending the price of capital for everyone.

It starves Bitcoin through three doors at once.

It took the story. Bitcoin’s deepest pitch was “the asymmetric bet on the future.” AI offered a better version — one with real revenue, demand it can’t build fast enough, and government backing — bought through the index you already own. Allocators now lump Bitcoin in with the most speculative tech; its correlation with unprofitable “story” stocks hit the 97th percentile. When two things share a risk bucket, and one suddenly has earnings, money rotates. That’s the outflow streak in a sentence.

It took the capital. The build-out is increasingly debt-financed — hyperscaler bond issuance already past all of last year, private credit to AI over $200 billion. When the best borrowers on earth flood the market with paper, capital gets absorbed from the top down, long before it reaches the speculative frontier where Bitcoin lives.

It took the rate cut. AI is physically inflationary — electricity and water up ~5%, memory up double digits. That helps pin inflation near 3.8%, which keeps a hawkish Fed at 3.50–3.75% and markets pricing in zero cuts this year. So AI doesn’t just outbid Bitcoin for capital; it manufactures the high-rate regime where liquidity can’t reach Bitcoin anyway. It suppresses it twice.

And then it got physical. AI and Bitcoin mining are the same business — turning electricity into compute, fighting over identical megawatts. AI won because a watt is worth far more in an Nvidia rack than a hashing rig. The average public miner spent ~$80,000 to make one bitcoin last quarter while bitcoin traded near $70,000 — a $19,000 loss per coin. So miners are defecting to the predator: over $70 billion in AI/HPC contracts have been signed, with listed miners on track to generate up to 70% of revenue from AI by year-end. Core Scientific is converting a 300-megawatt bitcoin site into AI data centres as part of a $10.2 billion deal. Riot leased to AMD and sold its bitcoin to buy the land. They’re dumping treasuries to fund the pivot — and they’re the same firms that secure the network.

Here’s why this is structural, not a dip. Compare it to the threat everyone fears: quantum computing. A powerful enough quantum machine could crack Bitcoin’s cryptography in minutes, maybe by 2029. Terrifying — but quantum attacks the machine, and you can change the machine: post-quantum standards are finalised, the migration soft forks are drafted, the roadmap is years. Quantum is a deadline, not a death sentence. You patch the lock. AI attacks what you can’t patch — the reasons to own Bitcoin, the capital that prices it, the power that secures it. There’s no soft fork for “something better took your narrative, your money, and your electricity.” Job one, gone.

Job two: the dollar replaced Bitcoin as crypto’s money

This is the part nobody connects, and it’s the keystone.

For most of crypto’s history, Bitcoin was the reserve asset — the on-ramp and the base pair. Fiat became Bitcoin; Bitcoin became everything else. Every coin was quoted in BTC. Every inflow hit Bitcoin first. That gravitational role is why alts used to rise and fall with it.

The stablecoin cut that cord. USDC just flipped Tether in transaction volume for the first time since 2019. Annual stablecoin volume crossed $30 trillion. The on-ramp is now fiat → USDC → whatever you want, and Bitcoin is nowhere in that path. When Polymarket rebuilt its exchange this spring, it launched its own dollar — Polymarket USD, backed 1:1 by USDC. Hyperliquid settles in dollars. The dollar has become, in one analyst’s phrase, money beneath app-level money — the reserve collateral under everything, with each app stamping its own label on top.

So when the market turns risk-off, the dominance charts show Bitcoin’s share falling while stablecoin share rises. Money isn’t fleeing crypto. It’s rotating into the dollar — inside crypto. You no longer have to hold Bitcoin to “be in crypto.” The dollar does that job now. Bitcoin can fall forever and never get a bid from on-chain activity, because on-chain activity runs on dollars. Job two, gone.

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What’s Thriving without BTC: Real Businesses

Strip Bitcoin out and look at what people actually use. These aren’t lottery tickets waiting for a BTC pump. They’re businesses.

Hyperliquid is the proof that should end the “crypto is dying” conversation. An on-chain exchange that trades like Binance — deep books, fast fills, self-custody — it did roughly $2.6 trillion in volume last year, more than Coinbase’s $1.4 trillion, generating $0.8–1.3 billion in annualised revenue. It routes 97% of fees into buying its own token on the open market — about $1.3 billion so far, ~7% of market cap a year, four to five times Ethereum’s burn rate, fourteen times Solana’s. No VC. A community airdrop and a buyback funded entirely by people using the thing. Its demand driver — traders trading — is bidirectional and BTC-agnostic. Volume can climb while Bitcoin falls.

Polymarket is the second: a $20 billion valuation, $25–30 billion in annual volume, a ~$365 million fee run rate, ~150,000 daily users (2.5x in five months), its own dollar, and a token coming. Its product is betting on elections, sports, and world events. None of that demand needs Bitcoin to go up.

These are now valued like companies: by revenue, users, and multiples. That is what a maturing market looks like.

The new differentiator: privacy — and two ways to get it

If Bitcoin’s transparent, surveilled ledger was the old default, privacy is the new premium. Something you can only get on-chain -- sovereign, untraceable money. But there are two completely different ways to buy it, and the difference is the whole point.

Privacy you own. That’s Zcash. ZEC ran ~70% in a week toward a ~$10 billion cap, up over 4,500% from its 2024 low — decoupled from Bitcoin, as its own move while BTC sat flat. It’s backed by mechanics, not hype: shielded supply has crossed 30% of all ZEC (from ~11% a year ago), and shielded coins tend to stay shielded, shrinking float as demand rises. The regulatory pressure that was supposed to kill privacy coins is the thing fueling them. Robinhood listed it; Grayscale filed the first privacy-coin spot ETF. Privacy went from use case to thesis.

But ZEC is still a coin you buy and a chain you move to. Which brings us to the more important model.

Privacy you use — anywhere. That’s NEAR, and it’s the most underrated idea in this entire shift.

NEAR isn’t asking you to buy a privacy coin or migrate your assets. Through chain signatures, a single NEAR account can sign native transactions on Bitcoin, Ethereum, and Solana — real BTC, real ETH, no bridges, no wrapped tokens — controlled by a decentralised MPC network. Layer on confidential intents, and you can move value across any chain privately, with counterparties and routing hidden, executed through a private shard. You keep your assets on your chains and get usable privacy as a layer on top.

That’s a categorically bigger claim than any single privacy coin. You don’t need ZEC. You don’t need to leave Ethereum or Solana or Bitcoin itself. Privacy stops being an asset you hold and becomes a property of how you transact, everywhere at once.

The unifying layer for an economy Bitcoin no longer dictates

Step back and look at the shape of the thing. The crypto economy isn’t consolidating — it’s expanding and fragmenting. Dozens of chains. A dollar base layer underneath all of them. And now a new class of participant entirely: AI agents that hold credentials, call APIs, and move money on their own.

A sprawling, multi-chain, agent-inhabited economy needs connective tissue. For a decade, that connector was Bitcoin — the reserve asset everything routed through. That role is now vacant. And the thing filling it isn’t another store of value. It’s a coordination-and-privacy layer: sign anything on any chain, settle in dollars, transact without broadcasting your every move, and let agents do the same.

NEAR is positioning to be that layer. It already lets agents pay each other in USDC with the counterparties hidden, runs confidential compute inside hardware enclaves nobody — not even NEAR — can see into, and turns its signing network into a key-management service for the agent economy. It’s usable privacy and cross-chain control for humans and machines, without forcing anyone onto a single chain or coin.

The consumer face of the same shift is Venice — a privacy-first, uncensored AI app with actual web2 users who just want private AI, wrapped in a token economy where staking VVV earns a share of inference via locking VVV and minting DIEM, tokens that represent a dollar a day of compute in perpetuity. It’s burned over 40% of its supply through usage-funded buybacks. Its demand curve is the number of people using AI. Bitcoin’s price is not an input. The token price and product growth clearly show it’s decoupling.

This is the new centre of gravity. Not a coin. A layer — and an economy that hangs off it, along with real businesses driving real value.

The Reframe

Put it together, and it resolves cleanly. The dollar is the cash. Protocol tokens — HYPE, POLY, ZEC, NEAR, VVV — are the equities. Coordination and privacy layers are the infrastructure connecting it all. And Bitcoin? Bitcoin is one node. The “digital gold” node is currently out of favour, because AI took the macro trade, gold took the safe-haven trade, and the dollar took the reserve trade.

For ten years, the only question in crypto was “what is Bitcoin doing?” because everything was Bitcoin-beta. That era is over. The new question is the one you’d ask about any company: does it have revenue, users, and a token that actually captures the value it creates?

The Takeaway

Stop watching Bitcoin to read crypto’s health. You’re staring at the wrong instrument.

Watch revenue. Watch users. Watch which networks are becoming the rails everything else runs on — the layers that let value move across any chain, privately, settled in dollars, usable by humans and agents alike.

Bitcoin lost two jobs this year. AI took the macro. The dollar took the money. And the connective tissue of everything left is being built by protocols Bitcoin can no longer dictate.

Bitcoin breaking $70,000 isn’t the end of crypto.

It’s the moment crypto stopped needing it.

Not financial advice. Small-cap narrative tokens are exactly where the gap between “great network” and “the token captures the value” is widest — and where the losses concentrate. Do your own work.

