That’s the plot of Nikolai Gogol’s Dead Souls, published in 1842, and it made sense because of how Russia counted.

Landowners paid tax on every serf they held, and the census that set the count came around rarely, so a serf who died last spring stayed alive on the books until the next one. Owners were paying tax on corpses. Chichikov, the stranger, offers to take those names off their hands. His plan is to collect enough of them, mortgage them to a bank and walk away with the loan.

Any system that pays per head and can’t see the heads ends up with fake ones.

Being human on the internet was free. So was pretending to be several.

Websites allowed multiple accounts, Twitter tolerated pseudonyms, and crypto let one person create thousands of wallets.

Give every account one vote or every wallet an equal airdrop, and whoever creates the most accounts has an advantage. AI now makes doing this at scale even easier.

Proof of humanity, then, is more interesting as an economic technology than as an identity technology. It attempts to manufacture something the internet has never naturally possessed, which is a scarce digital object corresponding to one human being.

You’ve heard why the internet needs proof of human, so I’ll skip that. The part I think is more interesting is the price. VeryAI, which now calls itself Very, raised $10 million in March on the idea that your phone can check you’re real by looking at your palm.

So I went looking for what a body goes for.

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

The price list

Cambridge researchers track how much it costs to buy an SMS verification code, the text message websites send when someone creates an account, across more than 500 platforms.

And the prices have been changing. On December 19, 2025, getting a verification code for Facebook using a US phone number cost just $0.20. There were 168,350 available for sale. By March 11, the price had jumped to $1.75, with only 115 available. The latest listed price is $3.02.

In other words, fake account creation has become a market of its own. When demand increases, or fewer phone numbers are available, prices rise, just like they do for ordinary products.

One way to create thousands of fake accounts is to use thousands of SIM cards, each providing a different phone number.

In September 2025, the US Secret Service seized 300 SIM servers and 100,000 SIM cards at sites within 35 miles of the UN General Assembly. Authorities were investigating potential threats to telecommunications and government officials, not ordinary fake-account farming. But the equipment shows how easily phone numbers can be managed on a massive scale. Websites often treat a verified phone number as evidence of a real user. The problem is when one person can control thousands of numbers.

The other toll was our time. Research estimated that people worldwide spent around 819 million hours solving Google’s reCAPTCHA puzzles, the annoying tests that ask us to identify traffic lights,or buses to prove we’re human. Failed too many times, sometimes I wondered if I am a robot.

If people had been paid the US minimum wage for that time, it would have amounted to roughly $6.1 billion in unpaid work.

Robots were learning to pass the same tests. In 2024, another team managed to solve every image puzzle in its tests using AI.

Crypto’s word for all this is Sybil. It comes from a 2002 paper by Microsoft researcher John Douceur. He examined how one person in a computer network could pretend to be numerous participants and gain influence they weren’t supposed to have.

Bitcoin’s answer was to stop counting people.

Satoshi Nakamoto knew that voting by IP address wouldn’t work, because one participant could control numerous addresses. Instead, Bitcoin introduced what its whitepaper called one-CPU-one-vote.

Bitcoin miners compete to find a number that, when put through a cryptographic function, produces a result that meets the network’s requirements. Each attempt is simple, but finding a valid result can take trillions of tries, consuming enormous amounts of computing power and electricity.

Bitcoin adapted that logic to securing a financial network. Proof of stake later made capital, rather than computational work, the scarce resource behind participation in consensus.

You still can’t tell whether ten addresses belong to ten people or one person. This wasn’t particularly important when all the system needed was agreement on financial transactions. It became important when crypto started distributing rewards, governance rights and opportunities to individuals. Essentially it was first electricity, then capital, now bodies. That’s how the cost to participate went.

Two ways to count a body

Very keeps the system in your mobile phone rather than a separate machine. At registration, users scan their palms to create a mathematical representation of their unique features. Later, when they need to verify themselves, Very asks for another scan.

The app asks you to touch a randomly chosen spot on your palm with your thumb while scanning. This helps prove that you’re holding your hand in front of the camera, rather than someone playing a recorded video of it. Very says it can recognise you when you return without sharing your name, ID details or other personal information with the app you’re using.

VeryAI claims a false-acceptance rate of one in ten million for a single palm and an even lower rate when both palms and multiple enrollment scans are used. CEO Zach Meltzer told Axios that crypto exchange MEXC was its largest early customer, using palm verification for withdrawals in place of traditional one-time codes.

World does its job with an Orb.

Orb photographs a user’s face and irises, establishes that the person is human and unique, and creates a World ID that can subsequently be used to prove unique humanity without revealing the underlying identity. World says the original verification data is encrypted and sent to the user’s device while the Orb deletes its copy. Uniqueness checking uses anonymised multi-party computation, while zero-knowledge proofs allow subsequent World ID use without revealing the person’s biometric information.

Read: Worldcoin’s New Trust Economy 🌎

World reports more than 18 million verified people. But an Orb has a problem. The better you control the sensor, the harder it is to distribute the sensor.

This means World builds its own Orb so it can control the cameras, lighting, and scanning technology. This can make verification more reliable. But it also has to manufacture the Orbs, ship them worldwide, and set up at different locations. Very uses the camera already on your smartphone. It has less control over camera quality and lighting, but people can verify themselves from almost anywhere.

World’s technical argument is that specialised biometric hardware provides unusually strong uniqueness and anti-spoofing guarantees.

World has made a concession to convenience too. After the initial Orb check, users can perform a quicker face authentication on their phone to confirm they’re the same person.

They also store things differently. World splits your iris code into fragments held by separate organisations, and Very keeps encrypted Palm Models itself, plus low-resolution palm images for up to 90 days.

Very has already put a price on verification. Businesses pay $0.50 to register a user, $0.02 for each subsequent verification, and $0.03 for a separate check that someone is physically present. So an exchange with one million customers would pay $500,000 just to register them. Every additional verification brings Very more revenue.

The bigger opportunity could be getting thousands of applications to accept the human credential. Think of payment networks. A card becomes more useful when more shops accept it. Similarly, when people can use it across exchanges, social platforms and other applications without registering again, then the human credential becomes more valuable. The company whose verification is accepted in the most places could have the bigger advantage.

And there’s another catch. Once a verified identity becomes valuable, people may start selling access to it.

But what a body goes for? Bodies are scarce, and they’re also cheap. This is what I’d worry about if I were building on them.

In 2023, Kenyans could get about $49 in tokens for an iris scan, and more than 350,000 had reportedly signed up when the government stepped in. Again in 2023, credentials were reportedly being bought for as little as $30. In April this year, ZachXBT posted screenshots of verified accounts going for as little as $0.50, with WLD about 98% below its peak. This is less than the average WhatsApp account on the Cambridge list.

Now compare the security. To attack Bitcoin you buy electricity, and to attack a staking chain you buy capital, and both are priced on world markets. To manipulate a proof-of-humanity allocation, one possible strategy is to recruit real people willing to participate.

And the price of recruiting a person isn’t uniform around the world.

The World Bank estimates that 811 million people live on less than $3 a day, measured using its purchasing-power-adjusted poverty line. So, $10 or $20 incentive may mean very different things to people in different economic circumstances.

If an airdrop is worth $50, someone could offer participants a fraction of the reward to register and claim it on their behalf.

World even considers using the country where someone registered as part of its verification system. Knowing where a credential was issued could help applications limit this kind of activity.

Very’s answer is the re-check, since a buyer needs the seller’s hand at every transaction. So even if someone buys your verified account, they still need your help to use it. Instead of paying you once, they could pay you a small amount every time your palm scan is required.

Software can pay that wage. In OpenAI’s 2023 testing, GPT-4, with researchers steering it, got a TaskRabbit worker to solve a CAPTCHA by claiming a vision impairment. In February, a site called RentAHuman launched so AI agents could hire people for errands and pay in stablecoins. I haven’t seen anyone sell palm scans this way, and the pieces are all there.

People have been leaving handprints for at least 67,800 years, as ancient cave art in Indonesia shows. Governments started using them to identify people much later.

In 1858, British administrator William Herschel asked a contractor in Bengal to put his handprint on a contract, hoping to stop him from denying his signature. Fingerprints eventually became a tool of colonial administration.

By 1906, authorities in South Africa’s Transvaal were forcing Indian residents to register their fingerprints and carry identity certificates. Thousands protested, helping launch Gandhi’s satyagraha movement.

But Gandhi wasn’t against fingerprinting itself. As historian Keith Breckenridge explains, his objection was to making it compulsory and using it to discriminate against Indians.

The voluntary vs compulsory identity checks could become relevant again. Very lets people request deletion of their biometric data. World has introduced technical protections intended to limit who can see a person’s original biometric information.

But, suppose a dating application starts offering verified-human badges. At first, the feature is optional. Then users begin avoiding unverified profiles. Similarly what if an exchange requires biometric verification for certain withdrawals.

You can argue saying this is the far future. But if enough of them adopt the same requirement, declining to enrol could become expensive.

A single universal identity provider creates a risk that several competing systems might reduce. If one company incorrectly marks you as a duplicate, can you appeal? can you use another method? If you no longer trust it, can you move elsewhere?

I think palm versus iris is the smaller fight. A two-cent check at the moment money moves is a good trade. I’d take that over uploading my passport to another application that doesn’t actually need to know my name.. What I don’t want is one credential from one issuer for everything.

When a password or wallet goes bad, you can replace it. But in this case, you still only get two hands. As the legend Vitalik once said -

“If you can only have one identity, you do not have pseudonymity, and you can be coerced into revealing it.”

Thejaswini

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Money’s utility depends on its mobility. Consider paying a friend who keeps their money in another bank. Behind the banking app interface, the two banks settle with each other, and the money remains worth the same in both banks.

Banks have spent the last few years rebuilding deposits for a world that never shuts. Enter tokenised deposits. JP Morgan, Citigroup, and HSBC all have tokenised deposit services. These tokenised deposits move at any hour and are programmable. But they work within the specific banking ecosystem.

A tokenised deposit is a claim on the bank that issued it, and other banks’ ledgers can’t use it. Although tokenised deposits are programmable, the hard part is connecting them across banking ecosystems.

In today’s story, I explain why the value accrues in making tokenised deposits travel across banks.

On to the story…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

The Porters Are Back

On September 24, seven British banks closed two remortgages in a way no British bank had before. The borrower’s money remained locked in their account, kept earning interest, and was released the moment the deal was done. Nobody had to wait for funds to clear.

The seven banks, including Barclays, HSBC, Lloyds, and NatWest, collaborated with one shared platform. UK Finance, the industry body, commissioned it from Quant, a London software firm. Without the shared platform, every bank’s token remains siloed.

The Bank for International Settlements (BIS) said as much in September. Without interoperability, a tokenised deposit can’t cross a bank’s “KYC boundary”. So, my bank’s token means nothing to another bank’s ledger because the latter has never vetted the former or agreed to hold a claim on the same.

This is the biggest bottleneck to what tokenisation promises. A tokenised bond or fund only settles when the asset and the cash swap at the same instant. On one of the largest live tokenised repo platforms, the securities are on-chain, and the cash still moves on old rails. Tokenised Treasury funds have grown to roughly $14.5 billion, yet most of that money still settles the old way.

It’s not a dead end. Finance has solved this problem before. In the early 1850s, New York’s banks settled with each other by sending porters around the city with cheques, each bank to every other bank, every day. The more banks opened, the worse it got. In 1853, they set up a clearing house that did a single settlement daily of all the cheques due. That institution is today’s The Clearing House, owned by about 25 of America’s largest banks, and it runs CHIPS and RTP, two of the country’s core payment systems.

Today, the problem is that separate tokenised deposits across banks need to align with central bank money on a Sunday.

To solve this, the banks are agreeing on a shared, common layer where the settlement of tokenised deposits issued by different banks takes place. On September 24, The Clearing House picked Quant, after a competitive process, to run the orchestration layer of its On-Chain Money network. It opens to banks in the first half of 2027.

A Once-in-a-Generation Opportunity

Clearing layers are close to natural monopolies. Once a country’s banks agree on a rulebook and a settlement venue, it stays that way for decades. When The Clearing House launched the Real-Time Payments (RTP) network in 2017, it was billed as the first new core payments system in the US in more than 40 years. That’s how this line of business works. When the window for such a stack opens and the seat is captured, the window is shut for a generation.

In 2016, after a competitive tender, The Clearing House hired Vocalink, a British company owned by UK banks, to build RTP. Vocalink had already built Britain’s Faster Payments. RTP now moves around $7 billion a day. A decade later, The Clearing House ran another tender and picked another London builder.

What’s different now is how many such seats of value accrual are opening at once. Tokenised money forced every major banking system to answer the same question within the same 18 months.

Quant has taken the two biggest domestic contracts awarded so far, America’s and Britain’s, on the same day.

Swift chose to build its own shared ledger. Seventeen banks are piloting it, and Swift expects at least 19 across five currencies by the end of 2026. Chainlink, IBM, ACI, and Oracle are all competing to plug banks into it. America’s regional banks started the Cari Network, built on ZKsync. The European Central Bank chose not to hire a private orchestrator at all. Its Pontes link, live since September 21, settles tokenised assets directly in central bank money.

So what is the seat actually worth? The liquidity.

CHIPS, The Clearing House’s large-value system, moved just over $2 trillion a day in 2025. Because it nets payments against each other, banks settled that with about $96 billion of funding instead of an estimated $442 billion. The Clearing House puts the savings at $5.5 billion a year. That is what a good clearing layer does. It lets the system run on a fraction of the cash it would otherwise need.

Now compare where tokenised deposits are today. JPMorgan’s Kinexys, the largest bank token network, averages about $7 billion a day. That’s roughly 0.3% of CHIPS, and it only works between JPMorgan’s own clients. The leader in tokenised deposits is a siloed garden.

That gap is the opportunity. No single bank can close it alone, however large it is. Only a shared layer can.

Who Captures the Value?

This layer will create enormous value. It’s how bank-owned infrastructure has always worked. The $5.5 billion CHIPS saves every year goes to its member banks. The Clearing House is owned by the banks that use it, and it prices like it.

Vocalink is a good example of how owners collect rent while the builders get paid. It built Britain’s Faster Payments and America’s RTP, two systems that now carry trillions. In 2016, Mastercard paid about £700 million for 92.4% of it, on revenue of £182 million. The prize went to the British banks that owned Vocalink. For the builder, that’s a superb outcome. But for the system it had built, it didn’t mean much.

The Quant Network operates inside the shared tokenised-money infrastructure of the world’s two largest financial centres. If it executes, it becomes a very valuable company. The banks that convene these networks will still capture far more.

So the real winners are the conveners of the ecosystem, like The Clearing House, UK Finance, and Swift. Those who control the rulebook of who gets in, on what terms, and at what cost capture the utmost value.

Close behind are the banks that join early, and that includes the small ones. America has 4,238 FDIC-insured banks, and 90% of them are community banks that will never run their own blockchain. Quant’s offer to issue tokenised deposits on their behalf, through The Clearing House, is the strongest case for a shared utility over a JPMorgan-only world. Without it, tokenised money becomes a big-bank privilege.

The risk is that banks solve fragmentation inside each club and recreate it between clubs. In America alone, there is The Clearing House network, Cari, Kinexys, and a 21-firm bank stablecoin planned for 2027. Further proof of the tokenised deposits ecosystem being a siloed garden is the fact that Huntington and KeyBank sit in both Cari and The Clearing House.

When the banks own the translator, the translator becomes a utility, and utilities don’t get to keep the toll. Enabling interoperability is where the value accrues, and where the players will crowd.

Although interoperability sounds like a technical word, it is more than that. It is the difference between a gift card and money. The former works only where it was issued. Money works everywhere because everyone accepts it at its face value. Banks spent a century making sure a dollar at one bank is valued as a dollar at every other. Central bankers call this the singleness of money. Tokenised deposits, as they exist today, break that perception. Interoperability puts it back.

Enabling it is not difficult. Ledgers can already talk to each other. The hard part is to figure out whose tokens a bank will accept, under which rulebook, and where final settlement happens. Britain answers through central bank reserves. The ECB built its own link. In America, The Clearing House is writing the rulebook, and Quant is building the switchboard.

A few things change if the industry gets this right.

First, money stops being a servant of time. A payment sent on Friday evening can sit idle until Monday because the settlement systems behind it close. Treasurers hold cash buffers to cover those dead hours. Bank money that settles across banks at any hour puts the cash back to work.

Second, tokenised assets finally get a till. The $14.5 billion in tokenised Treasury funds, and the repo and bond markets behind them, can settle against cash on the same rails at the same instant. The gap where one side has delivered and the other hasn’t paid disappears. That is the part of tokenisation institutions care about most, and it has been missing its cash leg all along.

Three, size stops being the moat. JPMorgan can run a token network for its own clients because it is JPMorgan. A community bank can’t do that. A shared layer gives the small banks that make up 90% of the system the same capability as the largest, on the same day.

The bigger shift is competitive. Stablecoins won their first decade by arguing that a dollar should be able to move around the clock between anyone. Interoperable tokenised deposits take that argument away while keeping deposit insurance, interest, and lending intact.

Within a decade, nobody will say “tokenised deposit”. It will just be a deposit, the same way nobody says “electronic payment” anymore. Customers will never see the clearing layer. That invisibility is exactly why it’s worth so much.

That’s it for today. I will be back with the next one.

Until next time, stay curious,
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

By the time you reach the hotel, the flight could be the smallest thing you paid for.

Sending money abroad is kind of the same story. Crypto made the crossing itself very cheap, and you’d think that would be the end of the story, but no.

Banks don’t publish what they make on an international wire, so three Harvard Business School researchers checked their own transfers.

In a paper published by the Bank for International Settlements, they looked at eight international transfers sent through large banks and found that customers paid about 2% to 4.7% extra through the exchange rate. This is before the banks even added a transfer fee.

In a separate study, researchers at the Bank of Italy used services available to ordinary customers to send 200 USDC between Italy and five other countries. They measured how much it cost and how long it took from start to finish.

The big numbers make stablecoins look the hero. IMF staff checked 270 routes, and Tobias Adrian, speaking for himself, presented the results at a BIS conference in June. On routes that could be matched with World Bank data, sending $200 by stablecoin cost roughly 1% against 5.8% the traditional way, so about $2 instead of nearly $12.

The Italians came back with a bit of bad news too. They compared their USDC transfers with Wise, the money transfer app and one of the cheaper ways to send money. Total costs ran from 0.30% to nearly 9%, which on $200 is about 60 cents on the best route and almost $18 on the worst. USDC beat Wise on three routes and lost on four, so it wasn’t reliably cheaper. The funny part is that the blockchain itself averaged only about 0.4% of the transfer, or roughly 80 cents. Most of the bill came from getting in and out of crypto.

Stablecoins look cheap next to the average money transfer. They have a harder time beating a good fintech service like Wise.

On October 29, a Hong Kong company called PhotonPay announced a $40 million Series C led by Blockchain Capital. If the blockchain leg was never the expensive part, I wanted to know what these investors think they are paying for.

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

On June 26, 1974, German regulators closed a bank called Herstatt, which had lost heavily betting on currencies. The timing made it famous. They shut it in the middle of the German business day, before US markets opened. Banks that had traded with Herstatt had already paid it in German marks and were waiting for dollars in return. The dollars never came, because New York was still asleep when the doors closed. Several institutions were hurt, and a major US payment system shut for 24 hours.

Payments people still call this Herstatt risk, and it taught them that a cross-border payment is two payments in two time zones. The money can finish one half of the trip and get stuck before the other. A late wire today is a far smaller problem, but money still crosses a border in stages, and each stage can stall.

A normal international payment hops. Your bank rarely has a direct link with a bank in Brazil, so the money passes through a chain of banks. Each of them keeping foreign currency parked so it can pay out. Each is able to take a fee and run its own compliance checks.

Fintechs like Wise and Remitly changed this by keeping money in local accounts. They could pay recipients without sending every transfer through a chain of banks. When you send dollars to Europe, Wise often pays from euros already there, so nothing crosses the border at that moment.

Stablecoins speed up a different step. A token can move even on a day when the banks are closed.

There can still be a wait at the other end. SWIFT says about 90% of its international payments reach the recipient’s bank within an hour. Much of the delay comes after that, while the bank checks the payment or waits to process it alongside other transfers.

The Italian test saw the same thing. Where instant systems like Brazil’s Pix existed, the whole transfer took under 20 minutes, and South Africa took one to two business days because of ordinary bank transfers on its end.

Plenty of cross-border companies have the working capital they need, sometimes in the wrong currency or country. For a business this is a big cost. A supplier who hasn’t seen the deposit won’t start the order, so the month’s stock runs late.

Atta Addo, a University of Surrey researcher, studied Lagos Computer Village, a market of more than 5,000 small businesses. He described a young trader paying a supplier in Guangzhou for 500 smartphones. Five years earlier, he’d have queued at a bank with foreign exchange forms and could have waited 7-21 days for clearance. Prices there can shift overnight, so a late payment could mean paying more, or getting your stock after everyone else has restocked. Now he sends USDT from his phone, and it reaches China within minutes.

Ads are the harshest case, because Meta pauses campaigns when it can’t collect payment. The money was there and so were the customers, and the ads stopped anyway.

What is Photonpay?

In its funding announcement, PhotonPay put annualised volume at $65 billion in August, up from more than $30 billion in January. It also reported over 200,000 business customers, 34 licences and authorisations, and 4.4 million cards issued in a year.

PhotonPay is trying to bring these arrangements into one service.

PhotonPay is a payments company that helps businesses collect, convert and spend money across countries. An online seller can receive payments in a local account, hold the money in different currencies and use it to pay a supplier or fund a card. The wallet holds USDC and USDT, on seven chains, next to fiat balances. A business can swap them into 17-plus currencies at any hour, then pay a bank account or load a card. The options depend on where the business operates.

It offers virtual cards, physical cards(plastic and metal) and custom branded cards too. The card is useful when a business holds stablecoins but needs to pay a bill in local currency. An advertiser can fund a virtual card with USDC and use it to pay for ads while the ad platform just think of it as a regular card payment. Some PhotonPay cards need to be topped up separately, while others draw from a shared balance. An agency running several campaigns can use that shared balance instead of leaving spare cash on each card.

