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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

—Thejaswini

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Then comes the Tron’s announcement.

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

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

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

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

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

Ethereum is taking the slow, unglamorous route of engineering.

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

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

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

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

The landscape is a study in contrasting power structures.

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

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

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

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

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

—Thejaswini

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

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

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

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

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

The Setup

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Four Stages of Extraction

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

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

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

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

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

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

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

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

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

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

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

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

Who Owns the Supply Owns the Market

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

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

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

That’s all for today.

Until then, stay curious.

Vaidik

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

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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.

Happy Monday.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The nuclear winter ended around 2003.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

—Thejaswini

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

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

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

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

Now here’s where it gets interesting.

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

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

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

Software is Now a Company

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

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

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

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

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

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

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

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

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

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

The Chains are a Vendor Now

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

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

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

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

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

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

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

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

The AI Token Graveyard

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

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

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

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

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

The Crack in the Thesis

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

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

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

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

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

Where The Money Actually Lives

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

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

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

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

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

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

That’s all for today, until next time!

Stay curious!

Vaidik

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

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

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

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

What The Money Says

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

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

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

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

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

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

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

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

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

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

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

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

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

—Thejaswini

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why does data before assets matter

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

—Thejaswini

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My favourite thing about the dot-com crash is that Bernie Madoff was one of the voices warning people it had gone too far. In late 1998, he said publicly: “It defies my imagination that so many people with so little sophistication are speculating on these stocks.” Bernie Madoff. Telling people to be careful with their money.

That’s the era we’re in this week. The part of the internet story that the “we’re in the Netscape moment” crowd leaves out. Let’s go.

In the spring of 1994, a twenty-eight-year-old named Jeff Bezos was the youngest-ever senior vice president at a Wall Street hedge fund. One of his jobs was to identify business opportunities on the internet. He came across a data point that stopped him cold: the amount of data transmitted over the web had grown by 205,700% in a single year. As Bezos later said, “Things just don’t grow this fast outside of petri dishes.”

He quit his job. He and his wife, MacKenzie, packed up their car and drove to Seattle, Bezos typing up a business plan on his laptop as they went. The company they founded in the garage of a rented house in Bellevue, Washington, would become Amazon. The original address was 10704 N.E. 28th Street. The domain was registered on November 1, 1994.

Bezos had thought carefully about what to sell first. He considered software, office supplies, and CDs. He landed on books because they were pure commodities: a copy of a book in one store was identical to the same book anywhere else, so buyers always knew what they were getting. There were also three million book titles in print worldwide, but no single store could stock them all. An online store could. “With that huge diversity of products,” Bezos said, “you could build a store online that simply could not exist in any other way.”

Amazon’s full website launched on July 16, 1995. In the first week, it took in $12,438 in orders and managed to ship $846 worth. The rest was a backlog. Early customers sometimes phoned in their credit card numbers rather than enter them online, not trusting the web. Others simply emailed them, “as if that was somehow more secure,” one early employee recalled. Credit card numbers were recorded on one computer, copied to a floppy disc, physically carried to a second computer, and processed in batches. This was known inside Amazon as a sneakernet.

By October, Amazon had its first hundred-order day. By 1996, it was recording 900% revenue growth. And then the Wall Street Journal put Bezos on the front page under the headline “Wall Street Whiz Finds Niche Selling Books on the Internet.” Overnight, Amazon went from experiment to standard-bearer. Kleiner Perkins invested $8 million. John Doerr joined the board.

A new motto started circulating inside the company. Netscape had coined the phrase, and Amazon turned it into a religion: Get Big Fast.

If you think about it, every DeFi protocol that launched between 2020 and 2022 was running some version of Get Big Fast. Subsidise users with token rewards, acquire market share, worry about sustainable economics later. Some of those protocols are still here, but a lot of them aren’t. Amazon survived Get Big Fast because Bezos had actually thought about what he was building. People wanted to buy books online. The books were actually worth selling. The thing Bezos was really testing in that garage was whether the internet made something possible that wasn’t possible before. That’s still the only test that matters, in everything you build.

While Bezos was building a bookstore, a French-Iranian programmer named Pierre Omidyar was running a small experiment on the side. He had built an online auction site called AuctionWeb mostly out of curiosity, just to see if the internet could create a decent market for secondhand stuff. He hosted it on his personal webpage, next to his resume. He started charging tiny listing fees because his ISP was threatening to move him to a business account, and he needed to cover the cost.

The fees covered the cost. Then they exceeded it. Within a few months, AuctionWeb was making Omidyar more money than his actual job. He eventually renamed it eBay.

What made eBay work? Buying something online from a stranger you would never meet was a deeply weird idea in 1996. What stopped fraud, what kept the whole thing from collapsing into a scam, was a feedback rating system. Buyers rated sellers, sellers rated buyers, and a number accumulated next to your username over time. A seller with a +847 rating had completed 847 transactions that ended with a satisfied buyer. That number became a reputation, and on eBay, reputation was worth more than anything being sold. McCullough is right to linger on this. The feedback rating system is one of the most quietly important inventions of the internet era. Uber, Airbnb, every marketplace that exists today is running a version of Pierre Omidyar’s experiment. The sharing economy is built on the premise that a stranger with a good reputation score is trustworthy enough to get into a car with or sleep in their apartment. That idea did not exist before eBay.

In crypto, the trust problem was solved differently, and it’s not through reputation, but through code. Smart contracts removed the need to trust the counterparty at all. You trusted the protocol, not the person. This is a more radical solution than eBay’s, and in some ways more elegant. But it created its own problem: people trusted the protocol without understanding it, and the protocol, as we’ve seen many times, did exactly what it was coded to do rather than what people thought it was coded to do. The bugs in early eBay could be patched. The bugs in an immutable smart contract on a Tuesday afternoon in 2022 could not.

By 1998, eBay was growing at 1,200% annually. Benchmark Capital had paid $5 million for a 21.5% stake in the company. That $5 million eventually turned into $4 billion.

Now we are in 1998, and something is changing in the atmosphere. It’s hard to describe how irrational things got, but McCullough makes an attempt here. In 1995, seven internet companies held IPOs. In 1996, twenty-seven. In 1999, two hundred and forty-nine. The market was losing its mind about the Internet. The companies being taken public in 1999 were, in many cases, barely companies at all. Pets.com launched in February 1999 and went public in February 2000 (12 months later), having lost more than $61 million on revenues of $5.7 million. The cost of the goods sold was $13.4 million. It was losing 57 cents on every dollar of revenue. It was selling heavy bags of pet food by mail and charging $5 for shipping when the actual shipping cost was closer to $10.

The company’s biggest expense was advertising. Pets.com had a sock puppet. You might remember the sock puppet. The sock puppet was a talking dog-like creature that appeared in a $2 million Super Bowl ad, a Macy’s Thanksgiving Day Parade float, and a spread in Entertainment Weekly. The puppet became a licensed toy. In a single quarter, Pets.com spent $17 million promoting the sock puppet and brought in $8.8 million in total revenue.

Pets.com was valued at hundreds of millions of dollars. It was out of business within a year of its IPO.

Crypto people reading this should be having flashbacks. The 2021 bull run had its own sock puppets, which were celebrity NFT endorsements from people who barely knew what a blockchain was, Super Bowl ads for exchanges, and stadium naming rights. The difference is that some of the crypto technology worked. The crash sorted out which projects had something underneath them and which were running on narrative. That is exactly what 1999 did too.

The mania had a specific structure. Lose money to acquire customers. Acquire customers to justify your valuation. Use your valuation to raise more money. Use that money to lose more, faster, to acquire even more customers. The assumption underneath it all was that once you owned your category, you could flip the switch to profitability. Amazon was doing this. The problem was that most of these companies were not Amazon. They were burning capital to fight for markets that would never be as big as they imagined, selling products at prices that could never be raised high enough to be profitable, and trusting that somehow the switch would flip.

“We’re in an environment where the company doesn’t have to be successful for us to make money,” one Benchmark VC admitted to McCullough.

When your investors are saying that out loud, you are at the top.

The 2021 DeFi and NFT cycle followed the same structure. Protocols subsidised users with token emissions. Lose money on every transaction to acquire liquidity, use liquidity to justify TVL, use TVL to raise the next round. The assumption was that once you owned your category, you could wind down the emissions and keep the users. Most couldn’t. The users were there for the yield, not the product. When the yield stopped, they left. Same switch. Same problem. It never flipped.

The crash, when it came, was not dramatic in the way crashes appear dramatic in retrospect. The Nasdaq peaked in March 2000. It fell 78% from peak to trough. Two and a half trillion dollars in market value evaporated. Pets.com shut down in November 2000, nine months after its IPO. Webvan, the grocery delivery startup that had raised $400 million and built 330,000-square-foot warehouses before establishing whether anyone actually wanted the product, filed for bankruptcy in 2001. Kozmo.com, the instant-delivery service that sent bike couriers to bring you a pint of Ben & Jerry’s at below cost, was gone. EToys. Furniture.com, which had learned too late that you cannot ship a couch by UPS. The tech press started calling it the nuclear winter.

AOL and Time Warner announced the largest merger in corporate history in January 2000, valuing the combined company at $350 billion. It was, in hindsight, the single worst merger of the era. AOL used its inflated stock to buy a real media company weeks before that stock became worthless. The deal that was supposed to represent the internet's conquest of old media instead became a tombstone for both.

Yeah..yeah.. Three arrows, FTX, Luna, leverage on leverage, I remember too, the only problem was why the heck did I not buy BTC at $16k?

But here is what McCullough is building toward, and it is the most important takeaway for us. The nuclear winter killed sock puppets, but it did not kill the internet. While the Nasdaq was falling, venture capital was drying up, and the media was writing the obituary of the dot-com dream, two things were happening. Across a series of server rooms, a search engine built by two Stanford PhD students was improving at an astonishing rate. And a nineteen-year-old college student named Shawn Fanning was about to break the internet open in a completely different way. What the crash did was clear the noise. What was left underneath was Google and Napster.

Part three is where it gets good again. Until then, stay extremely hopeful.

—Thejaswini

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

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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.

In January 2026, an anonymous trader on Polymarket placed a series of bets that Venezuela’s president, Nicolás Maduro, would be captured. The total wager was about $34,000. When the U.S. special operations carried out the mission days later, the trader cashed out over $400,000. The Secretary of State later confirmed that the operation was too sensitive for congressional notification. Think about that for a second. The United States Congress, the body that is supposed to authorize military operations, was not informed. The American public had no idea. But someone sitting behind a screen, on a crypto betting platform, knew enough to put real money on it. And they were right.

This has become the narrative of the prediction market industry today. To be known as what Polymarket CEO Shayne Coplan calls the “truth machine.” The argument is that, because traders have skin in the game, their collective bets produce the most honest picture of what is going to happen in the world compared to any polls, experts, or pundits who face no consequences for being wrong. The odds on Polymarket are arguably the closest thing to the truth you can find anywhere.

And that narrative has kind of worked. Prediction markets are no longer a niche corner of the internet where degens bet on elections for kicks. A recent dataset of 364 TikTok videos mentioning prediction markets found that 68% of the videos had nothing to do with trading. People were not betting; rather, they were citing odds from these platforms in political debates, just as they used to cite polls. Polymarket showed up in roughly 70% of those videos. A 22-year-old making a political video on TikTok is using a crypto betting platform’s odds as evidence for what is going to happen in the real world, and a significant number of people are nodding along.

That is wild. Two years ago, you could not have believed any of this was possible. But a serious question nobody is asking is whether the odds actually deserve that level of trust.

So let me ask: How accurate are these markets really? What happens when the odds start shaping the events they are supposed to predict? And what does the future look like when the whole world starts treating betting lines as the truth?

How Do You Even Grade a Prediction Market?

Before we look at the numbers, it helps to understand how you actually measure whether a prediction market is any good. Because most people have never thought about this, and without it, all the claims from Polymarket and Kalshi are just marketing.

There is a scoring method called the Brier score. Glenn Brier, a meteorologist, came up with it in 1950 to grade weather forecasts, because weather forecasters were (and still are) among the first people who had to take probabilistic predictions seriously for a living. The idea is dead simple. Say you predict there is a 90% chance of rain tomorrow, and it rains. That is a good prediction. Your Brier score is low. Now, say you predicted a 90% chance of rain, and the sky stays clear. That is a terrible prediction. Your score is high. A Brier score of 0 means you predicted everything perfectly. A score of 0.25 means you did no better than a coin flip. Anything above 0.25 means you would have been better off guessing randomly.

Why does this matter? Because when Polymarket tells you their market gave Trump a 60% chance, and he won, that sounds impressive on the headlines, but statistically, a single correct call tells you almost nothing. You need to grade the full track record across thousands of questions, over time. That is what the Brier score does. It is the only honest way to evaluate whether these markets are actually good at this.

A site called Brier.fyi does exactly that. They analysed over 84,000 questions across Polymarket, Kalshi, Manifold, and Metaculus. Polymarket’s overall Brier score came in at 0.047. Now, that’s a genuinely good score. To put that in plain terms, imagine a forecaster who says, “I am 90% confident this will happen” and gets it right at that rate, consistently.

But here is where it gets interesting, and where the “truth machine” narrative starts to fall apart.

That 0.044 is an average across everything Polymarket has ever listed. And averages, in this case, are doing a massive amount of heavy lifting. When you break the score down by what people are actually betting on, the grades swing wildly.

Science and economics? Polymarket scores a -A. The markets on CPI numbers, Fed rate decisions, and GDP prints. These perform well because the people trading them tend to be financially literate, the data is verifiable, and there is real money at stake from institutional players who actually understand the subject.

Politics? B+. Still decent, mostly carried by the massive presidential election markets where billions of dollars flow in. Culture and technology? Worse. Much worse.

And then there is sports. The overall Brier score across platforms for sports markets was 0.325. That is -D. Remember, a coin flip is 0.25. Sports prediction markets, across the board, performed worse than if you had just flipped a coin for every single question. Let that sink in.

The category that attracts the most casual bettors, the one that Kalshi has been aggressively expanding into (roughly 90% of Kalshi’s volume was in sports at one point), is the category where the markets are provably unreliable.

Now look at individual markets, this is where the story breaks even more.