P.S. This piece was originally published here.

Follow Nikshep on X

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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.

I think one of the hardest things to do in a market like this is to simply step back. Not step away, but just step back. There’s a difference, and unfortunately, few understand it. Stepping back requires you to still be aware, not shut down and see the whole canvas instead of the close-up view in front of your face.

Right now, the close-up view is incredibly loud. AI is reshaping entire job categories in real time. Companies that were private-market darlings are finally filing to go public, and the world is busy pricing them. Every other week, there’s a new headline about what AI will replace next. But there’s also the debate if this wave of technological disruption is the most important thing to happen in a generation or another metaverse moment that burns bright before it fades away . Remember when the entire conversation was about the metaverse? Major banks from DBS to JPMorgan were buying virtual land. That wasn’t very long ago.

The problem with these phases is that market excitement makes everything feel urgent and permanent. That makes it nearly impossible to separate what’s here to stay from what’s just passing through. The instinct to zoom out and get a bird’s-eye view of what’s happening is the hard thing to do, but it’s also the most crucial thing.

In today’s piece, Smac, a partner at Compound, offers a useful framework for understanding why these rolling waves of conviction keep forming using an excellent weather analogy (my favourite part of the piece). This weekend’s op-ed is unusually long, but that also explains why we carried it nevertheless.

On to Smac’s story,
Prathik

P.S.: This piece was originally published on Semi-Lucid Thoughts.

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Weather is a uniquely interesting field because all of the tools we use to accurately forecast the future have improved over the last 50 years. And as you’d expect, our ability to predict the weather has similarly improved. Today’s 5-day forecasts are now equally as accurate as 1-day forecasts were 30 years ago.

I think most people imagine weather as one big system that passes through. Clouds roll in, it rains, it stops raining, the sky clears out. I’d guess if I told you to imagine a winter front coming in, you’d picture some big, wide grey ceiling that lowers itself over a few hundred miles and lays down a blanket of snow. Meteorologists call this stratiform. Basically, layer-cake weather, with everything underneath getting the same treatment.

But this isn’t the only type of weather pattern. If you’ve ever watched a thunderstorm form over the plains in the summer, it behaves quite differently. Instead, what happens is you’ll get a single cell that builds (i.e. warm humid air at the ground rises into colder air above, the moisture condenses, and you get these crazy localised cloud towers) and within an hour there’s hail/lightening/rain so heavy you can’t see 100 feet in front of you.

That cell matures, dumps its energy, and then, importantly, it dies. The downdraft (cold, dense air falling out of the storm) fans out across the ground in every direction, moving as fast as 40mph, and when it slams into the surrounding, warm, humid air that hasn’t stormed yet, it acts like a wedge that shoves the warm air upward.

If there’s enough instability in the atmosphere, the wedge is exactly what’s needed to ignite the next storm cell, some ten or fifteen miles away from the first one.

The new cell was never going to form on its own. Sure, the energy was already loaded and waiting, but what it needed was a trigger, which is exactly what the dying storm provides. Then the new cell just does the same thing.

When you string these cells together, it forms what’s called a mesoscale convective system. From the ground, you experience one storm at a time, each one feeling like the entire weather system. One side of you is dead calm, completely oblivious to what’s about to come, while the other side has already cleared out. If you looked at this from a satellite, though, you’d see what looks more like a chain of events with a glowing line of separate cells, each at different phases of its life cycle, walking across the plain until they run out of warm air to feast on.

What makes these types of chains possible and different from single storm fronts is a symptom of the atmospheric conditions. Specifically:

• A boundary layer at the ground that is warm & wet (i.e. fuel)

• Cold & dry air above that makes warm air want to rise into it (i.e. instability)

• Winds at different altitudes blowing in different directions, which gives the storms rotation to move sideways (i.e. shear)

When you get all three of these, you get the procession-style storm I talked about.

Ok, so why did I just talk about the weather for 500 words?

I think the phenomenon described above is almost exactly what’s happened to financial markets today.

We used to live under a stratiform weather regime: one bull market, one bear market, slow rotations of leadership inside of it, with a few years of each. We had a secular bull market from 1982 to 2000. A dot-com bubble after this. A housing and credit cycle from 2003 to 2007. These were all long, layered and mostly legible from the ground in such a way that you could be wrong about timing for years and still come out fine if you understood the regime.

That’s just not the world we live in anymore. Today, we live under a convective chain regime: a procession of storm cells where each one feels total and unrelenting to whoever’s standing underneath it. Capital flows out of the dying narrative and ignites the next surge elsewhere (often in an adjacent area). The regime itself jumps from sector to sector on much shorter timescales, measured in even shorter time bands. AI infrastructure¹. GLP-1s. Stablecoins. Quantum. Nuclear. DATs. Robotics. Space. Each of these has its own complete weather event, with its own complete community of true believers, its own complete narrative arc and inevitably its own complete decompression. The cold downdraft then fans out and lights up the next cell a few miles away².

It’s almost silly to pretend this isn’t the reality we live in. People love to poke fun at anyone who says “this time is different”, but I think failing to acknowledge that the atmosphere of financial markets has shifted in a permanent way is either (i) intellectually lazy or (ii) a stubbornness of wanting the world to be one way rather than acknowledging the way it is.

Not Your Daddy’s Weather

For most of the post-war period, American financial markets operated like one very slow weather system. Bull markets lasted 10, 15, or 20 years, and sectoral leadership rotated within those secular trends.

The distinction here is that those rotations occurred within a coherent macro regime that only really flipped on specific generational pivot points.

The end of Bretton Woods. Volcker’s disinflation. The dot-com peak. The GFC.

I don’t necessarily think it’s a secret that there were structural conditions that made this the case. Trading was expensive³ which kept retail participation small and forced them to be patient. Pensions dominated household retirement⁴. The S&P 500 was a pretty balanced mix of manufacturing, energy, banks, retailers and a handful of media and tech names; the largest businesses produced earnings that grew with the economy in roughly linear and more predictable ways. Information also moved slowly. The gap between a 10-K filing and most investors knowing what was in it was measured in weeks.

At the same time, volatility was more symmetric. Bull markets were followed by significant corrections with drawdowns that flushed out leverage and reset positioning over longer periods of time. We didn’t see nearly the same degree of these kinds of violent sell-offs and V-bottoms. Bear markets similarly had long rallies that took time to play out. The market frequently spent meaningful time at different levels of agitation, and ~generally~ the transitions between regimes were measured in quarters or years.

If we revisit the atmosphere analogy, there was modest fuel, moderate instability and low shear. Broad, slow, layered weather that you could plan around. Every one of those conditions has now shifted with several actually inverting, and the result is a structural transformation of the conditions themselves.

What Changed?

A lot of these shifts ultimately overlap and amplify one another, but, to be honest, I think each has been large enough to reshape markets on its own. Taken together, they explain much of what has taken many smart people a long time to digest (some still haven’t, we will get to them later).

I count 8.

• The democratisation of the speculator class

• The infinite bid

• Passive investing & the inelastic counterparty

• Pod shops, HFTs & the hollowing of the middle

• Volatility suppression

• Composition mix

• The collapse of information lag

• Fiscal & monetary regime change

The democratisation of the speculator class

These aren’t necessarily in order of importance, but at the top of my mind lately has been the undeniable shift of who is actually in the market. In the 1990s, retail accounted for ~10% of US equity volume. There are a lot of reasons for this, but most retail investors held individual stocks for years. If they ever traded, it was through full-service brokers who charged commissions that made anything resembling active speculation pretty prohibitive. Robinhood famously introduced zero commission trading and pioneered the payment-for-order-flow model that made the economics work. But even that has been misremembered a bit because Schwab didn’t cut its commission to zero until the fall of 2019, and this was the real domino, as we saw Fidelity, TD Ameritrade & E*Trade, all follow suit within weeks.

Obviously, the pandemic accelerated the trend: stimmy checks, bored people stuck at home, and mobile-first trading apps intentionally designed to gamify the experience. Retail share of US equity volume jumped to 25% in 2020-21. Although many people thought this was a temporary blip at that time, it has remained elevated ever since. JPMorgan reported a record 48% of total order flow on April 29, 2025, during the tariff-driven volatility spike. On a normal day now, retail accounts for more than double its pre-2020 share, and on especially volatile days, it can reach as high as 35% of volume.

A related, and perhaps deeper, shift is what retail actually trades. Single-name options have become their expression of choice, and we all know the insane explosion of 0DTEs. The marginal new participant is younger, more concentrated, more thematic and maybe(?), most importantly, both (i) more leveraged in convex ways that don’t show up in margin debt statistics & (ii) far more reactive to price action than to fundamentals. They’re also way more likely to be tailing someone else⁵.

The boundary layer is wetter and hotter than it’s ever been, meaning the potential energy at the ground swells in a way that it never did before.

The infinite bid

This particular shift is one I’ve written about before. For more context, you can read that blog, but the TL;DR is that U.S. retirement plans have shifted from defined-benefit pension plans to defined-contribution ones. The individual, for better or worse, is now responsible for their own retirement in a way they weren’t historically. At the market level, this just means that every pay period, there’s a huge, mechanical, price-insensitive bid for equities. It’s autopilot flow.