PhotonPay holds 34 financial licences and regulatory authorisations. Those permissions cover different activities in different markets. The company says it will use the new funding to expand its wallets, issue more cards and build out the local payment networks it uses to deliver money.

Luban, an advertising platform under BlueFocus Media, shows why this matters. Its clients buy ads on platforms like Meta, Google and TikTok. According to PhotonPay’s case study, conventional cross-border funding could take up to two business days, making it difficult to adjust budgets while campaigns were running.

PhotonPay hasn’t split its $65 billion between stablecoins and traditional payments. From what I can see, the stablecoin here buys time and access more than it buys a better price.

The company earns small charges along the way. Its price list shows 0.3% to receive funds, $3 for a local transfer, $15 or $45 for a SWIFT wire and 1% on card transactions. At list price, receiving $100,000 and spending $10,000 by card costs about $400 before any currency conversion.

January’s Series B release promised yield on idle funds and credit lines as well.

PhotonPay hasn’t shared its revenue. For scale, Airwallex reported about $1.3 billion in annualised revenue on $287 billion of volume, or about 45 cents of every $100.

PhotonPay’s $40 million is small next to everything around it.

Mastercard closed its purchase of BVNK in August for up to $1.8 billion, after Stripe paid $1.1 billion for Bridge. Kraken’s parent agreed to buy Hong Kong’s Reap for up to $600 million. OpenFX raised $94 million, and Airwallex raised $320 million at an $11 billion valuation.

Wise owns its pipes. It has plugged directly into eight domestic payment systems and moved $243.5 billion across borders last fiscal year at a 0.52% take rate, down from 0.58%. Airwallex looks closest to PhotonPay, with accounts, cards and spend tools for businesses. Airwallex has about 80 licences and its CEO argued last year that stablecoins did little for payments between major currencies, and it has since co-led a seed round for blockchain startup Metal and put USDC payouts in early access.

Bridge sits a layer lower. Developers use it to move stablecoins and launch their own dollar tokens, so it sells to the company that sells to the ad agency. Corridor specialists like Zynta between Europe and Africa, or Conduit in Africa and Latin America, work where moving between dollars and local money is the hard part. That’s also where stablecoins have their clearest edge. PhotonPay’s version is an Airwallex-style bundle with stablecoin balances already in the wallet and a tilt towards ad buyers and online exporters. Reap, also in Hong Kong, sells a similar mix.

PhotonPay spent a decade building “the banking relationships, licenses and local payment infrastructure” to be that. TBlockchain Capital partner Yuanhan Li gave the investor’s answer. Businesses “want one provider,” and he numbers behind this sit in Going back to my favourite table of the whole Harvard paper. Wise gave the authors its costs for a pound-to-euro transfer. Wise spends 24p to process a transfer. Of that, 22p goes toward helping customers, running required checks and keeping the service running. The remaining 2p covers bank fees, managing cash across currencies and exchanging the money.

If you ask me who holds the strongest hand among blockchain access, licences and customer relationships, I’d say customers. Blockchain access is the easiest to buy, because Bridge and BVNK exist to sell it. Licences take years, but they only get you in the door. Wise holds 80-plus and still had its US trust bank application denied in July, and it plans to reapply. Customers are the hardest to replace, and even PhotonPay shares some of them.

Luban keeps the relationship with the advertiser, and its guide also lists PingPong, Payoneer and WorldFirst for funding its wallet.

You can see this in where revenue comes from. Almost half of Wise’s net revenue now comes from outside cross-border fees, through interest, cards and other lines. Airwallex says more than 90% of its revenue comes from customers using more than one product. Both are becoming the place a business keeps its money.

The price advantage in emerging markets is also shrinking. Stablecoin rates for rupees beat the benchmark by about 5.5% in 2021-24 and 2.9% in 2025-26. The paper puts these gaps down to capital controls, the rules that limit money moving in and out of a country, so that edge lasts only as long as the rules do.

Margins are the bigger worry. Consumer fintechs already charge about 0.4-0.6% on transfers above $10,000 to developed markets, and Wise keeps cutting its price. The fat sits in emerging markets, where the one B2B platform in the BIS sample averaged over 3%, and about 6% on reais. It’s a small sample, but I’d expect everyone above to go after that spread. If transfers turn into a commodity, the defence is selling things that aren’t transfers.

What does this mean for PhotonPay?

PhotonPay and companies like Airwallex and Reap are solving problems that banks leave behind. The economics of those problems explain why investors keep paying for them.

The first problem is fixed cost. Checking a customer and answering the phone cost about the same on a $2,000 payment as on a $2 million one, and Wise’s numbers showed those jobs are most of the bill. A bank makes far more on the big payment for the same work, so the small one is easy to drop. Between 2011 and 2022, the Bank for International Settlements counted about 34% fewer active correspondent banks in Africa and about 50% fewer in South America.

These are the banks that carry payments for other banks. A company like PhotonPay pays for its licences and bank links once and spreads the cost across 200,000 customers. You can call that economies of scale, and it’s how a small seller gets a service no bank would build for her.

The second problem is time, which we can price as opportunity cost, meaning whatever you gave up by waiting. Say a seller’s $1,000 of ads would bring in $3,000 of sales. A 1% card fee costs $10. A cheaper transfer that arrives too late for the ads to run costs the $3,000. That’s why a business will skip the cheapest route and feel fine about it.

The other scope is credit. Sellers often need money before their sales come in, to pay for stock or ads. Banks are slow to lend to them, because a small exporter abroad is hard to judge. When buyers can’t tell good from bad, good sellers get treated like bad ones. The Asian Development Bank put unmet demand for trade finance at $2.5 trillion in 2025. A company that sees a seller’s money come in and go out every week knows how that business is doing, so it can lend with more confidence. The company earns interest, which doesn’t depend on transfer fees. PhotonPay said in January that it plans to offer credit, and Reap already sells credit cards.

When one part of a business gets cheap and easy to copy, the money often moves next door.

Clayton Christensen and Michael Raynor had a long name: the law of conservation of attractive profits.

In cross-border payments, the transfer is the part getting cheaper. That opportunity may be becoming the account a business uses every day also, card they use to spend it

A payments company can earn a share of the fees when that card gets used. It also has a chance to handle the seller’s next payment because the money is already there.

So when investors put money into a payments company, I think they’re betting on a company that customers would have to reorganise their working day to replace.

Thejaswini

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Every Friday afternoon, treasury teams at large companies pull off a silly act out of helplessness. They sell part of their money market funds, which pay close to 4%, and move the cash into a bank account that pays nothing. All this so that they could pay the bills that are due over the weekend. Since the funds don’t trade on weekends, the money there could stay blocked until Monday.

When this habit recurs across corporate America, the number turns big. US companies hold over $2.5 trillion in checking accounts and cash. US banks hold over $4 trillion in non-interest-bearing deposits.

The issue is that someone is earning interest on all of this, just not the person whose money it is. Financial infrastructure being shut and moving cash being a hassle are two avoidable reasons that can keep money idle and unproductive. The good news is that both those reasons are going away.

As money compounds every hour, the profit from the arising value accrues to those who move the money. In today’s story, I explain how that’s happening.

On to the story…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Money’s Weekend Trail

On a Friday evening, corporate treasuries move cash back to the company’s operating account and earn no interest. But the bank does. Banks can park this cash at the U.S. Federal Reserve, which pays banks 3.90% on reserves since it raised rates in September.

On a billion dollars, that rate fetches a little over $200,000. By Monday morning, the bank pockets all this money that isn’t its own. What does the treasurer get in return? An earnings credit that the companies can use to claim a rebate or eliminate their monthly commercial banking service fees. Except that this works as a coupon and cannot be withdrawn in cash. So, the bank also decides what the idle cash is worth.

Weekends and public holidays in the U.S. add up to almost a third of the year. Consider every company that pre-funds payroll and supplier payments in this approach. McKinsey estimates interest income from this approach accounts for 40-45% of transaction-banking revenue, or $500-550 billion globally.

On the roughly $4 trillion worth of non-interest-bearing deposits, depositors give up about $160 billion a year in interest. Although not all of it goes to banks’ coffers, the earnings credits still is a minor dent in what is one of the biggest income streams for banking.

Even the commission-free brokerage, Charles Schwab, holds about $485 billion worth of client “sweep” cash at the end of August. That’s the money left in a brokerage account after you sell a stock, which Schwab puts in a bank account and pockets the interest.

It’s a similar pattern that repeats in multiple places. Money is never really unproductive. When it stays idle at a place, it’s more often than not the bearer who pockets the compounding growth and not the owner.

Last week, one bank gave up on this cash cow and threw away its own lunch.

On a Thursday, Uber used HSBC’s tokenised deposits to buy Franklin Templeton’s tokenised money market fund through Tradeweb. A tokenised deposit is an ordinary HSBC deposit recorded on a blockchain, so it can move at any hour, including Sunday. The trade settled on Canton’s chain. On Sunday, with banks shut, Uber sold part of the position via blockchain and earned about 1.5 extra days of interest compared with selling on Friday.

HSBC said it would offer the same service to its other U.S. clients.

For one trade, Uber’s gain is small. At 3.7%, keeping $1 billion invested for an extra day and a half earns about $150,000. But doing it every weekend compounds that prize to roughly $8 million annually. Any company that pays its suppliers, gig workers, or drivers over the weekend will be the biggest beneficiary of no longer having to lose interest on holding idle cash.

So why would HSBC give up that money? Because the alternative poses existential risk to the banks.

In July, Tradeweb ran its first on-chain Treasury trade on Canton. The cash side of that trade was USDCx, a stablecoin. If a company can settle its weekend trades with a stablecoin, it doesn’t need its bank on Sunday. Perhaps it may not need its bank deposit either. HSBC chose to lose the weekend spread rather than the client altogether.

Read: Defending the Deposit

The weekend business is going away anyway, whether banks like it or not. DTCC, which settles almost every US securities trade, turned on its tokenisation service on October 28, with 20 firms and about $30 trillion of eligible securities. On October 19, JPMorgan put its deposit token on Ethereum. DBS moves group cash across borders in seconds for clients like Ant International. Even the Federal Reserve will open Fedwire, its main payment system, on Sundays from 2028 or 2029.

Once the central bank works weekends, the industry loses its main excuse to keep the cash idle and pocket the interest.

But fixing money movement on weekends is an easier problem. Most idle money stays stuck because nobody bothers to move it. Code and blockchains have started solving that problem.

In April, Schwab’s shares fell 7.6% in a day on reports that JPMorgan was building an AI tool to manage cash. By October, Schwab was blocking outside AI agents from moving client money and steering customers to its own assistant.

Treasury teams are further along than retail investors. In Tradeweb’s 2026 survey, 22% said they already use AI for cash forecasting and liquidity. A treasurer can’t check every balance every hour. But software can. And once it does, frictions that a client faces stop adding free money to the bank’s coffers.

Who Keeps the Money?

You’d expect the yield to go back to the owner once idle cash starts earning. But not all of it does. In fact, in some cases, none of it goes back to the owner of the money. Like we see it happen with stablecoins.

Circle, which issues USDC, earned ~$1.4 billion in the first half of 2026, almost all of it interest on reserves. Out of this, it paid ~$819 million, about 60%, to the platforms that distribute USDC to users. Coinbase alone got ~$655 million. Eventually, Circle retained about 7 cents of profit per dollar of revenue. It holds the reserves and handles the regulators, yet retains the smallest share.

The one who owns the customer via the distribution takes the biggest cut.

Read: Who Captures Value in Web 2.5

The value flows similarly in corporate treasuries, too. Tradeweb paid $785 million for ICD, a portal where treasurers buy money market funds. It now serves 600+ clients, including about a fifth of the S&P 100. Fund managers pay ICD a small cut of every balance. Franklin Templeton charges about 0.20% a year on its on-chain fund. The funds compete for the cash. Meanwhile, it’s the portal that decides what funds a treasurer can see on Sunday.

All this explains HSBC’s move.

A bank that tokenises its deposits becomes the default route for the client to park their cash. Although the bank ends up earning less per dollar, it still stays part of every trade and retains the client. JPMorgan and DBS are also making the same bet. Even the banks that aren’t making this bet are busy lobbying. They fought hard against stablecoins paying interest, and the CLARITY Act stalled on a 49–50 Senate vote in September.

But this fight will just buy the banks some time, not a different outcome. Although the change won’t happen overnight, it will happen inevitably. Currently, only 1% of US treasurers say they use or pilot stablecoins or tokenised funds, per the 2026 AFP Liquidity Survey. But 86% of the respondents said they’d use real-time money market funds. Tokenised Treasury and money market products hold about $15 billion, or 0.2% of a $7.9 trillion money fund market. Zero-interest deposits hit a new high in the second quarter.

Corporate treasurers are continuously seeking cash that can earn every hour. But it’s not as easy. Some of the board policies are selective and sign off only on products they recognise. Auditors also need to consider if a tokenised fund in a digital wallet still counts as cash. But none of this is a technology problem. It’s got to do with paperwork.

There’s also a catch in the weekend trade. The U.S. Treasuries don’t trade on Sundays. When a fund lets Uber cash out on a Sunday, someone else is holding that redemption on their books until Monday, when the underlying bonds can actually be sold. On a weekend with low activity, that’s not a problem. But on days when many treasurers are panicking to cash out at the same time, it needs someone to have a lot of spare cash on a Sunday. That entity will charge for the extra liquidity they provide. This makes providing weekend liquidity a business arm in its own right. Not all the interest that used to accrue to banks on idle money will reach the corporate treasurer. A big slice of it could go to those who provide liquidity on the weekends.

Read: Making Funds Move at Internet Speed

There’s a behavioural study that explains the corporate treasurer’s phenomenon.

In 1952, an economist named William Baumol asked a question: why does anyone hold cash that earns nothing?

He rationalised that every trip to the bank costs money, time, and effort. If converting investments into cash were free, you’d keep everything invested and cash out the minute a bill was due to be paid. Because it isn’t free, you keep a pile of idle cash so you don’t have to make that trip too often or pay in the form of other costs. Baumol’s maths said the right amount of idle cash rises with the cost of each trip and falls as interest rates rise. Economists call these “shoe-leather costs,” after the shoes you wear out walking to the bank.

The weekend is that stretch of the week that allows no trips to the bank. So the treasurer makes one big trip on Friday, pulls out everything the company might need until Monday, and lets it stay idle.

As per Baumol’s model, the ~4% interest rate makes every idle dollar expensive. Infrastructure that never switches off removes the time cost of the trip, while software that moves money removes the effort cost. So when the trip costs almost nothing and holding cash costs 4%, it’s a no-brainer to hold almost no cash as idle money.

This forces the banks to eat their own lunch, because the alternative costs them more. HSBC gives up its weekend spread so that it can retain Uber as its client. By doing so, the client continues to do business with the bank for other products, where the margin is higher.

That’s it for today. I will be back with the next one.

Until next time, stay curious,
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

Everyone is talking about Hyperliquid’s token price and calling the ICE deal the biggest institutional validation in crypto history. So why did the company that owns the New York Stock Exchange just volunteer to give up the one layer that has defined the exchange business since electronic trading replaced floor pits? Matching and settlement.

Exchanges have spent 25 years telling shareholders that the matching engine is the moat. But if that layer can be outsourced to a two-year-old protocol, the question is what’s left of an exchange.

I think the exchange business model is about to go whack, and this deal is where it starts. Let’s drill!!

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

What’s Left of an Exchange?

When electronic trading killed the floor pit in the early 2000s, everyone assumed the specialists were the casualties. Those guys in the coloured jackets shouting across the NYSE trading floor, waving paper tickets, and making markets with hand signals and eye contact were replaced by servers in data centres in New Jersey, and the financial press wrote eulogies about the death of a romantic era.

But here’s what nobody pointed out at the time. The New York Stock Exchange itself was completely fine. The building on Broad Street turned into a television studio. The listing business kept printing money, even more now. The regulatory charter that lets companies go public on the NYSE was worth the same the day after electronic trading arrived as the day before.

The floor specialists thought they were the exchange. But in reality, they were just the exchange’s technology layer, and when better technology came along, the exchange shrugged and swapped them out.

I thought a lot about this while reading coverage of the ICE and Hyperliquid deal, because I think we are watching the exact same thing happen again, one layer up. For twenty-five years, exchange CEOs have been telling shareholders that the matching engine is the new floor. That the proprietary software that pairs buy orders with sell orders separates a great exchange from a mediocre one. That the billions spent on co-located data centres and microsecond latency improvements represent an irreplaceable competitive advantage. And the whole time, the real advantage sat underneath all that technology in the most boring place possible. It was sitting in a CFTC filing cabinet.

Let me explain: when a pension fund wants to trade crude oil futures. The fund manager does not shop around for the exchange with the prettiest matching engine. They call their broker at Goldman or JP Morgan, who holds a clearing relationship with CME’s clearinghouse, which in turn holds a derivatives clearing license from the US government that took decades of capital commitments and regulatory trust-building to obtain.

If their counterparty defaults, CME’s guaranty fund absorbs the hit. If the guaranty fund is not enough, CME’s own capital is on the line. The entire system runs on a chain of regulated guarantees that nobody can fast-track or replicate without going through the same decade-long process of proving to federal regulators that you can be trusted with institutional money. The matching engine, the software, is the least interesting link in that chain. A good engineering team can build one in a few months. What they cannot build is the government’s trust.

A wonderful bit of corporate history about McDonald’s parallels what is happening in the exchange industry right now. In 1957, Harry Sonneborn, McDonald’s first CEO, told a group of MBA students that they were wrong about what business the food giant was in. The students said it was selling hamburgers, but Harry denied it, saying it was real estate.

McDonald’s does not need to cook a single burger to make money. The franchisees cook. McDonald’s owns the land under the restaurants, and that land appreciates whether the burgers are good or terrible. Every strategic decision McDonald’s has made over the past seven decades follows from that one insight.

Jeff Sprecher figured this out way before, and honestly, the man might be the most underrated strategist in American finance. He has spent $35 billion on acquisitions across his career, and every single one followed the same logic.

He bought NYSE for $11 billion in 2013, for the market data business that now charges every institutional desk in America millions a year for feeds they literally cannot trade without. Between 2018 and 2023, he bought two mortgage platforms that now process most US residential mortgage originations. Analysts even questioned why an exchange company was buying mortgage software. But his strategy has always been to buy toll booths on capital flows that cannot be rerouted.

And now he has volunteered to hand the matching and settlement technology for perpetual swaps to Hyperliquid, and everybody is screaming why ICE would give away its keys. But Spencer answered with the most expensive action he’s ever taken: the matching engine was always the burger. The regulatory license is the real estate.

CME is the living proof. Eurex launched a U.S. exchange in 2004 to take Treasury futures away from Chicago with cheaper fees, and a consortium of Wall Street banks tried the same thing with ELX in 2009. Both folded, because every trader’s existing positions already sat at CME’s clearinghouse, where each new trade nets against everything they hold, and that netting saves more margin than any rival’s fee discount could ever return.

So if exchanges sell permission and not technology, and if ICE just proved it by outsourcing the technology to a two-year-old protocol, the obvious next question is why now. Why did Sprecher pick this exact moment to break character?

Because a product is coming straight for the exchange industry to whack its liquidity and its moat. Perpetual swaps are the fastest-growing instrument class in derivatives history, and they are structurally designed to gut the revenue model that every traditional exchange currently depends on to justify its stock price.

The real threat perps pose to exchanges is how they roll revenue. In traditional futures, contracts expire, so every time a trader wants to keep a position alive past expiry, they have to roll forward into the next contract; every roll is a transaction the exchange charges fees on, meaning a single position held for a year can generate six fee events.

That roll revenue is why CME trades at 30x earnings instead of 15x, and perps eliminate it completely. A contract that never expires never needs to be rolled, and the funding rate keeps them tethered to spot, flowing peer-to-peer without the exchange touching a cent. When perpetual swaps became a serious regulatory conversation in the US, exchange stocks collectively lost roughly $18 billion in market cap in two days.

Sprecher has no choice but to offer perps. The product did $90 trillion in volume globally last year, Hyperliquid alone pulled $900 million in revenue in its first full year without a single American user, and every institutional desk on the planet wants access. But offering perps also cannibalises the futures revenue that funds his entire business. When every option on the table is bad, and the only choice is between which one kills you slower. He picked the one that at least lets ICE survive the transition with its franchise intact.

Instead of spending billions rebuilding continuous clearing from scratch and then waging a multi-year regulatory battle to get the CFTC comfortable with it, Sprecher plugged into infrastructure that already solves the problem natively.

Another thing to note is that Hyperliquid is not an exchange. It is a protocol. The protocol does not know or care whether the entity deploying on top of it is the company that owns the New York Stock Exchange or some anonymous team launching a meme coin market from a beach in Bali. It just matches and settles.

The company that owns NYSE is now, in structural terms, an application running on someone else’s platform, the same way a startup runs its entire business on AWS and Amazon provides the compute layer without caring what gets built on it.

In the language of the Fat Protocols thesis that crypto has been debating since Joel Monegro published it in 2016, ICE has voluntarily turned itself into a thin application sitting on top of a fat protocol. The protocol layer captures the base infrastructure value. The application layer survives on whatever premium it can charge for regulatory compliance and institutional trust.

I think Sprecher is right that the premium is fat enough to sustain a massive business, and I will get into why in the section ahead. But I also think the valuation consequences for the broader exchange industry are something that nobody has properly thought through yet. If the matching and settlement technology that justified exchange stock premiums for fifteen years can be outsourced to a protocol that did not exist three years ago, what were those premiums actually paying for?

Those multiples priced in the fact that exchanges own irreplaceable proprietary technology, and ICE just demonstrated that the technology is a swappable module. What remains is the regulatory franchise and the distribution network, and those are genuinely valuable, but they are not technology company assets. They are utility company assets.

The Primitive That Eats Finance

Most coverage of the ICE trade.xyz deal treats perpetual swaps as a product, like ICE just added a new item to the menu. But Perps are not a product, they are a financial primitive. Think of them like Lego bricks. A single Lego brick is boring, but the shape is so fundamental that you can build anything literally out of enough of them.

Perps work the same way. A stablecoin like DAI is a perp, a margined future is a perp, even a Uniswap LP position is roughly a perp. They look like completely different financial products, but they are all the same underlying structure. If you stack enough of these bricks together in the right combination, you can reconstruct any financial instrument that exists, like options, structured products, insurance contracts, anything.