Polymarket ran a market on whether Bitcoin would hit $100,000 before January 2025. Bitcoin did hit $100,000. The market got the outcome right, but it spent most of its life pricing the probability wrong, hovering at low confidence for months, then spiking to near certainty only in the final stretch. Its Brier score was 0.4909. That is an F. Remember, you are better off just randomly guessing after 0.25 (the coin-flip line). This market scored almost double that.

The market on Kamala Harris winning the 2024 Democratic nomination is even wilder. She won the nomination, and the market got the outcome right, again, ironically. But the Brier score was 0.9098. That is so bad that it is hard to overstate. The market was confidently wrong for so long that even getting the final answer right could not save it. If you had been using that market as a signal for your own decisions, you would have been misled for the entire campaign cycle, right up until the moment it no longer mattered.

Now for the other side of the ledger, because this is not a simple story. The 2024 U.S presidential elections were a genuine win for prediction markets. Polymarket had Trump at roughly 60% when every major poll was calling it a toss-up. The Vanderbilt study ran Bayesian time-series models comparing Polymarket prices to national polls across all seven swing states. Polymarket was more accurate in every single one.

So what does this conclude? Prediction markets are very good at elections. Specifically, they are very good at the biggest, most liquid elections, where billions of dollars, tens of thousands of traders, and massive public attention all converge on the same question. In that specific scenario, they consistently beat polls.

But here is the problem with the “truth machine” label. Elections are maybe 2% of what these platforms list. Polymarket’s 2024 presidential market alone generated over $3.6 billion in volume with 63,000 unique monthly traders. Move one step outside that, to congressional races, state ballot measures, or anything in culture, tech, or sports, and the bid-ask spreads on contracts blow out to 20% to 100% of the midpoint price. Legislative and crisis markets had spread near 100%. A spread that wide means the market has almost no idea. It is just two people on opposite sides of a guess, with barely any money backing either view.

When the Odds Start Writing the Story

The accuracy problem would be manageable if it stayed inside the prediction market ecosystem. Traders who bet on bad markets lose money, learn, and the system improves over time. That is how every financial market has worked for a hundred years. But the problem is that the odds have stopped being an internal signal for traders and started being broadcast content for everyone else.

Over the past eighteen months, every major American newsroom has integrated prediction market data into its political coverage. The Wall Street Journal signed a formal agreement with Polymarket to run its betting data alongside editorial reporting. CNN started putting Kalshi odds on screen during election night coverage. CNBC did the same with Kalshi. In December 2024, even Substack announced a direct partnership with Polymarket so newsletter writers could embed live market data straight into their posts.

This is how the odds ended up on TikTok. The numbers traveled from Polymarket to the Wall Street Journal to cable news to Twitter to TikTok. By the time the odds reach a casual viewer, it has been routed through enough credible outlets that it feels like a fact. People were absorbing numbers that had already been pre-laundered through the mainstream press.

And this is where the problem with prediction markets starts, because once the odds are being consumed as news, they start to influence the very thing they are supposed to be predicting. There is a name for this. Economists call it endogeneity. In plain language, it means the measurement changes the thing being measured.

Let me give you a concrete example. Brian Armstrong, CEO of Coinbase, was on an earnings call. He became aware that Polymarket was running a contract on whether he would mention certain specific phrases during the call. So he modified the words he was going to use. Here, the market was supposed to be predicting what he would say. Instead, his knowledge of the market’s bets changed what he said.

Now scale that dynamic up to an election. In the 2024 U.S. presidential race, a French trader who goes by “Theo” (the media called him the “Polymarket Whale”) placed enormous bets on Trump winning. He eventually profited over $85 million. This was not some lucky gambler. He had commissioned his own private polling, separate from every public poll in the country, and his data showed Trump performing significantly better than the public numbers suggested.

Because of this, his bets pushed the polymarket price up, which was then picked up by the outlets I mentioned. The Wall Street Journal, CNN, and political commentators on every platform. The story turned into a prediction market favoring Trump even though polls say it is a toss-up. That single narrative shaped how millions of people perceived the race in the final weeks. Commentators debated whether the “smart money” knew something the polls did not. Voters consumed that, and Trump won.

I am not claiming that Theo changed the outcome of the election. That would be a stretch I cannot back up. What I am claiming, and what I think anyone paying attention should be alarmed by, is that a single trader with private polling data that nobody else had access to was able to move Polymarket’s price, and that price was then broadcast by the Wall Street Journal and CNN as the market’s collective wisdom. The function of a good prediction market is to aggregate many pieces of information from many participants into one clean signal. What happened in 2024 is that one person’s proprietary poll got laundered through Polymarket and rebroadcast as if it represented a consensus of thousands of traders.

And if one trader can do that with $85 million, imagine what people with actual money & power might do?

In February 2026, Israeli authorities indicted at least two people for using classified military intelligence to bet on Polymarket. They had placed wagers on contracts related to Israeli military operations before those operations became public. The potential profits were around $100,000. These were people with security clearances, betting on war using information the public would not see for days. It was the first prosecution of its kind anywhere in the world, and it confirmed that prediction markets are fast enough, liquid enough, and anonymous enough that they can be used to monetize secrets in real time.

The Maduro trade that opened this piece? Same pattern. Someone bet on a covert military operation before it happened and walked away with over $400,000. Whoever they are, they either had inside information or they made one of the luckiest guesses in the history of political betting. We would never know.

What Happens When Everyone Believes the Odds

The median Polymarket question resolves in four days. The average is 19 days, but that gets pulled up by a few long-running markets. Most questions on the platform are about what is going to happen this week.

This tells you that these markets are not forecasting the future in any meaningful long-term sense. They are just pricing the very near present. Will this vote pass on Friday? Will this person say this thing tomorrow? Will this number come in above or below the estimate on Wednesday? That is useful information. But it is a very different thing from what people mean when they call prediction markets “truth machines.” A truth machine, in the way the phrase is used, implies that the market can tell you what the world will look like in six months, a year, or five years. The data says it cannot. Not even close.

99% of prediction market volume shows up right before the event resolves. The money piles in during the final hours when the outcome is nearly certain. And the liquidity gap in these markets is even bigger. The total weekly volume across Polymarket and Kalshi combined reached roughly $2.5 billion by late 2025. Sounds enormous, right? But the U.S. options market alone clears around $760 billion in a single day.

Prediction markets are 0.05% of that. The entire prediction market industry, across every platform, every contract, every category, is a rounding error compared to the markets that institutions actually rely on for decision-making.

So here is the situation: Prediction markets work well for a very specific type of question: binary, high-profile, short-term eventswith millions of dollars at stake. That is a small sample of what these platforms actually list. For the other 98%, the prices are unreliable, the liquidity is nonexistent, and the output is closer to a Twitter poll than a financial instrument.

They are building the default source of probabilities for everything. The same way that when you want a stock price, you open a Bloomberg terminal, the vision is that when you want a probability, you open Polymarket. And the play is that once enough media outlets, newsrooms, financial analysts, and government researchers depend on that feed, the product becomes impossible to replace, regardless of whether the numbers on it are actually good.

I think this will work. And I think that should worry everyone.

Because the question is not whether prediction markets are useful. They are. For elections, major economic data, and a handful of high-profile events, they consistently outperform alternatives. That is real and that matters. The question is what happens when an entire information ecosystem starts treating the output of these markets as ground truth, even for the 98% of questions where the market has no idea.

Robin Hanson, the economist who spent decades arguing for prediction markets, described them as systems that force people to put money behind their beliefs. The resulting price, in his model, would be the best available estimate of truth. But that model assumed deep liquidity, diverse participants, and resistance to manipulation. The markets we have are dominated by a small number of whales, concentrated in two categories (elections and sports), with about 80% of all volume in those buckets. The other 20% is scattered across thousands of contracts where a few thousand dollars can swing the price by double digits.

These are attention machines. They work when the world is watching and break when it is not. The more people believe they are truth machines, the more power accrues to the people who can move the prices. And the people who can move the prices are not a diverse crowd of informed citizens. They are a handful of traders with deep pockets, private polling, and, in at least two confirmed cases, classified intelligence.

The most dangerous thing about prediction markets is not that they are wrong. It is that they are right just often enough, on just the right questions, to earn a level of trust they have not earned across the board. And that trust is being built into the rails of how the world processes information. The Wall Street Journal prints the number. CNN airs it. TikTok repeats it. And somewhere, one trader with enough money is deciding what that number says.

That is the reality of the truth machine. A system that produces a number, and a world that has decided to call it the truth.

Vaidik

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The entire history of the American retirement system is a story about defaults. In the 1980s, the default changed from pension to 401(k), and most workers went along with it without fully understanding what they had given up. In the 2000s, target-date funds became the default option in most plans, and tens of millions of people ended up in them without ever consciously selecting them. Each of these shifts moved enormous amounts of money and reshaped how an entire generation would eventually retire. Most of the people affected only found out later, when they looked at a statement.

Sometime in the next few years, a new default will be built. It does not look like a default yet. It looks like a proposed rule from the Department of Labour, sitting in a 60-day comment period, written in the language of fiduciary prudence and ERISA compliance. But defaults rarely announce themselves as defaults. They arrive as options, become common, and then become assumed.

On 30 March, the Department of Labour published a rule that could open the $12 trillion sitting in American 401(k) plans to crypto for the first time. Indiana already passed a law in March requiring state retirement plans to offer at least one crypto investment option by July 2027. Wisconsin holds $321 million in Bitcoin ETFs through its pension system. Michigan has $45 million in BTC and ETH ETFs. Florida and New Jersey are working toward similar positions.

Let’s start with the wall that kept crypto out.

Before this rule, crypto was not technically banned from 401(k) plans. The barrier was something more effective than a ban.

Under ERISA, the law governing retirement plans, fiduciaries can be held personally liable for investment decisions that lose money. Not the company or the fund, but the individual who made the call. More than 500 suits have been filed since 2016, alleging ERISA violations, with more than $1 billion in settlements since 2020. Plan administrators have watched colleagues get sued over excessive fees, over index fund selection, and over the specific share class of a mutual fund. The litigation is relentless, creative, and personal.

Think about the incentive structure that is created. You run a retirement plan, add Bitcoin, but then Bitcoin crashes by 50%. A plaintiff’s lawyer sends you a letter, and you spend three years in depositions defending your personal assets. Now, let’s say you do not add it. Bitcoin goes to $200,000. Nobody sues you for that. The rational move, every single time, was to stay away. And almost everyone did.

The Biden DOL made it explicit in 2022, with a formal guidance telling fiduciaries to exercise “extreme care” before touching digital assets. That guidance is now gone. In its place is a six-factor safe harbour. Fiduciaries who follow a documented review process covering performance, fees, liquidity, valuation, benchmarking, and complexity are presumed to have met their duty of prudence under ERISA. Follow the process, and you are protected against a personal lawsuit even if the asset drops in value.

Don’t mistake a change in the rules for a change in the market. The investment is just as volatile as it’s always been for the average person. This rule is really about protecting the fund managers, not the participants. It fixes the lopsided legal risk that kept crypto sidelined for a decade, making it ‘safe’ for the fiduciary to finally say yes.

The Mechanism

The DOL itself anticipates the main channel will be target-date funds. This matters enormously for what actually happens to ordinary savers.

A target-date fund is what most people pick when they start a job. You choose the fund closest to your expected retirement year, the 2045 fund, say, and it automatically adjusts its mix of stocks and bonds as you age, growing more conservative as 2045 approaches. Most people who have one have never looked at it twice.

If crypto is invested through a target-date fund, the investor never consciously buys Bitcoin. Their retirement portfolio simply acquires a 1 to 3 percent slice of it, professionally managed and automatically rebalanced. Most people who own gold in their 401(k)s do not know it is there. This is how gold got in. Through the same vehicle, and the same logic. Nobody asked the people whose money it was.

Fidelity moved first in 2022, offering plan sponsors the option to include Bitcoin before the Biden guidance pushed everyone back. Fidelity was offering plan sponsors the option to include digital asset investments in their investment menus, allowing participants to invest up to 20% of their account balances in Bitcoin. What has been missing is the legal cover for plan sponsors to use it without fear of personal liability. That cover is now being written.

The Money

U.S. 401(k) plans hold around $12 trillion in retirement savings. A 1% allocation amounts to roughly $120 billion flowing into digital assets, more than all DeFi TVL combined. Even 0.1% is $12 billion, roughly the size of a top-five Bitcoin ETF.

Every previous wave of institutional crypto adoption came from active decisions. ETF buyers chose to buy. MicroStrategy chose to hold. Banks chose to build custody products. Each of those decisions can be reversed. A CFO can sell the treasury position. An ETF investor can redeem. The 401(k) channel is structurally different in a way the industry has been waiting for since spot ETFs launched. Retirement money is passive, and it sits for 30 years. It does not panic-sell during crashes or respond to Fear and Greed readings. It does not care what oil prices did last week.

Morgan Stanley’s Amy Oldenburg noted that 80% of crypto ETF activity currently comes from self-directed investors rather than advisor-recommended allocations. The 401(k) market is almost entirely advisor-driven. The DOL rule opens a specific channel that has been structurally inaccessible because those controlling access had too much personal liability at stake to open the door. This is also the argument crypto has been making about itself for years. That the real adoption wave would not come from traders or tech early adopters. It would come when the infrastructure of ordinary savings pointed in its direction without anyone having to make a conscious choice. Target-date funds are that infrastructure.

A 50% drawdown in a trading account is a bad quarter. A 50% drawdown in a 55-year-old teacher’s retirement account is a different situation. Bitcoin has drawn down more than 80% in previous bear cycles. The current cycle has been closer to 50%, which some read as maturity. But losing half your retirement savings is not made more comfortable by calling it progress.

TD Cowen’s Jaret Seiberg wrote that he remains sceptical that the rule will encourage fiduciaries to act until courts have confirmed the safe harbour language actually protects them from litigation. ERISA is a process-based law, but courts interpret it. The Supreme Court’s removal of Chevron deference means agencies can no longer expect courts to defer to their own regulatory frameworks. The safe harbour may be real on paper. We don’t know whether it survives the first wave of litigation when a target-date fund with a crypto sleeve loses 40% in a bear market.

The comment period closes June 1. The DOL can modify the rule, withdraw it, or proceed after that date. Even if it finalises without changes, the path from proposed rule to actual crypto in actual retirement accounts runs through compliance teams, investment committees, recordkeeper integrations, and fiduciary review processes that will take months, more likely years. Indiana's July 2027 deadline is a hard mandate. The federal rule is a soft permission. The two will produce very different timelines.