Pensions didn’t work like this because defined-benefit pensions were managed against liabilities, which means managing duration risk. In practice, this meant that the people managing pension plans had to decide at times that “hey, equities look expensive here, we should shift the mix toward bonds”. Even if the flows were slow here, they were still more active than the passive infinite bid we see today.

This matters because we’re mostly interested in understanding what the marginal active dollar is doing, and today’s marginal dollar has more leverage on price-setting than it did in the past.

Passive investing and the inelastic counterparty

Passive investing by construction is an inelastic counterparty in the sense that it doesn’t care about price. It buys the index in proportion to weight, full stop. As a stock gets larger, passive buys more of it, and vice versa. The weird thing about this construction is that it sort of embeds momentum as infrastructure. The things working best get the largest mechanical bid moving forward, and this is at least partly how we end up with the Mag7 phenomenon⁶.

Plenty of people have written a lot about the concentration at the top of the indices and how much it has swelled over the last decade⁷. But I don’t know that the concentration is necessarily unjustified⁸. The point is that passive flows don’t really have a natural off-switch.

Pod shops, HFTs & the hollowing of the middle

So the market has introduced a permanent, passive price-insensitive bid. At the same time, we’ve seen the active layer transform in important ways, the defining structural change being the rise of the multi-strat pod shop. Citadel, Millennium, Point72, Balyasny, etc. These places house hundreds of independent PMs, each running a specialised strategy and operating on strict, tight risk leashes. AUM growth for these entities has been hilarious, and honestly, capital is concentrating in the top names here, much as it has at the equity index level.

Combine this with the fact that High-frequency trading (HFTs) now accounts for 50-60% of U.S. equity trading volume and 75% of futures volume, and you get a uniquely fragile environment. Intermediaries trading with themselves aren’t functioning as a price discovery mechanism. So a lot of the “volume” we see on the tape is just plumbing. The ultimate effect of this dynamic is that you get extremely tight spreads in normal conditions, which is obviously a good thing. But when something actually moves, i.e. a narrative cracks or positioning has built up too far in one direction, or when a pod shop’s risk limits start tripping in correlated ways⁹, the microstructure basically gets out of the way. The pod shop structure makes this worse in a very specific way because every PM across every platform is running the same factor exposures and has the same drawdown limits. So when one team is forced to de-gross, everybody else is too. February 2018. August 2019. March 2020. August 2024. These will keep happening because the structure that produces them is now baked into the financial system.

In a lot of ways, the guys who got squeezed out in this world are the traditional fundamental L/S managers who have some theses, a 20-40 name book and multi-quarter time horizons. That model got pushed either up into the platforms or drifted sideways into private markets, family offices or concentrated single-strat funds. I’m biased, but there is likely meaningful alpha to be generated by understanding the narrative shifts and having the patience to sit between marginal new flow and the marginal new exit.

Volatility suppression

Ok, so we’ve got these 4 ingredients:

• how wet boundary layer (democratising speculative class)

• perpetual passive bid

• inelastic passive counterparty

• pod-shop microstructure with synchronised risk discipline

Now the volatility regime almost has to look the way it does today. Empirically, we know the VIX spends most of its time at low levels: somewhere around two-thirds of trading days since 1990 have closed with a VIX below 20. Day-to-day autocorrelation of VIX is up to ~85%, meaning that today’s volatility looks almost exactly like yesterday’s. But the transitions are now violent and asymmetric. There are studies that suggest most of the volatility explosion occurs in the days immediately following a compression break (duh!), but that the decay back downwards takes much longer and is spread over a handful of weeks.

There are some structural reasons for this: a huge industry now exists for selling volalitity¹⁰. But inevitably, tail risk shows up, and everyone unwinds all at once. The 0DTE proliferation makes this even more complicated because dealer hedging of these products creates gamma profiles that further suppress intraday volatility.

This was a lot of words that really just say the distribution of volatility has gotten worse. So the market spendsmore time in compression phases and then pays for that with bigger, more violent releases.

Composition mix

The sixth shift is the actual composition of the market. In 1980, the S&P 500 was a manufacturing-heavy index. Industrials, materials, energy, financials and consumer staples made up the bulk of it. These companies produced earnings that grew at roughly the rate of GDP and in an almost linear way, which meant they could be valued using approximately mean-reverting multiples. Estimating the 5-year forward earnings of Proctor & Gamble was not exactly something you could be orders of magnitude off on.

Compare that to today…

Information Technology + Communications Services + some of the tech-adjacent slices of Consumer Discretionary (Amazon, Tesla) account for more than 40% of the S&P 500. Many of these companies don’t operate on linear economics. Software was lauded famously for having near-zero marginal cost of distribution. AI is even more of a Rorschach test: AI labs are either enormous capex sinks that turn into the most valuable economic infrastructure of the next half-century or they’re some kind of delusional money pit, doomed for disaster. Estimating short-term earnings is difficult enough for these companies, let alone terminal value math, where uncertainty is the highest and prone to the most violent swings in valuation. In many ways, these companies have shifted from P&L exercises to narrative ones. Again, this means there’s alpha for investors who can take a view on the trajectory of a frontier technology, the durability of emerging (and existing) moats and the shape of markets that don’t really exist yet.

Widget factories used to add capacity in linear chunks, so the discounting cash flow models mostly converged on reasonably stable estimates. Multiples mean-reverted far more often in that paradigm. An increasing share of today’s multiples is a function of how much investors believe the story that is being told. And, for what it’s worth, I don’t actually think this means valuation frameworks are broken, but it’s just the reality of the types of companies that exist in 2026.

The new financial market indices are full of these long-duration, narrative-driven businesses. Each of these has a much steeper lapse rate¹¹. The steeper the gradient, the more potential energy is locked in the system and, by extension, the more violently that energy converts to motion when something triggers it.

The collapse of information lag

Everyone has lived through this in some way and probably, intuitively, understands it. So, it’s perhaps less interesting but also maybe now under-appreciated. Throughout most of financial history, any kind of market-relevant information was rate-limited by some speed-of-publication bottleneck. Today, information latency is literally zero.

Positioning information, in particular, seems to propagate faster than anything, as investors immediately see how every “smart” person they respect reacts to news in real time (more than ever, a lot of people are disclosing positions as well). Part of this onslaught of real-time information is a constant buzz of comparison brewing under the surface. PnL screenshots are everywhere, stories of someone turning $1K into $5M regularly go viral. There’s a permanent engine of FOMO roaring louder every day.

Fiscal & monetary regime change

I actually don’t think this needs to be explained in detail at this point. So the cliff notes are

• US monetary policy is structurally easy with real rates near zero for a long time

• QE expanding Fed balance sheet by trillions

• Every duration-sensitive asset traded at a premium, with the discount rate so low

• Then, fiscal added to this with stimmy checks, PPP, Inflation Reduction Act, CHIPS Act

• War-time deficits while at full employment

• K-shaped economy dynamic, as stimulating financial markets & stimulating the main street economy are very different things

How The Storm Forms

It’s much easier, given all the above, to now visualise how these rolling bubbles become inevitable.

I won’t go through all these stages individually because they’re reasonably self-explanatory, but let me call some things out.

The compression stage is something we’ve uniquely seen at Compound investing across a bunch of (seemingly) disparate areas for a long time. Categories/sectors/industries come in and out of favour all the time. With that comes a lack of attention and interest. But there are always talented people building regardless of the outside perception, and the reality is that most of the time they’re not going to be in the spotlight.

Eventually, some legitimate change happens that brings the interest back around, be that a real technological breakthrough, a regulatory shift, or an earnings surprise that changes the scale of the underlying business reality. The domain specialists will should notice this first.

At some point, the narrative is crystallised (AKA, the story gets a name), which does a few things. One of which is that it flattens what’s actually happening. Some people find this annoying because they hate memes or think it’s bastardising something they hold dear, but the reality is this makes the narrative easier to understand (and, importantly, to share). It becomes way more legible to a broader audience who can’t necessarily evaluate the entirety of it on merit alone (even with LLM help).

At some point, you reach a stage where youwe see bifurcation. The modern context of this is that the marginal buyer outside “the believer cohort” shrinks to zero. You reach a point where the valuation gap between believers and sceptics becomes too large such that even those people chasing the narrative fall off as marginal buyers.

The crack is always obvious in hindsight, though in 2026, people are way too quick to try to call this ahead of time. This again is just the reality of the hyper-online, comparison, and clout-chasing world we live in. But this crack gives way to the de-grossing as factor exposure unwinds for everyone, and the outflow then goes searching for a new pocket of warm, humid air to trigger a new cell.

Future Forecast

The downstream effects of this regime are significant. And I think this is more an acknowledegment that the shape of the weather is, in theory, more predictable without necessarily knowing the exact location of any given storm.

But what does this mean about the future?