The math behind this is called a Taylor series, and in plain terms, it means a settlement engine designed for perps isn’t an engine for one product. It is an engine for building every financial product. ICE did not add a product to its shelf. It plugged into the primitive layer that could subsume every product on the shelf. Equity settlement is a 1-perp, stablecoin clearing is a 0-perp, options exposure is a combination of higher-order perps, and ICE’s mortgage platforms process collateralised loans that are functionally a variant of the same primitive.

ICE has just connected itself to the infrastructure that could eventually route all of those flows through a single settlement layer. I don’t think Sprecher has fully thought through what he plugged into, and I mean that as a compliment to the Hyperliquid team, because I don’t think anyone outside a very small circle of crypto-native researchers has thought through what perpetual contracts actually become when you take the math seriously.

CME is the most dangerous stock in the exchange sector right now, but if you break it down, its entire monopoly rests on liquidity gravity and its regulatory franchise, and liquidity gravity only holds when there is no alternative venue where a trader can get the same exposure with better economics.

Perps are that venue. A trader holding a position through a perpetual contract pays zero roll fees, and when the math on keeping your position alive goes from six fee events a year to zero, institutional desks will migrate regardless of how long they have been clearing at CME. This is like Kodak inventing digital photography and then spending twenty years marketing film as the future because their entire profit depended on chemical processing. CME has the engineering talent to build continuous clearing and the regulatory relationships to get CFTC approval, but it cannot survive admitting that everything it built over four decades is a swappable module that a crypto protocol already does natively.

And things are already coming from the opposite end of the spectrum, Kraken acquired NinjaTrader, which holds an FCM license, and Bitnomial, which holds both a DCO and a DCM designation from the CFTC. A crypto-native company now owns the exact regulatory stack that I just spent the entire first section arguing is the irreplaceable moat protecting traditional exchanges.

ICE is moving down the stack by outsourcing technology to a crypto protocol and keeping the regulatory franchise. Kraken is moving up the stack by acquiring the regulatory franchise and keeping the crypto-native technology. Both are converging on the same spot, regulated perpetual swaps cleared on continuous settlement infrastructure and distributed to institutional clients through licensed intermediaries, and honestly, we do not yet know which direction builds the more durable business.

If I had to bet, Sprecher is right on a five-year horizon but very wrong on a fifteen-year one. The institutional trust premium is real and fat, and it will take years for a crypto-native company to build the reputational weight that makes pension fund compliance officers comfortable. But the premium is a wasting asset. Every year that Kraken operates its DCO without a blowup, every year that Hyperliquid settles trillions without a systemic failure, the trust gap shrinks. Trust is being evaporated by the simple passage of time and the accumulation of a track record crypto didn’t have five years ago and now does.

People in the crypto industry are excited because the HYPE token price is going up. The people in TradFi are excited because a real institution validated their favourite protocol. Today, Hyperliquid is not competing with Binance or Coinbase anymore. It is competing with CME Clearing, the DTCC, and every post-trade settlement layer in global finance, and its technology is already better than all of them at the one thing that matters most, settling trades that never stop.

Sprecher accepted that the technology was never the moat, plugged into the best infrastructure available, and bet everything on the regulatory franchise being valuable enough to sustain a massive business even after the technology premium goes to zero. I think he is right. I also think he just drew a map showing every other exchange exactly how expendable they are.

That’s all for today!
Vaidik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Hyperliquid has earned about a billion dollars in fees over the last 12 months. It did this without ever raising a dollar from a venture fund. Fragment, the marketplace where Telegram users trade usernames and phone numbers, earned over $350 million over the same period without ever launching a token or raising funds from a VC.

For a decade, an average crypto startup first raised funds from VCs, launched a token, and then hoped business sense would prevail to justify the former two. That model has flipped as the best crypto projects prefer to build from a business-first approach.

Hyperliquid and Fragment aren’t exceptions. The broader market has taken note of such protocols and has followed suit. More crypto companies are doing away with tokens and VC money as the industry rapidly merges with fintech.

Even where tokens exist, investors now prefer to bet on cash flows to stories.

1kx’s 2026 blockchain revenue report tracks fees across more than 2,600 crypto protocols and 100 listed companies. The report establishes how the industry is fast entering its earnings era. And, oddly, it’s happening in a year when total crypto revenue fell about 30%.

Read 1kx Blockchain Revenue Report

In today’s piece, I argue three things. Revenue has replaced the token as the preferred way for crypto companies to fund themselves. Markets now price tokens based on what they pay. And, lastly, I show how value flows to whoever owns distribution, rather than to a token.

On to the story…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Why Launch a Token?

A token was a default option for crypto startups for multiple reasons. It not only brought in the money to launch but also handled the marketing. A token launch always made enough noise to grab attention. That attention also brought in customers.

Revenue has made all these jobs redundant.

Among crypto protocols that earn fees, 157 launched a token in 2025. Only 24 had done so by the end of September this year. 1kx partly attributes that to the ongoing bear market. Launches do dry up when prices fall across the board, but the protocols that are skipping the token route aren’t exactly those struggling to survive.

Protocols without a token now collect 31% of all fees paid to crypto applications, up from 20% last year. They earned more in absolute terms this year than in 2025, even as fees at token-backed protocols fell by more than a third.

One of the reasons for skipping tokens is how fast they can start making money. In 2026, 22 protocols reached $100 million in annualised fees within six months of switching on charges. Seven of those started charging fees only in 2025 or later.

Blockchains lend this speed to crypto businesses that is not otherwise available to off-chain businesses.

An average fintech must build or license its infrastructure, sign up partners and gather enough liquidity before earning a dollar. However, a crypto startup can just plug into public blockchains, shared liquidity and wallets that its users already hold on day one.

The other aspect is capital efficiency. The top 20 revenue-earning protocols earn $6.30 in fees per dollar raised. Axiom, a trading app with no token, earns 607 times what it raised.

What makes this observation even more stark is that the media crypto app outside the top 20 earns about 8 cents a year for every dollar raised.

The head of the market raised revenue. The tail raised money. For years, venture rounds and token launches hid the difference between raising revenue and funds. This capital efficiency highlights how the leaders raised revenue while the tail raised money.

This worked until the token was all that mattered to the industry and investors. A token could be listed and sold whether or not anything beneath it worked. With a mature industry and investor collective, that cover is now gone. Although Hyperliquid, one of the most profitable protocols, has a native token - HYPE, it followed the revenue and not preceded it.

So, revenue first, token last, if at all.

Read 1kx Blockchain Revenue Report

Markets Price the Payout

Once a token stopped being the preferred fundraising route, it raised an important question: what does it pay for then?

For most of crypto’s history, tokens rose and fell on narratives, supply schedules and exchange listings. What separated the ones that rose from the fallen? Whether a protocol sends value back to its holders, usually by using its fees to buy its own token from the market.

This year, that factor won outright. A 1kx index of tokens that pay holders returned 82% between January and September. During the same period, Bitcoin lost 5%, and the top 100 tokens outside Bitcoin and Ether lost 7%.

Some of these tokens also beat publicly listed crypto companies.

Coinbase stock fell 18% this year. Robinhood was flat, and Circle rose 4%. Tokens with no legal claim, board of directors, or regulator outperformed companies that had them all. How? By doing the one thing equity investors understand. They handed value back to the investors in cash.

In 2024, the same index rose 63% while Bitcoin more than doubled. In 2025, it lost 14% while Bitcoin lost 6%. This year, the market has changed what it rewards.

Valuations have caught up. A decentralised exchange that pays its holders now trades at a median of six times its annual fees. Listed exchanges and brokers mostly trade at single-digit multiples of their sales, with Coinbase at eight.

Protocols are responding by paying generously. In Q3 2026, they transferred $1.5 billion to token holders, about 40% of all fees paid on blockchains. Apple’s dividends and buybacks came to 27% of its revenue in the second quarter.

A token that doesn’t pay back its investors is just a worse asset than the token that does.

Distribution Is the New Token

The token’s third job was bringing in users. Airdrops, liquidity mining and points programmes all paid people in tokens to show up. That job has been taken over by distribution. The user base is now either bought or borrowed from someone.

This is evident in the case of Circle. The USDC-issuer earns interest on the Treasury bills backing its dollar stablecoin and hands about 60% of that to the partners that distribute it. Coinbase alone received $655 million in the first half of this year.

The company that issues the dollar keeps less of it than the exchange that hands it out.

Uniswap also has a similar story. Without Robinhood Chain, its fees would be down 54% from a year ago. Two-thirds of Uniswap’s fees now come from a single distribution partner.

Hyperliquid has built distribution into its design. Its HIP-3 protocol version lets outside developers build their apps and launch their markets on top of it. In return, it parts with a portion of its fee income with them. Those builders and market creators now take 16% of Hyperliquid’s fees.

All three elements taken together show that every time a new financial technology matures, the instrument stops being the point, and the cash flow takes over. I’ve argued before that value in this new layer of finance accrues to whoever owns distribution, not to the asset class. Crypto tokens are also now being judged by this rule.

Read: Who Captures Value in Web 2.5?

That’s healthy. A token that pays is an asset that can be valued. A protocol that can fund itself from its own revenue doesn’t have to sell its future to a fund before it even has a product. This doesn’t dismiss the need for a token. It just shows that launches of new tokens will now have to mimic share classes that are issued by businesses that already earn revenue. These tokens will be priced based on what they pay out.

The pleasant irony here is that one of the most convincing cases for crypto’s best businesses no longer needing venture capital and tokens comes in a 78-page report written by a venture fund.

That’s it for today. I’ll be back with the next one.

Until then, happy reading!
Prathik

Read 1kx Blockchain Revenue Report

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

I am back this week after a brief hiatus last week.

Welcome to Edition 23 of The Reading List. Every Saturday, I share my favourite reads across crypto, finance, technology, culture, and beyond, alongside what I’m watching and reading.

This week, markets had to absorb two sharp shocks: a reported $6.3 billion security breach at Binance and renewed doubts about how quickly companies can earn returns on their AI spending. Strong US growth and persistent inflation made the rate outlook harder to dismiss, while conflict around the Strait of Hormuz kept energy supply in focus.

Before we get to the reading, here’s what moved markets and what we covered at The Token Dispatch.

Let’s get going…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

The Week That Was

Markets witnessed a widespread downturn across asset classes this week. Bitcoin was down 3.65%, while silver fell 9.24% and gold 3.12%. The dollar index rose 0.29%, and spot crude gained 0.54%. The S&P 500 CFD was down 0.88%. The US cash-market figures differ slightly: by Friday’s close, the S&P 500 was down 0.6% for the week, the Dow 0.9% and the Nasdaq 2%.

On Friday alone, the Nasdaq fell 2.9%, its steepest daily drop since June.

The sell-off worsened as investors questioned whether the returns from AI investment justify the cost. Intel fell 28.6% after its outlook disappointed, while Amazon’s results renewed scrutiny of the cash required to build AI infrastructure. The Nasdaq’s weekly decline was larger than the S&P 500’s, and small caps finished higher. That divergence suggests the pressure was concentrated in technology rather than spread evenly across the market.

The economic data gave the Federal Reserve little reason to relax.

The first estimate of US third-quarter GDP showed annualised growth of 3.4%, up from 2.2% in the second quarter. September’s PCE price index rose 3.6% year over year, while core PCE rose 3.1%. The Fed held its policy rate at 3.75%–4% in a 10–2 vote; the two dissenters preferred a quarter-point hike, and the statement said inflation remained elevated.

The message was that growth is holding up, but the inflation problem has not gone away.

Other central banks moved in different directions.

The Bank of Canada raised its policy rate by a quarter point to 2.5%, citing persistent energy-price pressure. The European Central Bank held its rates steady but warned that higher energy costs could raise inflation and weigh on growth. The Bank of Japan also held the rate at 1.25%, while signalling that further increases remain possible.

Geopolitics kept that energy pressure alive.

Brent futures rose 2.4% on Friday to $117.64 a barrel after the UK Maritime Trade Operations agency reported that a tanker had been hit by explosive devices in the Strait of Hormuz. Iran separately claimed that several tankers had caught fire after hitting mines; Reuters said it could not immediately verify that account. Qatar presented Iran with a proposed framework to end the US-Israeli war, but an Iranian official called the ideas unacceptable.

Crypto’s biggest story was Binance’s reported security breach.

The exchange estimated the impact at about $6.3 billion and said roughly $3 billion had been frozen with help from blockchain partners; it also said user funds were safe. Binance’s initial account pointed to a compromised engineer’s laptop, while blockchain-analysis firms attributed the attack to North Korea-linked actors. That attribution remains an assessment as the investigation continues. Bitcoin fell below $79,000 after the news, and crypto-futures liquidations rose sharply.

What We Covered This Week

At The Token Dispatch, we followed a common question: what makes an asset or an AI service useful in practice, and who provides the trust, liquidity or infrastructure that lets it work?

In Turn a Ticker Into a Financial Product, Vaidik wrote on how tokenised assets can gain attention, access and financial uses in a different order from traditional markets. Memecoin trading helped create liquidity for stock tokens, while institutional products still rely on trusted intermediaries.

In Stocks Will Borrow Where Brokers Can’t, I looked at what changes when tokenised shares can back loans. Borrowers may gain access to credit beyond a broker’s usual hours or reach, but stale weekend prices leave both lenders and borrowers exposed.

I then examined Aave’s new lending structure in Marrying DeFi Funds with RWA Collateral. Its Hub-and-Spoke model lets specialists such as Sentora assess tokenised real-world assets as collateral, while Aave retains control over shared liquidity and the ability to switch a lending desk off.

Thejaswini explored two less visible inputs behind AI.

In Life-Coach the Robots, she followed businesses that collect people’s physical actions to train robots, and asked whether contributors receive fair compensation or retain meaningful control over their data.

In AI Inference 2, she examined how buyers can tell whether an AI provider has served the model it claims to offer.

We have got more lined up for you in the coming weeks. Until then, we’ve got some recommendations that will keep you in good company.

On to this week’s reading list.

Regulation & Policy

• Criminal: Scott Galloway argues that political corruption weakens the rule of law and institutions that help make the US attractive to entrepreneurs, and calls for voters to hold leaders accountable.

Crypto & Tech

• North Korea gets away with $3 billion from Binance: Molly White examines the reported Binance wallet theft, efforts to freeze the stolen assets and the unanswered questions around user reimbursement.

• Not Your Keys, Not Your DAT: Matt Levine looks at Datavault’s reported inability to withdraw Bitcoin held through an exchange account, and what the episode says about custody and digital-asset treasury companies.

• From Exposure to Claim: The Formation of Frontier Financial Markets and ISDA: Lauris argues that a market can lack a workable financial instrument even when buyers, sellers and demand exist, and explores how ISDA-style contracts could help create one.

• The Biggest Opportunity In Crypto: Marlon makes the case for on-chain options as a major growth opportunity, pointing to expanding options demand and crypto’s still-small market.

Finance & Economy

• When banking could be a capital offence: Byron Gilliam recounts how Sweden’s first bank created paper credit, overextended its lending and brought its founder close to execution.

• The Heisenberg principle of investing: Byron Gilliam argues that precise measures, such as near-term earnings forecasts, can be less useful to judge a company’s long-term value.

AI & Innovation

• Verification Is Not Knowledge: Cosmos Institute and Jessica Hullman argue that formal verification cannot replace human understanding, judgment and accountability in science.

• The AI Boom Has a Power Problem: Delphi Digital examines the electricity demands of AI data centres and the companies that may benefit as the race to supply power accelerates.

• Charts of the Week: Attention is All You Need: A collection of charts looks at competition for viewers’ limited attention, the continued demand for GPUs and the growing pressure on SaaS companies to prove their security.

Culture & Beyond

• In praise of the dilettante: Jay Miller makes the case for the curious amateur, whose wide-ranging interests counter an age organised around specialisation and optimisation.

• An Afghan woman recalls how her country’s devastation led to her own: In a short documentary, an Afghan woman recounts how war and social upheaval reshaped her childhood and family life.

• What Is Special About Human Beings?: Using Frankenstein, The Culturist asks what makes human life valuable and how that should guide technological progress.

• Thoughts on Humanity in the Age of the Machine: Priyanka Desai reflects on humanity’s place in an era shaped by machines.

That’s all for this week. I’ll be back next Saturday.

Until then, happy reading!
Prathik

P.S.: Read, watched or listened to something worth sharing? Send it my way for a future edition.

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Thanks for reading! Subscribe for free to receive new posts and support my work.

Snapper was the worst. Of 120 samples sold as red snapper, only seven were red snapper, and some of the rest were cheaper farmed fish like tilapia. The “white tuna” was even stranger. 84% of it turned out to be escolar, a fish that can give some people serious stomach trouble if they eat more than a few ounces.

White fish on a plate looks like white fish on a plate. So people at the table couldn’t say.

Not so many can see it now, but AI has a blind spot. AI has a lot of them, but this one builds on something we already talked about earlier.

A few weeks ago I wrote about AntSeed, an open market where anyone can sell access to AI models. Also, I know we never had an Inference 1 article to begin with, but this is 2. So essentially, that one was 1.

Read: GPT 97% Off

In the antseed article, we saw two sellers offering OpenAI’s GPT-5.6 Sol at 7 cents per million input tokens, 97% below the price the market’s own dashboard compares against. A token is a chunk of a word, and a million of them is roughly 750,000 words. Only OpenAI and its cloud partners can run that model. So either the seller had found some cheap way to resell real access. Or it was serving a different model and calling it GPT or Opus.

Antseed isn’t the only one. Surplus Intelligence also buys and sells models. Orbio does something closer. OpenRouter is the famous one, but they review applications and test endpoints themselves.

Today we’re picking up where that piece left off, with the second possibility, and with the handful of companies trying to make it easy to check which model you got.

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

A quick map, since this is a series. Every time you type into ChatGPT, a computer somewhere runs the model on your words and sends back a reply. That’s called inference.

Around that activity, now there’s a business of hosting models and selling access, also called inference-as-a-service. Running the software that handles those requests is called model serving.

Our first piece covered the open markets. This part covers verification. Later parts look at the companies that run models for you, like Together AI and Groq, and then at the chips built just for this job.

From the buyer’s side, buying AI is very easy. The buyer here is usually a developer or a company building an AI product, rather than someone using a chatbot directly.

The buyer sends a message with a model name attached, say kimi-k3, a model from the Chinese lab Moonshot. An answer comes back, and Moonshot charges $3 per million input tokens for Kimi K3, according to the price comparison on Engy, which itself charges $1.95. Some AntSeed sellers go lower. All of them send back text that reads like Kimi. I picked Kimi for this over GPT or Claude for a reason.

Moonshot makes Kimi K3’s model files available to download, so providers can run it on their own hardware and compete to sell access. Some sellers may instead forward requests to another provider.

Proprietary GPT and Claude models are different. Their files are not publicly available, so genuine access ultimately comes through OpenAI, Anthropic or their supported cloud services. You can compare a seller’s answers with those from an official service, but you cannot download the model and independently rerun it to check.

On the seller’s side, they can cut costs in two ways. It can run a smaller model under the big model’s name. Or it can run the real model in a squeezed form. An AI model is a giant pile of numbers, billions of them. Normally, each one is stored with a lot of detail. A seller can store them with less detail, so each number gets rounded to the nearest value in a smaller set. The whole model then fits on fewer chips. That’s called quantisation, and it’s a bit like saving a photo at lower quality. Done well, you barely notice. Done badly, the model gets worse at long, careful reasoning, and on easy requests you’d never spot it.

Researchers have checked. In mid-2024, a Stanford team tested 31 services from nine companies selling Meta’s Llama models, and 11 of them were serving something that didn’t match what Meta released.

It was even worse for GPT and Gemini, models you can’t download. In March, researchers at CISPA, a German security research centre, studied what they call shadow APIs, the unofficial resellers selling cheap access to these models, often to people in countries where the official services are blocked. Some 187 academic papers used them. The researchers ran identity checks on 24 reseller connections, and 11 failed. Two services selling GPT-5 answered like much cheaper open models, GLM-4 and DeepSeek-V3.

Gemini 2.5 Flash scored about 84% on a US medical licensing exam through Google and about 37% through the resellers. One item asked which test confirms HIV in a mother during labour. The official service got it right, meanwhile three resellers got it wrong.

A bad answer doesn’t always mean cheating, though. When Moonshot saw other hosts of its Kimi models scoring below its own service, much of it came down to setup mistakes, like getting the temperature wrong, which is the dial that controls how random a model’s word choices are. In April it released a free test kit for checking hosts.

OpenRouter, which routes requests to model providers, analysed billions of requests. It found that when AI failed to use an app or carry out an action, the problem usually came from the software running the model.

Sending traffic only to hosts with good records lifted its test scores 10-20%. So you’re dealing with careless sellers and dishonest ones at the same time, and from where you stand they might seem identical.

You can test a little of this yourself. The Stanford method needs about ten answers per prompt, compared against a copy of the model you trust, and for GPT or Claude nobody outside the labs has that copy. A Berkeley team also found these checks easy to dodge. A seller can serve the real model some of the time and a cheap one the rest, or spot the test prompts and send only those to the real thing. Moonshot’s own full check took about 15 hours on two big servers.

AntSeed has started adding checks. Since June, sellers have digitally signed their answers, creating a record of who supplied each response. That does not establish which model produced it. AntSeed says it checks answers against patterns associated with each model. Basically, you know who sold it, but you don’t know what you bought.

Who’s trying to fix it?

The companies here fall into two camps. One camp is trying to prove which model ran. The other is making models cheaper to run.

Engy is the most direct fix for the AntSeed problem, at least for open models, the ones anyone can download, like Kimi, DeepSeek and Qwen. It sells them through independent GPU owners on the crypto network Bittensor, some of them running ordinary gaming cards.

For example, someone who owns a server with several NVIDIA graphics cards installs Engy’s software, loads a supported model and makes their server available to process requests. When Engy sends them your prompt, their GPUs run the model and return the answer with verification evidence.

As a model generates text, it produces internal numerical values called activations. Engy’s verification method, TOPLOC, records a compact fingerprint of selected values. This gives a checker something to examine.

A checker loads the same model version on its own hardware and recomputes the relevant calculations using your prompt and the returned text. It compares the resulting activations with the operator’s fingerprint. A significant mismatch can indicate that the operator used a different or modified model.