Stocks were added to retirement accounts through mutual funds in the 1980s. International equities through target-date funds in the 2000s. REITs, inflation-protected securities, and commodities. None of them arrived because retirement savers demanded them. The product infrastructure and regulatory alignment happened simultaneously, and the industry moved before most people noticed.

Crypto is at that inflection point now. The spot ETFs are the product. The DOL rule is the regulatory alignment. Fidelity, Schwab, and Morgan Stanley are the distribution. The CLARITY Act, which codifies crypto asset classifications into statute, gives fiduciaries the legal footing to complete a prudence review with documented confidence. Every piece is in place except one.

Somewhere down the line, a plan administrator adds Bitcoin to a target-date fund. Bitcoin drops to 60%. A retiree loses a chunk of their savings. A lawyer files a suit.

At that point, the only thing that matters is whether a judge agrees the safe harbour protected the person who made that call. Nobody knows the answer to that yet. The DOL thinks it will. TD Cowen thinks it will take years to find out.

Until that first case gets heard and decided, every plan administrator in America is being asked to trust a piece of paper that has never been tested in court.

—Thejaswini

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Happy Monday.

What is the one hope that crypto runs on? It’s definitely not just the number-go-up kind, but the deeper belief that the people who continue building after every crash carry with them: the belief that the only thing missing is the moment when it clicks for everyone else, the way it clicked for you.

The way they describe this hope, when trying to explain it to someone outside the space, always circles back to the same reference point: the early internet. Netscape. 1995.

I’ve used that reference myself, but recently I realised I had been using it the way people use historical analogies to sound smart and feel reassured at the same time, without actually knowing the history. So, I went and read the history itself. Now you’re stuck reading a book report on a book about a different book on a Monday morning before your coffee.

How the Internet Happened by Brian McCullough covers the period from 1993 to 2007, from the first downloadable web browser to the first iPhone. It is the best book I have found for understanding what the Netscape moment actually was, what made it happen, and what it took for the internet to go from a technology that worked to a technology that everyone used. Those are two different things. And all of it maps onto crypto in ways that are sometimes encouraging and sometimes a little uncomfortable.

Three parts. Today is where it starts.

On August 9, 1995, a company called Netscape went public.

Netscape had existed for just sixteen months. It had generated $17 million in revenue but had never made a profit. When the markets opened that morning, buyer demand was so overwhelming that Schwab customers were greeted by a special recording: “Welcome to Charles Schwab. If you’re interested in the Netscape IPO, press one.” One investor even called and offered to take out a mortgage on her home.

The stock was priced at $28. The first trade of the day hit at $71. By the time it was over, the market had valued Netscape at $2.1 billion.

The Wall Street Journal put it on the front page the next day: “It took General Dynamics Corp. 43 years to become a corporation worth $2.7 billion. It took Netscape Communications Corp. about a minute.”

A joke went around Wall Street. August 9 also happened to be the day Jerry Garcia died. What were Jerry Garcia’s last words? Netscape opened at what?

The twenty-four-year-old cofounder of the company, a few months out of college, was worth $58 million before lunch. By December 1995, his stock would be worth $171 a share— six times the IPO price. And his face would appear on the cover of Time magazine, barefoot, sitting on a throne. His name was Marc Andreessen.

That IPO was the big bang. Everything that came after, Amazon, Google, Facebook, the iPhone, crypto, AI, the whole mess, traces back to that day. To know why, let’s go back two years, to a university basement in Illinois.

The internet existed before Marc Andreessen. It had existed for decades, connecting universities and government labs in a dense, arcane network that required a working knowledge of Unix to navigate. Tim Berners-Lee invented the World Wide Web in 1990, a layer on top of the internet built on the genius of the hyperlink, where one page connects to another, one idea links to the next. Berners-Lee was a researcher at CERN in Switzerland. He imagined the web as a tool for physicists to share data.

What nobody had done yet was make any of it usable for a normal person. Yeah, the crypto people in the room finally know why I am reading this out loud.

In 1992, Marc Andreessen was a twenty-one-year-old part-time student programmer at the National Center for Supercomputing Applications at the University of Illinois, earning $6.85 an hour. He was large(six feet two), loud, enthusiastic, and allergic to authority. A colleague named Rob McCool remembers meeting him: “All of the computer science students I’d come across were quiet, kind of nerdy types. And here’s this gigantic Scandinavian guy with a purple computer, and he’s wild-eyed and telling me about all this stuff that’s gonna be great.”

Andreessen’s big idea was nothing but what if the web looked good?

At the time, the web was still a text-only research instrument. Berners-Lee, in fact, had a fairly pure vision for it, centered on scientific papers and academic collaboration. When someone proposed adding images, he reportedly said that images were “the first step on the road to hell. And the road to hell is multimedia content, magazines, garishness, games, and consumer stuff.”

Andreessen disagreed. “I’m a Midwestern tinkerer type,” he said. “If people want images, they get images. Bring it on.”

We are fighting with the same ghosts in crypto right now. The purists want the protocol kept clean, self-custody, trustless, and permissionless. Every time someone builds an abstraction layer that makes it easier, there is a version of Berners-Lee’s objection. You’re compromising the vision, you’re reintroducing trust, you’re building something that just looks like a bank. And sometimes those objections are right. But Andreessen was also right. The people who made it beautiful are the ones who changed the world.

In December 1992, he and a colleague named Eric Bina started coding. In a little over a month of round-the-clock work, they had a browser. It was called Mosaic. On January 23, 1993, it was posted online. Tim Berners-Lee himself forwarded the announcement: “An exciting new World-Wide Web browser has come out, written by Marc Andreessen of NCSA.”

Within eighteen months, Mosaic had been downloaded three million times, roughly equal to the entire web population at the time. Websites were multiplying by the thousands. Fortune magazine named the browser one of its products of the year, alongside the Wonderbra and Mighty Morphin Power Rangers, noting: “This software is transforming the Internet into a workable web, instead of an intimidating domain of nerds.”

The NCSA promptly tried to sideline Andreessen, assigning seasoned managers to oversee his team and asking him to “give someone else a chance to share the glory.” He didn’t even pick up his diploma when he graduated. He went to California when he was just twenty-two.

Now let’s talk about the founding myth of the modern internet.

Jim Clark was the founder of Silicon Graphics, the company that made the computers behind the dinosaurs in Jurassic Park. By early 1994, he had been eased out of his own company. He was angry, he was fifty, and he had billionaire envy. Clark had built a billion-dollar company and had a net worth of only $20 million to show for it. He was looking for a second act.

A friend mentioned Marc Andreessen. Clark loaded Mosaic onto his computer. Then he emailed a kid he had never met, saying:

“You may not know me, but I’m the founder and former chairman of Silicon Graphics... I plan to form a new company. I would like to discuss the possibility of you joining me.”

Andreessen wrote back the same day.

They met for dinner. Clark offered Andreessen the chance to build a proper company around the browser, with funding, staff, and stakes. He flew the entire Mosaic team, a bunch of corn-fed Midwesterners who had been making six dollars an hour, out to California, put them up at a hotel, and typed up identical offer letters on his laptop, then printed them on the hotel’s fax machine. Clark offered each of them $65,000, a week in Tahiti on his yacht, and 100,000 shares of stock in the new company.

“We didn’t really know much about Jim Clark,” one of the team recalled. “But we trusted Marc. He gave us all these papers to sign. We just met him for one night. The next morning, we all walked in and quit. That was on Thursday. By Saturday, we were in California picking out apartments.”

The company they built above a Mexican restaurant in Mountain View would become Netscape. The browser they built, called Navigator, captured 80% of the market within two years. It was fast, beautiful, and it worked. Berners-Lee’s academic dream had been handed to the masses.

And then Bill Gates noticed.

Here is the thing about Bill Gates in 1994. He was not an idiot. He was, in fact, one of the shrewdest technology strategists alive. Microsoft controlled 70% to 90% of all computers sold worldwide. Windows 95 was about to drop. The company had a $38.5 billion market cap. Over the past five years, revenue, profit, and stock price have all quadrupled.

Gates and almost every other powerful person in technology, like the cable titans, the Hollywood moguls, and the telephone executives, had spent years building toward something called the “information superhighway.” It was the future everyone agreed on, which involved interactive television, five hundred channels, movies on demand, and shopping from your couch. The living room was going to be the center of the networked world. The TV was going to go smart. And whatever platform ran on those smart TVs, that was where Microsoft wanted to be.

To Gates, the internet was a tool for geeks. It ran on dial-up modems. It required Unix knowledge. People watched television, not the internet.

He was wrong, of course. The information superhighway was already here. Berners-Lee had built it. Andreessen had made it beautiful, and Netscape had given it to the world.

The correction in Gates’s book is one of my favorite details in the entire book. In November 1995, Gates published The Road Ahead, his vision of technology’s future. The hardcover index had 68 references to “information highway” and only 46 to “Internet.” A year later, the paperback was released, heavily rewritten. “Information highway” dropped to 39 mentions. “Internet” shot to 169.

Netscape came out between those two editions, and Gates had to rewrite the whole thing.

On May 26, 1995, Gates sent a memo to every senior executive in the company. It became one of the most famous documents of the internet era. It was titled “The Internet Tidal Wave.” Everything stops, everything reorients, now. They licensed browser code, put a small team on it, and shipped Internet Explorer. Microsoft was in the browser business. The browser wars were on.

But a completely different company had already figured out the mass market, and it wasn’t doing any of this through browsers or tech-savvy. America Online was mailing CDs to people’s homes. Literally. You’d find one in a cereal box, or tucked into a magazine, or stuck to the back of an airline headrest. You put it in your computer, clicked a few things, and you were online. So easy.

At its peak, AOL had 35 million subscribers paying $21.95 a month. A third of all time on the platform was spent in chat rooms, where strangers talked to strangers around the clock about nothing and everything. Facebook was seven years away, but the instinct was already there. AOL had built the first mass-market social network and barely knew what to do with it.

This is where I thought about Coinbase. This is what crypto was for a long time. A walled garden, custodial, simple, slightly expensive, occasionally clunky, that brought millions of people in. You can argue about whether that was the right trade-off. AOL’s users mostly didn’t graduate to the open web either. But they came.

Then there was Yahoo. Two Stanford PhD students, Jerry Yang and David Filo, had started keeping a list of good websites for the same reason everyone needed one: The web was growing faster than anyone could keep up with. They called it Jerry’s Guide to the World Wide Web. Traffic exploded. They renamed it, cleaned it up, and went public in 1996. Yang and Filo became overnight heroes of the internet age.

Reading all now, McCullough’s argument is hard to shake.

The user experience was the only bottleneck, not the technology. The internet existed for decades before most people used it. What changed things was Netscape making the web clickable, AOL making it approachable, Google making information findable, and the iPhone making all of it portable. Every time someone simplified the experience, adoption exploded.

Crypto is where the internet was in 1995. You all said it, and I agree. The infrastructure and technology—amaze amaze amaze. You can move value across the world in seconds, earn yield without a bank, and own things that can’t be taken from you. But most people cannot do any of this without feeling like they are defusing a bomb.

Netscape closed it for the web. Crypto is waiting for that moment. Square auto-enabling Bitcoin payments for merchants might be it. Aave building a savings app that looks like Revolut might be it. Some twenty-two-year-old in a university basement right now, annoyed that this is still so hard, is probably building it.

The browser didn’t explain the internet. It just made it easy enough that nobody needed an explanation.

Part 2 is next Monday: The gold rush goes mad. Amazon builds e-commerce from a garage. eBay figures out how to make strangers trust each other. And then the whole thing inflates into the most spectacular financial bubble since the tulips.

—Thejaswini

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

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If you own Bitcoin in America and want to buy a house, you only have two options, and both of them are bad.

Option one: sell your Bitcoin, pay the capital gains tax on every dollar of appreciation, use the cash for a down payment, and lose your position entirely. Option two: keep your Bitcoin. But when you apply for a mortgage, the system treats you as if that wealth does not exist. Until recently, it officially did not.

Around 52 million Americans own crypto. 53% of millennials under 35 hold some form of it. But the median age of first-time homebuyers in America just hit 40, the oldest on record. So you’ve got a whole generation building wealth in crypto and then being asked to sell it just to qualify for a home loan.

But that’s finally changing…

Better Home & Finance and Coinabse launched a mortgage product last week where you pledge your Bitcoin or USDC as collateral for the down payment. You get a Fannie Mae-backed loan. You keep your crypto, you do not need to sell it, you do not trigger a capital gains event. And the most important part, even if Bitcoin’s price drops, you don’t lose your home.

That’s the key part. Here’s how it actually works.

Lessons From Every Crypto Lending Failure

In 2021, a wave of companies figured out how to offer something that sounded impossible – interest rates of 6 to 10% on your Bitcoin or Ethereum, when a bank savings account was paying close to zero. BlockFi, Celsius, and Voyager were the biggest names. The model was simple. You deposited your crypto with them, and they lent it out to institutional traders and hedge funds who would pay high rates to borrow it, and they passed some of that yield back to you. At their peak, Celsius had $12 billion in customer funds. BlockFi was valued at $3 billion. Voyager had $5.9 billion on the platform.

Then crypto prices crashed in 2022. The hedge funds and traders who had borrowed crypto to run their strategies started getting margin called. They could not repay. The collateral backing their loans lost value faster than anyone could liquidate it. Celsius froze all customer withdrawals in June 2022. BlockFi filed for bankruptcy in November with $3 billion in liabilities against barely $150 million in assets. Voyager went down right after. All obituaries were now saying the same thing: Crypto lending is too risky.

What they actually meant was that particular model of crypto lending is too risky. The problem was that the collateral and the loan were in the same asset class. When crypto prices fell, the collateral lost value at the same time as the borrowers’ losses mounted. Margin calls were fired, forcing people to sell. That selling pushed prices down, which triggered even more margin calls. The whole system was wired to accelerate its own collapse.

The Fannie Mae product is something very different. Here, your collateral is Bitcoin, your loan is a fixed-rate dollar mortgage, and your monthly payment is a dollar amount that does not change, regardless of what Bitcoin does. And the trigger for losing your collateral is not price. It is 60 consecutive days of missed payments. The same standard practice, applied to every conventional mortgage in America.