One argument that a lot of people made just after COVID was that “this is an anomaly” or “[redacted] was a zero interest-rate phenomenon”. Some of this was obviously true. But to me, it’s become clearer that the broader regime shift is permanent. How permanent is a debate for another day. But each of the shifts I described above is pretty one-directional…

• trading commissions are not going back up

• passive investing doesn’t just unwind

• defined-benefit pensions are gone forever

• social media isn’t going to get slower in the future

• pod shops — maybe these dissolve quicker than I appreciate, but given the parabolic growth and how much money they’re making, that seems unlikely

• information lag isn’t going to lengthen

You can almost think of these conditions as the new climate we live in. Candidly, I think anyone expecting us to return to the previous stratiform regime of the 80s or 90s is just in denial.

Another claim that’s maybe(?) finding some kind of local maxima is the idea that these rolling bubbles will keep compressing in duration. This is one of those things that I think is tougher to say just because it turns into a never-ending meta game of “I know that you know that I know that you know…”. But the simplest lesson is that each cycle teaches participants what the pattern looks like, which in theory speeds up the next one. Crypto participants now feel comfortable trading TradFi assets because many of the survivors there have so many reps already with these types of behaviours. And while I don’t necessarily subscribe to the idea that crypto traders are best suited for this new world, I understand the argument. Some probably are. To me, though, there is some half-life of narrative-driven markets that can only be compressed so much.

Maybe the most critical claim I’ll make about what the implications are is that this new rolling bubble world rewards a couple of types of investors. Domain specialists who actually understand what would need to be true for the narrative to deliver on its priced-in promises (or exceed them). But this means understanding true technical bottlenecks, regulatory nuance, manufacturing supply chain dependencies, and competitive dynamics, all the way down to unit economics. Realistically, this is not where most people will sit. I think AI models probably make people believe they live in this category more than they actually do, which is especially dangerous¹². The reality is most actually sit in an adjacent seat where they are trying to understand how the next-most-informed participant will react. There’s probably a completely separate piece on why the combination of these two is likely to become an even more rare trait over the years, but this blog is already long enough.

One thing that’s clear is we are certainly not at a loss for future narratives:

• AI infrastructure & applications

• Robotics

• Physical AI

• Precision medicine

• Crypto

• Materials science

• Fusion, advanced fission, grid storage

• Space

• BCI

• Quantum

Even within these, we’ll see ripples form and dissipate as investors move up and down the supply chain and tech stack within these.

Retail’s biggest advantages in financial markets have always been time and flexibility. That is to say, they don’t need a few days to schedule investment committee decisions or convene on allocation sizing. They also aren’t beholden to the same shorter-term pressure that many institutional managers face (i.e. there’s no quarterly redemption in your PA). This may explain some of the outperformance retail has enjoyed. Decades of BTFD working also probably helps. So, in some sense, retail participants who can manage risk are in a great position.

The View From Above

A lot of what I’ve (too verbosely) written above may leave the impression that I care one way or another about the things that have led us to the market structure we have today. Sure, I have opinions on some of those things. And when investing on the private side, I still think there are plenty of ways to make money without funding things that accelerate net-bad societal outcomes. But one of the most common mistakes many people make in public markets is making decisions based on how they want the world to be¹³. I get this. We’re all humans with emotional triggers that have been hard-coded into our DNA for centuries. It’s why Newton re-entered with a lot.

But emotional triggers are a huge barrier to performance. For years, we’ve seen asset managers and hedge fund guys come on TV and talk about why markets are wrong or wax poetic about impending recessions. Much like our friend Sisyphus, these guys do not come on TV for charitable purposes. Ray Dalio has been calling for a debt crash throughout this historic bull market.

The world is not going back to your father’s market. Is this convective regime a more degenerate version of the old stratiform one? I’m not actually sure, to be honest. Potentially? But at the same time, a lot of the shifts that are producing it I actually think are net-good things: democratised access¹⁴, automated retirement saving, passive ownership, more real-time information. All of these things reflect a market that is way more available to ordinary people than it was in the past.

An aside that I tweeted about the other day (there’s a more philosophical piece to be written here):

From the ground, every storm is total. The world narrows to whatever is happening to you right now. This is the experience of market participants in any of these sector cycles. On the ground, it feels like a black hole of liquidity sucking up everything around it. Only if you are able to step back and move to an altitude, are you able to actually see the chain. One cell after another, lighting up. Each of them at different phases and each of them feeling total and complete to the people underneath them.

Markets are so intoxicating because they are simultaneously always changing, and yet, at least today, humans still have control over price discovery. And humans are inherently emotional beings that can’t help but make the same mistakes over and over. That tension helps create the conditions we experience each day, which ~feels~ quite frenetic in the moment but, taking a step back looks a lot more like rolling bubbles.

Really, the point of all this is to try to drift above the immediate storm and operate at a higher system level to allow yourself space to recognise which way the front is moving, and to detach yourself as much as possible from the immediate sensory-overload effects of any individual cell underneath.

As with most things that require immense discipline, this is simple but not easy.

References

1 which within itself has its own rolling narratives

2 one of the funniest things about this is seeing people on Twitter say things like “nobody is even talking about xyz yet” and it’s an entire massive sector that has tons of people talking about it they just don’t happen to overlap at all with this random narrative trader

3 a round-trip stock trade through a full-service broker costs roughly the equivalent of a steak dinner in real terms

4 in 1980, almost ⅔ of private retirement contributions still went to defined-benefit plans which meant most of the wealth being saved for retirement was deployed by professional fiduciaries operating on multi-decade liability horizons

5 https://www.finra.org/media-center/newsreleases/2026/finra-foundation-research-examines-characteristics-behaviors-outcomes

6 obviously, these companies are also exceptional at making money and growing earnings

7 there is some truth to it, but i also think it’s not necessarily that fair to think of some of these companies as 1 company

8 guy who thinks you’re not allowed to use em dashes anymore bc AI’s corrupted it

9 btw these guys are in all the same trades

10 covered call ETFs are a massive category, dispersion trades (short index vol, long single-name vol) are a core book at every multi-strat, structured products from banks systematically harvest premium. all of these things just manufacture the calm by collecting income in exchange for taking tail risk

11 the steepness of the temperature gradient between the surface and the upper atmosphere is the lapse rate

12

13

14 fwiw Robinhood is one of my least favourite companies in the world. I think the fact that they pretend they’re helping people on some type of financial journey, but in fact design the entire company/product around turning their users into degenerate gamblers is gross.

Disclaimer: This piece was originally published here.

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Our team at Token Dispatch has Friday editorial calls, where we talk a little bit about a lot of things. We also discuss what’s exciting and worth writing about in the upcoming weeks. Lately, the conversations sound less and less like crypto conversations. We end up talking about lending businesses, AI subscription models and payment rails that Stripe and Mastercard are fighting over. Last Friday, we spoke about how the upcoming trillion-dollar IPOs of OpenAI, SpaceX and Anthropic will shape the broader financial markets. Even if someone brings up a crypto project, halfway through the discussion, you’d realise nobody has mentioned ‘token price’ even once.

This shift is also reflected in what we have been publishing lately. In the last couple of fortnights at Token Dispatch, we have moved toward stories that sit at the edges of crypto. Think of fintech that uses blockchains as plumbing, consumer products where the token is a distribution mechanism rather than the product itself, infrastructure companies getting acquired at valuations that have nothing to do with the cycle. The developments that keep moving, whether BTC is at $100k or $70k.

Originally published by Hepworth Iron Capital, this week’s piece puts a framework in place for this phenomenon. Charlie Booth argues that crypto’s era as a single Bitcoin-sensitive factor is ending, paving the way for a cycle driven more by non-crypto factors and not cryptocurrency prices.

Onto Charlie’s story,
Prathik

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Historically, crypto has traded as one Bitcoin-sensitive factor. That era is ending.

The crypto economy is bifurcating into two groups: endogenous and exogenous.

The former is the crypto of old: tokens and projects whose value depends on crypto prices. The latter is crypto in name only, with value increasingly exogenous to crypto prices.

Bitcoin’s value comes from its properties and reflexively from its price. A rising price reinforces the perception of the properties. At the peak of a bull market, Bitcoin is perceived to be interplanetary money, the scarcest digital bearer instrument known to mankind. At a bear market bottom, it’s dismissed as a digital collectable without any cashflow.

Hyperliquid sits in between these endogenous and exogenous groups. Much of its business still depends on crypto prices, but both the supply and demand sides are broadening. A lot of on-chain financial infrastructure sits here, with the assets underneath shifting toward tokenised real-world assets.

HIP-3 Open Interest is a rough proxy for non-crypto-related Open Interest. The percentage of total Hyperliquid Open Interest attributable to HIP-3 sits at ~30%, up from ~4% in November 2025. HIP-4 (outcome markets) should further boost this, pulling in new demand (traders) and new supply (markets, assets) at once.

On the purely exogenous side, the driving forces behind a project like Venice are completely exterior to the crypto market. Customer profiles overlap, but the business model looks a lot more like consumer AI than Uniswap. The latter still largely relies on users trading assets with endogenous value, making its business a function of those asset prices. Venice packages private multi-modal inference into a usage-plus-subscription model.

The only crypto connection is the choice of a token as the instrument that captures business value, plus the fact that some of its inference suppliers happen to wear crypto labels. Perhaps also the crypto-native understanding of Venice’s steward Erik Voorhees that tokens can be an excellent marketing tool when done well.

Figure is an easy example on the listed equity side, a fintech lender using its own chain to cut home equity approvals to under five minutes. The blockchain is incidental; the business is the point.