Engy’s network checks sampled requests. Customers can also request evidence and verify responses independently, but they need the model files and suitable hardware. With all of this, Engy is able to offer cheaper prices.

Attestable does this with heavier maths. It launched in August with $20 million led by Altimeter Capital and TLV Partners, and it makes zero-knowledge proofs.

ZK proves that the specified model produced the answer from the given input, without requiring the checker to see the model’s private weights or rerun it.

The proof ties those together, so the provider cannot substitute another model or alter the answer without invalidating it.

The buyer checks that proof using Attestable’s standalone verifier, which runs on a CPU. Unlike Engy’s approach, the checker does not need the model’s weights or hardware capable of running it. That also makes the approach potentially useful for proprietary models, provided their owners cooperate in generating the proofs.

So in theory OpenAI could prove GPT answered you without showing GPT to anyone. Attestable reports verification times below a second in its alpha benchmarks, but that excludes proof generation. Its current system also limits context length and changes the numerical precision of some calculations.

Inference Labs has been at this the longest, and its story took a turn this month. It uses ZK proofs to verify AI computations. From 2024 it ran a Bittensor network called DSperse, where computers competed to make these proofs fastest by chopping a model into slices and proving the slices side by side. ZK Proofs are expensive. One of its team members said on a podcast last year that proving even GPT-2, a small model from 2019, could cost $100 of computing on Amazon’s cloud. In October, the network was handed to DaniLabs, led by DSperse’s lead author Dan Ivanov, which plans to turn it into a cheap, private service that runs AI models rather than proving them. Inference Labs still sells verification for AI agents and robotics.

EigenLayer’s EigenAI makes AI answers repeatable. Normally, asking the same question twice can produce different answers, making comparisons unreliable. EigenLayer’s EigenAI fixes the chip type and software versions so the same prompt always returns the exact same answer. Anyone can rerun a request and compare, and operators caught cheating can lose staked ETH.

Some sellers lean on hardware instead, using chips with a sealed section, called a trusted execution environment, that can sign a note saying which program ran inside it. AntSeed, for example, lets buyers send requests only to sellers running these chips. Google’s Private AI Compute is built the same way. The Berkeley team called those chips the most practical fix today, though you end up trusting the chipmaker instead of the seller.

Now there’s another camp. Zro, from a team called MoonMath, writes its own low-level code for AI chips and says it gets 220 tokens a second out of a DeepSeek model.

Inferact, started by the people behind vLLM, free software many AI hosts use to run their models, raised $150 million in January at an $800 million valuation. Neither of these tries to prove which model you got. They matter here because a low price can also mean an honest seller found a smarter way to run things, and companies like these make that kind of cheap more common.

Moonshot said it now works with the teams behind vLLM and similar software to fix the setup errors it found, which tells you where a lot of the honest mistakes come from.

Most people will never touch any of this directly. But many of the apps they use run on these services, and some of those apps buy their AI from the cheapest reseller available. If a study app or a health chatbot runs on a shadow API, you could be getting the 37% Gemini instead of the 84% one, you wouldn’t know.

So the first thing verification could give you is the name of the model that answered. The second is privacy, and here the big labs are involved. In June 2025, Anthropic and a security firm called Pattern Labs described a setup where Claude requests stay encrypted except at the exact moment they’re processed. Then the keys go only to a machine whose chip proves it’s running approved code. Anthropic called it a sketch to start a conversation. The third is a record of what the model said, which starts to make sense once AI agents spend money for you and someone has to show what was asked and what came back.

My take is that if you use ChatGPT in OpenAI’s own app, none of this reaches you as of now. Because there the brand does the job a proof would do. It matters wherever there’s no brand, which is most of the cheap market.

“Cheap” always gets a bad name.

In 1970, an economist named George Akerlof wrote about used cars. The seller knows whether a car is good or a lemon, and the buyer doesn’t. As a result, buyers offer only what an average car is worth. Owners of good cars refuse to sell at that price, the average gets worse, buyers offer even less, and soon mostly lemons are left. He later won a Nobel prize for the idea.

If you can’t tell an honest cheap seller from a fake one, you treat every cheap listing as possibly fake. The good sellers get nothing for being good, and they’re the first to give up

Warranties and vehicle history reports partly rescued used cars by giving buyers what sellers used to keep to themselves. Proofs try to do that for AI, and for open models it’s closer than I expected. Once a model has a public seal, “kimi-k3” means one exact file, and OpenRouter already publishes each host’s speed and reliability. Choosing between two Kimi sellers feels like me picking mobile data plans, where the product is the same and you just compare price and reliability.

None of the checkers can verify GPT or Claude from the outside, which are the names on the flashiest discounts.

Big labs can ask you to trust them. Cheap sellers have to show their work, and that may be what gets cheap AI taken seriously.

Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Exactly a month ago, a company called Sentora posted a proposal on a governance forum. About 25 days later, it was running a lending business. It’s surprising because it didn’t raise any capital, applied for no licence, and has no back office. Starting a lending business is not that easy. In 2025, only four new banks opened in the U.S., and each had to raise between $20 million to $40 million before accepting a single dollar in deposits.

Sentora’s lending desk runs on Aave V4’s Hub-and-Spoke architecture. The arrangement is unique because it lends PayPal’s and Ripple’s digital dollars against tokens tied to American home-equity loans, a Wellington Management credit portfolio, and short-term loans to payment companies. It’s a case of unlocking DeFi liquidity by using real-world assets as collateral.

In this arrangement, Aave retains the contracts, the pooled money, and the power to switch Sentora off. But it has handed over the job of deciding what is safe to lend against to Sentora. Although that might seem like ceding power, I think it’s a bet that the venue hosting everyone’s lending desk ends up worth more than any single desk on it.

Let’s explore what Aave needs to price in to make this bet pay and what value arises out of the new lending architecture.

On to the story…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Unbundling the Lending Desk

On-chain lending has so far come in two shapes. In the first, one protocol runs one big pool, and one committee decides what goes into it. Aave V3 worked this way. Every new asset went through the DAO and its paid advisers, and every saver shared the same pool, so a single bad asset could hit all of them, as April’s KelpDAO exploit showed.

In the second shape, anyone can open a market, but each one starts empty, and nobody can veto a bad one. Aave’s V4 is a third shape. It separates where the money sits from where the lending happens.

Aave’s V4 hosts a Liquidity Hub where all the savers pool their deposits in a shared vault. This is then routed into lending counters called Spokes, where each counter has its own rulebook. Each spoke decides independently which collateral it accepts, how much it lends against each asset, what interest it charges, and where it gets its prices from.

Borrowers pledge their collateral at any of these spokes. If the collateral matches its standards and covers the loan, then the Hub disburses the loan.

Sentora runs one such Hub on Ethereum with three spoke counters. Only three stablecoins can be borrowed through these spokes: Ripple’s RLUSD, PayPal’s PYUSD, and Origin’s OUSD. The interesting part is the collateral it accepts.

• PRIME, a token backed by Figure’s home-equity loans

• mWIN, a Wellington credit strategy issued by Midas

• PST, Huma’s payment-financing token

• Ethena’s USDe and Kraken’s bitcoin token kBTC

Sentora lends up to 89.5% of PRIME’s value and 84.6% of mWIN’s. The borrowing costs move based on how much of the liquidity from the Hub is being exhausted. The interest rate is 4% when 90% of the pool is lent out, but it jumps to 16% when it runs dry.

Although Sentora reserves the right to decide the interest rate and assess collateral quality, Aave retains the kill switch. The Aave DAO owns every contract and can strip Sentora’s permissions with a vote. Any change that adds risk is put on a public 48-hour delay, and new collateral faces a two-week review.

In return for opening its architecture to external lending desks, Aave charges 50% of all the fee income the desks make through the lending business.

But isn’t Aave giving away its core business by opening the lending model to external players? It’s difficult not to mention how Hyperliquid’s HIP-3 deployer markets often overtake its native perp activity by volume.

The Hub-and-Spoke architecture is walled off from Aave’s main markets. The V4 explicitly bans USDC and USDT in the Hub, so Sentora or any other lender cannot poach Aave’s core business.

This is what sets V4 apart from the earlier models. On Aave V3, the DAO and its paid advisers decided everything, and every market shared one pool. That meant a single bad asset could hit every saver in it. In April, an attacker minted unbacked rsETH, a staked-ether token, posted it as collateral on Aave V3 and borrowed against it. Estimates of the resulting bad debt ran as high as $230 million, and Aave’s deposits fell by about $10 billion as savers pulled out. On Morpho, Aave’s main rival, anyone can open a market, but nobody can veto a bad one. V4 sits in between. One pool feeds several counters with different lending rules, which are set by an outside specialist. Yet Aave’s governance keeps a veto.

There’s one more break with banking. Unlike a bank, the loan on V4 doesn’t burden anybody’s balance sheet. Neither Aave’s nor Sentora’s. A bank books your deposit as its liability. Here, the savers are the lenders. They own the shares of the deposit pool. Every loan, limit, and pending rule change is visible on a public ledger in real time.

The entire architecture brings together stablecoin liquidity and marries it with a tokenised pool of real-world assets as collateral.

Much of the $300 billion in circulating stablecoins earns little yield or is chasing crypto-native interest. Meanwhile, some of the steadiest credit in traditional finance, such as home-equity lines, investment-grade bonds, and trade receivables, is hard to borrow against unless you’re big enough to negotiate a facility with a bank. Tokenisation has made those assets transferable. But the same tokenisation, when married with lending counters, can also make the real-world assets financeable.

Let’s follow a dollar in this architecture

In Aave’s V4, a saver first deposits PYUSD into Sentora’s Hub. A borrower pledges their PRIME - a token backed by Figure’s Home Equity Line of Credit (HELOC), and receives the loan in PYUSD.

PRIME earns its yield from Figure’s warehouse of home-equity loans that are going to be packaged into bonds. Senior slices of those bonds have carried AAA ratings. It’s interesting because a stablecoin deposit now funds American homeowners’ credit lines, which was a job that belonged to warehouse banks.

Sentora’s PRIME vault on Morpho has drawn over $211 million of PYUSD since its launch in May this year.

Even other collaterals that Sentora accepts follow similar logic.

mWIN is a tokenised investment strategy that tracks the performance of an investment-grade credit portfolio run by Wellington, which manages more than $1.3 trillion. Borrowing against it is what private banks call securities-based lending, except that here it runs around the clock on-chain without any relationship manager.

PST, Huma’s payment-financing token, represents one-to-five-day loans to licensed cross-border payment firms that need to pay out before money arrives. Pledging it turns trade finance into dollar liquidity on a Sunday when banks don’t function.

All these strategies have existed in the past. Warehouse lending, securities-based lending, and receivables financing are all decades old. But what Aave’s V4 enables is the low cost of entry for specialist lending desks, removing the time restriction and democratising who can run them.

It also changes who makes money throughout the flow, and how much.

Follow the Money

Take a desk with $100 million deposited and $90 million lent out at 4%. Borrowers pay $3.6 million a year in interest. At least 20% of that, $720,000, goes into a fee pool, which Sentora can raise but never lower. The pool splits evenly, with Aave’s DAO pocketing $360,000 and Sentora taking the other half.

Savers keep the remaining $2.88 million, about 2.9% on their money.

The actual numbers might still be insignificant. In October, Aave’s DAO made barely a million dollars from fee income on V4. Aave V3 already earns more than that daily. But short-term revenue isn’t the point. The DAO wins anyway because every dollar it makes from V4 is virtually free revenue that it earns from someone else’s labour. All this while it retains the kill switch if any tenant misbehaves.

The entire risk rests in the hands of the savers. Aave’s V4 model still ends up pulling in more savers, who pool more of their deposits. Sentora, too, earns its fee share without investing a single dollar of its money. But it captures value for managing the depositor relationship. It already curates about $440 million of RLUSD and $366 million of PYUSD in vaults on Morpho.

PayPal and Ripple also benefit from increased borrowing. If nobody borrows a stablecoin, then it just remains as a source of float revenue. RLUSD has hit an all-time high circulation of $2.6 billion this week. PYUSD’s circulation is around $3 billion, down by a third from its March peak. Both need borrowers to survive, and they pay for increased visibility.

When Sentora’s mWIN vault launched on Morpho in August 2026, PYUSD rewards supplied 7.61 percentage points of its 8.31% headline yield. The savings rate of depositors is often subsidised by the stablecoin issuer.

Figure, Wellington, and Huma gain more adopters, and consequently more business. A token that you can borrow against is a better asset to hold. Lending counters help turn financial instruments of these asset managers from investments into working capital.

Aave earns a thin rent and hasn’t settled the price. Sentora pays 50% of the fee pool.

Value in this stack flows to those who control the relationship with depositors and borrowers. The lending venue in the middle earns the least. Here, Aave is looking like a landlord who rents neutral rails to specialists by moving underwriting off its own books.

Lesson from the Past

Hyperliquid ran this experiment a year earlier, with trading instead of lending. Under its HIP-3 programme, a builder who stakes 500,000 HYPE, can launch its own futures markets on Hyperliquid’s engine. Those markets charge double fees, where the builder retains half. Validators can confiscate the stake if the builder runs bad price feeds or lets markets go down. Builder markets reached $4 billion in daily open interest (OI) this week.

HIP-3’s share of daily OI out of the total daily OI across Hyperliquid has remained above 20% consistently since February this year.

Both Hyperliquid and Aave are renting their core engines to specialists so that they can earn excess revenue and split it down the middle. All of this still happens under their respective brand names.

Hyperliquid prices trust before a market opens by asking the deployer to post a bond and losing it if they mess up. In Aave’s case, if a borrower’s collateral collapses and debt is left over, Sentora’s savers absorb the shortfall.

This makes the lending business into something that can be rented by specialists. Specialists can compete on judgment instead of carrying the burden of loans on their balance sheets. There are many high-quality collateral waiting to be financed. Home equity, fund units, and receivables can back dollar loans at any hour. They need not be saved for the end of the month valuation cycle.

Such a Hub-and-Spoke model unlocks gains for even those who seldom think about crypto. It enables a savings app to pay a rate funded by home-equity loans. It also allows homeowners to use their lines of credit and draw on a global pool of dollars instead of a handful of warehouse banks. A fund investor can borrow against their holdings on a Sunday.

Most of these institutions might never hear about Aave. And probably that’s the entire point. To make crypto work as an underlying infrastructure that is running someone else’s product.

That’s it for today. I will be back with the next one.

Until next time, stay curious,
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

You can tie your shoelaces without thinking much about it. But explaining every finger movement to someone else is tough and confusing. You know how to do it, even if you struggle to put it into words.

AI has made that problem commercially valuable. Much of what people could easily explain is already available in books or online. Getting at the knowledge expressed through their actions requires a different arrangement. Someone has to persuade them to demonstrate it and capture enough of what happens. They need to work out whether a machine can learn from the result.

We are discovering a market for knowledge that rarely received much recognition. But does this mean the people who possess it will get a good price?

Physical AI is a newborn. Annoying, a little innocent and mostly clueless. They desperately need more information on mundane things, and they have no character arc to build over years. It has to be now.

Companies are building businesses around different parts of that process, with consequences for who owns human experience once it becomes training material.

Today we are looking at those working on teaching AI how to fold a T-shirt and the sustainability of that work.

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

In his 1988 book Mind Children, the roboticist Hans Moravec noted that it was fairly easy to get a computer to perform like an adult on an intelligence test or at checkers, and close to impossible to give it the perception and movement of a one-year-old.

This became known as Moravec’s paradox.

In July 2025, systems from Google DeepMind and OpenAI reached gold-medal level at the International Mathematical Olympiad. Meanwhile, robotics companies still post videos of a robot folding a T-shirt as evidence of progress.

How to fold a t-shirt as a human?

Lay the cloth flat on a table. Smooth out the wrinkles with your hands. Pick up the two corners nearest you and bring them to the corners on the other side. Put them down together. Smooth the cloth again, then fold it in half once more. Now it is folded.

You can find a corner without looking. You know whether you’ve grabbed one layer of fabric or two. If you notice a sleeve is inside out, fix it by shaking it out. Nobody ever wrote these steps down because nobody needed to.

Chelsea Finn, a co-founder of Physical Intelligence, has described an early laundry model that worked well on a controlled set of shirts but failed when it encountered a mixed pile.

This is the issue people are now paying to fix. Language models had a giant pile of human writing on the internet, produced for other reasons and easy to scrape. Ken Goldberg, a roboticist at UC Berkeley, estimates that the text and images used to train today’s big vision-language models work out to roughly 100,000 years of human experience when converted into time. Robots have nothing close to that for physical work. YouTube is full of people cooking and cleaning, but a video shows what hands look like, not how hard they grip or what a robot’s own camera and joints would register.

For example, there are so many types of bottle caps. Is there a tutorial video on how to open each one of them? How much pressure to apply, pull up or twist. Is there a course? no.

When Tesla shifted Optimus training towards mostly video in mid-2025, an outside researcher pointed out this missing sense of touch and contact.

Simulation helps, but not always, because if it’s a cloth, for example, it changes shape every time you touch it. In a 2024 study, researchers trained a system to find the corners of towels and T-shirts using only synthetic images. It reached 64% precision, climbed to 74% after fine-tuning on real photos, and the authors concluded that simulated cloth still needs to be much more realistic. Synthetic data also needs a judge. A simulator can produce a million folds, but a person still has to tell which of them count as folded.

Kled pays people to collect photos, videos and audio for AI companies. Its app shows tasks, such as filming an everyday activity, with instructions and payment details. Kled checks submissions, pays for accepted work, and sells access to datasets that AI companies use to train and improve their AI models. Kled can send a notification to people who are already standing in their kitchen, and with V3 it says it can steer contributors to specific jobs.

Late in September, Kled changed what you see when you open its app.

The old home screen was one big button for uploading whatever was already sitting in your camera roll. The new one is a feed of jobs, with the pay shown before you start. Kled says it can now push a request like that to more than 500,000 contributors and start getting data back within 72 hours.

Kled belongs to Nitrility Inc., a San Francisco company Avi Patel started in 2025. You upload files, Kled pays you, and Kled licenses them, cleaned up and packaged into datasets, to companies training models.

Patel has written that bigger rivals keep roughly a third of every dollar, which makes his own cut an obvious thing to ask about.

Contributors are paid in dollars through PayPal, Venmo or a Solana wallet. There’s also a KLED token on Solana, with a market value of about $10 million. This is small for a network claiming 500,000 contributors. KLED isn’t equity and gives holders no claim on company assets. The company can buy back tokens at its discretion. Kled does not claim labs must buy the token, and most contributors are paid in dollars.

It claims more than 500,000 contributors and about 8 million datapoints a day, and earlier this year it said it had paid out millions and had over $30 million of buyer demand in the pipeline.

What people earn is murkier. App Store reviewers complain about getting a dollar for a hundred photos, and Kled’s replies say pay isn’t fixed and depends on how unique the upload is and what buyers want. Kled has also said its top contributor was making around $7,400 a month.

Public materials do not establish a standard contributor share of customer revenue or a reliable average hourly income.

Kled’s terms say contributors retain ownership, while granting a perpetual, irrevocable and transferable licence. That licence is exclusive for specified uses involving third-party AI development and training. Keeping ownership therefore does not mean being free to license the same material to another AI data buyer. The terms also put responsibility for necessary permissions on the contributor. A home recording can still contain someone else’s personal information.

San Francisco startup PrismaX focuses more directly on robot training. PrismaX raised $11 million in 2025 in a round led by a16z’s crypto accelerator.

One way it collects this data is by having a person control a robot remotely. This is called teleoperation. The person might guide a robot arm to pick up a cup and place it in a bin. During the task, the system records both the camera footage and the robot’s movements. Those recordings help a model learn which movements to make based on what the robot sees.

A Kled video of someone tidying a room can show the actions involved. A recording made by controlling a robot also captures the commands used to move its arm and gripper. That gives the model information about how the robot performed the task, beyond what is visible in the video.

PrismaX claims more than 120,000 episodes delivered and capacity for 5,000 hours of custom data a month, and says it has paid contributors over $1 million.

Robot owners run sessions on their own hardware and get paid per valid session. It’s also opening first-person video collection, paying for clips that meet its “Operator Intent standard”. Every upload carries the serial number of the registered robot that produced it, and the main camera feeds go through QA and duplicate checks.

Kled mostly captures the world from the outside, which it can do cheaply and in bulk. PrismaX records action from inside a machine, which is slower and costs more because every session needs a robot. The two are also drifting towards each other. Kled’s V3 asks for first-person video of people reorganising rooms, and PrismaX’s video waitlist accepts smartphones.

These are early-stage products, and established names are already doing the same thing.

Scale AI records people steering robots through tasks. It also has what it calls a “robotless egocentric platform”, which records a task from the person’s point of view, with no robot involved. Its customers include robotics companies Generalist and Physical Intelligence.

Meta bought a 49% stake in Scale in June 2025. Scale says it delivered more than 150,000 hours of physical AI data that year. Its website says it now collects more than 1,000 hours of demonstrations a day from people’s homes and commercial and industrial sites.

Appen also collects recordings of people doing physical tasks. It adds information explaining what happens in the footage, such as how a person’s hands move and whether an object has been picked up or put down. These details help turn a video into material a robot can learn from.

Appen reported $230.8 million in operating revenue for 2025 across its business, not just robotics. Its business outside China fell about 21%, while growth in China helped lift overall revenue. Rising demand for AI training does not benefit every part of a data supplier’s business equally.

Some familiar tasks, such as labelling objects in images, can now be partly automated. At the same time, customers are buying work that requires different skills, such as checking a legal answer or recording a useful robot demonstration. A company can have thousands of contributors and still lack the people or equipment needed for a new contract.

Suppliers can also lose work that remains valuable. Google ended its Appen contract in 2024 and said the quality-rating work would move to other providers. That customer generated $82.8 million for Appen the previous year.

This matters for Kled and PrismaX. Building a large contributor network gives them people to call on, but buyers need particular kinds of data. Their prospects depend on how quickly they can organise the right collection work and prove that it meets the customer’s needs.

Reaching more contributors could make collection easier for Kled and PrismaX. They would still need to make sure the recordings are useful. Scale already uses homes and workplaces to collect data. That’s not the moat anymore.