That means, if Bitcoin drops 70% right after you take the loan, your mortgage doesn’t change. The collateral simply stays with Coinbase, and your loan continues as usual. You keep making the same monthly payments, and that’s all that’s required. The price volatility of your collateral is entirely absorbed by you as the borrower. The lender is not exposed to it at all.

This became possible in June 2025, when the FHFA issued a directive ordering Fannie Mae and Freddie Mac to start accepting crypto as financial reserves without requiring conversion to dollars. That reversed a 2022 policy that had locked digital assets out of the mortgage underwriting system entirely.

There is also a tax angle to this, pleading crypto as collateral is not a taxable event. You have not sold the asset, so no capital gains liability is triggered. The only scenario where taxes enter the picture is if the collateral gets liquidated, and this product is specifically designed so that price drops never cause that to happen. So the only path to liquidation is for a borrower who stops paying for 60 days.

Of course, this isn’t free; the rate premium is 0.5-1.5% above a standard 30-year mortgage, depending on borrower profile. But for a homebuyer sitting on Bitcoin that has appreciated 300% and does not want to sell, paying a slightly higher rate to preserve the position and avoid a tax hit is the better deal.

The Real Risk

The mortgage product itself is pretty conservative, but the real question is what happens when it scales.

On October 10, 2025, crypto markets saw $19.16 billion in liquidations in a single day, one of the largest to date, wiping out 1.6 million trading accounts. At this point, we know how these cascades work. When leveraged positions cluster at similar liquidation prices, each forced sale pushes the market down enough to trigger the next position’s liquidation threshold, which triggers the next, and so on.

The Fannie Mae product doesn’t create that kind of cascade because there are no price-based liquidations. But as this scales, a different kind of risk comes in, which is, what will happen if a market crash hits at the same time people start defaulting on their loans?

Consider the scenario where a severe crypto crash coincides with an economic downturn. Bitcoin falls 60%, layoffs rise, and some borrowers lose income while also watching their portfolios fall, which leads them to start defaulting on their mortgage payments. At 60 days, liquidation begins, and Coinbase starts selling the bitcoin collateral into a market that is already distressed. If this happens at scale across thousands of mortgages simultaneously, you have introduced a new vector for crypto selling pressure that is correlated with economic stress. It’s not a doom loop, but definitely a feedback mechanism worth watching as the product scales.

Ledn’s example is useful here. It is a crypto lender that has been making Bitcoin-backed loans since 2018, and by most measures, it is one of the most conservative operators in the space. It has originated hundreds of millions in loans without a single loss on record.

In early 2026, it was in the middle of closing a $188 million bond deal, essentially packing a pool of its Bitcoin-backed loans into a security that institutional investors could buy, the same way banks bundle mortgages into mortgage-backed securities. Before the deal could close, Bitcoin dropped 27% in a short window. Because Ledn’s loans had price-based margin call triggers built in, the collateral backing roughly 25% of loans in that pool fell below the required threshold, and those positions had to be liquidated. The bond deal survived, but a quarter of it has to be unwound before it even reached investors.

Ledn’s product and the Fannie Mae mortgage are structurally different. But the episode shows that even well-run, conservative crypto lenders operating at a relatively modest scale had to face liquidations due to one price move.

Beyond The Mortgage

Fannie Mae’s product is only a few days old. There are no stress test results because there has been no stress. We will get those answers over time, most likely when it is no longer hypothetical. What we do know is that it is the first crypto-backed mortgage in America, and it will not be the last.

Companies like Figure Technologies have been offering Bitcoin-backed mortgages up to $20 million with 30-year terms since 2022, without Fannie Mae backing. SoFi, a U.S.-regulated bank, is expanding into crypto-collateralized products and launching a native stablecoin in 2026. The idea of “putting your bitcoin to work without selling it” is expanding and moving beyond just homeownership into auto loans, business loans, and margin facilities for investors. Anywhere that collateral currently has to be converted to cash before a bank will accept it represents a potential expansion of this model.

The mechanism is the same in every case. You hold an asset. The system accepts that as proof of your creditworthiness, and you borrow against it. The asset stays on your balance sheet. You do not pay capital gains tax because you never sold it.

For the past decade, one of the biggest inefficiencies in crypto has been that it sat outside the productive capital system. You could hold bitcoin, watch it appreciate, and then do exactly the one thing you could: sell it, pay taxes on it, and re-enter the dollar economy. There was no way to let it function the way a stock portfolio functions for a wealthy investor, as something you borrow against while keeping the underlying asset intact.

Regulators are moving in the same direction. When FHFA reversed its 2022 policy to accept crypto as a financial reserve, it was a change in how they classify crypto. When CTFC launched a pilot in December 2025, allowing Bitcoin and USDC as collateral in derivatives markets, that was another one. They are signaling that crypto belongs in the same bucket as other assets the financial system uses to function.

Today, US Treasuries are the world’s default collateral because they are liquid, predictable, and backed by something everyone has agreed to trust. That took decades. Bitcoin is earlier in that same process, with more institutional infrastructure already in place than Treasuries had at a comparable stage.

That’s it for today. See you next week.

Until then, stay curious!
Vaidik

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

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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.

We know this because Microsoft spent years saying it had nothing against Linux. Then it started showing up at government procurement meetings with security briefings about why open-source software was a risk to national infrastructure. Google told publishers for a decade that it was just organising information. Then it started funding research that found journalism paywalls were bad for democracy. What used to be “we built something different” becomes “what they’re building is dangerous.” And if you are good enough at it and you have the right relationships, you do not even have to win the technical argument. Just make sure the technical argument gets decided by people who trust you more than they trust your competitors.

Canton is doing that now. And the crypto industry, for the most part, is missing from the conversation.

In January, I wrote that Canton chose efficiency over freedom and that institutions chose Canton. The framing was that Canton and Ethereum were solving different problems for different audiences. That framing was right at the time. Since then, Canton has moved.

Read: Wall Street’s Stealth Chain - by Thejaswini M A

Canton’s founders have been making the case to buyers and regulators, both publicly and in closed-door meetings, that zero-knowledge proofs (ZKPs) pose unacceptable risks for mission-critical financial systems. I think it’s a regulatory positioning campaign, which runs alongside a fundraising effort that brought in major players like Goldman Sachs, Citadel, DRW, Circle, Paxos, and Polychain. JPM Coin went live on Canton in January. Visa joined as a super validator in March. On March 27, LayerZero became the first interoperability protocol operating directly on Canton, enabling institutions to route tokenised assets across more than 165 public blockchains. The $CC token trades at a $5 billion fully diluted valuation.

None of that is the story. I’m going to talk about how Canton is now trying to shape what technology banks are allowed to use. And so far, it is the only one having that conversation.

The Argument Against ZK

Canton’s case runs like this. ZKP bugs could go undetected because the underlying data stays private. A flaw that spreads silently, with no audit trail and no accountability, is kind of a dealbreaker.

They point to a real example. On April 16, 2025, Solana patched a zero-day vulnerability in its Confidential Transfers feature, which is powered by ZKPs. The bug could have allowed attackers to mint unlimited tokens. No one knows if it was ever exploited.

The person making this argument is Shaul Kfir, Canton’s co-founder and chief operations officer, who co-authored libsnark, a C++ library for creating zk-SNARK proofs. Is he dismissing a technology he does not understand? Highly unlikely.

His argument is that when a ZK proof fails, nobody sees it fail. The data stays private, the error stays hidden, and by the time anyone notices something is wrong, the damage has already spread. For a regulator whose job is to prove that a bank did not launder money, a system that says ‘trust the math’ is not a satisfying answer. They want to see the records.

And in Canton’s model, the only entities that can view those records in real time are the super validators, the same institutions whose keys, if compromised, are the single point of failure.

That argument does not have to be airtight to be effective. It just has to sound reasonable to someone who already does not fully trust crypto. To people who built their careers on paper trails and audit logs, the proof is invisible when it breaks hard. You do not need to win the technical debate, but you still need to make the other option feel risky.

Where It Breaks

ZKsync co-founder Alex Gluchowski answered publicly last week. Canton’s logic, he says, proves too much. If a technology can have bugs, and those bugs could be catastrophic, then we should never use it. By that reasoning,, we would have grounded every commercial flight in the 1970s and never built another plane. Fly-by-wire systems have bugs. Engine controllers have bugs. Autopilot software has failed in ways that have killed people. We did not stop flying. We built planes with multiple independent systems so that when one system fails, another catches it before the plane goes down.

Is Canton answering what happens when an operator’s keys get compromised? There is no backup system or second layer checking the work. The trusted operator is the only line of defense. If that fails, the damage spreads silently through the network with nothing watching. By Canton’s own standard, that is the architecture that should worry regulators.

The solution to fallible technology has never been to find an infallible technology. It has been to build systems that expect failure and survive it anyway. A nuclear reactor does not stay safe because the software never breaks. It stays safe because when something breaks, five other things have to break simultaneously before anything catastrophic happens. Same with a pacemaker. Same with a commercial aircraft. The engineering principle is redundancy and containment. Build multiple independent layers so that when one fails, another catches it. And design the system so that when something does go wrong, the damage stays contained instead of spreading to everything connected to it.

Gluchowski applies the same test to Canton’s own architecture. Canton’s privacy and integrity model relies on a single mechanism, which is trusted operators segregating data between participants. There is no cryptographic verification layer or independent check. If operator keys are compromised, the manipulated state propagates silently across opaque chains of UTXOs with nothing watching. By Canton’s own logic (a single point of failure with catastrophic consequences), this is the architecture that should concern regulators.

The ZK bugs, Solana zero-day, all of these are real issues. But the response to fallible components is not to replace them with a different single point of failure dressed in institutional branding. The response is multiple independent lines of defense, blast radius limited by design, and the kind of adversarial stress testing that comes from a decade of open scrutiny. The EVM exists today as the product of continuous adversarial testing by the world’s most sophisticated actors, with hundreds of billions at stake, over more than a decade. Every maturity concern Canton raises about ZKPs applies to DAML, with a fraction of the mitigation available.

None of this settles the debate. But it reframes the debate. One approach to institutional risk management is arguing, in venues where regulation gets written, that it should be the only permitted approach.

There is something Canton’s argument leaves out entirely. ZK technology does not sit at a fixed risk level. It gets more secure as more people are allowed to verify. The core mechanic is a ZK proof lets someone prove a statement is true without revealing the underlying data. The verifier checks the proof, not the data. The more independent verifiers checking the same proof system, the harder it becomes for any bug or manipulation to go undetected. Nethermind formally verified the correctness of ZKsync’s on-chain ZK verifier using EasyCrypt in 2025, completing the first formal proof of this kind in a live zero-knowledge system. That is the adversarial scrutiny of an open system producing a demonstrably stronger result over time.

Canton’s model works in the opposite direction. Trust concentrated in a small set of approved operators does not compound in the same way. A closed system with approved verifiers has a ceiling on how much scrutiny it can absorb. The question of who is allowed to verify is not a footnote to the security debate. An open ZK system gets harder to compromise as the verifier network grows. A permissioned trust model stays as strong — and as vulnerable — as its weakest operator. A 2024 systematic analysis of known attacks found that approximately 96% of documented circuit-layer bugs in ZK systems were due to under-constrained circuits, a category of vulnerability that open adversarial testing is specifically designed to catch and eliminate. The bugs Canton points to are real. The open ecosystem is the mechanism for finding and patching them. Keeping that ecosystem closed does not make the bugs disappear. It just means fewer people are looking.

Canton stopped staying in its lane

Going back to the January piece I wrote, it positioned Canton as a parallel system. Solving a different problem for a different audience. Not competing with Ethereum for the same ground.

The LayerZero integration changes that positioning. It enables traditional financial institutions in Canton to route tokenised securities, digital bonds, and equities across more than 165 public blockchains, while maintaining compliance and confidentiality requirements. Investors can now use stablecoins on external public chains to fund primary purchases of Canton-based tokenised real-world assets. Canton-native tokenised instruments can move into other ecosystems for secondary market trading.

LayerZero Labs CEO Bryan Pellegrino says that “Canton has already built the rails for traditional finance, processing more than $350 billion in daily US Treasury repo volume. LayerZero’s job is to make sure those assets are available in every global market, across blockchains.”

This is Canton reaching into crypto’s liquidity pools, not remaining separate from them. Which creates a tension. Canton’s founders are in closed-door regulatory conversations, arguing that zero-knowledge proofs are too dangerous for institutional finance. At the same time, Canton-based assets are now flowing through LayerZero into a public blockchain ecosystem where ZK proofs underpin significant infrastructure. Including the products being positioned as the institutional alternative to Canton.

What that looks like in practice? A bank holds tokenised US Treasuries on Canton. Through LayerZero, those Treasuries can now move to Ethereum or Arbitrum, where they can be posted as collateral in Aave, borrowed against on Ondo, or used as a base asset in DeFi lending protocols. The instrument stays institutional-grade and compliant on Canton’s rails. The liquidity it accesses is crypto-native. Ondo Finance is already doing a version of this with LayerZero. Its tokenized Treasury product USDY operates across four blockchains with $700 million in TVL, usable as collateral across DeFi. Canton now has a direct path into that same ecosystem. The bank gets the yield and the composability. DeFi gets the institutional collateral. And Canton gets to argue to regulators that ZK proofs are too dangerous, while its assets flow freely through chains that run on them.

Using crypto’s infrastructure while arguing to regulators that crypto’s foundational privacy technology is a systemic threat is a coherent strategy if the goal is regulatory capture. It is not incoherent. You can say out loud, because the ZK camp has not yet organised a response at the same level.

The ZK camp’s most visible contribution to the regulatory conversation, as of this week, is Gluchowski’s thread. It is a good thread. Canton has lawyers in those meetings. Goldman’s relationships. A decade of work building trust with regulators whose sign-off determines what a systemically important bank can run.

What Is At Stake For ETH

This is not abstract for anyone holding ETH or watching where institutional tokenisation lands.

If Canton wins the regulatory argument, which is if ZK proofs get categorised as too risky, too opaque, too novel for the frameworks that govern systemically important institutions, then Ethereum’s path to institutional settlement gets closed before it fully opens. The $100 trillion tokenisation opportunity that Larry Fink wrote about in his annual letter stays on permissioned rails. Ethereum settles DeFi. That is not nothing. But it is not the settlement layer for global finance.