The emergence and growth of the exogenous class at scale in listed equity and token markets is meaningful. Historically, pure bottom-up investing has been difficult given the high sensitivity of most business models to crypto prices. It’s not as though crypto hasn’t had exogenous narratives before; every “blockchain, not Bitcoin” cycle promised them. In most cases, they collapsed back into crypto-beta because demand never materialised, revenue wasn’t there (if it was, it was not captured by the token), and once the token stopped going up, there was nothing underneath.

What’s different this time is that you can answer who pays and why, that demand is measurable and less reflexive in many cases, and that the token as an instrument is slowly improving (more on that later). Venice’s signup revenue is real money from users buying inference. There’s no obvious reason it reverses when crypto draws down, because it was never a function of prices. You have two things that prior cycles lacked: usage that persists, and buyers underwriting on fundamentals rather than solely on narrative.

Consider the stablecoin corner of private markets. In March 2026, Mastercard agreed to buy BVNK for up to $1.8 billion, 15 months after BVNK’s Series B closed at a $750 million valuation. Bridge (acquired by Stripe for $1.1 billion in February 2025) is growing fourfold year-on-year within Stripe, according to Stripe’s annual letter. None of these tracks the crypto cycle.

This isn’t a bearish call on the endogenous category. Just as gold and even junior gold miners might have their time and place in a portfolio, there’s a time and place for something like BTC and the endogenous class. But fundamentally different drivers will likely continue to drive performance and correlations. You can see both relationships in the data:

The analogy made literal: Junior gold miners track gold at a correlation that never really leaves the ~0.75 region. That’s how crypto broadly trades today: junior miners to Bitcoin’s gold, and levered bets on the same underlying. The blue line is the other relationship. Gold and the S&P share some macroeconomic correlation but trade on their own drivers. That’s the destination for the exogenous class. Over time, these names should migrate from the gold line to the blue line, from levered proxy to independent assets that occasionally share the weather.

These exogenous names are tokens, both illustrating the point and serving as exceptions to it.

Much of the “endogenous class” still moves closely with Bitcoin. A few exogenous names sit lower, but the window’s too short to mean anything yet. Fundamentals change first; the correlations follow.

This changes the analytical approach. The exogenous class needs underwriting like a normal business: who pays for the product, how the unit economics work, and where the moat sits. BTC price stops being the most important variable, and you start sounding like a fintech investor with weird custody.

Exciting “exogenous” categories, in no particular order and with miscellaneous notes attached:

• on-chain exchanges and brokers

• credit/redemption solutions for longer-tail tokenisation (Grove Basin looks interesting here)

• real crypto x AI (private inference, distributed open-source model training à la Psyche by Nous Research)

• neobanks (I tend to like the more privacy-focused players like Payy and Raycash, the programmable privacy infrastructure, Aztec and Zama, that enables them is also interesting)

• lending (Morpho is becoming the institutional standard for repo-like markets, smaller names like Valinor and 3jane target interesting niches in private credit)

• stablecoin and real-world asset/tokenisation issuers

• payment rails (Stripe with Tempo is the incumbent to beat in broad payment rails; in agentic payments, it’s Coinbase, for now)

• non-finance consumer crypto (products like Venice and Collector Crypt come to mind, unique cases where imbuing a token with value derived from a non-crypto business can drive marketing value and adoption)

• agentic economy (the prize is agent<>vendor/creator coordination at the access layer, which is less substitutable than the rail. Cloudflare is well placed, but whether it taxes the flow or just sells the toggle is the open question)

Most durable exposure to this theme is equity today, not tokens. Good tokens are the exception and will only earn a larger role if the instrument improves, which regulators and industry have to deal with. There’s progress on both, the CLARITY Act on the regulatory side and transparency efforts from the likes of Blockworks on the other. The token has a lot of work to do.

None of that changes the main point. The driver is shifting from one factor to many; the work is no longer reading the Bitcoin chart but underwriting businesses. Don’t spend the next decade confused about why “crypto” stopped moving as one.

Follow Charlie

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Disclaimer: This piece was originally published here.

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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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.

I am not a connoisseur of art, music, or food. But there’s one trend I have noticed about businesses that I feel gets often overlooked. The most extraordinary businesses were successful because they did not ignore some non-business things that others paid little attention to.

Apple’s competitive edge was not hidden in the chip they used but in how Steve Jobs cared about the weight of a thing in your hand. Sony’s golden run began because its founder loved music enough to consider what a portable music player should feel like.

The common pattern I found in such businesses is that they gave equal, if not greater, importance to their non-business instincts than to their business ones. These non-business instincts are still rooted in human behaviour, like hospitality, craft, cultural sensibility, and a sense of atmosphere. Often, these are the instincts that hold the entire workplace together by acting as a centre of gravity. They have the power to connect people from different geographies, cultural backgrounds, socio-economic backgrounds, and many other differences.

I think about this when I look at how we discuss technological shifts. The conversations in boardrooms are almost always about competition, infrastructure, bureaucracy, and regulation. Don’t get me wrong. These are useful and unignorable, but not holistic. It misses a crucial layer where things often get built between people. What connects someone’s ambition with someone else’s risk appetite and a founder’s context with a policymaker’s expectation is one of the oldest interfaces. Like a table, a room, or a shared meal on a nice wooden table in a room curated with the right people and the right food for the occasion.

In today’s piece, SuperAI and TOKEN2049 co-founder Peter Noszek argues that the age of AI needs its human layer built with the same intentionality as its supply chain – through bridges, dinners, houses, and rooms where trust can form.

Onto Peter’s story,
Prathik

Follow Peter's works here

Follow Peter on X

America’s version of Pax Silica is the US Department of State’s flagship effort in AI and supply chain security.

ASEAN’s framing is more concrete: a US-led initiative to build a secure, prosperous, and innovation-driven silicon corridor, from critical minerals and energy inputs to semiconductors, AI infrastructure, logistics, software, and models.

The subtext is obvious: Trump’s America is looking for trusted partners with whom it could build around China. It is adversarial by design.

But Pax Silica should mean more than securing the stack. It should describe the human layer and human implications of the AI age: the trusted relationships, rooms, and institutions that help people navigate the biggest technological shift of our generation.

The Official Frame

Securing the AI supply chain with trusted partners runs through Southeast Asia.

Compute needs power. Chips need minerals, fabs, packaging, logistics, and trusted routes. AI companies need neutral places to deploy across Asia. Singapore, as a Pax Silica signatory and regional coordination point, sits in the middle of that problem.

I’ve just landed from an immersion into the Chinese AI ecosystem via Shanghai, at a moment when Washington and Beijing were circling each other on chips and AI — the public gestures running well ahead of anything actually moving.

A few weeks ago in San Francisco, Cindy and I hosted the first Pax Silica dinner. Alvin Wang Graylin was there and shared a frame that has stayed with me since. As he later put it in the South China Morning Post: “The responsible move is to find common ground, not raise the fence.” He called this moment the “doorstep of peace” and warned that fanning rivalry between two great powers was both irresponsible and immoral.

That was not the China I met in Shanghai.

The people I met were open, curious, and collaborative. The energy was pointed outward: founders, operators, platforms, and city-level ecosystems trying to understand where their companies, products, capital, and talent should go next.

Some of that energy pools in Shanghai and Beijing. Some of it hums in Shenzhen and Hangzhou. Some of it stirs in tier-2 cities like Changsha: 10-, 20-, 30-million-person cities with serious young AI founders who are hungry to internationalise, but need trusted bridges into the right markets.

Southeast Asia’s role becomes clearer from that angle, with Singapore at the centre: a trusted landing point for AI companies moving between China, APAC, and the global market.

The Name

The name matters because it puts peace and the technology race into the same sentence.

Pax

Peace. Stability. Long-term prosperity.

Silica

The compound is refined into silicon, which is one of the base materials of the AI age.

Put together, the phrase asks a question that feels much bigger than export controls or data centres:

Most institutional answers begin with security. Secure the supply chain. Protect sensitive infrastructure. Reduce coercive dependency.

All of that’s sensible, and I take it seriously. But peace also asks for translation between people who normally operate in different systems.

It asks for rooms where a founder can speak honestly in front of capital, where a policymaker can understand what builders actually need, where an enterprise buyer can explain the risk behind a slow decision, and where someone is paying attention enough to translate between all three.

The age of AI will need secure supply chains and people who can keep building when geopolitics, capital, and deployment timelines get messy.

Relationship Infrastructure

The AI supply chain spans chips, minerals, energy, data centres, models, and networks. Every layer eventually runs through people.

A Chinese founder entering Southeast Asia needs more than market research. A frontier lab deploying in Asia needs more than policy slides. A sovereign fund looking at AI infrastructure needs more than deal flow. An enterprise buyer evaluating AI needs more than demos.

They need trusted translation.

Who understands the founder’s ambition? Who knows which institution can move? And who keeps the thread warm after the event ends?

Pax Silica is the public thesis. The operating stack underneath it has three parts: bridges move value, dinners build trust, and houses create continuity. Together, they point toward a larger gathering where the system can find itself. The mechanism is practical: trust reduces coordination cost, rooms increase bandwidth, and repeated contact improves the accuracy of the models people hold of one another.