Even DoorDash now pays couriers to record tasks such as washing dishes for AI training.

A more lasting advantage would be knowing what a customer’s robot struggles with and reliably collecting demonstrations that help it improve. That knowledge could grow with each project, as a supplier learns which recordings work and which contributors can produce them.

NVIDIA’s AdaDemo research supports the idea of targeting those weaknesses. The researchers collected additional demonstrations for situations where a robot struggled. Across 22 simulated tasks, this approach improved performance more efficiently than the methods they compared it with.

But better human demonstrations are only one way to improve a robot. NVIDIA’s SPARR research explores another. It trains robots on assembly tasks in simulation, then lets them practise on real hardware to correct mistakes, without human demonstrations. NVIDIA reports higher success rates and roughly 30% shorter task completion times than using the simulation-trained system directly.

Does that make Kled or PrismaX unnecessary? Not really. For a particular task, buying demonstrations might help a customer improve its robot more cheaply or quickly than collecting its own data or training through simulation and practice. Neither company’s contributor count nor hours of footage tells us whether it can deliver that advantage.

Even when a data supplier wins a customer, it may have to wait weeks to get paid. Meanwhile, contributors need to be paid, and the next collection project needs funding.

Other industries already have ways to cover this gap. Citi, for example, runs a programme that lets Walmart’s suppliers receive payment early. Once Walmart approves an invoice, the supplier can ask Citi to pay it before the due date, accepting a deduction for receiving the money sooner. Walmart pays Citi when the bill falls due.

That is how an agreed customer payment can help fund a supplier’s ongoing work. But there’s a big distinction for AI data businesses. With Citi, the buyer has already approved payment. A data supplier whose work is still being checked could face rejection.

Financing that earlier stage requires more than knowing that the buyer has money. A lender needs to understand what the lab ordered and whether the supplier’s recordings meet those requirements.

Masterpiece is a small startup trying to serve this particular market. Backed by Y Combinator, it says it lends to AI data suppliers while labs review their work and process payments.

Masterpiece says it checks a supplier’s invoices, past payments, data quality and finances before deciding whether to lend. Approved suppliers can receive money within two to four days. It reported securing a credit line of more than $10 million to fund loans and lending $200,000 at a reported annual yield of 24%. It also reported more than $65 million in signed letters of intent, which indicate potential business rather than completed loans. We have no public information about the borrowers yet.

It is still early. That makes it an example of lenders exploring this business, but it’s still too early to judge how well they can manage the risks.

Masterpiece says it gets lending money from banks and private credit funds, as well as a stablecoin pool called USDml. Crypto users can deposit stablecoins into that pool, which targets an annual return of 8% to 12%. That return is not guaranteed.

Masterpiece says the pool helps meet its “haircut capital requirements”. This usually means covering the portion of a loan that the main lender will not fund. Money in that position can absorb losses before the main lender does. If USDml works this way, its depositors could be taking greater risk than the credit fund.

Crypto has financed invoices before, with some familiar problems. In 2023, ConsolFreight warned that its largest borrower in a $2.7 million lending pool on Centrifuge faced liquidation following a legal dispute. That put $1.84 million of MakerDAO’s funding at risk. Moving the financing onchain did not remove the risk that the borrower would fail to pay.

AI data adds another complication when financing happens before the customer accepts the work. Even a well-funded lab can reject recordings that fail its quality checks. If it accepts only part of a delivery, the supplier may receive less money than expected and struggle to repay the loan.

Cheap data can be expensive

A lab that trains on fakes wastes compute and can end up with a worse model, so every company here is selling checking as much as collecting. A 2023 study by Swiss researchers estimated that up to 46% of workers on Amazon’s Mechanical Turk were using AI models on tasks meant for humans. A phone makes faking harder, since a first-person video of your own kitchen is harder to generate than a text summary, but not impossible.

Kled says its fraud model, FD 1.0, removes 98.4% of bad actors within their first ten uploads, a figure from its own internal tests. It also writes a receipt for each accepted file to Trace, a public ledger run by The Data Foundation. I went through Kled’s Trace audit page. It showed about 282 million records, 99.2% of them images and 0.7% videos. Southeast Asia accounted for 74.5% of contributions and Sub-Saharan Africa for 22.7%, with North America at 2% and the EU at 0.4%. Today’s Kled corpus is mostly camera-roll photos from two regions, a long way from the first-person task video robotics labs want.

A recording can still be poor training material. The robot might learn unnecessary pauses from the person controlling it, or struggle because its camera shows a different view from the training footage. What matters is whether the data helps it perform the task better. PrismaX describes testing this by giving Physical Intelligence’s π0.5 model extra training on recordings of a robot picking up and placing objects, then testing changes to lighting and camera position.

There are different ways to check the work. Sapien asks data labellers to put up tokens they can lose if reviewers reject their submissions. NVIDIA and Lightwheel test trained robots in simulation to see how well they perform.

I haven’t found an independent checker that both suppliers and AI labs widely trust. I think a company that earns that trust could charge more for checking data than others earn from collecting it. Lenders like Masterpiece might also rely on its checks when deciding whether to lend.

I think this industry is pricing something real, which is why the price will eventually fall. What Kled or PrismaX pays for a tidied room shows what robots still cannot do, and as they improve, that rate gets cheaper.

For most people, this is a decent side income for now, but not something to build a life around.

Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Robinhood’s stock tokens are traded all around the clock. Even on weekends, when the Nasdaq shuts its shop. Between September 24 and October 23, ~60% ($2.2 billion out of $3.7 billion) of all the stock-token trading on Robinhood happened outside regular US market hours. About $532 million of it changed hands on weekends and holidays.

It was only last Thursday when Robinhood’s stock token holders got some added value to their tokenised shares.

On October 22, Morpho opened markets on Robinhood Chain that let stock-token holders borrow stablecoins by using their tokenised stocks as collateral. Aave has offered the same against Coinbase’s tokenised shares since September 25.

Yet, none of this makes a tokenised share a better way to own a stock. It still has imperfect voting rights in most cases. What lending against tokenised shares does, though, is to make that share work for people who already prefer tokenised shares. Borrowing stablecoins against their holdings moves these tokenised shares from assets of globalised access to assets of usefulness.

The caveat is that these loans run all weekend, even when the prices of the underlying shares don’t get updated on the conventional markets. This could lead to price-driven manipulations.

In today’s piece, I look at what lending against tokenised stocks unlocks, who gets paid along the way, and what it will take to price the hours when Wall Street sleeps.

On to the story…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Credit without the Broker

Borrowing against shares is a bare-minimum feature. Every good asset lets its holders do multiple things besides just holding the asset.

In the US, that privilege has often been with the broker. A Reg T margin account needs $2,000 to open and lends up to half the stock’s value. Schwab’s pledged asset line starts at $100,000 and charges 8.27%. Every version has one precondition: the lender holds your collateral and decides whether you qualify.

Outside the US, the conditions often fail. Many brokers outside the country don’t allow investors to borrow against their holdings. India’s Liberalised Remittance Scheme rules issued by the Reserve Bank of India explicitly restrict investors from engaging in speculative trading abroad. Vested Finance, one of the apps Indians use to buy US stocks, offers only cash accounts. Interactive Brokers’ Indian arm also bars residents from margin products on foreign markets.

So, an Indian investor who owns $20,000 worth of Nvidia through one of these apps can trade their holdings, but cannot borrow against them. Same Nvidia stock, but less useful than the shares owned by someone in the U.S.

This geographical gap is not universal, though. Interactive Brokers accepts clients from many countries and earned an average of 4.25% on its margin loans in Q3 2026. Nigeria’s Trove, a retail investing app, started offering twice the buying power on US stocks in August. So, credit against stocks does exist in most places. But it comes with minimum balances, paperwork, and a broker at whose discretion your qualification is decided.

Tokenisation changes one main thing in this entire setup. It moves the collateral out of the broker’s books and into the holder’s wallet. Once that happens, any lender that accepts the token can lend against it.

How much they’ll lend comes down to the haircut, which every venue prices differently. Kraken began accepting 10 xStocks as futures and margin collateral on July 3. It knocks 10% off index trackers like SPYx, 20% off single stocks like Nvidia, and 30% off volatile names like Robinhood’s own stock.

Aave lets borrowers post seven Coinbase stock tokens for USDC, up to 79% of value for Microsoft, 70% for Nvidia and 65% for Tesla and Meta. On Aave, that limit is also the liquidation line, so a borrower who takes the maximum starts at the edge. Paboxo reached Robinhood Chain on October 19, and Morpho followed three days later. Morpho liquidates Nvidia loans at 62.5% and SPY loans at 77%, a gentler line for an index that moves less than its parts.

On Aave, the number of borrowing wallets rose from 8 to 144 by October 18. Three in four had never used Aave on Base before.

LlamaRisk, the firm that sets Aave’s risk parameters, found that most of the borrowing happened while the stock market was shut. These are new users who are borrowing at hours when no other broker lends.

But tokens just replace the broker with the issuer, who keeps a wider gate. The gatekeeper still exists. Neither Robinhood’s nor Coinbase’s tokens can be sold to US persons.

What the holder owns is also thinner than a share. Robinhood’s stock tokens are debt securities issued by Robinhood Assets (Jersey) Limited. Its own filings say they confer no legal or beneficial rights in the underlying companies. Coinbase’s tokens are certificates from a Coinbase special-purpose vehicle that reinvests dividends instead of paying them out.

Read: A Token Can Be the Asset

Even for someone who already has a margin account at Interactive Brokers, a stock token does a few things a conventional share can’t. The loan comes without a margin application, the collateral settles on-chain in minutes rather than a business day, and the cash lands on a Saturday if that’s when you need it. But beyond all this, the real edge is offering lending in markets where conventional brokers don’t lend at all.

A simple rule about the nature of assets is that any asset that lenders accept is worth more than one that they don’t. US Treasuries are the best example. Investors accept a lower return on them because they are safe, liquid, and can be turned into cash almost anywhere by borrowing against them.

Tokenised stocks have none of that support, which is why the haircuts on them run from 10% to nearly 40%. The haircuts are the price of everything Treasuries have, and tokenised stocks don’t. Their collateral premium will be smaller, and borrowers will pay for the missing pieces through those haircuts.

Where Value Accrues

Lending is just the base layer. Once a token can back a loan, other products start building on top of it. Each of them pays someone.

First is plain cash. Aave founder Stani Kulechov says loans on Aave’s stock market carry rates of about 5%. Although that is more expensive than what Interactive Brokers charges, the main appeal is not the rate. This offers a loan without requiring any account minimums and broker’s approval. Also, this is available on a Saturday afternoon.

The second is leverage. Pendle’s new PendleLoop can borrow USDC against a stock token on Aave, buy more of that token, and repeat the cycle. This leads to a leveraged bet on Nvidia or Microsoft at 2.6 to 4.2 times, built in one click, with fees waived until December 31. A US retail margin account starts at twice the cash you put in. Aave’s risk team sets its limits as ceilings. Pendle’s interface turns them into targets.

The third is a margin for trading. Kraken lets xStocks back futures positions, capped between $100,000 and $1 million per stock. More than 300,000 people held xStocks as of September. Their Apple position can now fund a Bitcoin futures trade without being sold.

The fourth job extends from savers to borrowers. Robinhood Earn lets eligible US users lend USDG through a Morpho vault run by Steakhouse Financial. Steakhouse acts as the vault’s curator, the risk manager who decides which loans a pooled fund can make. The vault held about $543.6 million from roughly 75,000 wallets this week. Steakhouse has now approved it to fund loans against Robinhood’s stock tokens, with each market capped at 1 million USDG.

The irony here is that American investors in Robinhood’s Earn product now fund loans against tokens that Americans are not allowed to buy. Although the dollar exposure is still small today, the structure is now in place to enable a US savings product to become the lender behind global stock-backed credit.

Each of these jobs pays a different layer.

The issuers sit at the top of the stack. Robinhood issues the stock tokens, runs the chain they trade on, and owns the app where savers deposit the dollars that fund the loans. Both sides of the loan, the borrower’s collateral and the lender’s deposit, sit in one company’s app. It also earns from the float, because Paxos shares reserve income on USDG with Global Dollar Network partners. Coinbase runs the same playbook on Base, and Kraken owns Backed, the issuer behind xStocks.

The packaging layer sits just below. Pendle doesn’t lend a dollar. It owns the leveraged user’s click and routes the loan to Aave.

The lending protocols earn the spread between what borrowers pay and what savers receive. At the current business size, that is still a thin business. Aave caps borrowing on its stock market at $21 million. Fully used at 5%, that is about $1 million a year in interest, and most of it goes to the lenders.

The curators and risk managers charge a fee to sell judgment. Steakhouse decides how much of a $543 million savings pool may touch stock loans. LlamaRisk decided that liquidators on Aave get at most a 5.5% discount on the collateral they seize.

Chainlink owns the time factor. It prices both Coinbase’s and Robinhood’s tokens for these lenders. It decides when that price moves and when it doesn’t. I’ll come back to that in a while.

This fits the pattern I’ve written about before. Distribution decides who shows up, while risk managers judge how much they can borrow.

Read: Who Captures Value in Web 2.5

Nobody is making significant money from this yet. Robinhood has disclosed no revenue from its stock tokens. Its third-quarter results arrive after Wall Street closes today.

But the prize everyone is chasing is large. Interactive Brokers held $105.2 billion of customer margin loans at the end of September and earned $1.15 billion in net interest income that quarter. US margin debt hit $1.48 trillion in September. Aave’s stock market had $4.08 million borrowed on October 18. That’s about 0.004% of Interactive Brokers’ book.

Pricing the Time Gap

All these loans depend on what the collateral is worth right now. Chainlink’s equity feeds publish from Sunday 8 pm to Friday 8 pm, New York time, and hold the last price through weekends and U.S. holidays. The lending markets never close. Borrowings and liquidations stay open, and interest keeps accruing.

That leaves two ways to game the system. With the price frozen, a borrower who sees bad news break on a Saturday can borrow the maximum against Friday’s price, knowing the stock will open lower. The lender ends up holding collateral worth less than the loan.

Pricing the weekend from on-chain trading invites another problem. Weekend markets are thin on liquidity: Aave’s own assessment found only $0.3 million to $1.1 million of stock tokens on Base could be sold before prices moved 2%. A pushed price liquidates honest borrowers. On July 28, a single SK Hynix share printed 30% below its previous close on a Korean pre-market venue. An oracle carried that print into Hyperliquid and liquidated 960 accounts holding $57.4 million. The stock recovered within minutes, though.

Even without anyone gaming it, the gap comes back to bite. The whole weekend’s move lands in one update on Sunday night. In January 2025, Nvidia fell 17% after DeepSeek news broke over the weekend, then recovered. A borrower at 60% against Aave’s 70% limit would have been liquidated in one step, before the recovery.

Lenders are working the problem. Caps can help keep exposure small. Paboxo pauses liquidations while markets are shut, which protects borrowers but pushes the gap to the reopen. Jupiter’s Offerbook offers one-to-30-day loans with no price-based liquidation, pricing the risk into the rate. From December 6, US exchanges will trade 23 hours a day, five days a week. That shrinks the overnight gap, but the weekend stays dark.

I don’t think oracles will solve the weekend problem. Lenders will still keep pricing what they can’t see into the haircut. Tokenised stocks will borrow a little less than real shares do. For someone whose broker won’t lend at all, that’s still better than nothing.

So lenders will keep charging for the hour, or will cap the prospective damage.

That’s it for today. I will be back with the next one.

Until next time, stay curious,
Prathik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000+ subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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Robinhood launched a blockchain in July to let people trade tokenised stocks on-chain, and the first wave of real traction on the chain came from memecoin traders. That might sound like a product-positioning failure, but surprisingly, it became the best go-to-market strategy ever for on-chain wrapped assets.

The memecoins were paired with the stock tokens in shared liquidity pools, and enough degen demand poured in that over half the supply of a real healthcare company’s stock got locked up as collateral for a cat memecoin. Memecoin speculation became the liquidity engine for a real equity.

Every asset that matures into a financial product follows a very similar path. People pay attention to it, then someone builds a way to access it, and then the financial system figures out how to own and structure it. Gold drew attention for five thousand years before the GLD ETF wrapped it in an access layer in 2004. Bitcoin ETFs are doing it for crypto now. On-chain, these layers are compressing and sometimes arriving out of order, reshaping what the end product looks like!

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

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The Financial Layers and the Trust Problem

If you look at every major institution building tokenisation infrastructure today, they are all doing what State Street did in 2004 when it gave gold a ticker symbol. State Street launched GLD, the first US-traded gold ETF, which let anyone with a brokerage account buy gold exposure without dealing with vaults, insurance, or physical bars.

BlackRock, JPMorgan, Nasdaq, and Franklin Templeton are positioning themselves as the translator between an asset class and the capital that wants exposure to it, because the translator permanently captures the flow. State Street has collected fees on GLD for over 20 years.

The entire modern history of Wall Street is a history of owning these translation points, from JP Morgan brokering the bridge between European capital and American railroads, to the investment banks of the 1980s owning the bridge between mortgage borrowers and bond investors. Whoever encodes an asset into the language that institutional money already trusts gets to charge tax on every dollar that crosses it, for as long as the encoding remains the standard. Which is why the race to tokenise is really a race to become the next encoding monopoly.

The encoding itself is no longer the hard part. On-chain, anyone can tokenise anything. The monopoly has moved one layer up, from who can encode the asset to who can make the encoding trustworthy enough for institutional money to touch it. That is what Securitise does for BlackRock, what the KYC gate does for BUIDL, and what no memecoin has ever needed.

The GLD ETF is the simplest example of how that encoding works and what it costs, because gold already had the strongest possible attention layer, five thousand years of universal consensus that it was the hardest money on the planet, and yet if you ran a pension fund in 2003, your options for getting exposure were only as sophisticated as renting a safe deposit box with physical bars.

What GLD did in November 2004 was build the access layer by performing a trust translation. It took gold’s five-thousand-year reputation and re-encoded it into something a brokerage account could hold by listing gold on the NYSE, establishing a clearing relationship with DTCC, and reclassifying it from “operationally impossible” to “one click in your brokerage account,” and moving a billion dollars in three trading days.

Central banks that had been net sellers of gold for decades now reversed course, and seven years later, GLD briefly became the largest ETF on Earth, holding 723 metric tons of physical gold. But every trust translation is lossy, and the losses compound as you build higher.

Converting gold into a ticker meant surrendering the property that made it valuable for years: you could hold it, and no counterparty, government, or intermediary could touch it. GLD made gold investable by embedding it inside the very financial system it was supposed to be insurance against, and then the ownership layer built on top of that, with banks using GLD shares as collateral, other financial products referencing its price, and lending desks borrowing against it, each adding another counterparty between the investor and the actual metal.

A pension fund holding a structured note linked to GLD was not really holding gold in any meaningful sense, it was holding a chain of promises that tracked gold’s price.

Notice that the translator always decides which properties of the original asset survive, and the properties that always get cut are the ones that would make the wrapper unnecessary. If you could hold gold directly in your brokerage account as easily as holding GLD, State Street’s fee stream would evaporate, so the translation has to be lossy, because a lossless one would eliminate the translator entirely.

This is why different layers exist in sequence in traditional finance, and each translation creates dependencies, and each dependency must prove itself trustworthy before the next layer builds on top. For example, once GLD became collateral across thousands of portfolios, the next sequentially important thing was the custodial and legal wrapper rather than the gold itself, because if the wrapper failed, the gold would still sit in the vault while the financial system built on top of it would collapse.

The sequential process also protects incumbents like State Street and BlackRock, because if banks and asset managers could build structured products that referenced gold or Bitcoin directly rather than through an ETF wrapper, the wrapper becomes irrelevant and its fee stream dies. The slow march from attention to access to ownership is presented as prudence, and it partly is, but it also happens to be the optimal strategy for maximising the value captured at each intermediary step.

Bitcoin ETFs compressed this seven-year sequence to about eighteen months, with $57 billion in first-year inflows. Gold survived by being ignored by its attackers, and Bitcoin survived by being attacked, through fifteen years of hostile regulators, exchange collapses, China banning mining and 80% drawdowns that lasted years.

When a compliance committee evaluates whether an asset belongs in an ETF wrapper, they assess downside survivability, and Bitcoin’s record of surviving sustained attacks gave them more conviction faster than gold’s record of never facing one.

But faster does not mean better. Bitcoin’s entire point was that you could hold value without trusting a bank, move it on a weekend, and nobody could freeze your account. The ETF strips all three. You get a ticker and a tax advantage, in return, BlackRock holds your Bitcoin, trades it only during market hours, and can be subpoenaed to freeze your shares. GLD did the same with gold.

The whole system moves slowly because trust takes years to build and seconds to destroy, and every gatekeeper in the chain, from the SEC analyst reviewing an ETF application to the custodian deciding what qualifies as collateral, charges a fee for the time they spend. Some of that caution is genuinely protective, but the slowness also makes them money, because instant, free access would put most of them out of business.

On-chain, none of those gatekeepers exists, and anyone can issue a token, build a liquidity pool, and wrap a position into a composable instrument without asking permission from a single intermediary.

On-chain Is Running the Layers Out of Order

This is where memecoins came in and filled the vacuum. Billions of dollars moved through tokens named after dogs and frogs on infrastructure built for tokenised securities and structured products. In the three-layer model we discussed. Memecoins skipped the middle step entirely. People showed up with attention and capital, and the liquidity infrastructure they created by trading became the access layer for everything that followed.

Robinhood’s RWA share of the chain’s volume rose from 0.39% to 8.58% in three weeks, built entirely on liquidity memecoin traders created. BONKbot, a Telegram trading bot, plugged into the $BONK community on Solana and hit $200 million in daily volume without negotiating a single partnership.

Memecoins have super-extended the velocity of attention and capital by paving a new way to invest in culture. It is the first instrument where you invest in a go-to-market strategy directly rather than a company that has one.

The institutions that showed up were all for defensive reasons. BlackRock listed its BUIDL fund on Uniswap and bought the protocol’s governance tokens to distribute a tokenised Treasury fund to its clients. BUIDL itself is GLD on a blockchain with the same KYC-gated access, and Securitise sits in the middle with about $2.8 billion in AUM across eight chains, deployed on rails that were specifically designed to remove that restricted access.