If the ZK camp wins, which means if zkSync’s Prividium and emerging institutional-grade ZK infrastructure earn regulatory approval alongside or ahead of Canton’s model, then the calculus shifts majorly. The chain that secures DeFi starts securing institutional tokenisation. ETH’s position in the financial stack expands. The asset that seemed like it might be getting sidelined by the institutional layer ends up being the foundation of it.

Visa just joined Canton as a super validator. DTCC is moving toward production. Broadridge is already processing hundreds of billions in daily volume on Canton rails. Testing participants include Goldman Sachs, BNP Paribas, Tradeweb, and Citadel Securities. The institutional flywheel is moving and has been for long enough to have real momentum.

In January, I said that Canton is not trying to replace Ethereum. That is still true. The goal is narrower and more effective than replacement. The goal is to ensure Ethereum never gets the chance to compete in the same market. Not by building better. By certifying first. By getting to the regulator before the regulator gets to the technology and forming the frame through which everything that comes after gets evaluated.

Crypto has always competed on visible metrics, such as TVL, fees, users, transactions, and token price. Canton never competed on any of those. It is based on trust, which is invisible, accumulates slowly, and is almost impossible to replicate once someone else has it.

The funny thing is that ZK proofs were invented to solve exactly the problem Canton is exploiting. You should not have to trust anyone if the math can prove the thing without showing you the thing. Canton’s argument is that the math cannot be trusted either. So trust the institution instead. The one whose risks got named last wins.

—Thejaswini

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To clarify, let me explain it this way: a person is not someone who happens to be somewhere. Rather, they are shaped by the place they inhabit. Nishida was writing about consciousness and being. Sounds like a silly commonsense packaged in sophisticated words to some of you, but apologies, I am going to use this theory today.

Talking about Base here. The active addresses hit an 18-month low last month. Reflecting on it, I realised what Base built was a location, but what it never built was the conditions for something to grow and become.

When Coinbase launched Base in 2023, the crypto-native believed, and “belief” is not very common for us. Here was the answer to the oldest problem in Ethereum. All this infrastructure, but no users. Coinbase had its 100 million users and unmatched distribution, which had a unique advantage. When the door opened, the users were already waiting.

For a while, the belief seemed justified. Base grew faster than any Layer 2 (L2) before it. Total Value Locked (TVL) hit $5.6 billion in October 2025. Fee revenue was unmatched across the entire L2 layer. Given that, the token confirmation in September 2025 felt like a moment when a successful experiment became permanent. Yes, a location becoming basho. (Now you know what it means.)

Then, the users left.

To get to the numbers. Active addresses on Base are back to where they were in July. The token confirmation delivers exactly what airdrop farmers needed it to. A final paycheck and nothing more.

Base’s 2025 bet on the creator economy didn’t help either. The mechanism was Zora, a protocol that tokenises content by default. By the end of the year, 6.52 million creator and content tokens had been issued through Zora on Base. Of those, only 17,800 showed sustained activity throughout the year, representing 0.3%. The other 99.7% were gone before anyone came back for a second look.

Base peaked at 1.72 million daily active addresses in June 2025. By March 2026, it is at 458000- a 73% drop from the peak. After Armstrong’s September 2025 statement that Base was exploring a token, Base contracted 54% in active addresses over the following six months, meaning the speculation had left.

The sociologist Ray Oldenburg studied what makes people return to a place without being paid to. He called them third places, which include bars, barbershops, and town squares. Not productive spaces, but places that gave people a reason to come back that had nothing to do with an incentive. The whole point was that the wanting to return cannot be manufactured. It has to emerge from what the place makes possible over time. Crypto designed its places for extraction and then wondered why nobody stayed.

This is what a location without a basho looks like: people pass through, extract what they need, and leave because there’s no cost to leaving. No identity was formed here. Or no capability built that could not be rebuilt elsewhere in three weeks. Nothing that makes departure feel like a loss rather than a simple redirect. Is there a relationship that exists only on this chain? We did not build stuff that way, did we?

You cannot build basho with financial incentives. You sure can use incentives to get people through the door. Cannot use them to make people want to stay. The wanting to stay has to come from what the place makes possible over time. Nishida called it the logic of place, which is the way a field of relations shapes what emerges within it. Crypto designed the field for extraction and was surprised when extraction was all that emerged.

Brian Armstrong publicly said that the Base App is now focused on being the self-custodial, trading-focused version of Coinbase.

The social and creator vision, the one that was supposed to make Base sticky, was supposed to give users an identity on the chain that was worth protecting, but it is gone. It is a rational call given what the data showed. It is also an admission. The condition never formed. Base has a location, and it is now optimising for people passing through, because that is what it has.

One chain, one category

Base is the most visible example of a pattern seen across the entire L2 landscape.

Usage across smaller L2s has declined 61% since June 2025. Most chains outside the top three are now zombie chains. Active enough not to be shut down, not active enough to matter. The L2-to-L1 daily active users ratio dropped from 15x in mid-2024 to 10 to 11x today. Most new L2s saw usage collapse after the incentive cycles ended. The whole layer is cooling, not just Base.

Read: Vitalik Didn’t Blink 👾

The rollup-centric roadmap used to be a theory about how adoption would work. Lower the cost of participation, users arrive, ecosystems form, and everything compounds. The Ethereum Foundation published a 38-page mandate this year about what Ethereum wants to be. The biggest L2 just hit an activity low and left the OP Stack. The second biggest is flat.

Read: What Ethereum Wants to Be

Lowering the cost of arrival is not the same as creating the condition for something to become. They solved the access problem and assumed belonging would follow. It did not follow. It cannot follow automatically because belonging is not a feature you ship.

Farcaster is the closest crypto has come to building it. Because a specific group of people built a specific culture on top of it. Builders sharing work, arguing about Ethereum, forming opinions about each other over months. That takes time, and it cannot be replicated by a competitor offering higher rewards. Friend.tech tried the incentive version of the same idea. It peaked in a week and died in a month. Same mechanic, no culture. The difference was not the product. It was a question of whether anyone stuck around long enough for something to form.

What holds?

The chains holding users through the cooling are not holding through better incentives.

Arbitrum peaked at 740,000 daily active addresses in June 2024 and is now at 157,000, also down 79% from peak. So both chains are falling.

But the mechanism is different. Base's users show up to trade. When trading slows, they leave. Arbitrum's users are there regardless of fee levels. The correlation between its user count and fee revenue is near zero. Base attracted tourists. Arbitrum, somehow, kept locals.

Hyperliquid holds because the trading experience is different, and the community around it has developed an identity that exists nowhere else. Token incentives hardly matter there. Being there has become part of how they operate and what they are. Something is shaping the users, and the users are shaping it back.

The crypto industry is still optimising for arrival. The question of condition is only asked after the numbers go wrong, never before the chain is designed.

I would argue that Base has the distribution to solve this better than any chain that has ever existed.

Right now, it is a trading app. That is a legitimate thing to build. It is also the thing that 40 other products already are, and trading apps do not produce basho. They produce sessions. Users come when they have a trade to make and leave when it is done. The condition for becoming requires something more continuous than that. It requires the kind of relationship that builds between visits, making the next visit feel like returning rather than arriving.

Armstrong’s pivot is a lot about what Base learned from the data. The social layer, the creator economy, the on-chain identity that was supposed to make Base a place people inhabited rather than used - those required patience that the metrics did not reward. Quarterly active addresses and TVL are measures of location. And so the condition was never prioritised.

The Ethereum ecosystem needs Base to be more than a trading venue. The whole L2 thesis rests on the assumption that chains can become the kind of infrastructure people build lives around. If the best-distributed L2 in the history of crypto settles for being a fast Coinbase, the thesis answers itself.

Nishida believed that the deepest basho is one where the distinction between self and place begins to dissolve. Where you cannot fully separate who you are from where you have been shaped. That sounds abstract. But then think about what it would mean for a chain. A user who cannot imagine their financial life without a specific chain. A developer whose entire toolkit is native to one ecosystem. The identity hardly exists somewhere else.

That has never been built on an L2, to my knowledge. It might not be buildable on an incentive schedule at all.

I know I stretched “basho” a bit too much. The point is easy. You can have 100 million potential users and still end up with an empty room if there is nothing worth staying for. Base knows this now.

It hasn’t figured itself out yet.

—Thejaswini

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And lying always has consequences, now or later.

A trader made nearly $1 million since 2024 from dozens of well-timed Polymarket bets that correctly predicted US and Israeli military actions against Iran. The bettor won a staggering 93% of their five-figure wagers about Iran, even though the events they predicted were unannounced military operations. The bets were placed hours before strikes in October 2024, hours before US airstrikes on Iranian nuclear facilities in June 2025, and hours before the joint US-Israeli surprise attack in February that started the current war.

Bubblemaps traced the pattern. The accounts are still anonymous. Some still had open positions when the analysis was published. And it is not even the worst case in the dataset. Israeli prosecutors filed indictments against an IDF reservist and a civilian for allegedly using classified military intelligence to bet on Polymarket. The pair are said to have wagered on the timing of Israel’s strike on Iran during the June 2025 Twelve-Day War. The officer allegedly sent a follow-up message on WhatsApp right before the attack, stating, “It’s starting.”

The Maduro trade I wrote about in January was a single account, a single event, a single day. This is a pattern across two years, two countries, and multiple military operations. The argument that prediction markets are a monetisation layer for information asymmetry, is no longer theoretical. Israel arrested people over it.

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The government that uses it hates it. The US Army published a piece recommending that intelligence analysts use Polymarket and Kalshi data to detect national security threats. The Federal Reserve published a paper saying Kalshi has called every single FOMC rate decision correctly on the day before the meeting, every time, since 2022.

The very product that may be leaking classified military intelligence is also being recommended by the military as a threat detection tool. The platforms that states are prosecuting as illegal gambling are being studied by the Fed as superior forecasting tools.

On one Monday, approximately 15 minutes before President Trump posted about “productive” talks with Iran, stocks and oil futures on CME saw an unusual burst of volume that predicted precisely what happened once Trump made his announcement.

The government is both the victim and the customer. It is the source of the leaks and the consumer of the signal.

Not all of this is the same thing, and that distinction matters. A contract on whether the US strikes Iran is a legitimate financial instrument. Oil companies, airlines, and governments all have real economic exposure to that outcome. The Fed’s FOMC contracts are the same logic. These markets produce useful signals because real money rides on the underlying event for people who are not just gambling. Sports is different. Nobody’s balance sheet depends on who wins the Super Bowl. That market exists because retail volume funds the platform, and the platform calls all of it information infrastructure. The insider trading problem lives specifically in the first category. The casino lobby problem lives in the second. Conflating them is how everyone avoids fixing either.

The people fighting them

There are more than 20 civil lawsuits over Kalshi’s legal status. Arizona filed criminal charges as the first criminal prosecution ever brought against a CFTC-registered prediction market operator, 20 misdemeanor counts, including illegal sports betting and election wagering. Nevada won a temporary restraining order forcing Kalshi to remove sports, entertainment, and election contracts from the state. Washington sued Friday. And the legal map is expanding weekly.

The public framing is consumer protection. Utah’s governor said prediction markets are “destroying families.” Washington’s attorney general said Kalshi targets college students and promotes addiction.

But look at who is actually doing the lobbying. A casino industry group called Gambling Is Not Investing hired former Trump budget director Mick Mulvaney to lobby against prediction markets. DraftKings stock is down 50%. FanDuel’s parent is on its worst performance streak in years. Kalshi is being downloaded four times as much as either.

This isn’t about consumer protection, it’s a market share fight. The Nevada Resort Association told courts that prediction markets are an existential threat to the gaming industry. DraftKings and FanDuel spent years building sports betting operations in states that carved out regulated gambling markets. Kalshi walked in under federal CFTC jurisdiction, bypassed every state licensing requirement, and started eating their customers. The lawsuits are competitive threats wearing consumer protection as a costume.

Kalshi and Polymarket companies spent $14 million on lobbying in 2025. The casino industry has Mick Mulvaney. The prediction market industry has the White House.

Meanwhile, Kalshi was also fighting a different kind of war over what a word means.

Kalshi published a seven-page document trying to define what counts as a word. The rules include gems like “plurals count if the given word is singular, but singulars don’t count if the given word is plural” and mispronunciations count if “minor” but not if “severe”. Nobody defines who decides. Linguists called the rules arbitrary and traders called outcomes that resolve on technicalities “rulescucks.”

Both problems come down to the same thing. Someone has to decide what counts. And when large sums are involved, that decision is never neutral. Settlement requires someone to decide what counts. And when large sums are involved, that decision is always political.

Who gets to say what is gambling

The broader litigation and enforcement fight now spans roughly 20 federal and state actions across at least 14 states. Courts have split sharply on the core federal preemption question: a Tennessee federal court granted Kalshi a preliminary injunction in February 2026, while a Massachusetts state court issued an injunction the same month calling Kalshi’s preemption theory “overly broad.”

Kalshi’s argument is that the CFTC has exclusive federal jurisdiction over derivatives and event contracts. States cannot regulate a federally regulated exchange. CFTC chairman Michael Selig called Arizona’s criminal prosecution “entirely inappropriate” and a jurisdictional dispute. The federal government filed an amicus brief in the Ninth Circuit in February arguing that federal law pre-empts state gambling enforcement.

Now the states’ argument is that gambling regulation has been a state power since before the Constitution. A federal agency labeling something a “futures contract” does not transform it into something other than sports betting. Nevada has regulated gambling for decades, and Arizona has a constitutional ban on election wagering. No federal agency can override those laws through administrative classification.

Legal experts believe this will eventually reach the Supreme Court. Multiple attorneys tracking the cases have called it inevitable. All I see here is the federal government asserting that the administrative state can reclassify gambling as derivatives trading and thereby strip states of a regulatory power they have held for generations. The prediction market company is the proxy in a constitutional fight about federalism.

The $35 million bet that none of this matters

The CEOs of Kalshi and Polymarket are arch-rivals. They are also investing in the same $35 million VC fund focused on prediction markets. 5c(c) Capital, named after the clause in the Commodity Exchange Act that governs event contracts, is being raised by two former Kalshi employees and backed by Marc Andreessen through his Moneta Luna fund, Ribbit Capital’s Micky Malka, and Multicoin Capital’s Kyle Samani.