Bridges

To bridge is to speed up the diffusion of technology, information, and value across systems that would otherwise move too slowly.

Some bridges are powered by capital. Funds can move an entire market by deciding which infrastructure deserves conviction.

Some are powered by advisory. The right operator can help a frontier company enter a new region, find the buyer who matters, and anticipate institutional friction.

Some are powered by community. In San Francisco, I saw this through the builder-house and residency graph around HF0, The Embassy, The Residency, Frontier Tower, AGI House, and Cerebral Valley. These bridges connect people who might live a few miles apart and still never properly meet.

Ray Del Vecchio, co-founder of Cerebral Valley, used an interesting phrase: “MBSF”, months behind San Francisco. It describes the lag between discovery in the Bay Area and adoption elsewhere. The same could be true in the other direction: “MBSZ”, months behind Shenzhen. A bridge shortens both lags.

China has its own bridges. Google China is one of the clearest examples I saw in Shanghai: helping Chinese companies internationalise across APAC, with Singapore increasingly important as an operating centre.

Singapore operates its own bridges as well. I’ve been fortunate to meet and work alongside people across EDB, Singapore Global Network, and Enterprise Singapore who understand that Singapore’s role is to be the trusted crossing point.

Pax Silica starts with bridges that move people across systems, and rooms that let trust compound over time.

Dinners

The smallest complete unit of Pax Silica is a dinner.

A dinner is intimate enough for trust to form and serious enough for people to show up with intention. It lets the host compose the room across layers that usually sit apart: frontier capability, infrastructure, capital, policy, enterprise deployment, culture, taste, and human-centred design.

Curation is the seating, the rotation, the music, the food, the pace of the evening, the first introduction, and the comfort people feel to speak plainly. I like to think about events as an interface. A dinner is the layer underneath: the seeded conversation, the useful encounter, and the natural follow-up the next morning.

A room works when the worlds inside it are close enough to notice each other properly.

At the Pax Silica dinner in San Francisco, several people were adjacent without having properly crossed. Some lived within a few miles of each other. Others were already moving between Asia and the US, but needed the right shared container.

In June, during Singapore AI Week, Cindy and I will host the next Pax Silica dinner in Singapore.

Houses

The house is where the corridor stops being a calendar item.

The space Cindy and I are building is a tech and cultural house at the edge of the Pacific. Teak floors. Jade tile. A long table for twenty-four. Peranakan light through tall windows. A garden courtyard where someone is writing code and someone else is cooking.

Part salon, part dining room, part gallery, part working room, part recovery space. A San Francisco node for Southeast Asia’s frontier-tech corridor, with the intimacy of a home and the seriousness of an institution.

A founder visiting from Asia should be able to land in San Francisco and find the right first table. A Singapore delegation should be able to understand the Bay Area without turning it into a study trip. A frontier builder should encounter Southeast Asia through people, food, art, and music.

This is where compassion becomes operational. Everyone who enters the room is carrying something: ambition, mandate, exhaustion, risk, family, national interest, the desire to be understood. A good room lets people put some of that down.

For Pax Silica to mean peace in the age of silicon, it has to be practised through hospitality. As Donatus Schaumburg-Lippe reminded me, embassies were built for ongoing dialogue: permanent rooms where trust could be maintained before, during, and after negotiation. Pax Silica House carries that instinct into the age of AI. The chair, the menu, the music, the guest room, the first ten minutes, the follow-up the next morning: these are how an abstract corridor becomes a place people trust.

Schelling Points

A Schelling point is where people converge without coordinating every step in advance. Everyone comes because everyone believes the others will come.

AI needs this badly. The work I care about – connecting the distinct nodes of Silicon Valley and Asia, between founders and sovereign capital, between the people building AI and the people deciding how it lands – only compounds when those people are repeatedly, reliably in the same place.

Dinners and houses do the intimate work. But intimacy has a ceiling. A dinner seats twenty. A house holds a season. Past a certain point, the bridge needs a wider span: a gathering large enough that an entire scattered ecosystem – labs, funds, ministries, platforms, data centres, universities, founder houses – can assume the others will be there, and plan their year around it.

That gathering sits a layer up, and Pax Silica feeds it. The dinners seed the conversations, and the house keeps them warm; the gathering is where they become unavoidable – where a partnership that was a maybe in April becomes obvious by June, because the right people were finally, unmistakably, in the same room.

Pax Silica’s job is to make that larger room work better: to fill it with people who already trust each other, conversations already seeded, and relationships that are already warm. The dinner is the smallest complete unit. The gathering is the largest. Singapore is where the largest one can hold – close to China, fluent in the West, trusted across Southeast Asia and Europe, serious enough that institutions show up without being asked twice.

As silicon becomes the operating layer of civilisation, peace has to be hosted – as a room, at every scale: the table, the house, and the gathering they point toward.

Pax Silica is the thesis on the human layer of the AI age. Field Notes is the running notebook behind it: dispatches from the rooms, cities, and conversations where this corridor is being built.

Disclaimer: This piece was originally published here.

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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I hear a loose thought floating around me, used so often at this point: AI will take away our jobs and make us redundant. The bigger irony is that people across domains use this without really questioning exactly what these AI models are replacing in their line of work.

As writers, my team often discusses this idea in our internal calls. Although there are a lot of ideas about how AI will evolve from here, there’s one thought we all seem to agree on. When everyone has access to the same data, similar models and common building blocks, data no longer remains the moat. The content creation game flattens out at the level of data aggregation and research. But there’s something else that AI also does.

Every door closed shut, more often than not opens another one. Once these AI models and the software built on top of them make building things easier, they force humans to differentiate based on our ability to imagine and express diverse tastes.

What I find under-appreciated about AI-enabled software is that they strip away the excuse that building something is difficult or costly. What hurts humans more about AI is not that it takes away our jobs or makes us redundant. But it’s that they act as beautiful filters and ask a difficult question: “Do you actually have a vision that separates you from the rest? Can you build something different when the playground is levelled for everyone to build?

Today’s piece by Kinjal Shah, General Partner at Blockchain Capital and Co-founder of Komorebi DAO, takes that instinct and grounds it in what’s actually happening. It’s about the explosion of personal social software and why open protocol infrastructure like the AT Protocol ensures that small, specific, human experiences don’t have to start from scratch. She explains why the bottleneck isn’t code anymore but imagination.

Give it a read.

Onto Kinjal’s story…

Do you remember when your friendships translated for the first time? I was thirteen, ranking my “top eight” shelf on MySpace, only to realise I didn’t have eight friends I’d include. Ranking them made my stomach sink.

Fast forward to 2026, and numbers are still the fastest way for a stranger to determine your status. The platform on which you’ve gained them adds context. Your name, profession, and appearance are all scrutinised in the flick of a scroll, quickly adding up to a first impression.

What’s real or not is an afterthought. Perception is reality.

The early internet was a companion to many users, whether it was through Tumblr communities or the early blogging world. It allows anyone to expand their network beyond geographies and gain access previously held by gatekeepers.

When the algorithm became the discovery tool for default social experiences, it forced everyone into the same doomscroll. In doing so, it fundamentally changed how humans form connections online, creating parasocial relationships that are optimised for the wrong metrics.

What if it wasn’t the desire for human connection that broke — but the game itself?

The barrier just collapsed. With today’s agent functionality, the cost of creating software has collapsed to nearly zero.

But a more interesting consequence of this is the friction AI removed.

Before, if you had an idea for an app specific to your community or context, you had two options: find an existing platform that approximated it, or hire someone to build it and hope they understood what you meant. Either way, building yourself was for a technical user. It involved real friction for an app that most people might not even want.

That friction is effectively gone. Intent can now translate directly to action.

My brother-in-law recently built an app for his friends where they share weekly sports parlays. He vibe-coded it in a week, and now they use it every week. That wasn’t possible 12 months ago.

We’ve moved from personal computers to personal software.

The parlay app will never have a million users, but it doesn’t need to. It is working perfectly on the one metric that matters the most: deepening specific relationships.

The numbers confirm this.

Apple app store submission grew 30% in 2025, followed by an 84% year-over-year jump in Q1 2026, the largest surge in a decade.

This is after declining 48% from 2016 to 2024. The inflexion point correlates precisely with the release of more accessible agentic coding tools.

What’s even crazier is 63% of the vibe-coding users today classify themselves as “non-developers,” many of whom have never written a line of code.

Personal Social Software is the Future

When I think of the early web, I remember the cosy corners where lifestyle blogs, independent journalism, and the personal website proliferated. One of the biggest enablers of websites was an open-source content management system called WordPress.

As of 2026, WordPress powers nearly 44% of all websites published on the internet.

WordPress didn’t succeed because a single blog had millions of readers. Most WordPress sites have relatively low traffic.

It succeeded because it enabled millions of distinct, self-publishing experiences on shared infrastructure.

The metric that mattered was the number of websites deployed. That’s the same frame for what’s coming.

What would a world of thousands of social experiences, many of which are pop-ups, look like? Each is built for a specific group, ritual, context or use case.

A weekly parlay app for eight friends. A darkroom photography community for a neighbourhood. A shared reading log for a family. A group planning tool for a climbing crew.