Even JPMorgan said that tokenised money market funds are a defensive play against stablecoins pulling deposits out of the banking system. The money is already leaving, and the GLD-era encoding monopoly is being rebuilt on-chain because the alternative is losing the fee stream entirely. BlackRock understood long before that permissionless finance would eat its deposits, and instead of fighting the new infrastructure, it rebuilt the old tollbooths on top of it.

And while institutions were recreating the sequential playbook, the permissionless side was building ownership-layer products on assets that had barely finished building their attention layer.

For example, Ethena’s USDe makes money from the very thing that keeps institutions away from crypto: the volatility. A product like this would never survive a single risk committee at any bank. But within two years, Ethena became the foundational collateral for other on-chain protocols, building on it the way banks build on treasuries. Pendle then split Ethena’s yield-bearing token into principal and yield components, each trading separately. Once those principal tokens became collateral on Aave, traders could loop the position five to ten times, turning a 5% return into 33% or more.

In traditional finance, this same recursive loop would have required its own ISDA master agreement, compliance review, and counterparty evaluation. Hyperliquid even showed us that the access layer itself can run in reverse. Traditional finance encodes alternative assets into its institutional language, the way GLD encoded gold. But Hyperliquid encodes traditional assets into crypto’s language. When Iran struck on a Saturday, and oil spiked, the CME was closed. But Hyperliquid priced the shock in real time for the entire market.

Arcus, built by dYdX Labs on Robinhood Chain, collapses all three layers into a single token. A leveraged perpetual futures position packaged as an ERC-20 that is transferable, composable, and works with every other protocol without custom integration. That means you could hold 3x leveraged Bitcoin in your wallet, deposit it as collateral in a lending protocol, borrow against it, and send it to someone else, all without closing the position or asking a broker.

You’ll see traditional finance keep building products that look like BUIDL, because the permission gate is how every institution makes money. Permissionless finance will keep building products where the layers merge, invert, and recurse, because the architecture allows it and nobody has the authority to stop it. Both systems are expanding what a financial product can be, in directions the other cannot follow.

That’s all for today!
Vaidik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

Wordle, one of my favourite games of all time, started as a small game that a software engineer in Brooklyn made for his partner.

On November 1, 2021, 90 people played it. By early January, more than 300,000 did, and a week later, over 2 million. It was everywhere. You could share your score without revealing the answer. At one point, group chats and social media feeds were full of it.

The New York Times bought it before the month was over.

As with anything loved too much, a few months later, people began writing that Wordle had lost its appeal. It also showed up less often online, and it looked like people had moved on.

But in 2023, Wordle was played 4.8 billion times. People kept coming back every day, even though they didn’t post about it.

Pokémon and Fortnite were written off the same way, and both came back with record numbers.

Robinhood Chain’s big number went quiet in a similar way. On September 4, memecoins paired with tokenised stocks jammed the network. The chain runs on Arbitrum’s technology, where the minimum fee per transaction climbs faster and faster as traffic piles up, and that one day earned it $8.27M in fees. By mid-October, daily fees were around $30K-40K.

In today’s guest essay, 100y from Four Pillars explores whether we're using a bad yardstick again.

On to 100y’s story,
Thejaswini

Follow 100y's works

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

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Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

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Key Takeaways

• Launched in July 2026, Robinhood Chain saw daily network fees surge to $8.27M in early September, fueled by the frenzy around stock token-memecoin pairs. However, as memecoin activity cooled and network congestion eased, fee revenue plunged more than 99% from its peak.

• Despite the sharp decline in network fees, Robinhood Chain’s core ecosystem remains surprisingly resilient. The USDG Vault continues to hold around $540M in TVL, tokenized stocks maintain roughly $150M in outstanding supply, and memecoins still account for a significant share of spot DEX trading volume.

• Lighter and Arcus, two perpetual DEXs strategically backed by Robinhood, are emerging as potential drivers of the ecosystem’s next growth cycle, with trading volumes and open interest (OI) steadily increasing through points programs. The real test will be whether they can retain users and liquidity after their TGEs and airdrops.

• For Robinhood Chain to experience another explosive growth cycle, it needs more than just a stable foundation. Stronger builder support, sustainable activity across existing protocols, and differentiated financial products will be critical. In particular, entirely new onchain financial primitives, much like stock token-memecoin pairs, could become the catalyst for its next wave of growth.

1. Is Robinhood Chain Dead?

If you had to name the hottest blockchain launch of 2026, Robinhood Chain would probably be everyone’s answer. Despite an unfavorable crypto market and Robinhood’s own crypto business losing ground, with crypto’s share of total revenue falling from 27.2% in Q1 2025 to 12.6% in Q1 2026, Robinhood made the bold move of launching Robinhood Chain on July 1, 2026.

Robinhood Chain was built with an ambitious vision: to create an onchain financial ecosystem centered around tokenized stocks. But shortly after launch, memecoin launchpads like Noxa and tokens like Cashcat began stealing the spotlight, making Robinhood’s grand vision of an RWA-driven ecosystem seem somewhat overshadowed by speculation.

Things got more interesting when launchpads like LONG and Pons introduced a novel concept: pairing memecoins with tokenized stocks. Trading volume exploded, and in some bizarre cases involving stocks like HIMS and AMC, memecoin-driven volatility in their tokenized counterparts even began influencing prices in the traditional stock market.

On September 4, 2026, Robinhood Chain generated an astonishing $8.27M in network fees in a single day. To put that into perspective, Robinhood’s entire crypto business generated roughly $100M in revenue during Q2 2026. That makes the chain’s single-day performance pretty remarkable.

But the excitement didn’t last long. Against a backdrop of unfavorable macro conditions and a weak crypto market, the memecoin frenzy on Robinhood Chain began to cool. As network congestion eased, fee revenue collapsed just as quickly as it had surged. By mid-October, daily network fees had fallen to around $30K-40K, less than 1% of the September 4 peak.

So, did Robinhood Chain really become yesterday’s news almost overnight? Let’s take a closer look at what’s actually happening across its ecosystem.

2. Taking a Closer Look at Robinhood Chain

There are four main pillars supporting the Robinhood Chain ecosystem. These are Robinhood’s Four Pillars:

• Steakhouse USDG Vault

• Stock Tokens

• Launchpads & Memecoins

• Perpetual DEXs

2.1 Steakhouse USDG Vault

Robinhood Chain currently has around $1.1B in TVL, and there’s one particularly important vault that accounts for nearly half of it. That vault is the Steakhouse USDG Vault, curated by Steakhouse Financial, which allows users to earn lending interest by supplying USDG to Morpho lending markets. It currently holds approximately $540M in TVL.

What’s interesting is that, unlike the network metrics we looked at earlier, USDG Vault TVL has been steadily climbing. The biggest reason is its attractive yield. To attract users and grow TVL, Robinhood has been offering existing Robinhood users an APY of up to 7%, boosted by additional incentives. Considering that stablecoin-based Morpho vaults on other networks typically offer around 3-5% APY, a 7% yield is an extremely competitive proposition for investors.

Looking at the vault’s depositor distribution, funds are fairly concentrated among larger holders, but roughly 75,000 wallets have participated in deposits. Interestingly, the largest depositor is NetNet, a protocol that uses USDG as a reserve asset. Taking this into account, the actual number of wallets indirectly participating in the USDG Vault could be considerably higher.

Within the crypto community, Robinhood Chain has developed a reputation as a playground for degens actively trading memecoins. But behind the scenes, Robinhood Chain has been quietly building a base of passive investors through its USDG Vault.

2.2 Stock Tokens

Robinhood Chain was originally designed to build an ecosystem around RWAs, particularly tokenized stocks. True to that vision, stocks and ETFs account for more than half of all RWA tokens issued on the chain, followed by commodities and U.S. Treasuries.

The value of tokenized stocks surged in early September. As discussed in the first section, this was largely driven by massive trading activity in memecoins paired with stock tokens, which required substantial amounts of new stock tokens to be minted to meet demand. Although the memecoin frenzy has cooled somewhat, many of those newly minted stock tokens have not been redeemed, leaving their outstanding value relatively stable. The roughly $150M worth of stock and RWA tokens issued on Robinhood Chain could serve as a solid liquidity foundation as the ecosystem moves into its next phase.

2.3 Launchpads & Memecoins

Starting with Noxa and followed by Pons and LONG, memecoin launchpads have played such a massive role in building Robinhood Chain’s current ecosystem that it’s hard to overstate their contribution. Traditionally, memecoins launched through these platforms are paired with a network’s native assets, such as ETH or SOL. But Robinhood Chain introduced a new concept: memecoins paired directly with tokenized stocks, which helped spark the chain’s memecoin frenzy.

If you only looked at the network fee revenue chart, you’d probably think Robinhood Chain’s memecoin trading ecosystem was completely dead. But spot DEX volume tells a different story. On September 4, when network fee revenue peaked, spot trading volume reached $3.27B. Even after the hype faded, volume climbed to $3.24B on October 7, and the network has continued to consistently generate at least $600-700M in daily trading volume.

More importantly, memecoin-related trading hasn’t disappeared either. Memecoins still consistently account for more than half of total spot DEX volume. The memecoin ecosystem that everyone thought was dead hasn’t actually gone away. The fire may have cooled, but the embers are still burning.

2.4 Perpetual DEXs

While Robinhood Chain aims to build an ecosystem centered around tokenized stocks, it also has a highly active perpetual DEX ecosystem where users can trade a wide range of assets, including RWAs. Two major perpetual DEXs operate on Robinhood Chain: Lighter and Arcus. Robinhood has made strategic investments in both protocols and has been actively supporting them. Lighter’s trading functionality is directly integrated into Robinhood Wallet, while Arcus has been integrated as a liquidity partner for tokenized stock spot trading.

If memecoins drove Robinhood Chain’s first wave of growth, perpetual futures are arguably the strongest candidate to drive its second. Both Lighter and Arcus have been aggressively attracting traders through points programs, and both trading volume and open interest (OI), which measures the value of outstanding positions, have been steadily trending upward. If these protocols can attract a larger user base and successfully execute their airdrops, Robinhood Chain could be on track for another golden era.

3. What Robinhood Chain Needs for Its Next Leap

The reason Robinhood Chain appeared to be dying was largely due to how dramatically its network fees surged during the congestion spike in September 2026. Robinhood Chain uses Arbitrum’s technology stack, which employs a fee pricing mechanism designed to increase the base fee exponentially when network usage exceeds its target processing capacity.

Meanwhile, key ecosystem metrics such as USDG Vault TVL, outstanding stock token supply, and memecoin trading volume have remained relatively resilient, showing nothing close to the collapse seen in network fee revenue. In fact, looking at the perpetual DEX sector, there’s a compelling case that Robinhood Chain could experience a second wave of explosive growth.

In my view, several things need to fall into place for Robinhood Chain to enter its next golden era.

• Ecosystem Metrics: Key indicators representing both passive and active investors, including USDG Vault TVL, outstanding stock token supply, and memecoin trading volume, need to remain healthy. Maintaining these metrics would provide a strong foundation for Robinhood Chain’s next growth cycle.

• Success of Perpetual DEXs: Leading perpetual DEXs in the Robinhood ecosystem, such as Lighter and Arcus, need to successfully attract traders and liquidity through their points programs. More importantly, they need to sustain meaningful activity even after their TGEs and airdrops.

• Ecosystem Support: Compared with other L1s and L2s, Robinhood Chain clearly has fewer ecosystem and builder support programs or initiatives. Although the Arbitrum Foundation has been running various official programs to support Robinhood Chain’s ecosystem since early 2026, no separately operated program directly run by Robinhood has been identified. When it comes to investments, Lighter and Arcus remain the only confirmed cases. Of course, there’s plenty of debate over how effective ecosystem and builder support programs actually are. Still, if implemented thoughtfully, these initiatives could help Robinhood Chain attract higher-quality dApps and ultimately deliver a better experience for its users.

• Original Concepts: Much of Robinhood Chain’s early success came from genuinely novel ideas, such as memecoin-stock token pairs and stock token airdrops. With core ecosystem metrics still holding up and perpetual DEXs potentially setting the stage for another growth cycle, the emergence of entirely new initiatives or primitives that don’t yet exist elsewhere in the onchain ecosystem could become a powerful catalyst for its next golden era. For example, we need to see more innovative initiatives like Standard Reserve, which will soon be featured in Four Pillars research article by Ponyo.

Robinhood Chain isn’t dead. It’s simply waiting for its next big leap. If you missed Robinhood Chain’s first wave of growth, now might be a good time to start paying attention again.

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

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

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Welcome to Edition 22 of The Reading List. Prathik is on leave, so I’m the one choosing your weekly reads.

I began the week with How to Be an Explorer of the World by Keri Smith, a beautiful gift from a dear friend. The pages are orange and grey, hand-lettered, with cut-out photos scattered across them like someone’s scrapbook. You can call it a field guide to life and experiences.

Smith keeps a small bowl of sea glass on her desk. She tips the pieces out and sorts them by size, then shape, then colour. She holds each one up to the light and wonders how many hands touched it before the sea did. Some of that history is gone for good, and she works out the rest by looking for a long time.

A good amount of finance comes down to that kind of patience. This reading list is my own small bowl for the week, pieces I picked up because they caught the light.

On to the article….

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

The Week That Was

The Nasdaq had its worst week since June last week. It fell 4.5% on fears that AI companies borrowed too much to build data centres. By Wednesday, it had recovered almost half of those losses. The Nasdaq Composite rose 1.1% on Monday, was flat on Tuesday, and rose another 1.1% on Wednesday. The S&P 500 added 0.4% on Monday, 0.3% on Tuesday and 0.6% on Wednesday.

The AI worry found its way to Korea as well. Elice, a Korean data centre company, listed on Tuesday after retail investors oversubscribed its IPO more than 400 times. It closed 32% below the offer price. Kospi fell almost 4%. A day later, the Kospi bounced 3.3% as foreign investors bought back in and Samsung Electronics and SK hynix each rose more than 5%. On Thursday, it gave back almost all of that, falling 3.5%. Feels like all three of these moves came from nerves.

Oil kept climbing as ships kept getting struck by projectiles in and around the Strait of Hormuz. Brent futures crossed $111 a barrel on Tuesday, the highest since May.

Higher fuel costs are now feeding into inflation well beyond the US. UK inflation jumped to 3.5% in September from 3.1%, and motor fuel was the biggest push.

The Fed is in its quiet period before the 27-28 October meeting. It raised rates to 3.75-4% in September. On Kalshi and Polymarket, traders priced in about a 12% chance of a rate hike at next week’s meeting and about 77% for December. The 10-year Treasury yield ended Wednesday at 5.13%, barely changed on the week.

France now pays about 1.55 percentage points more than Germany to borrow for ten years. This is the widest gap since the eurozone debt crisis, as protests spread and investors doubt it can pass next year’s budget. That budget also includes a plan to tax crypto-to-stablecoin swaps at 31.4%, which the National Assembly passed by a single vote on 16 October.

Bitcoin stays between $81,000 and $85,000. US spot bitcoin ETFs took in $526.7 million on Monday, their best day this month. BlackRock’s IBIT took $665.2 million, while $138.5 million left Fidelity’s and Grayscale’s funds. Tuesday was much quieter, at $23.2 million. Wednesday nearly matched Monday at $524.8 million, and this time Fidelity’s fund took in $212.7 million. BlackRock filed for a bitcoin fund that limits how much you can lose and how much you can gain. BlackRock earns either way, nice business.

What We Covered This Week

We started the week with Vaidik’s Data: The New Asset Class. AI labs have nearly run out of public text to train on, so they now buy the Slack threads, emails and work logs that companies have piled up over years. Vaidik argues that this emerging market resembles early commodity trading, where brokers understand prices better than sellers.

Vaidik also wrote A Premier on Conditional Markets, on what comes after prediction markets. He looks at markets that make DAO decisions, which he thinks run into Goodhart’s law, and markets that price an asset if a specific event happens.

This week’s product piece took me into private markets. In Can’t Sell? Borrow, I looked at Techdollar, which lends against vested shares in private companies like Anthropic, OpenAI and Stripe, at 0.80% to 1.5% a month. Most companies now take about 12 years to go public. A loan buys employees time without selling, and it only makes sense if the shares hold their value.

We have more lined up for you in the coming weeks. Until then, here’s what you can read. On to the list…

Regulation & Policy

• AI Is Transforming How Economies Develop and Compete: BCG’s 2026 AI Maturity Matrix scores more than 75 economies on how exposed they are to AI and how fast they can put it to use, and moves Japan and South Korea into its top tier this year.

Crypto & Tech

• Maybe We Were Wrong About Blockchain: Joe Weisenthal admits he used to laugh at conference speakers who were excited about blockchain but not bitcoin. He has joined the Hyperliquid fan club while also appreciating stablecoins and agentic payments. (Paywall)

• Don’t rage against the machines: Byron Gilliam uses the Hindu tradition of Ayudha Puja, which honours the tools people use in their work, to look at how humans should treat artificial intelligence.

• The Crabification of Capitalism: Jonah Burian shows fintech apps, such as Robinhood and Coinbase, all evolving into the same bank-plus-brokerage superapp. He sees AI startups heading the same way.

• Data quality as a contract: Coinbase’s Xuan He explains why thousands of data checks buried in pipeline code didn’t stop incidents at the exchange, and how the team now gives each check an owner and a consequence. Pairs nicely with Vaidik’s Frozen 2 piece.

Finance & Economy

• The Shrugflation Triangle: Kyla Scanlon starts in a Las Vegas casino and builds a loop out of oil, AI borrowing and Treasury yields. A lengthy read, but also a good one.

• The Near-Bag Experience: This one goes with what I wrote this week. Kram and Emma examine why employees at successful startups often fail to make substantial money from their equity.

AI & Innovation

• Can a Million AI Agents Create Superintelligence?: Ramez Naam questions the assumption that running millions of AI agents simultaneously will produce superintelligence. Some pretty good infographics in there.

• Citrini Therapeutics: Citrini Research describes an experiment to see whether AI could help people without extensive biotechnology experience design a potential drug. There’s a winner. (Partly paywalled)

• The Beauty of Something Grown: Goodfire researcher Nick Cammarata claimed this month that most neurons inside large models can be read and understood. Author Dean Ball thinks that could be a turning point for knowing how AI works, also suggesting that Cammarata might be wrong.

• I Can See Everything: Zvi Mowshowitz reads the same result and calls it progress, but says some people hyped it well beyond what it shows.

• Do You Trust Apple?: (For the record, no.) Apple’s HomeView now shipped after years of delays. Ben Thompson says its success now depends on whether people trust a slower, more error-prone Siri enough to mount it in their walls.

• AI is ushering in an age of personal media: Benj Edwards fed about 50 old voice memos into Suno and got back hundreds of songs that (by his own admission) nobody else will ever want to hear, his kids included.

• Swarm Intelligence: Kevin Kelly says “Instead of a central AI brain, AI is like a gas, composed of zillions of tiny interacting AIs that create an atmosphere.”

Culture & Beyond

• The Man Who Taught Us How to Pay Attention: A.O. Scott makes the case for art critic John Berger as an antidote to endless scrolling. Berger believed that looking was an active process shaped by personal experience, history, politics and social relationships. (Paywall)

• How intuitions work: Although I read this one to justify my gut feelings, Marc A Moffett suggests that the gut feeling can be as rational as seeing something with your own eyes. Because intuition works much like perception.

• How is music possible: Henrik Karlsson explores how arrangements of sounds produce such powerful emotions and alter our perception of reality.

• Nora Ephron Anticipated Our Narcissism: Ellie Kemper and I admire Ephron’s willingness to write something simply because it amused her, without worrying whether everyone would understand the joke. Very fun. (Paywall)

Next on my bedside table is Heart Lamp by Banu Mushtaq. If you’ve read it, tell me which story to start with.

That’s all for today!

Thejaswini

P.S.: Read, watched or listened to something worth sharing? Send it my way for a future edition.

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

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Thanks for reading! Subscribe for free to receive new posts and support my work.

A goat rancher in Northern California needed to protect himself against this summer’s wage-rule changes. His entire operation depends on seasonal hired labour, and if the state raised minimum crew wages mid-season, his margins would disappear overnight. There is no insurance policy in the country that covers legislative risk for small livestock operations, and there has never been a futures contract listed for it on any exchange in the world. So Castle Tech, a firm that builds custom derivatives for exactly these kinds of uncovered risks, created a contract tied to whether California’s proposed wage legislation would pass by a specific date, settled the whole thing through Kalshi, and got Susquehanna, one of the largest quantitative trading firms on the planet, to take the other side for half a million dollars.

For nearly two years, conversations about prediction markets have focused on whether Polymarket and Kalshi can forecast elections better than polls. But prediction markets are becoming much more than forecasting tools, and the new financial products growing out of them are starting to touch governance, hedging, and speculation in ways I think nobody is paying nearly enough attention to.

Let’s dig in!

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

What Happens When Governance Becomes a Market

Every prediction market today, from a contract on Polymarket to a listing on Kalshi, produces only one kind of output, a probability. Which is nothing but a number that represents the market’s collective estimate of whether a specific event will actually happen.

Over the past two years, the infrastructure behind this has shown us that markets can aggregate dispersed knowledge about future events more accurately than any polls, expert panels, or any other forecasting mechanism humans have ever built.

But probability is a strangely incomplete piece of financial information. If you hold bitcoin and see a 65% chance the Fed will cut rates by 75 basis points next month, you might learn something tangible about the world and almost nothing useful about your own position. You know the event is likely, but you still have no idea what it would actually do to the price of the asset you’re holding. Would Bitcoin trade at $110,000 in that world, or $95,000, or $140,000? No prediction market can ever tell you that.

There are events, and then there are the impacts those events have on companies and assets, and the financial system has been collapsing those two entirely separate things into one for as long as markets have existed. That difference is exactly where the next generation of financial products is being built.

Two very different things are being built right now. One starts with a question prediction markets made inevitable: if markets aggregate information better than anything humans have ever built, why are we still using votes to run organisations? These are called decision markets. The other is something that has never existed before, a market that actually tells you what Bitcoin would be worth if the Fed cuts 75 basis points, not just the probability of the cut happening, but the dollar price of Bitcoin in that specific version of the future. These are called impact markets, and they’ll be far more important than anyone in the space is giving them credit for right now. I will get deep into impact markets in the next section, but the fastest way to understand why they matter so much is to see exactly where decision markets hit a bottleneck.