The fund is investing in market makers, index designers, data infrastructure, and compliance tools. Which means the capital is going into the layer that survives a court ruling, a state ban, or a change in administration.

Kalshi reached a valuation of $22 billion in the first quarter of 2026. Polymarket hit $20 billion after securing investment from the Intercontinental Exchange. They have started looking like financial infrastructure companies that have decided, as crypto did in 2015 and 2018 and 2022, that building through the legal uncertainty is better than waiting for it to resolve.

If you are asking me what I see for the future, Prediction markets will not be killed. They will be restricted in some states, grudgingly permitted in others, eventually embedded in financial infrastructure. The insider trading problem will not be solved by self-regulation. It will be solved when someone actually goes to prison in the US.

They are settlement systems that happen to produce accurate data as a byproduct, when insiders are not front-running the outcome. Still not truth machines. The data is useful, but the structure is broken.

The rulebook for what prediction markets are allowed to be is still being written. In courtrooms in Nevada and Arizona and Washington. In a Senate that has six competing bills and no consensus. In a White House whose son advises one of the companies being sued.

I checked and Polymarket doesn’t have a market for that. Probably because nobody wants to know the answer?

—Thejaswini

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

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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.

When the first commercial websites launched in the 1990s, the obvious model was to charge users directly — a few cents per article, a small fee per search, and pay for what you use. However, there was a major issue: no system to collect such small amounts. Credit card processors charged minimum fees that made sub-cent transactions uneconomical for merchants to accept. Bank transfers were built for amounts large enough to justify the overhead. The smallest practical unit of internet payment was roughly a dollar, which meant anything priced below that could not be sold at all. So the websites did the only thing that worked: they made the content free and sold ads against it instead. One workaround, scaled for thirty years, is how global ad spending reached $1 trillion in 2025.

The engineers who designed the web knew about this problem. They even reserved an HTTP status code for it, 402 “Payment Required”, but it sat unused for nearly three decades because the infrastructure to honour it did not exist. We covered how x402 finally activated that code and made pay-per-request a live standard. What that piece did not address is what this means for how the internet gets paid for, who captures the value when it scales, and why AI agents are the reason this moment is different from every previous micropayment attempt.

AI agents have started doing the work that humans used to do on the web – searching, comparing, pulling data, calling APIs, at machine speed and machine volume, for amounts that are often fractions of a cent each. Circle’s Nanopayments now make those fractions of a cent economically viable to settle. This innovation fundamentally alters the way the internet gets paid for.

The $0.000001 Payment

Micropayments have been tried before. Flattr, launched in 2010, allows users to voluntarily tip content creators tiny amounts. BitPass tried something similar in 2003. Both failed, so did a dozen others in between. The standard explanation is that transaction fees were too high. But that explanation stops short of the real problem.

The reason every previous micropayment system failed is that humans are bad at making thousands of small decisions about money. Even when the fee is $0.001, there is cognitive overhead to every “should I pay for this?” that kills the behaviour. Nick Szabo described this in 1999: the mental cost of evaluating whether a piece of goods is worth a fraction of a cent often exceeds the fraction of a cent itself. You can engineer the fee to near zero and still lose the customer, because the act of choosing is what breaks the behaviour. We explored this in depth here, and the follow-on piece on payments in the agentic economy covers where this leads next.

AI agents do not have this problem. An agent executing a research task pays for an API call the same way it calls a function. Payment is part of the execution, and not a separate decision. There is no evaluation of whether the cost is worth it. The demand side of micropayments has fundamentally changed, and it changes what is possible on the supply side too.

Circle built Nanopayments to solve the supply side. The core problem has always been that every blockchain transaction costs gas. On even the cheapest chains, the gas fees run at least a fraction of a cent per transaction, which means paying for something worth $0.000001 would cost many times more in gas than the payment itself. Sub-cent on-chain payments were just expensive to process; they were practically impossible because the overhead always exceeded the value being transferred.

Circle’s solution is batching. A buyer deposits USDC once into a Gateway wallet smart contract, a single on-chain transaction. After that, every individual payment is an ERC-3009 TransferWithAuthorisation, a cryptographic signature created locally on the buyer’s device with no gas cost at all. That signature basically says, “move this specific amount to this address, and this authorisation expires at this timestamp. The buyer attaches this signature to an HTTP request. The seller verifies the signature and serves the resource immediately, before anything settles on-chain. Gateway collects thousands of these signatures over time, computes the net balance changes across all participants, and submits a single on-chain transaction that applies to all of them at once. By batching, the gas fee is split across thousands of transactions, reducing the minimum payment from $0.01 per transfer to $0.000001.

There is also a fine detail in how the batching runs. Before a batch goes on-chain, the entire settlement computation runs inside an AWS Nitro; it’s basically a clearinghouse built by Circle for machine payments, where the operator cannot unilaterally tamper with the settlement. The settlement is verified in tamper-proof hardware before it touches the blockchain. That means two AI agents with no prior relationship and no trust in Circle can transact through the gateway with mathematical assurance that no one in the middle falsified the outcome. Instead of relying on the operator, the system verifies the settlement before it goes through.

What happens if the seller or the API doesn’t serve your agent? Well, every ERC-3009 signature makes the payment void if the API doesn’t respond within 500 milliseconds. Neither our banks nor our cards can replicate this function. There’s no need for escrow or a dispute mechanism. The conditional payment is built into the cryptographic signature itself.

How does Circle make money from this?

Everyone covering the payments space often frames the revenue question around transaction fees. But that’s the wrong lens to look from.

When you operate a clearinghouse that processes machine-to-machine payments across the internet, the fees are the least interesting part of the business. The interesting part is the behavioural data. Every x402 call that flows through Circle’s gateway produces a record: which agent bought what, from which service, at what price, how frequently, and at what time of the day. At scale, that is the most granular map of the digital economic activity ever assembled. Human transactions on card networks are noisy, full of personal spending, cash, and offline behaviour. Machine transactions tell you exactly where the economic value in the agent economy is flowing.

This is what DTCC (Depository Trust and Clearing Corporation) did for equities. DTCC clears roughly $2.4 quadrillion in securities annually, and its real moat is not the clearing fee; it is being the single truth for who owns what after every equity trade in America. The data is worth more than almost any fees.

Just to give an idea, what the data reveals is the pricing fiction at the heart of the SaaS industry. Right now, a company selling a data API for $10,000 a month has a straightforward business: fifty enterprise clients, predictable churn, $6M ARR, valued at eight times that. What the subscription obscures is the actual usage. Some of those fifty clients are querying the API thousands of times a day. Some are querying it twice a week. The contract does not distinguish between them, so the price does not either.

When agents start paying per call, that ambiguity collapses. Clients who are underusing the product will not pay $10,000 a month for something their agent uses for the equivalent of 80 hours a year. They will pay the actual usage price, which might be $200. CAC approaches zero because agents discover and pay without any sales process, which sounds good until you see the other side, where there is no stickiness either. Every API becomes a spot market. The business that survives the transition will be the one whose actual per-unit value justifies the per-unit cost, and a lot of current SaaS pricing will not clear that bar once usage becomes visible.

The same selection is running beyond software, in any part of the economy where agents need to interact with vendors. Andrej Karapathy framed the agent economy in three zones: A fully digital world, where agents operate today with almost no friction, the physically autonomous world, still years from meaningful agent autonomy, and the interface between them, where agents need to commission data collection, source physical goods, hire contractors, and verify real-world states. That interface is largely unbuilt, and it is where most of the economic growth will come from.

At that interface, vendors who expose APIs and accept programmatic payments get inbound from every agent-driven company in the world automatically. Vendors who require phone calls, manual quotes, and net-30 invoices become unreachable to agents entirely, because agents cannot interact with a workflow that requires human intervention. Any supplier that builds an API-accessible, x402-payable service is making itself available to a rapidly growing share of commercial activity. Any supplier that does not is becoming invisible to it, the same way businesses without a website became invisible through the 2000s, just faster.

When the Real Value Accrues

Every company building payment infrastructure for the agent economy – Circle, Coinbase through x402, Stripe, through its $1.1 billion acquisition of Bridge, or Skyfire, is positioning for the same underlying prize. The fee revenue is real, but it’s not the business. The data and trust position of operating the settlement layer creates the business.

Circle generated $2.78 billion in 2025, almost entirely from interest on the dollar reserves backing USDC. That is a good business while rates are elevated and USDC supply is growing. But it is not a durable moat. The genuinely durable position is the one that Skyfire’s CEO, Craig DeWitt, named directly: attestation.

In a world where agents pay other agents to perform tasks, the most valuable service is proving that the work happened. A research agent pays $0.50 to a data agent for a market analysis. The payment clears in milliseconds. But did the data agent actually produce valid research, or did it hallucinate a response and pocket the fee? The agent that commissioned the work has no way to verify this without a third party that can attest to the quality and authenticity of what was delivered. That attestation service, a cryptographic proof of work completion, is signed by a trusted verifier and is paid for by the buyer as a part of the transaction, either bundled into the task price or charged as small verification fees. In many cases, it’s worth more than the payment itself, because it determines whether the payment should exist at all. It effectively becomes the court of record for the machine economy. And whoever builds that layer first and gets it embedded into the standard payment flow ends up owning something close to a monopoly on the trust infrastructure of autonomous commerce.

The scale of what is at stake becomes clear when you look at where today’s internet revenue actually lives. Google’s search advertising generated $237 billion in 2024. That entire business runs on humans doing the discovering and seeing the ads. By 2025, automated traffic already accounted for 51% of total web activity, and AI-driven traffic to retail sites grew 4,700% year over year. As more of that activity shifts to agents calling APIs directly and paying per call through x402, the ad impression simply does not occur.

The stablecoin network that would settle those transactions already processed $33 trillion in volume in 2025, up 72% year over year. Most of that is still institutional and financial flows. The commercial machine-to-machine layer has barely started. When it does scale, every service that adds an x402 endpoint makes the network more valuable for every agent, which draws more agents, which draws more services. That is the same loop that made card networks impossible to displace after they hit critical mass.

The infrastructure is here. x402 is live and being integrated by major players like Cloudflare, AWS, Anthropic, and Coinbase. The next stop is determining which clearinghouse and attestation layer will dominate this new landscape.

That’s it for today. See you next week.

Until then, stay curious!
Vaidik

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Bitcoin is down 44% from its October high. ETH is sitting at ~ $2000, less than half of its value from seven months ago. The altcoin market cap has shed $470 billion since its peak. The Fear and Greed Index is at 11. Not just 11 on a bad week, but 11 out of 100. This is when people have stopped arguing about the bottom and started selling whatever is left.

And into this, on March 17, the SEC and CFTC published a document that finally tells you what your tokens are. After ten years of lawsuits, hundreds of enforcement actions, and billions in legal fees. Companies that packed up and moved to Singapore rather than play a guessing game with Gary Gensler. And the answer arrives the week that ETH broke below $1,900.

But here’s the thing, while the token economy bleeds, everything beneath it is thriving. Stablecoins crossed $316 billion in circulation, and real-world assets (RWAs) are at $26.5 billion on-chain and growing. So much so that Morgan Stanley is building a crypto trust bank. Meta gave up on the metaverse, but is putting stablecoins into WhatsApp. Stripe is processing $400 billion in stablecoin volume. Nasdaq is building a tokenised equity venue. Crypto is becoming the backbone of global finance, most of the time without a token attached.

Crypto is no longer just a speculative asset class. The regulatory clarity that arrived on March 17 was made for the first version of crypto. But it arrived during the second one.

That does not make it meaningless.

SEC Chairman, Paul Atkins once said, “We’re not the Securities and everything commission anymore.”That sentence may have come a bit late?

For the first time, U.S. regulators have a shared vocabulary for what crypto is. Five categories, and every token fits into one of them. I’m going to give you the definitions, read as if you have never heard this before.

Digital commodities are the big one. A digital commodity is a crypto asset whose value comes from the programmatic operation of a functional crypto system combined with supply and demand dynamics. Its value does not depend on the managerial efforts of a central issuer. If the network is genuinely decentralised and operational without any single company holding it up, the asset is a commodity. The CFTC jurisdiction, not the SEC.

Sixteen major tokens are officially named as digital commodities including Bitcoin, Ethereum, Solana, XRP, Cardano, Avalanche, Polkadot, Chainlink, Dogecoin, and Shiba Inu. DOGE and SHIB qualify because there is no promoter whose efforts drive their value. There are no promises, roadmap, or team whose continued work is essential to the token’s worth. That is why they are commodities and not securities. The test is whether someone is promising you returns based on their work.

Digital securities are tokenised stocks, bonds, and Treasuries. Basically, assets that were securities before they got put on a blockchain and remain securities after. The SEC keeps these. Full stop.

Digital collectibles are NFTs tied to a specific item or experience. Digital tools are assets you use to access software or services, with no expectation of a return on investment. Stablecoins have their own category under the GENIUS Act framework.

Staking, mining, and airdrops are cleared. The ruling explicitly states that receiving mining rewards, participating in on-chain staking, or receiving airdrops of digital commodities are not considered securities transactions. This removes one of the biggest legal risks hanging over proof-of-stake networks since the Gensler era. Wrapping a non-security token is also cleared.

The 16 named tokens are all base-layer infrastructure with years of decentralisation behind them. DeFi protocol tokens — JUP, POL, METEOR, and most of what launched in the last two years — are not named and do not obviously qualify. A functional crypto system with no central party overseeing participation is a high bar. Most actively developed protocols don’t clear it. The grey zone the interpretation was supposed to resolve is still grey for most of what people actually hold.

Value must come from the programmatic operation of a functional system, not from someone’s promises. That single test separates a decade of ambiguity into something a compliance officer can really work with.

There’s A Catch

The release does not constitute formal rulemaking under the Administrative Procedure Act and does not have the binding force of a statute or duly promulgated regulation.

You want to read that sentence again. The 68 pages that we have been waiting for is an interpretive release. Not a law or even a regulation. It’s just an agency statement of position that the current SEC and CFTC chairs issued and they can rescind it anytime.

This interpretation is a formal agency action binding on the SEC and CFTC, though absent legislation it could be modified by the agencies under a future administration. The document itself reserves the right for the agencies to refine or expand their views. A future SEC chair with different politics can reverse it without going to Congress. The next administration does not even need a new law. Just needs new leadership.