None of these needs to win the internet. All of them deepen specific relationships in ways that no generalised platform can, because none of those generalised platforms was designed with those specific people in mind.

When software doesn’t need to scale, it allows for personalisation and community formation.

When the cost of building a social experience approaches zero, the bottleneck shifts from infrastructure to imagination.

Why AT Protocol Matters as Infrastructure

The problem with building your own social experience from scratch is that you start from zero. Your social graph lives somewhere else. Your identity lives somewhere else. You’re effectively building on sand, where the ground can shift quickly under your feet.

This is what killed most of the ‘build your own community’ tools of the last decade. The only audience you’ve been able to own is your email list. Every new network asks users to start over - new account identity, new followers, new context. AT Protocol breaks this constraint.

When you build on AT Protocol, you’re not building on a platform. You’re building into a network that users already belong to. Their identity travels with them. Their followers travel with them. Their context travels with them. The parlay app isn’t a closed garden but a node in an existing social graph.

This means a builder who creates something for eight friends doesn’t have to solve the cold start problem. The network is already there. When someone discovers the parlay app and joins with their existing AT Protocol identity, their connections see that they joined and the context compounds across deployments rather than within each one.

Why Should You Build on AT Protocol?

Shared identity on the AT protocol. Social graph portability lets you bring your users with you. Moderation primitives let you focus on the product rather than on trust- and-safety tools. A whole suite of à la carte building blocks is coming to you.

The things that take months to build from scratch, AT Protocol gives you right away, out of the box.

Try AT Protocol

Attie, the feed-building tool the Bluesky team recently launched, is a live demonstration of what this infrastructure unlocks. It’s a portal into creating your own feed experience on top of the underlying network. What you find there is a vibrant set of users already organising events around science and academia, sports, news, analogue hobbies, and niche interests that you rarely see spaces made for. The like-minded users are already in the network. The tools to build for them are already available.

There are some really unique examples being built today.

One of my personal favourites, Anisota, created a gamified social interface where each post becomes a collectable trading card with rarity ratings and stamina limits to discourage compulsive scrolling. It makes social feel like play. It’s built on AT Protocol, which means it inherits the network rather than having to recruit it.

Tangled is another example, building a social coding platform where coding is a social, owned experience rather than a solitary professional one.

These aren’t competitors to Bluesky. They’re what a healthy protocol ecosystem looks like: multiple distinct experiences, all running on a shared identity and data layer, each finding the specific community it was built for.

The Open Question

The thing I keep coming back to is if AI makes creation cheap for everyone, doesn’t everything converge toward the same AI-generated aesthetic? If the tools are the same, don’t the outputs homogenise?

I don’t think so, and here’s why. The inputs to a 1-of-1 social experience are human taste, specific relationships, and local context. Those don’t homogenise. The medium becomes generative, but the intent stays personal. My brother’s parlay app is shaped by the specific way his friend group talks about sports, the running jokes, and the particular format of their picks. No amount of AI tooling makes someone else’s version of that app feel like his.

The broader ‘human-only’ framing emerging across new social apps is responding to this desire to prioritise human connection. When synthetic content saturates the feed, genuine human context becomes a scarce resource. We are seeing this permeate our everyday interactions. Recently, the World launched a “humans only” concert to address the challenge of bots sniping tickets online.

We’ve also seen this in our digital spaces, on apps like Retro or Subtext, which emphasise private social for humans only.

1-of-1 social is, among other things, a structural defence against that. It’s a social space designed for specific people, making it legible to them in ways that generic content can never be.

Build Something

Cosy corners of the internet are coming back. Not as a nostalgic throwback but as a return to the fundamentals that made the internet a meaningful place for connection. This is the structural consequence of tools that enable personal software.

The social graph can be open. The identity layer can be portable. The moderation primitives are available. The barrier isn’t technical anymore. It’s imagination.

Think about the ritual your group already has that no app serves perfectly. The shared behaviour that happens in spite of the tools you’re using, not because of them. The group chat that’s doing work it was never designed for.

That’s the brief. Build the thing that makes it better — and build it on infrastructure that doesn’t make you start bringing together your users from square one.

That’s it for today,
Kinjal Shah

Try AT Protocol

Try Attie

Disclaimer: This piece was originally published as an X article.

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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.

As a writer, I have been pleasantly amazed by what Substack has become over the past few years. It’s not so much what the platform chose to do that made me stick around as what it chose not to do. Substack doesn’t crowd my screen with engagement metrics or algorithmic noise. It doesn’t turn every interaction into a performance. Each time I open it, I see a clean surface to write on, discover people who align with or oppose my thoughts, and communities I feel like engaging with in some cases or ignoring in others. In the era of short-form content with even shorter shelf lives, platforms like Substack choose the path that slowly builds trust between a creator and a consumer.

It’s this restraint that I find extremely rare in most social networks. This phenomenon becomes clearer when you take a step back and look at other platforms.

I find most of them suffocating with metrics thrown all over the screen, including likes, shares, view counts, and boosted replies. All of these factors collectively determine what you see in your feed. The platform has already decided what the content means, so there’s nothing left for you to decide. You stop participating and start performing. The medium eventually eats itself with too much optimisation.

In today’s guest piece, Anderl makes this argument, albeit with much better examples. He uses McLuhan’s hot-and-cool media framework to explain why SocialFi collapsed, why NFT culture evaporated, and why the platforms that actually work are the ones that know where to let money in without letting it take over.

Onto the story,
Prathik

Follow Anderl’s Works

There is a sentence McLuhan wrote in 1964 that has been quoted so often it has lost most of its meaning. The medium is the message. By now, it sounds like the kind of thing you put on a tote bag. But if you stop and read it as a working diagnostic rather than a slogan, it does something useful, especially for anyone trying to understand why so many recent attempts to fuse social networks with finance have collapsed in slow motion.

McLuhan’s actual argument was narrower and stranger than the slogan suggests. He thought that every medium reshapes the people who use it, not through what it says, but through the shape of the signal it sends. A medium that delivers a finished, high-resolution signal turns its users into receivers. A medium that delivers a partial, low-resolution signal forces its users to fill in the gaps, and in doing so, they become participants. He called the first kind hot and the second, cool.

Print is hot because the page is fully written. Radio is hot because the broadcast is fully produced. A lecture is hot because the speaker controls the signal. By contrast, a telephone call is cool because voice alone carries too little information, and the listener has to construct the missing context. A cartoon is cool because the brain finishes the drawing. Television, in McLuhan’s analysis, was cool because the early signal was so low-resolution that watching required constant active reconstruction. He thought, controversially, that this is why television felt addictive in a way that cinema did not.

The point that matters here is not the specific examples, which date badly, but the underlying claim. The temperature of a medium predicts the kind of behaviour it produces. Hot media produce consumption. Cool media produce participation. And, crucially for what comes next, you cannot turn one into the other without changing what the medium fundamentally is.

What’s This Got to Do with Social Networks?

In McLuhan’s terms, most of what we call social media is cool. A tweet is a fragment. A photo without context is a fragment. A ‘like’ is a fragment. None of these are finished signals. Their meaning emerges only through others’ participation, replies, reposts, threading, and association. A post without engagement is barely a thing. A post with two thousand replies becomes something else entirely, even though the original content has not changed by a single character. This is exactly the McLuhan signature of a cool medium: the artefact is incomplete on arrival and gets completed through use.

This is also why social networks feel the way they do. They are not content delivery systems. They are participation engines that happen to look like content from the outside. The platforms that have understood this, even without ever reading McLuhan, have flourished. Most platforms that tried to professionalise their participation and deliver finished signals to passive consumers have drifted into irrelevance.

The interesting thing is what happens when someone tries to add an economic layer to a cool medium. This is where SocialFi enters the story.

What Did SocialFi Try to Do?

The promise of SocialFi was elegant on paper. The argument was that social capital is real economic value. People generate it constantly, but the platforms capture it all. If we could put social acts directly on a market, then the people who actually create the value would also capture it. Each follow becomes a stake. Each post becomes a tradeable asset. Each connection has a price. The result, in theory, would be a social network that is also an economy, where reputation has a market and creators get paid in real time for the attention they generate.

For a few weeks in late 2023, with Friend.tech, this looked like it might be a real category. People bought and sold each other’s keys. Influencers commanded thousands of dollars in opening prices. The interface looked like a social network and behaved like a brokerage account. Other projects followed quickly, each promising a slightly different version of the same logic. Stamps, gated chats, social tokens, attention markets, and on-chain creator economies. The pitch decks were everywhere.

Then the whole category collapsed. Friend.tech faded. The follow-on projects mostly never reached escape velocity. Token prices crashed and never recovered again. By 2024, SocialFi had become a slightly embarrassing phrase that founders avoided in their next deck.

The standard explanation is that it was a speculative cycle, that people were there for the gains and left when the gains stopped. This is true but shallow. Speculation cycles do not explain why participation collapsed in the underlying social product. People did not just stop trading the keys. They stopped posting, reading, and showing up. The social activity died, along with the financial activity. Why?

The McLuhan Reading

The deeper diagnosis is that SocialFi did not fail because of speculation. Speculation was the symptom, not the cause. The problem was that the entire category was built on a misreading of its own medium.