Decision markets were first implemented in governance because DAO governance and token voting have had a fundamental flaw for as long as they have existed. People with the most tokens rarely have the most information, and people with the most information rarely have enough tokens to matter. Decision markets invert this by letting anyone with capital express a view, regardless of token holdings. Ultimately, this lets people price information in real time.

Robin Hanson proposed a fix for this in 2000 called futarchy, and the core idea is beautifully simple. Say a protocol needs to decide whether to cut its staking rewards. Instead of a token vote, traders price the protocol’s token in two competing futures: one where rewards get cut and one where they stay, and whichever version of the future trades higher is the one that gets implemented. And if you ever trade that decision without understanding the protocol’s economics, you lose money to whoever did the work, and that loss directly shrinks your weight in every future governance call.

The system gets smarter over time because uninformed capital bleeds out and informed capital accumulates, the opposite of token voting, where you can make the worst possible decision on every proposal for a year and your governance power still stays the same. Futarchy makes ignorance expensive, something no governance system before it has ever managed.

MetaDAO was the first team to actually ship this. They launched on Solana in late 2023 with a binary model where every proposal is a straight pass or fail, and proposals go through conditional markets instead of token votes, and the market’s answer is the one binding.

But the binary design creates another kind of problem. Since the market can only answer yes or no, every parameter (Budget, timeline, technical changes) of the proposal must be negotiated in a forum before the market even opens. This defeats the whole purpose of decision markets because figuring out what the right version of the proposal looks like still happens through the same informal consensus process, where the biggest token holders dominate the conversation.

You have moved the final yes-or-no into a market, which is a real improvement, but you have left all of the design work in the broken system you were trying to replace.

In September, Umia launched a multi-outcome model for the MetaDAO version. So, instead of a single pass-fail question, multiple proposal configurations compete simultaneously, and the market filters out weaker parameter sets through trading. The discovery of what the right version of a proposal should be happens inside the market rather than outside it, which is where it always should have been if the whole point was to let price discovery do the work.

Across all 22 DAOs running this, 96 proposals have gone through market-based governance so far, and the total capital sitting inside market-governed treasuries is $50.5 million, with MetaDAO holding $39.3 million of that. So the trajectory toward real scale is there, but even if treasuries grow by a hundred times from here, decision markets have a ceiling, and it doesn’t even have anything to do with capital or adoption or how good the teams building them are.

A rule in economics called Goodhart’s Law says that when a measure becomes a target, it stops being a good measure. Credit ratings were designed to measure default risk, and when banks started using them to decide which mortgage products to buy, investment banks began engineering securities to meet rating criteria rather than select for actual loan quality, and the ratings kept printing AAA while the underlying mortgages rotted.

Decision markets have this vulnerability baked in. When a proposal passes, the token moves partly because the proposal itself might create value, and partly because passage signals team confidence. A strong project could propose burning treasury funds on a decorative bronze bull, and the token would still trade up on approval because traders react to confidence in governance, not the statue. Apple stock does the same thing every time it announces a buyback at absurd valuations.

The whole thing breaks because the market’s answer feeds back into the thing it is trying to measure, and you cannot fix that without destroying the reason traders show up in the first place. But strip the governance layer out entirely and point the same conditional mechanic at events happening outside the protocol, events that no trading outcome can influence, and that feedback loop disappears on its own. There is nothing left for the price to corrupt against. And that is what an impact market does.

Impact Markets

Hedging in crypto right now is impossibly blunt. If you hold Bitcoin and you’re worried about what a specific regulatory ruling would do to your position, you either sell and give up all the upside from every other scenario, or you buy volatility products priced against general market movement that have nothing to do with the specific event you’re actually worried about. There has never been a financial product that isolates one event and lets you trade its dollar impact on another, and that’s exactly what impact markets let you do.

For example, let’s say the SEC is about to rule on whether a category of DeFi protocols qualifies as securities, and you hold a large ETH position. You believe a negative ruling would crush ETH to $1,800, but the impact market on ETH conditional on a negative ruling is trading at $2,300, which is the market’s collective estimate of where ETH would land if the ruling goes against DeFi.

You sell at $2,300, the ruling drops, ETH falls to $1,800, and you pocket $500 per token because your read on the severity was better than the crowd’s. If you’re wrong and ETH only dips to $2,200, you still pocket $100. A trade like this couldn’t have existed before impact markets, because no product has ever let you trade the magnitude of a specific event’s impact on a specific asset, with a live market pricing your estimate against everyone else’s.

It works because every asset price you have ever looked at is really just an average across all possible futures, compressed into one number, and impact markets are the first product that lets you trade beneath it.

Right now, if you want to protect yourself against a specific risk, you diversify across assets, which is really just a crude guess about which things might go up when others go down. Impact markets let you skip that entirely. You can hold a concentrated Bitcoin position and buy protection against the exact regulatory or macro event you’re worried about, without selling the position and without paying for volatility insurance that covers a thousand scenarios you don’t care about.

Instead of diversifying across assets and hoping correlations work out, you hedge specific scenarios directly, a completely different way to manage risk that only becomes possible when the underlying product gives you a conditional price rather than a probability.

I think impact markets will be far more consequential than prediction markets ever were. A prediction market closes and pays out a binary result, which is useful but, on its own, finite. You can’t build anything financial with that number because a probability isn’t a tradable object.

Impact markets produce a dollar-denominated price for where an asset would actually trade under a specific condition. You can hedge against it and write derivatives on top of it, instruments that literally could not exist before because there was no underlying conditional price to build on. The products that eventually get layered on reliable conditional prices will dwarf the conditional markets themselves, the same way the options and derivatives market is orders of magnitude larger than the equity market it’s written on.

The TAM for “how likely is this event?” is interesting but fundamentally capped, because there’s only so much you can do with it. But the TAM for “what would this event do to this asset?” has far more outcomes and runs as deep as finance itself.

The first implementations are already live and scaling faster than ever. Protocols like Branchpoint are even building something called Branchmap, a shared naming system for how conditional outcomes get defined across protocols, which matters because impact markets only become a real financial layer if “ETH conditional on a negative SEC ruling” means the exact same thing on every platform that trades it. Without that standardisation, liquidity stays fragmented and conditional prices on different platforms aren’t actually comparable.

Branchpoint calls this “multiverse finance.” Every conditional market is a window into one version of the future, one branch of reality where a specific event did or didn’t happen. As impact markets mature and liquidity deepens, what emerges is not just a single conditional price for one event but a full spectrum of an asset’s value across parallel futures, each conditioned on a different scenario.

Finance has tried to decompose asset prices this way for decades. The entire discipline of event studies exists to work backwards through historical data and estimate how much of a price move was driven by one specific event versus background noise, and the best anyone has ever managed is a rough estimate after the fact.

Impact markets produce this decomposition forward-looking and in real time, and that information changes the foundations of how risk gets priced the moment it exists.

The next level is when conditional prices start plugging into risk models and portfolio engines that currently run entirely on spot prices and implied volatilities. Once that integration begins, the framework for capital hedging and exposure in crypto gets rebuilt from the ground up.

But there is no reason this stays just limited to crypto. The goat rancher couldn’t buy insurance because actuarial tables don’t cover goat pasture in a specific Northern California county. In every case, the missing piece has always been a liquid price for what would happen to X if Y occurs.

Once that price exists, capital allocation changes in compounding ways. Once an insurer can lay off risks too weird for its own book into a conditional market, it can underwrite policies it currently has to decline. A VC fund that can hedge sector risk separately from company risk can concentrate more on its best founders instead of diversifying across thirty bets as a substitute for conviction. A government considering a minimum wage increase can watch a conditional market price the employment impact in real time, with real money behind every position, instead of arbitrating between economic models that disagree with each other.

Options did that to equities in the 1970s; it rearranged the industries built on top of them in ways their inventors did not predict. Conditional markets are the same class of primitive. The difference is they decompose risk across events, not just across assets, and the number of events in the world is unbounded.

That’s all for today!
Vaidik

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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They borrow against the assets and spend the loan. When they die, their heirs in the US inherit the assets at today’s value, so those gains are never taxed as capital gains. Planners call it “buy, borrow, die.”

The habit needs two things, an asset a lender will accept and a lender willing to deal with you. Public stock and property qualify easily. Shares in a private company mostly don’t, unless you’re a founder or a senior executive with a big stake. That leaves out a lot of people.

Borrowing against shares can be risky even for someone extremely wealthy.

When Elon Musk agreed to buy Twitter for $44 billion in April 2022, banks offered him a $12.5 billion loan secured by his Tesla shares.

But Tesla’s share price was falling, which created a problem.

If those shares lost too much value, Musk could be required to provide more shares or cash to keep the loan. He dropped the loan plan and increased the amount he would contribute through other financing. As of August 2025, about 236 million of his 715 million Tesla shares were pledged to secure personal loans, according to Tesla’s proxy filing.

The lenders expect repayment regardless of what happens to the share price, and a fall in value can leave borrowers scrambling for additional collateral. By a startup’s Series C, its employees typically own more of it than its founders do, according to Carta data. These people can’t easily cash out before their company goes public.

Techdollar wants to give them a loan against those shares and, if they’d like, an Apple Pay card to spend it with.

Techdollar launched in June with a $3 million pre-seed round. It lends against vested shares in private companies. They call this “Build. Borrow. Thrive.” Building is up to the borrower, leave that part, and we are going to check whether the other two stay true.

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Why did the wait get so long?

In 1989 the average company going public was six years old, and by 2021 that had stretched to 12, according to Morgan Stanley’s wealth management research.

According to Jay Ritter, the University of Florida professor who keeps the standard database of US listings, the median age during the dot-com years of 1999 and 2000 was 5. Which became 11 years between 2001 and 2025, and 12 years in 2025 alone.

SpaceX was founded in 2002 and listed in June 2026. Stripe, founded in 2010, is still private.

Big private companies let staff sell through tender offers instead. Stripe has run tender offers at $91.5 billion in February 2025, $106.7 billion in September and $159 billion in February 2026.

During Facebook’s 2012 listing, even David Choe, the artist who accepted Facebook stock for painting its offices, became wealthy when the company went public. But it is different for someone who joins a promising startup, spends eight years building it, and leaves before the company lists. They may have substantial equity and still need to pay a mortgage or fund their next venture using their salary and savings.

In October 2025, OpenAI’s current and former staff sold about $6.6 billion of stock at a $500 billion valuation. The company had authorised up to $10.3 billion, and only about two-thirds of it was tendered.

Anthropic ran its own tender between February and April 2026 at a $350 billion valuation, open to people with at least a year at the company. Investors had lined up about $6 billion, and employees sold less than that, which could be staff expecting the shares to keep rising.

Reuters reported that the value of tender-offer transactions executed through Nasdaq Private Market rose from around $3 billion in 2023 to just under $15 billion in 2025.

So tenders do benefit, but they run on the company’s calendar. The company sets the date, says who qualifies, and sets limits on how much each person can sell. According to Morgan Stanley, companies typically limit these sales to 20–30% of an employee’s vested shares.

Selling outside those tenders is also possible. Employees can approach platforms such as Forge or EquityZen.

Most private companies require board approval for any transfer, and most have a right of first refusal, usually shortened to ROFR. It lets the company, and sometimes its investors, match whatever price you negotiate and buy the shares themselves.

OpenAI and Anthropic treat any unapproved transfer as void. Anthropic barred SPVs(Special Purpose Vehicles) and named new offerings on Forge and Hiive among those it would not recognise.

A sale that clear, becomes a taxable event, and seller also gives up whatever the shares do next. OpenAI staff sold at $500 billion in October 2025. Five months later, the company was valued at $852 billion.

Banks lend against private stock selectively. They usually take only unrestricted shares, which rules out much of what employees hold. Even so, Goldman’s San Francisco private wealth loans are up 50% since 2023, and banks see a loan today as a way to win the IPO later.

What is Tech Dollar?

For founders and employees, the core product is a loan secured by vested shares.

That means, unlike tenders or secondary sales, Techdollar lets employees get cash without selling their shares or losing future gains. Also, unlike traditional banks, which prefer public stocks or private stocks from wealthy investors, Techdollar specialises in lending against private-company shares, though borrowers still need to qualify and pledge shares they’ve already earned.

You keep the shares, but if you fail to repay the loan, the lender may have the right to sell them. You don’t necessarily need substantial wealth beyond those shares to qualify.

The money can arrive in whatever form the borrower prefers. It can be a bank withdrawal, a physical card, an instant digital card in Apple Pay, or in stablecoins.

But unlike its crypto competitors, Techdollar does not tokenise the shares. The pledge is documented in standard legal documents as a registered claim over the shares, known as a share charge, with the accounting firm RSM verifying ownership. It uses blockchain only to move the money borrowers receive.

The money moves through its USDte settlement rails. Investors financing a loan can settle funds through that infrastructure, while a borrower can choose to receive ordinary dollars, spend through a card, or take stablecoins. From the borrower’s perspective, the crypto portion can remain largely invisible.

Funds and family offices can borrow against stakes in companies that haven’t exited to pay their own investors or back a later round without selling at a discount.

They also list a 30-to-60-day bridge for funds and loans for employees to exercise options, both still coming.

Each loan is funded by outside investors, either approved investment partners or groups of private lenders. The money moves through USDte, a dollar-backed stablecoin issued by a partner.

The collateral it wants is narrow. The site groups its focus into eight areas, from AI and compute to aerospace and defence, advanced chips, energy and data centres. It says more than 300 companies are accepted, including Anthropic, OpenAI, Stripe, Databricks, Ramp, Cerebras, Blue Origin and Apptronik.

A private company’s shareholder agreement may restrict pledging shares as well as selling them.

Plenty of tokenised crypto projects have tried to put private-company stock on a blockchain, and the Anthropic and OpenAI notices showed us how that ends when the company hasn’t agreed.

The lender cannot assume that a borrower’s name on an equity statement is enough to make the collateral legally recoverable. Techdollar says professional-services firm RSM checks ownership and transferability.

RSM’s job is to check what the borrower is actually allowed to do with those shares. It reviews ownership records and restrictions so Techdollar can identify whether company approval is needed before accepting them as collateral.

Techdollar offers advance rates of 25% to 40% of the collateral value, depending on the company and its liquidity prospects.

Once the facility is signed, capital from approved investors funds it.

None of this would surprise a private banker. Banks also review the shares, take a pledge, and lend a portion of their value. Enness, a London broker that arranges these loans, says lenders typically advance 25% to 40% against shares in an established private company, the same range Techdollar gives.

Techdollar’s case rests on who can get a loan and how fast it is done.

Enness says many lenders only make these loans above about $1 million, against shares worth at least $3 million to $5 million. Techdollar says its software makes loans as small as $400,000 economical. It still isn’t for every employee, but it goes further down the staff list than a private bank usually does.

Speed is the second. Techdollar says it makes decisions in one to two days for companies it already knows and pays out within 72 hours of signing, while a bank takes two to four weeks.

The company says wealth managers, private banks and brokerages could eventually offer private-share loans to their own clients using Techdollar’s underwriting and servicing infrastructure. That would make Techdollar a potential technology provider for traditional lenders, not just their competitor.

Techdollar charges 0.80% to 1.5% a month. On a $300,000 loan, that’s $2,400 to $4,500 every month, or $28,800 to $54,000 over a year, before any other fees. Techdollar says interest is paid monthly and the loan itself is repaid at the end, with no penalty for paying early.

The firm says its rates are more than 2.5 times cheaper than traditional lenders. At 0.80% monthly, simple annual interest is 9.6%. At 1.5%, it is 18%. These calculations exclude compounding and any additional fees, so the annual borrowing cost could be higher. Both are significant amounts to pay to keep the shares intact.

At a 16% annual rate, a competing lender would need to charge 40% for Techdollar’s rate to be 2.5 times lower. Techdollar’s CEO says banks quoted some founders around 20%, while Morgan Stanley puts specialist lending rates at 15% to 20%. Based on these figures, Techdollar may be cheaper than some alternatives

But, if an employee owns $2 million in private shares and needs $200,000. A secondary-market buyer offers 20% below the estimated value, so she must sell $250,000 worth of shares to receive $200,000.

Alternatively, she could borrow $200,000 from Techdollar at 16% annual interest, costing $32,000 over a year.

If her shares don’t change in value, borrowing leaves her slightly better off because she avoids selling at a discount. If the shares rise 50%, borrowing becomes much more attractive because she kept all her shares and their gains.

But if the shares fall 50%, selling would have been better. She would have avoided the debt and the interest bill.

Tech Dollar is not the only option here. Secfi, ESO Fund and Equitybee offer non-recourse financing, where you owe nothing beyond the shares if they go to zero, but you hand over a slice of your gains at exit.

Speed isn’t much of a difference. Secfi says it can fund in three days, and ESO offers 24-hour funding. Every lender here also picks its companies.

The loan structure is a trade-off. If the company soars, a loan is cheaper than giving away gains. If it fails, a non-recourse provider takes the hit, and a lender doesn’t. The key differences are the payout options, which no other private-share lender offers, and the funding, which comes from stablecoin holders seeking yield.

This whole thing makes me think equity is a weird way to pay people.

When companies listed within a few years, asking people to wait together was a fair deal. Now that the wait runs past a decade, it asks for a big piece of someone’s life, and a loan is one way of giving some of it back.

Ten years ago, nobody could say what a private share was worth between funding rounds. Today firms like Caplight and Forge record those trades, so a lender can mark the collateral as prices move instead of guessing. The GENIUS Act, passed in 2025, also gave dollar stablecoins a legal footing in the US, which Techdollar is counting on for USDte.

One employee pledges her share against the loan. The loan is in a pool with others, and the pool is split into eUSDte tokens held by many investors. So a think slice of a person’s share in a private company ends up backing a thn slice of token in someone’s portfolio?

That’s fractional finance. That’s the same thing that turned home loans into mortgage-backed securities. The pressure point is the token. If investors could borrow against their eUSDte tokens, the same private shares could indirectly support another loan. That ties additional debt to the same collateral, so losses from falling share prices could spread beyond Techdollar’s original lenders. The hard transfer restriction Techdollar described to the SEC is what stops that for now.

For employees, trade cuts both ways. They get the loan, but then it also ties their salary and debt to the company. Well, startups want staff who think like owners, and hopefully some will think like borrowers too.

Techdollar only uses crypto to move the money. Crypto has been trying to make tokenised stocks usable as collateral, and Ondo with morpho, xStocks with kamino, Coinbase and Aave are already making it possible.

Now we have Techdollar, testing whether traditional ownership records and stablecoin funding can do the job.

Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

A data broker was recently trying to sell a dataset to an AI lab. He priced it by the terabyte, the current standard because nobody in the industry has figured out how to calculate the data’s actual worth yet, and he shipped it without ever opening the files himself. When the lab opened it, they found about 4,000 copies of Frozen 2 buried at the bottom of the folder structure because someone padded the whole thing with Disney movies to make it look bigger.

I don’t think the broker will ever hear from the lab again, but the wild part is that this isn’t some shady back-alley operation. AI data brokering has become a proper commodity market assembled in the past two years, purely around selling all kinds of proprietary data to frontier labs. One brokerage alone is pulling in $144 million a year in revenue.

I spent the last few weeks pulling apart how this market actually works and how companies see it; let’s dig in!

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

How a Market Forms From Nothing

The first thing you need to understand is why this market exists at all, because just a few months ago nobody could have imagined it as a market.

For the last decade, every major AI lab trained its models on the open web. They used Wikipedia, Reddit, digitised books, old forum posts, literally anything they could scrape and feed into the machine. When GPT-3 was launched, it consumed roughly 500 billion tokens of this material, and GPT-5, three years later, consumed several trillion.

Each generation was hungrier than the last, and around 2025, consumption outpaced the available new knowledge base. Epoch AI estimated that high-quality public text will be functionally exhausted anytime between 2026 and 2032. Reddit, which every major lab has used to scrape for free, now charges roughly $60 million a year for access to its archives.

Millions of websites today have blocked crawlers entirely. And a 2024 paper showed that training models on AI-generated text, also called synthetic data, the obvious workaround when human writing runs short, causes outputs to degrade across successive generations. The researchers called it “model collapse,” which is a polite way of saying that if you feed a machine its own words often enough, it forgets how to think.

So the open web kind of completely closed as a variable training source and the labs then went looking somewhere else.

And what they found was that the richest remaining source of the data they needed had been accumulating for years inside ordinary companies’ private systems. That meant Slack archives, email threads, CRM databases, internal wikis, the operational exhaust of daily work generated as a byproduct of people doing their jobs- had been piling up in cloud storage for a decade, and nobody had ever thought it could be sold too.

This data is often called “data exhaust” because people produce it so unconsciously. Nobody sits down at their desk in the morning thinking about the training value of the Slack message they are about to send. The data is a byproduct of doing the job, the way exhaust is a byproduct of driving. You leave it behind without realising it, and it accumulates in volumes nobody inside the organisation has ever thought to measure, let alone monetise.

Hernando de Soto once captured this kind of moment in his book The Mystery of Capital, even though he was writing about something entirely different. He argued that in the developing world, families owned homes, ran businesses, and farmed plots of land but could never turn any of it into capital. The assets were real, and people lived on them and fed their families from them. But from the financial system’s perspective, they didn’t exist.

In these dead assets, wealth couldn’t participate in the broader economy because the institutional machinery to convert it into tradable instruments had never been built. Every company with five years of Slack history was pretty much in the same position until about 2024. Their employees had been generating valuable operational data as a byproduct of daily work, and it had been accumulating in cloud storage with no mechanism to price it because the market for trading it simply had not been built yet.

What is also interesting is how quickly that infrastructure appeared once demand materialised. Oil had been actively traded for more than a century before the NYMEX introduced standardised crude futures in 1983. But for AI training data, the market compressed that entire trajectory into less than 24 months.

There are now standardised deal terms with two-year exclusivity windows, thirty-day inspection periods for buyers, sixty-day payout schedules, and referral networks that pay brokers up to 50% of the transaction price.

The average deal falls somewhere between $200,000 and $300,000. If you described the mechanics of a typical transaction to someone, they would mostly guess commercial real estate or a speciality financial instrument. But they would never be able to guess five years of a roofing contractor’s Slack messages.