Atkins knows this. He said it on the day of the release, calling for Congressional action to provide more durable clarity. He framed the interpretation as a bridge measure pending Congressional action on comprehensive market structure legislation. That legislation is the CLARITY Act. And the CLARITY Act is in the Senate.

The CLARITY Act

The House passed the CLARITY Act in July 2025 by a vote of 294. Bipartisan is a good margin that suggests a real consensus.

Then it hit the Senate and stalled.

The issue that stopped it was stablecoin yield. Banks argued that allowing crypto platforms to pay interest on stablecoin balances would trigger deposit flight. People pull money out of savings accounts and park it in USDC for a higher return. The banking lobby went to work. The Senate Banking Committee pulled the scheduled markup in January 2026. The bill went nowhere for two months.

Read: The CLARITY Paradox ⚖️ - by Prathik Desai - Token Dispatch

On March 20, Senators Thom Tillis and Angela Alsobrooks confirmed an agreement in principle on stablecoin rewards, backed by the White House. The deal is that, passive yield on stablecoins is banned. Activity-based rewards tied to payments and platform use remain permitted. Both sides are unhappy, which is usually how a compromise works.

But the yield deal is one of five things that need to happen before the CLARITY Act becomes law. The four remaining legislative steps run against the tightest calendar of the year.

• A Senate Banking Committee markup; and a full Senate vote (requiring 60 votes)

• Reconciliation with the Agriculture Committee

• Reconciliation with the House version

• A presidential signature

The Banking Committee markup is targeted for the second half of April, after Easter recess. Senator Bernie Moreno has warned that if the bill does not reach the Senate floor by May, digital asset legislation may not move forward for years.

There is also the Iran war eating up Senate floor time. There is the voter-ID bill that Trump said he wants passed first. There are still unresolved DeFi provisions, with Senate Democrats citing illicit finance concerns. There are unresolved ethics provisions, specifically whether senior government officials should be barred from personally profiting from crypto assets, a question with obvious political sensitivity given the current administration’s crypto holdings. And Senate Republicans are now discussing attaching community bank deregulation to the bill as a political trade, which introduces a whole new set of negotiations.

The House Financial Services Committee recently held a hearing on “Tokenisation and the Future of Securities: Modernising Our Capital Markets.” The witness list is Kenneth Bentsen of SIFMA, Summer Mersinger of the Blockchain Association, Christian Sabella of the DTCC, and John Zecca of Nasdaq. Nasdaq and NYSE are both building tokenised equity venues. The DTCC controls current settlement. If the DTCC validates blockchain efficiency, the argument is effectively over.

So, the infrastructure is being built on top of a rulebook that might not exist in two years. That is the tension the industry is living in right now. Companies are making billion-dollar decisions to build custody systems, tokenization platforms, and staking infrastructure, all based on an interpretive release that carries persuasive authority but not statutory force.

What is Permanent, And What Isn’t

For the reader with a position in any of the 16 named tokens, your ETH, your SOL, your XRP. These are formally recognised digital commodities under U.S. law today because two agency heads said so. That classification remains in place as long as those agency heads, or their successors, agree.

If the CLARITY Act passes, it becomes law. No future chair can undo it without Congress. The named assets become permanently defined, and the taxonomy becomes binding.

If it doesn’t pass by May, the taxonomy rests on the opinion of a single administration. The 16 named assets are safe for now, but everything is not named. Most DeFi, most new tokens, anything permissionless with no identifiable issuer is still in a grey zone that the interpretation explicitly didn’t address.

The most awaited sentence is written in pencil.

Someone needs to pick up a pen to make it permanent. It all depends on what happens in the Senate in the next six weeks. Could the rules last long enough for all of it to matter?

—Thejaswini

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

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On March 13, the EF published a 38-page document called the EF Mandate. Constitution + manifesto + guide. It opens with “Dearest Friends” and closes with “with the greatest love in the world.” The document was stored on-chain and has illustrated pages. One page even reads: “May the Foundation fall on its own sword if it fails to uphold its solemn promise to Ethereum.”

Ethereum must function independently, even if the EF ceases to exist tomorrow. The core idea of the document is that the EF’s success is measured by how unnecessary it becomes.

That is possibly the most principled thing anyone in this space has committed to paper. Beautifully said, but also an impressive way to say we are already on our way out.

I also perceive this as the sound of an institution that has been losing the argument for a year, writing down its values before anyone else can define them for it?

For context, this document dropped two weeks after Tomasz Stańczak left. Stańczak was the co-executive director who spent 2025 pushing the EF toward pragmatism, builder engagement, and paying attention to the real world. Vitalik praised him on the way out. Yet, the foundation just declared it was going back to cypherpunk fundamentals.

Why are they saying this now?

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What The Mandate Says

The EF mandate is structured around CROPS, an acronym for censorship resistance, open source, privacy, and security. The document is explicitly saying that CROPS override everything, permanently, and proposals that trade any of these properties for convenience face a harder bar. Non-negotiable.

The EF sees its role as stewardship, and not leadership.

What the EF is not about:

• Building products

• Doing the BD work that attracts institutions or retains builders

• Chasing adoption metrics

Instead, they want to be doing:

• Protocol hardening

• Privacy research

• Security

The foundational infrastructure that a neutral settlement layer needs to remain neutral over decades. Fair?

Now let’s talk about the walkaway test. Ethereum must work even if the EF disappeared tomorrow. If any system requires the foundation’s presence to function, it fails the test. The whole document is a case for why Ethereum should be able to live without the people writing the document. Call it a coherent philosophy.

What Is The Problem?

This is what Yuga Cohler, a senior engineer at Coinbase, says: “Just as Netscape wasted time on a rewrite from version 4 to 6 at a time when Microsoft was absolutely killing them, the EF insists on focusing on cypherpunk values at a pivotal time when the institutions are finally coming on-chain - often to other networks. An EF determined to win would focus on how to make Ethereum the best chain for finance.”

Netscape had the internet browser market almost entirely to itself in the mid-90s. Then Microsoft bundled Internet Explorer with Windows for free. Netscape’s response was to scrap their entire codebase and start over. By the time they shipped the new version, it was too late. Microsoft had won. The rewrite took so long that the window closed. Netscape went from 90% market share to irrelevant in three years. This is where you SIGH.

They were focused inward at exactly the moment the competitive landscape shifted. Is EF doing the same?

The mandate does little to address practical concerns about how the ecosystem serves real users.

Feist’s departure is itself a data point. He co-created Danksharding, one of the most important technical contributions to Ethereum’s scaling roadmap. He left for Tempo which is the Stripe and Paradigm payments chain. Which i would like to look at like something that moved faster and served several financial use cases. He voted with his feet.

So did the builders who moved to Solana and the institutions that went multichain. Base left the OP Stack. But let’s unleash a philosophy paper.

Read: The Best Thing Optimism Ever Did - by Thejaswini M A

Since the bottom of the previous crypto bear market, Bitcoin has rallied over 348% while Ethereum has gained over 130%. But look at the network itself. Ethereum commands about 58% of all DeFi TVL. This has been consistent throughout the last few years.

Ethereum maintained roughly 55% of total stablecoin supply, adding $50 billion in new issuance in 2025 alone, driven primarily by institutional flows, tokenisation, and RWA infrastructure. Tokenised real-world assets, the thing every bank wants to do on-chain are 56.8% on Ethereum, which holds nearly $10 billion of the RWA market. When institutions want to park something serious, they still come to Ethereum.

But there are other competitors who are also not doing badly either.

Solana? Doing fairly well for a bear market. Not the same conversation yet.

Hyperliquid processed nearly a billion dollars in oil trading over a single weekend during the Iran conflict. Institutions are going multichain as a default, not even an exception anymore. BlackRock launched a staked ETH ETF but also has positions on other chains. The world is not waiting for Ethereum to decide what it wants to be.

The ecosystem feels like it is losing the narrative.

There is no section on how to make Ethereum more attractive to builders who currently prefer Solana. No mention of the competitive pressure from chains with better UX and more aggressive roadmaps. The document is not addressed to the market, but to the EF itself.

The Case For Disappearing

Bitcoin has no foundation, yet it is the most valuable crypto asset in the world. The less a base layer is controlled by any single entity, the more credible its neutrality. Every time a foundation accumulates power, it becomes a target - regulatory, political, and eventually internal. The EF is trying to avoid becoming what it became.

I will explain the strongest case the EF has. Ethereum’s value to institutions is not speed or cheap fees. It is credible neutrality and the assurance that no single actor controls it, that the rules will not change based on who is in power today, and that it is safe infrastructure for the next thirty years. Every time the EF shrinks its footprint, its credibility increases. Every time it steps back, Ethereum becomes less like a company.

The EF does the protocol work nobody else will do. The ecosystem does the product work the EF should not do. If both work as intended, Ethereum becomes the neutral base layer that the financial system builds on, regardless of whether any particular EF employee is still there.

The Power Vacuum

When an institution steps back, something fills the space. The EF is choosing to shrink its footprint. That is the whole point of the walkaway test. But Ethereum does not exist in a vacuum, and the entities that have the most influence over its future do not share the EF’s relationship to CROPS.

Coinbase has Base. It has 100 million users, a developer platform, and more economic interest in Ethereum’s direction than almost anyone else. Lido controls a significant share of staked ETH, which gives it governance weight that no document can revoke. a16z has invested in protocols that shape the roadmap. These are not neutral stewards. They are participants with their own interests, their own timelines, and their own ideas of what Ethereum should be.

The mandate says the EF is “one steward, not the sole one.” But it does not say who the other stewards are, what they are optimising for, or how their interests stay aligned with CROPS when those interests conflict with growth.

The talent and institutional weight that used to sit inside the EF is now distributed across entities with commercial incentives that the EF explicitly says it does not want. Who are they building for, and do those interests converge with Ethereum’s long-term neutrality or pull against it?

Decentralisation does not eliminate power dynamics, but it sure makes them harder to see.

What Does Ethereum Want to Be When It Grows Up?

The mandate offers an answer: Sovereignty infrastructure. It aspires to be the neutral base layer for human freedom in the digital space. A thousand-year project, all about the machinery of freedom.

Also want to be the most liquid stablecoin settlement network. The most credible neutral chain for institutional finance. The place where RWAs are tokenized because the regulatory standing is cleaner than anywhere else.

These goals aren’t mutually exclusive, but the path to the first requires slower growth and ideological purity. The path to the second requires engaging with the competitive reality, where Solana and others are currently winning the battle for builders and mindshare.

Tell me this a year ago, I would clap and reshare. But today, we hardly find adoption from the people who want to use blockchains. Everyone wants a product; they want convenience.

Principles may win in a long enough timeline. Though I would never say this, timelines are shorter than Vitalik thinks.

There is also the asset problem, which the mandate does not touch at all. Ethereum is not just a protocol. ETH is something people hold. An asset that keeps underperforming its peers stops attracting capital, which reduces the security budget, which eventually comes for the protocol itself. Principles can sustain a network, but cannot sustain a token price. And in crypto, those two things are more connected than we assume.

The one bet that matters most is the one the builders make when they decide where to build next.

In my view, the mandate is right about one thing and silent about another. It is right that a protocol whose value depends on a foundation’s presence is a fragile protocol. It is silent about the fact that a protocol whose steward is focused elsewhere while the competition moves faster is also fragile. These are two different failure modes and the document only addresses one of them.

Ethereum has survived tougher times than this. It has also never been written off with more conviction by people who used to believe in it the most.

—Thejaswini

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

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

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

Imagine running a 40-person startup and deciding to pay your team in stablecoins. Faster settlements, no banking middlemen, and lower fees across borders. Sounds like a win, right?

But then, reality sets in. The moment you process your first payroll, every single salary becomes visible on-chain. Your lead engineer can see what the new hire earns. Your competitors can access your total burn rate. A random blockchain explorer can piece together your entire treasury activity just by watching one wallet.

This is not hypothetical. Researchers analyzed 22.7 million stablecoin transactions and extracted customer spending habits, peak sales times, and average order values from businesses that accept crypto straight off the public ledger. They could identify transaction timing patterns that reveal peak sales periods. They could conduct a geographic analysis to determine where a company was expanding. All of it sitting there, in public, forever.

Your bank doesn’t publish your account balance on the internet. But on Ethereum, Solana, or any major Layer 1 (L1) blockchain today, that’s exactly what happens by default.

Stablecoins processed over $33 trillion in volume in 2025. That’s a 72% jump from the previous year. The market cap went from $205 billion in January to over $306 billion by November. The GENIUS Act was signed into law in July, providing a stable regulatory framework for stablecoins for the first time. Stablecoin issuers are now the 7th largest purchasers of U.S. government debt. By every metric, this market is exploding.

Yet, despite these numbers, almost no businesses use crypto for payroll. Stablecoin-based B2B payments only grew past $6 billion monthly by mid-2025, which is still a tiny fraction of the $55 trillion global payroll market. Privacy, the most basic expectation in finance, is still missing from almost every blockchain.

This is what Aleo was built to close.

The Chain That Encrypts Everything

Most privacy solutions in crypto operate as bolt-ons. You take an existing transparent blockchain and add a privacy layer on top. But the base ledger is still public.

Aleo works differently. It’s a Layer 1 blockchain where privacy is the default, and not an option you toggle on. Every transaction on Aleo is encrypted. Wallet balances are private, transfer amounts are hidden, and the entire chain runs on zero-knowledge cryptography, which means the network can verify that a transaction is valid without ever seeing the actual data inside it.

To understand why that matters, think about how blockchains normally process transactions. On Ethereum, when you send 100 USDC to someone, the network needs to verify a few things. Does your wallet actually have 100 USDC? Is the transaction properly signed? Is everything following the rules? To do those checks, every validator on the network looks at your balance, the amount, and the recipient. All of that gets stored permanently on a public ledger.

Aleo uses a record model, a variation of Bitcoin’s UTXO system. Instead of keeping a global balance sheet that everyone can read, each transaction creates encrypted “records” that only the owner can decrypt. The network validates everything using zero-knowledge proofs, mathematical guarantees that the transaction is legitimate without revealing what’s inside.

The result is a blockchain that provides the same security and trustlessness you expect from a public chain, but without the public visibility. Your transactions are verified. They’re final, they’re on-chain, they’re just not visible to everyone.