Social networks are cool media. Their value lies in the fact that participation completes the signal, and that meaning accumulates through repeated low-resolution acts whose significance becomes visible only over time. SocialFi took that medium and replaced its constitutive signal, the social act, with a high-resolution one: a price.

The moment you put a real-time, visible, tradeable price on a ‘follow’ or a post, you have not added an economic layer to a social medium. You have replaced the medium. The new artefact is a fully resolved signal. There is no gap to fill in. The ‘follow’ no longer means anything ambiguous. It means this exact dollar amount right now. And once a signal is that resolved, the rational response is no longer participation. The rational response becomes allocation.

This is why Friend.tech, in its own internal logic, was not a social network. It was a Bloomberg terminal for micro-reputation, with a social network-shaped interface on top. Users were not posting. They were trading. The fact that the trades happened to be on each other’s identities did not make the activity social. It made the activity a financial one that was using social vocabulary as the skin. And once the financial dynamics turned (when prices stopped rising, when the obvious arbitrages closed, and when speculation became less profitable), there was no underlying social medium left to fall back on. The social layer had been consumed by the financial layer at the moment of creation.

This is what McLuhan would have predicted. Hot signals do not coexist with cool media. They replace them. You cannot have a partial, ambiguous, participation-driven act when one of the simultaneous attributes of that act is its current market price, updated in real time, visible to everyone. The price wins. It always wins, because it is more resolved than anything else on the screen.

The early SocialFi designers thought they were building a social network with an economy underneath. But what they were, in fact, building was a market with a social aesthetic on top. The category failed not because of excessive speculation, but because it had quietly become a hot medium and was still being marketed as a cool one.

Follow Anderl’s Works

Why This Matters beyond Crypto

It would be tempting to read this as a niche post-mortem on a niche product category. But the same logic applies far more broadly, and it explains a pattern in platform history that goes back decades.

Cool media die when they get too hot. This is not a metaphor; it is a recurring failure mode. Platforms that begin as low-resolution participation engines tend to add features that raise their resolution over time. Think of verified accounts, engagement metrics that are visible to everyone, creator funds that pay per view, and algorithmic ranking that shows you exactly how well your post performed. Each of these additions seems harmless or even helpful. But collectively, they describe a slow thermal drift from cool to hot. The medium gets clearer, the signals become more finished, and at some point, the user shifts from participating to performing, and from performing to consuming the performance metrics, and then increasingly stops showing up at all because there is nothing left to fill in.

This is why platforms that look unstoppable at peak engagement often turn out hollow a few years later. They have removed themselves from the regime where they actually generated value. Twitter circa 2012 was cool. Twitter circa 2024 is mostly hot. The drift is not anyone’s fault in particular. It is the natural pull of every metric, every monetisation layer, and every product team trying to make the signal sharper. Hotness is what optimisation looks like, applied to a medium that does not benefit from being optimised.

SocialFi made this same drift in fast-forward, compressed into months instead of decades. By starting with the hottest possible signal, a real-time market price, it skipped the entire cool phase that gives a medium its initial gravity. There was nothing to drift away from. It was hot from the first day, and it died quickly because hot media without distribution moats die quickly.

The Way Out: Condensation Points

If you accept this diagnosis, an obvious question follows. Does this mean that any attempt to fuse social participation with capital is doomed?

No, because there is a third option that early SocialFi missed entirely. You can leave the medium cool and let capital condense at specific points within it, rather than dissolving capital into the medium itself.

The metaphor is borrowed from physics. In a fluid that stays mostly gaseous, there are specific local conditions under which droplets form. The droplets are not the gas. The gas is not the droplets. Both coexist, and the interesting thing is the geometry of where condensation happens. The bulk of the medium remains in its original state, while a small number of points become dense, liquid, and load-bearing.

Cool media can work the same way. The base substrate stays cool. Most acts in the medium remain low-resolution, ambiguous, and participation-driven. But at chosen, specific moments, capital is allowed to condense out of the social substrate and become real, financially anchored, weight-bearing. Crucially, these condensation points are not the medium. They are local intensifications inside it. The rest of the medium is left alone.

I think this is the correct way to read certain platforms that quietly worked while SocialFi did not. Substack is a cool medium for writing. The writing itself is fragmentary, ongoing, accumulative, and completed by readers who reply, forward and quote the posts. Capital condenses at one specific point: the recurring subscription. That subscription is a hot signal, an explicit recurring price, but it is structured as a long commitment rather than a spot trade, which means it does not contaminate the rest of the medium with continuous price discovery. You do not see real-time tradable share prices for individual essays. The medium stays cool, and capital condenses at the subscription edge.

Bandcamp does the same thing for music. Wikipedia does it through donations rather than per-edit pricing. Patreon does it for creators. Each of these platforms has intuitively positioned the condensation points where capital can enter without heating the entire medium. None of them has tried to put a market price on every social act. They have all understood that the substrate must stay cool for the platform to keep generating gravity.

The lesson SocialFi missed is that capital and cool media are compatible, but only on specific terms. The capital has to be local, infrequent, illiquid in the right ways, and structurally separate from the bulk of social acts. It has to condense, not saturate. The moment you try to make every act capitalisable, you have replaced the social medium with an economy. And economies do not produce the kind of accumulating, ambiguous, participation-driven meaning that cool media generate.

What Comes Next

There is a generation of projects that quietly figured this out, often without naming it in these terms, and the pattern is beginning to look stable. They tend to share a few characteristics. The base layer is a social or cultural artefact whose meaning accumulates through participation.

If there is a single sentence that captures the lesson of SocialFi’s collapse, it might be this. Liquidity is heat. Adding it to a cool medium does not make the medium more efficient. It changes the medium into something that can no longer do the thing the original was good for.

The interesting design space, then, is not how to put a price on every social act. It is the much harder, much more specific question of where exactly to let capital condense inside a cool substrate without disturbing it. That question has barely been explored. SocialFi failed to ask it, because it was too busy trying to dissolve everything into a market. The next wave, the one that actually works, will probably be the one that takes McLuhan seriously enough to leave most of the medium alone.

NFTs as the Sharper Case

If SocialFi shows what happens when you build a hot medium and call it social, NFTs show something more revealing. They show what happens when you take a deeply cool medium that has worked for centuries and watch it heat up in real time.

Collecting is one of the oldest forms of cool media. Browsing record bins, stopping in antique shops, trading Pokémon cards on a school playground, and showing a stamp collection at a club meeting are all activities where the object itself carries only half the meaning. The other half is built through participation, recognition, the slow accumulation of a collection over the years, stories attached to specific pieces, and the conversations in which someone notices what you have. The value of a collected object is low-resolution, ambiguous, and context-dependent. That is not a deficiency. It is the mechanism that makes collecting a cultural practice rather than a transactional one.

The early NFT waves, in 2020 and early 2021, still carried some of this logic. CryptoPunks began as an insider thing, almost a joke among crypto natives, with unclear meaning and a value that came from shared culture rather than from a price feed. Early Art Blocks drops had a similar texture. There were forums, Discord channels, places where people talked about individual pieces, traded stories, and built community. The collecting was cool. The artefact was unfinished. It needed participation to mean something.

Then the marketplaces matured, and the thermal drift began, in a form so extreme it deserves to be studied as a case in itself. OpenSea made floor prices visible everywhere. Rarity tools quantified every trait into a numerical score. Real-time charts made every collection look like a stock ticker. Sniping bots made human reaction times irrelevant. Wash-trading volume became a status signal. Each of these features, taken individually, was a rational market optimisation. Collectively, they tipped the medium from cool to hot at a speed no cultural practice had ever experienced before.

The result was almost painfully consistent with what McLuhan would have predicted. The collector became a trader. The trader became a bot operator. The bot operator reduced the artefact’s meaning to a single floor-price number, and once that number fell, nothing was left. The communities that had formed around early collections did not mature into a richer cultural form. They evaporated the moment the market stopped moving. This is not how collectors behave. Collectors stay when prices fall. They keep talking, trading, and tending to their collection. What happened to NFT communities after the crash was not a collector exodus. It was proof that there were no collectors left. Only market participants disguised as collectors stayed, and when the market closed, the veil came off.

This is an even sharper illustration of the medium thesis than SocialFi. SocialFi was a new medium that was hot from its birth. So its failure could plausibly be blamed on novelty or speculation cycles. NFTs took a medium that had worked for centuries, survived wars, technological revolutions and changes in taste, and destroyed its working dynamics in barely thirty months. The medium had worked. The platforms broke it; not by neglect, but by relentless optimisation. Each step made the experience more precise, more measurable, and more efficient. It also made it slightly less collectable, until at some point, there was nothing left to collect.

The cautionary lesson is that thermal drift is not slow. It can happen on a timescale measured in product cycles, especially when the platform layer is built by people who do not recognise that they are operating inside a cool medium. The temptation to add another metric, another leaderboard, and another real-time price feed is constant. Each of these additions raises the temperature by a small amount that feels harmless in isolation. But the cumulative effect is the disappearance of the practice that the platform was supposed to host.

That’s it for today,
Anderl

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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

We will be featuring good writing and writers we love from time to time. If you have recommendations, send them our way.

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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.

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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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.