This is different from what projects like Tornado Cash tried to do on Ethereum, where funds were mixed in a privacy pool. That approach got sanctioned by OFAC in 2022 precisely because it was a privacy tool layered on top of a transparent system, making it impossible for regulators to distinguish legitimate privacy from illicit activity. Aleo’s design starts from privacy and builds compliance tools into the protocol itself. That matters a lot when you’re trying to work with the financial system instead of around it.

One of Aleo’s understated engineering feats is how it handles fees. Running zero-knowledge proofs is computationally heavy. On many privacy systems, that cost is passed onto the user, making private transactions expensive enough to defeat the purpose. Aleo’s design routes a chunk of the computation off-chain: instead of broadcasting all transaction data to every node, the chain only needs to see a short cryptographic proof that everything checks out. The actual data stays with the parties involved. For users, this means lower transaction fees, even as privacy guarantees run in the background of every single transaction.

Aleo launched its mainnet in 2024, and since then, the pace of what’s been shipping is worth paying attention to. In July 2025, the team released snarkOS 4.0.0, the largest protocol upgrade since launch, delivering 500% faster confirmation times. In August 2025, Aleo became the first privacy-preserving blockchain listed on Revolut. If you’re not familiar with Revolut, it’s the largest crypto-friendly fintech in Europe, with 60 million users, operating across the UK and EEA, giving users across Europe direct access to buy, stake, and hold ALEO tokens with staking yields of up to 19% APY.

The same month, Aleo joined the Global Dollar Network, a stablecoin consortium founded by Paxos, whose members include Robinhood, Kraken, Standard Chartered, and Worldpay. This is the network that major financial institutions use to settle in dollar-backed stablecoins. Banks and payment processors in that network need privacy too, and now, Aleo is the chain that can offer it.

Making Stablecoins Usable

Cross-border payments are already broken before you add any blockchain to them. A company paying contractors across different countries today typically goes through SWIFT, which chains together multiple correspondent banks, each taking a cut. The World Bank’s 2025 data puts the average global remittance cost at 6.49% per transfer. For a $5,000 contractor payment, that’s $325 gone before the person even receives it. Settlement takes anywhere from 2 to 5 business days. And none of this cost includes the FX markup that banks add on top, typically 2-3.5% above the mid-market rate, which, as a standard practice, most businesses don’t track separately.

Stablecoins solve most of these issues. They provide same-day settlement, eliminate the correspondent bank chain, and offer fractional fees. The payment infrastructure problem has been genuinely solved. The privacy problem wasn’t, until now. So, what does private stablecoin infrastructure actually unlock for these real financial workflows?

The most concrete answer is Toku. Toku is an Employer of Record (EOR) platform built specifically for companies paying distributed teams. They handle tax withholding in each jurisdiction, local employment contracts, labor law compliance, and real-time FMV calculations for token-based compensation. On January 29, 2026, Toku integrated Aleo’s infrastructure and added USAD as a payroll settlement currency. A company using Toku can now run payroll across 100+ countries in stablecoins with full compliance in each jurisdiction and without any of that payroll data going onto a public ledger. Toku handles the compliance layer, USAD handles the settlement, and Aleo handles the privacy. The company’s existing payroll software doesn’t change.

This changes the economics for a business entirely. For example, International wire transfers average around $50 each. If a midsize company pays 80 contractors monthly, that’s $4,000 a month, $48,000 a year, just in transfer fees, before the FX markup. Private stablecoins bring that cost down to cents per transaction. When you also remove the 6-7% in fees that SWIFT-based payments typically carry, a company paying $5 million in annual international payroll saves somewhere between $250,000 and $350,000 in friction costs alone.

Then there’s USDCx. It runs on Circle’s xReserve infrastructure, which means it stays 1:1 backed by USDC held in a smart contract. When a company wants to move USDCx back into regular USDC on Ethereum for liquidity purposes, xReserve issues a cryptographic attestation that settles the transfer without a third-party bridge. Private settlement on Aleo, public liquidity on Ethereum, connected natively. For a treasury team, when you need to access DeFi liquidity or convert to fiat on an exchange, you move back to USDC without paying bridge fees or trusting a third-party protocol. The private rails and the public rails interoperate.

Paxos introduced another option with USAD, backed 1:1 by USDG and regulated across Singapore, the EU, and the U.S., giving institutions in those jurisdictions a fully compliant private stablecoin option with cross-border bridge support on Ethereum and Solana.

One often overlooked use case in this context is humanitarian aid. NGOs operating in conflict zones or under hostile governments face a specific problem with transparent financial rails. If aid distribution transactions are on a public ledger, the recipients can be identified. In environments where receiving foreign aid can make someone a target, that’s a genuine security risk. Private stablecoins on Aleo are one of the first financial infrastructure solutions that can move regulated, compliant dollar payments to recipients in sensitive environments without creating a permanent public record of who received what. This is not speculation; Aleo has explicitly identified aid relief as a core use case in its documentation, and the infrastructure is already live.

Regulator-Friendly Privacy

There’s a common assumption in crypto that privacy and compliance are opposites. That if you build for privacy, you’re building against regulators. Aleo’s approach is built specifically to break that assumption.

The protocol includes something called view keys, which are baked directly into the account model. Think of them as cryptographic windows that stay sealed unless the account owner decides to open them for someone specific.

There are two types. An Account View Key gives read-only access to an entire account’s history, past, and future. You can hand this to an auditor, a tax authority, or an internal compliance officer at the start of the year, and that authority can verify everything they need throughout the year without the company having to produce records on demand. They can see everything they need, but cannot move any funds or modify anything. And the key is derived from the user’s private key in a way that makes it mathematically impossible to reverse-engineer access to the actual funds.

Then there’s the Transaction View Key, which is automatically generated for every single transaction on the network. It decrypts only the inputs and outputs of that one action. Its hash gets recorded on-chain as part of the transaction, so its authenticity is verifiable by anyone. A company settling an invoice can share the transaction view key with the counterparty to prove payment without exposing anything else about their treasury.

For banks and compliance teams, the comparison point here is traditional correspondent banking, where AML checks happen through a patchwork of monitoring systems that generate false positives constantly. JPMorgan estimated that financial institutions spend over $31 billion annually on AML compliance in the U.S. alone, and still miss an estimated $800 billion to $2 trillion in illicit flows globally each year. The compliance infrastructure is expensive, and it still doesn’t work well. View keys with on-chain verifiable attestations give compliance officers a cleaner audit trail than the systems most banks are currently running.

Standard Chartered predicted the stablecoin market will hit $2 trillion by 2028. B2B cross-border payments alone represent over $10 trillion annually, with the market expected to grow at 7.1% CAGR through 2030. The question isn’t whether enterprises will eventually move payments onto stablecoin rails. The economics are too clear. The question is whether blockchains can offer the confidentiality that every serious business finance team already takes for granted.

That’s what Aleo is solving. Not with privacy as a feature bolt-on, but as a chain where privacy is the starting point, selective disclosure is the compliance tool, and the products built on top, USDCx, USAD, Toku’s payroll infrastructure, are already in production. As the $33 trillion stablecoin market grows, the need for financial privacy is just getting started.

That’s it for today! See you next week!

Until then, stay curious!
Vaidik

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An AI agent, however, has none of those qualities.

It does not collect points. It does not feel safer with fraud protection. It does not aspire to a black card. It has one instruction which is to complete the task. And when the task involves paying for something, the agent does the math that no human would ever bother to do. The cheapest rail. The fastest settlement. The lowest fee. Every time, automatically, without sentiment.

Last month, a SubStack post titled”The 2028 Global Intelligence Crisis” crashed Visa 4%, Mastercard 6%, and American Express 12% in a single trading session. The report was labeled a scenario, not a prediction(their words). The market did not care. The technical claim didn’t matter. The problem was that the agents would route around interchanges by 2027 and settle in stablecoins instead. Visa has spent fifty years perfecting a product for a customer that is being replaced.

In machine-to-machine commerce, the 2-3% interchange rate is an obvious target. That line from Citrini Research is the whole thesis. Not that AI will destroy Visa tomorrow. That the fee structure Visa built its empire on was always a tax on human irrationality, and agents are perfectly rational. That is the whole point of them.

What Is Visa Selling?

To understand why this matters, you have to understand what the interchange fee actually funds.

When you buy something on a credit card, the merchant pays 2-3% to the card network and your issuing bank. This fee covers your rewards points, fraud protection, purchase insurance, and dispute resolution. The entire consumer value proposition of a credit card is funded by merchants, who pass the cost to everyone through slightly higher prices. It is a beautiful, stable system that has run for fifty years because the human being in the transaction was willing to pay for all of it, just not directly.

An AI agent does not need any of these things. It does not dispute charges or want cash back. The protections that justify the fee are those against human error, fraud, and impulsiveness. Remove the human from the transaction, and the fee loses its justification entirely.

American Express is the cleanest version of this problem. Its customers are premium cardholders who are high-earning, high-spending, and aspirational. Its fees are higher than Visa or Mastercard precisely because its customers are willing to pay for the status and the perks. That whole model assumes a human is making the purchase consciously, choosing Amex over Visa because the lounge access is worth it. An agent does not choose Amex. An agent finds the cheapest option that closes the task. The premium tier does not exist in a world where software holds the card.

Agent-driven commerce routing around interchange poses a much greater risk to card-focused banks and mono-line issuers, who rely heavily on the majority of that 2-3% fee and have built entire business segments around rewards programs funded by the merchant subsidy. Visa and Mastercard have network businesses that can adapt. The issuers who built entire profit-and-loss (P&L) models around interchange and rewards have nowhere to go.

The week everyone shipped at once

The Citrini report and the infrastructure launches landed in the same three-week window.

Tempo went live on mainnet on Wednesday. Stripe and Paradigm’s payments blockchain, purpose-built for high-volume stablecoin settlement, launched alongside the Machine Payments Protocol, which is an open standard that lets AI agents pay for services autonomously, without human sign-off at each step. The protocol introduces sessions. An agent authorises a spending cap once, then streams micropayments continuously as it consumes services like data, compute, or API calls. OAuth for money. The human authorises a budget. The agent spends it. No card required at each step.

Anthropic, DoorDash, Mastercard, Nubank, OpenAI, Ramp, Revolut, Shopify, Standard Chartered, and Visa were all listed as Tempo design partners. The entire payments and commerce stack is acknowledging the structural change.

The same day Tempo went live, Visa’s crypto division launched a command-line interface tool for AI agents to make payments from a terminal without API keys, without accounts, without a human authorising each transaction. Visa framed it as “Command Line Commerce” - machines transacting without human intervention.

Mastercard agreed to acquire BVNK, a stablecoin infrastructure startup, for $1.8 billion. Circle launched Nanopayments on the testnet. Sub-cent, gas-free USDC transactions built specifically for agents paying for pay-per-call APIs without accounts or credentials. Sam Altman’s World project launched AgentKit, which allows agents to carry cryptographic proof that they represent a real human and integrates it directly into Coinbase’s payment rails, enabling platforms to verify agent identity without blocking legitimate commerce.

In my view, what happened this week was companies racing to become the new Visa before Visa figures out what it has already lost.

The Paradox in Plain Sight

Now, what is not said clearly enough is that Visa is not standing still.

It contributed to Tempo’s Machine Payments Protocol, launched Visa Crypto Labs, and its head of crypto was quoted in Fortune explaining how agents can pay with card rails through the new standard. Mastercard is spending $1.8 billion on stablecoin infrastructure. Stripe has acquired Bridge and Privy. The incumbents understand the shift and are positioning themselves in the new infrastructure before it fully arrives.

Visa’s argument is that it can extend its rails to cover agent-driven commerce before agent-driven commerce builds rails that make Visa irrelevant.

That argument is not obviously wrong. Stripe processed $1.9 trillion in total payment volume in 2025, a 34% year-on-year increase. These companies are not shrinking. The card network’s distribution advantage is not easily replicated. I’ll admit I’m reluctant to say that out loud, because historically, the moment someone makes that argument, something ships and makes them look stupid.

So here is the gap in that argument: Visa’s distribution advantage is built on merchant relationships and consumer trust. Merchants accept Visa because consumers carry Visa. Consumers carry Visa because merchants accept it. The entire flywheel runs on the human being in the loop. The moment agents become the primary buyers in a meaningful commerce category, the flywheel slows. Agents do not have brand loyalty, nor wallets. What they have are budgets and instructions. Whatever rail is cheapest and fastest wins their business every time, with no switching costs.

I want to be precise about where we are, because the narrative is running ahead of the data.

Despite a roughly $7 billion ecosystem valuation around x402, on-chain data show the protocol processed only about $28,000 in daily volume last week, much of it from testing rather than real commerce. That number, and what Visa processes in a single day, are in completely different leagues.

x402 has crossed 50 million transactions. The volume per transaction is tiny. But the transaction count suggests that the infrastructure is being used. Developers are building on it. The merchant side of the equation (services that accept agent payments) is growing. That is how payment networks start.

McKinsey has estimated that AI agents could mediate between $3 trillion and $5 trillion of global consumer commerce by 2030. That estimate may be right. It may be wildly optimistic. What is not in dispute is that agent-driven commerce does not exist at scale today. The merchants building agent-native services, the enterprises deploying agents as primary buyers, and the volume that would actually stress-test interchange economics are still being built.

The Citrini report spooked the market because it modelled a credible sequence of events. Mastercard’s Q1 2027 earnings are not going to cite “agent-led price optimisation” as the reason volume slowed. Not yet.

The first place this hits is micropayments for AI infrastructure, not consumer commerce.

An agent completing a research task calls specialised data APIs hundreds of times per session. Each call costs fractions of a cent. Over a week, those calls might generate $40 in revenue for the developer running the service. Card networks cannot handle this. The minimum transaction economics do not work. The merchant onboarding process does not work. The fee structure does not work. This category of commerce was never going to run on Visa rails. It needed something new, and x402, Nanopayments, and Tempo are building it.

The disruption to consumer commerce, as Citrini modelled it, comes later, if it comes at all. It requires agents to handle a meaningful share of discretionary spending, which in turn requires consumers to trust agents with purchasing decisions they currently make themselves.

Visa is being disrupted by a better customer. One that has no use for what made Visa great. The 2-3% interchange fee is not a tax on transactions. It is a tax on human irrationality. Agents are perfectly rational.

How do I know this will matter? Because Visa spent $1.8 billion this week making sure it is not left out of the answer.

—Thejaswini

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

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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.