What the Labs Are Actually Buying

The AI labs are no less insane in their data demands, with very high specificity about what they’re actually requesting.

Brokers receive detailed intake lists from the labs specifying exactly which industries they want data from at any given moment. When I started pulling these apart, the granularity was very interesting data in itself! They were looking for Chip design verification workflows, wet-lab experiment logs, clinical trial records, and factory-floor head-cam footage.

One broker described getting requests for bizarre internal workflows of a roofing contractor or a tax firm or a freight company. And the labs want these records across every conceivable industry vertical, from aerospace to insurance to veterinary medicine. Think about what this tells you. When a company spending billions of dollars a year on training sends a broker a shopping list that specifically asks for freight companies’ and roofing contractors’ internal workflow documentation, it reveals its product roadmap.

That level of granularity isn’t just for vague ambitions about making models smarter. It is for building AI systems that can perform the actual day-to-day work of specific industries. The list of industries they target is broad enough that the implications extend to almost every white-collar profession. But the supply side of this market barely understands what any of this means.

The Quality Problem

About half of everything submitted to the major brokers gets thrown out immediately. The rejection stories are as important because they show how far sellers are from understanding what buyers actually want.

Brokers have reported receiving forty gigabytes of WhatsApp messages labelled “HIGH QUALITY CONVERSATIONS” that turned out to be one person’s entire chat history with their friends and family. They have received thirteen hours of doorbell camera footage marketed as a “real human emotion” dataset.

The submissions that look the best usually the most absurd and funny. Because the market still hasn’t figured out how to measure what makes data valuable. So sellers mostly price by the terabyte because nobody has proposed anything smarter, creating a direct incentive to make files as large as possible regardless of what’s inside. Buyers use employee headcount and years of operation as rough proxies because those are the only metrics both sides can agree on. But nobody actually captures much about the quality of the underlying material, which means the entire market is flying on vague instruments and measurements.

This is very similar to the cotton trade in the 19th century, where the merchants who worked the Liverpool Cotton Exchange had access to telegraph reports from ports across the American South, to real-time intelligence on crop yields and shipping schedules, and to demand figures from textile mills across Lancashire.

But the plantation owners who actually grew the cotton knew almost none of this. They sold at whatever price the merchant offered and had no independent way to verify whether that price was fair, and the asymmetry persisted for decades before futures contracts and public pricing mechanisms gave the growers enough visibility to negotiate on equal footing.

The data market is at the very beginning of that same arc. And the sellers here are even more disadvantaged than the cotton growers were, because at least the growers understood what cotton was used for.

How AI Labs Evaluate Data

Let me explain with a real-life example I heard from an AI broker I chatted with.

A SaaS business owner submitted ten years of internal communications spanning Slack, WhatsApp, and Discord. It was a decade’s worth of operational data from a company that was still running. On paper, that is exactly the kind of archive the frontier labs are paying millions of dollars for.

But when the broker assessed the dataset, they valued only the most recent two years. Why? Because the company had switched messaging platforms every few years, and each switch shattered the continuity that makes operational data useful for training.

What the labs need to see is how a team escalates a problem, the full arc of how a group of people actually worked through something together. When the conversation jumps from Slack to WhatsApp halfway through a project, that arc disappears. The record becomes fragmented, and fragmented records have almost no training value.

The reason they have no training value is that the labs are not paying for the messages themselves. They are paying for the behavioral patterns embedded in those messages, the accumulated record of how experienced people actually exercise judgment when the situation is ambiguous and the manual does not help.

Michael Polanyi, the Hungarian-British polymath, coined the term “tacit knowledge” in the 1950s to describe this. His classic example was riding a bicycle: you know how to do it, but you cannot write down instructions that would teach someone else, because the knowledge lives in your reflexes and your muscle memory and the only way to transfer it is to let another person spend time on the bicycle until their own body figures it out.

Organizations are full of this kind of tacit knowledge. A senior engineer at a SaaS company knows which production alert at 3am requires waking up the team and which ones can wait until morning, and that judgment has nothing to do with the severity labels in the monitoring dashboard. It comes from years of accumulated pattern recognition, from having been wrong enough times to develop an instinct that no runbook captures.

Management consulting firms have spent half a century trying to extract and codify this kind of operational judgment. McKinsey built entire practices around the effort. And the fundamental challenge was always the same: you could sit inside an organization for months, interview dozens of people, observe how they worked, and still walk away with only a fraction of what the organization actually knew how to do, because the knowledge was too deeply embedded in daily habits for anyone to articulate it clearly enough to hand over.

AI training changes this completely. A model can ingests five years of continuous internal communications from a company extract it, compress into model weights, and reproduce in a completely different context.

That is what created this market. The labs figured out that the richest source of tacit knowledge on earth is the operational data that companies generate as a byproduct of doing business, and nobody had ever thought of it as something worth buying because nobody had ever had the technology to process it at that scale.

That is also why dead startups were the natural starting point. The moral friction was zero. But the market did not stay there, since the dead company supply is limited by definition, and the labs are building toward something that requires a continuous, renewable feed of behavioral data from people who are still actively working.

So the gradient kept moving. From the archives of companies that no longer exist, to active companies selling their employees’ ongoing communications while those employees are still generating them, to a tech giant installing monitoring software on its own workforce’s computers to record the granular process of how they do their jobs.

If the next generation of AI depends on proprietary behavioral data rather than public internet text, then the competitive advantage in AI shifts in ways that might not even be visible. Right now the conversation is dominated mostly by the labs, the GPU manufacturers, the cloud providers, and the companies that won the compute race.

But compute is a scaling problem, and scaling problems can be solved with money. Behavioral data is a trust problem. You cannot buy your way into a hospital’s patient interaction history or a law firm’s thirty years of case files with a bigger check if the people who generated those records do not trust you with them. The organisations sitting on the deepest archives of tacit knowledge, the manufacturers, the financial institutions, the professional services firms with decades of accumulated operational judgment, hold something the labs cannot replicate and cannot route around. And most of them have no idea about this!

That’s all for today!
Vaidik

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Money’s value is in its utility. Casino chips are real only until we stay on the floor. We can use them to play at the poker table or any other table. But once we walk out the door, it buys nothing until the casino cage turns it back into dollars.

We are surrounded by so many kinds of money. Gold, airline miles, gift cards, and reward points. They are all worth something, but they aren’t considered at par with fiat currency due to how hard each one of them is to spend.

Even when stablecoins were introduced over a decade ago, people jumped to ideological sides and claimed that the digital dollars would replace the entire payment system as we knew it. Stablecoins moved balances instantly between exchanges, protocols, and wallets. But that’s not the end of the journey for money. The difficult part is to make the digital dollars usable once they step out of the crypto ecosystem.

Last week, I argued remittance won’t have a global winner because the value sits in plugging on-chain rails into licences, ramps, and local banks that no blockchain can overwrite. Spending stablecoins also works the same way.

In today’s guest essay, Sebastien Davies, Partner at Primal Capital, shows how.

Onto Sebastien’s story,
Prathik

Follow Sebastien’s Works

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

Stablecoins have largely solved settlement for crypto-native flows. Getting a token balance accepted at a till is a separate job, and for now, the most practical route runs through the card networks.

A Balance the Counter Can’t See

Put $2,000 of USDC in a wallet on Base and walk into a restaurant. The balance is real and dollar-denominated. The terminal on the restaurant counter has no idea it exists.

Over the past five years, stablecoins have become a credible settlement infrastructure for many types of transactions. They carry large, continuous volumes across trading, DeFi liquidity, treasury & remittances, on rails that run around the clock, finalise in seconds, and cost fractions of a cent per transfer. For crypto-native and cross-border flows, settlement feels close to solved.

Paying a merchant involves complexity. A checkout transaction needs a payment credential, an authorisation engine, a compliance check, fraud controls, chargeback rules, FX conversion, and clearance through a card network. A transfer between two wallet addresses needs none.

The interesting work in digital assets right now sits in between. Call it the spend layer, the part that turns a blockchain-native balance into an authorised, compliant card transaction that a merchant can accept through the terminals, software, and bank workflows it already runs.

Settlement And Spend Are Different Jobs

Narratives across the digital asset ecosystem tend to merge the two. A smart contract can move dollar tokens from Alice to Bob in two seconds, so it’s easy to assume checkout got simpler, too.

But, settlement answers one question: Did the value move?

Spend answers a more nuanced question. Can the merchant approve the sale, hand over the goods, manage fraud & disputes, reconcile the day’s takings, and get paid through the systems it already has?

Merchant checkout runs on ISO 8583 (the message format card terminals and processors use to talk to each other), point-of-sale software, acquirer relationships, and network rulebooks. When a customer taps a card or phone, the merchant’s system expects an authorisation answer in a fraction of a second. Nobody at the checkout counter wants to think about gas fees, block confirmations, chain reorganisations, or slippage on a token pair, and most of them shouldn’t ever have to learn what a reorg is. Nor does the cardholder want the payment experience to feel like crypto. Apple Pay and Google Pay matter because they turn the card credential into something familiar, fast, and invisible at the moment of spend.

Settlement is the backend clearing of value. Spend is the trust, rules, authorisation, messaging, and commercial guarantees that make a merchant willing to hand over the goods before that clearing happens.

Merchant Acceptance Is the Bottleneck

Blockchains can move value globally without permission. However, they can’t make a merchant accept cryptocurrency.

Merchants take Visa and Mastercard because those networks are built into point-of-sale hardware, e-commerce checkout software, acquirer relationships, accounting workflows, and customer habits. A retailer prices a payment method on the total cost of running it across the whole business, and the transaction fee is only one line in that. Changing how payments land in the ledger means touching POS integrations, store training manuals, end-of-day reconciliation scripts, tax software, and acquirer liability agreements. That’s what makes acceptance path-dependent. Unless a new payment rail is dramatically better for the merchant, inertia wins.

Raw stablecoin acceptance runs into three specific problems:

1. Retailers won’t put a separate QR scanner next to the terminal they already have.

2. Finance teams expect settled funds in a bank account by ACH or wire on a predictable cycle.

3. Merchants lean on card-scheme chargeback rules to defend against fraud & processing errors, which an irreversible transfer doesn’t give them.

Therefore, the holder of that USDC is liquid and unable to spend it at the same time. That turns the useful question around. Rather than persuading tens of millions of merchants to change hardware and software at once, can crypto-native balances inherit the acceptance merchants already have?

Cards Are Still the Front Door

Early crypto writing assumed stablecoins would route around the card networks entirely. No interchange, no intermediaries, just peer-to-merchant transfers.

That thesis treated the networks as merely expensive message pipes. But these card networks are also acceptance networks, rules engines, dispute systems, issuer/acquirer coordination layers, and merchant integration standards, which is why they’re so hard to route around.

For now, cards are becoming the main channel that carries stablecoin balances into the real economy. Stablecoins improve the funding rail, cards own the acceptance rail, and the spend layer is where the two meet. This does not mean that cards forever own the payments landscape. For the current phase of stablecoin adoption, acceptance may matter more than replacement.

The result is not a clean swap of one rail for another. Stablecoins fund the transaction. Cards translate it into something the merchant can accept. The spend layer coordinates the handoff.

The Stablecoin Spend Stack

Turning a token balance into a completed card payment takes eight layers working together in well under a second:

• Wallet & balance. Where the user holds the assets, whether that’s a self-custodial mobile wallet, a browser extension, or a regulated exchange account.

• Stablecoin funding. The APIs, protocols, or smart contracts that reserve, lock, or debit tokens when an authorisation request comes in.

• Program infrastructure (the spend layer). The engine that turns an incoming card authorisation message into a real-time balance check, policy evaluation, and risk decision.

• Processor. The technical processor that handles ISO 8583 messaging formats, PIN verification, and authorisation routing between the scheme and the program management system.

• Issuer & network access. The regulated issuing bank or principal member with legal authority and scheme permissions to issue credentials.

• Card network. The scheme, such as Mastercard, that routes authorisation, clearing, and settlement messages between issuers and acquirers.

• Acquirer & merchant processor. The merchant’s bank-side partner, which processes transactions, manages terminals, and settles local currency to the business.

• Merchant point of sale. The terminal, mobile reader, or online checkout where the customer presents the credential.

The focus here is on the third layer, which facilitates the translation between wallet balances and payment network specifications. Without it, on-chain dollars stay disconnected from everyday commerce.

Immersve as a Case Study

Immersve is a useful example because it sits in the translation layer. The company has assembled the B2B card infrastructure around Mastercard-enabled card issuing. Wallets, exchanges, and fintechs use it to offer their users card credentials funded by crypto balances, without building the issuing, authorisation, compliance, and settlement stack themselves.

It’s a complex stack. An in-house card programme means network memberships, issuer-processor connections, transaction monitoring, dispute workflows, and multi-currency treasury operations. For most wallet teams and exchanges, it’s a poor use of engineering and compliance headcount, which is the gap a platform like this fills. End users stay in the app they already use, and Immersve runs the programme behind it. The wallet or fintech gets a branded card and recurring payment activity without taking on the card stack itself.

Recent reference points show the thesis across both sides of the custody spectrum. Immersve’s work with MetaMask brings the spend layer into a major self-custodial wallet, where the user stays inside the wallet environment, and the infrastructure underneath turns a blockchain-native balance into a card-network transaction. Binance, Bitget, and KuCoin show the same need from the custodial exchange and wallet-account side. In those environments, the user’s balance may sit inside a platform ledger rather than directly in a self-managed wallet, but the payment problem is the same: how does that balance become spendable at merchants that already accept cards?

That custody distinction matters because the technical mechanics differ. A self-custodial wallet has to solve permissions, balance reservation, and on-chain funding without waiting for a fresh transaction at the counter. A custodial platform can debit or reserve funds inside its own ledger, but still needs card issuing, authorisation, compliance, settlement, and reconciliation. Different balance models, same merchant-facing goal.

Immersve is not alone in that fight. Rain, Baanx, Marqeta, Bridge, Gnosis Pay, and others all touch parts of the same problem, with different models for custody, issuing, programme management, settlement, and distribution. That is what makes the category interesting. This is not one company trying to bolt a card onto crypto. It is a market forming around the operating layer between blockchain asset balances and merchant acceptance.

What Happens at Checkout

Here’s the sequence in a typical card-linked stablecoin spend flow, when someone pays in a shop. Programmes differ in the details, so treat this as the general shape:

1. Tap. The user taps a virtual card stored in Apple Pay or Google Pay at a contactless terminal.

2. Authorisation request. The terminal sends a standard card authorisation request through the acquirer to the card network, Mastercard in this example.

3. Routing. Mastercard reads the card’s BIN (Bank Identification Number) and routes the message to the programme’s processor and spend infrastructure.

4. Policy & balance check. Depending on the programme structure, the platform checks account status, runs fraud rules, scores the transaction, and confirms the linked account or wallet holds enough stablecoin to cover the amount plus an FX buffer.

5. Approval. An approval code goes back through Mastercard to the terminal, and the customer and clerk see it immediately.

6. Ledger movement. Behind the scenes, the programme debits, locks, or reserves the user’s stablecoin balance, depending on how the program is structured.

7. Clearing & settlement. In the normal clearing cycle, funding and settlement run through the relevant program, processor, issuer, network, stablecoin, and acquirer relationships.

8. Merchant gets paid. The merchant sees a normal card transaction and receives settlement through its usual acceptance setup, typically in local currency, without touching digital assets, managing cryptographic keys, or altering its corporate accounting routines.

That cardholder experience matters. The closer the front end feels to Apple Pay, the less the user has to understand about the funding rail behind it. The user experiences crypto-native funding, while the merchant continues to offer card-native acceptance.

Custodial and Self-custodial

People hold stablecoins in very different places, from exchange accounts to hardware wallets, and a spend layer has to handle both ends of that range.

In a custodial model, the exchange or custodial wallet keeps user balances in its own database. When an authorisation arrives, the spend infrastructure queries the platform’s API to confirm funds, the platform typically deducts or locks the balance internally, and the programme’s settlement arrangements fund the scheme obligation.

Self-custody is harder. The user holds their own keys, and their stablecoins sit on-chain. An authorisation window can’t wait for a fresh on-chain transaction, and block times, network latency, and gas spikes can’t be counted on to fit inside it. So the programme has to be able to approve the card transaction before the final on-chain movement happens. That takes something arranged in advance, whether pre-positioned permissions like smart contract allowances, reserves, and delegated spend, or liquidity arrangements that let the programme pay first and settle on-chain afterwards. The exact mechanics vary by programme.

Handling both behind one authorisation layer means the cardholder gets the same experience wherever the money actually sits.

The Boring Parts Dig the Moat

Stablecoin payment pitches tend to lead with speed and low gas fees. The real work is somewhere else, and it’s the strongest evidence that settlement and spending are different jobs:

• Compliance & sanctions. KYC, AML, sanctions controls, transaction monitoring, and wallet-risk screening. This happens across onboarding, monitoring, and authorisation.

• Fraud. Real-time scoring for card-not-present risk, geographic anomalies, velocity, and credential misuse.

• Disputes & chargebacks. Card networks let consumers reverse unauthorised charges or non-delivery. The spend layer has to reconcile irreversible on-chain funding with a dispute system built on reversibility.

• FX & treasury. A USD stablecoin spent in Paris or Tokyo needs real-time conversion to EUR or JPY. The programme has to hold FX buffers on every authorisation and manage spreads, liquidity, and slippage, or it eats the losses.

• Reconciliation across three ledgers. On-chain records, the processor’s transaction stream, and the scheme’s clearing files all have to reconcile every day.

• Issuer & program obligations. The issuing bank and programme manager answer to the scheme and to regulators for every card in the programme, and those obligations don’t shrink because the funding happens on-chain.

• Network rules. Scheme rulebooks govern authorisation, disputes, settlement, and what a programme is allowed to support. Every programme operates inside them, and they change.

“Pay with stablecoins” fits on a slide. Reconciling three ledgers every night is the difficult part. This is where payment businesses get hard. Getting authorisation, compliance, disputes, FX, and reconciliation to work without the merchant or the user noticing the machinery is the job, and it’s most of the job.

What Still Needs to Be Proven

Card distribution gives stablecoins immediate reach, and it comes with its own open questions:

• Network dependency. Programmes on card rails live under scheme rules, fee changes, and policy shifts. If a scheme restricts certain digital-asset structures, providers have to adjust quickly.

• Regulation. Rules on card issuing, stablecoin reserves, consumer disclosure, and cross-border transfers are still moving, in different directions in different places.

• Consumer behaviour. It’s unclear whether people will use stablecoins as everyday spending accounts or keep them for narrower jobs like travel, trading payouts, freelancer remittances, or inflation hedging.

• Technical risk. Self-custodial funding depends on smart contract integrity, network uptime, and predictable gas. Congestion or a contract bug can interrupt payments.

• Direct competition. Large merchants or acquirers may eventually build a cardless stablecoin checkout for high-value B2B or e-commerce payments.

Card-mediated stablecoin spend doesn’t eliminate the card stack’s economics. It inherits interchange, scheme fees, processing costs, programme costs, compliance overhead, and dispute obligations. Direct stablecoin checkout can be cheaper at the rail level, but only if the merchant is willing to absorb new integration, reconciliation, refund logic, accounting treatment, and operational risk. For many merchants, the cheaper rail isn’t cheaper if it forces the enterprise workflow to change.

Cards solve acceptance. Building on them means inheriting their rules and constraints along with their reach.

Making Stablecoins Ordinary

Wallets own the consumer relationship. Merchants own the checkout. Schemes own acceptance and brand trust. Issuers and programme managers hold the licences, BIN access, and scheme relationships. Someone has to make those four work together on every tap. Coordinators like Immersve get paid for a job the wallet does not want to staff, and the merchant does not want to think about.

Asking merchants to drop their terminals, acquirer flows, and accounting to accept blockchain transfers directly has so far looked unrealistic. Stablecoins made settlement work. Spend brings its own problems in authorisation, compliance, merchant integration, and the user’s experience at the till.

If stablecoins are going to become ordinary, the winning architecture may not ask every participant to change at once. Users can hold dollar tokens. Wallets can own the interface. Card networks can preserve acceptance. Merchants can receive ordinary settlement. The spend layer coordinates the translation between those worlds.

Settlement made stablecoins useful. Spend will determine whether they become ordinary.

Disclosure: The author is a partner at Primal Capital, which is an investor in Immersve. This note was not paid for or sponsored by Immersve or any affiliated party.

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

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

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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

Welcome to Edition 21 of The Reading List. Every Saturday, I share my favourite reads across crypto, finance, technology, culture, and beyond, alongside what I’m watching and reading.

This week’s selection spends some time with the machinery behind the AI boom: the chips, the lenders, the inference businesses, and the people trying to steal its secrets to capture value.

First, a look at the week that was.

Let’s get going…

StablesReward: Your AI Agent Spends, You Earn

AI agents can surf the web, read reviews, and book hotels for you. All these tasks cost money. But what if these tasks could earn you back something?

StablesReward does just that. Once you connect your agent to an MCP endpoint, it gives your agent a menu of paid tools. Every call your agent pays for earns you miles or stocks. And this is in addition to the rewards you earn anyway on your own spending across 800+ merchants.

Every purchase pays you back. The best part is that you can choose how you’d like to be rewarded. Transfer miles to airlines and hotels, or let your stocks build a portfolio from the spend you were making anyway.

Launching soon. Free to join.

Plug in Your Agent

The Week That Was

Oil was the standout on our chart last week, gaining 7.83% while Bitcoin fell 1.88% and the S&P 500 CFD slipped 2.09%. Silver lost 6.29%, gold eased 0.51%, and the dollar index fell 0.49%. Expensive energy and weaker risk appetite made for an uncomfortable combination.

Wednesday’s US inflation report added to that discomfort. Consumer prices rose 0.6% in September, taking annual inflation to 3.7%, from 3.4% in August. Gasoline accounted for more than half the monthly increase, but the pressure extended beyond fuel: core inflation rose 0.3% over the month and accelerated to 2.6% annually.

That strengthened the case for further tightening without settling when the Fed would act. Policymakers also have weaker September payrolls and the tightening already delivered through borrowing costs to consider. The Conference Board highlighted that tension between what the inflation data suggest and the more cautious signals coming from Fed officials.

The market reaction changed as the week progressed. Yields and the dollar initially jumped after the inflation release. Later, anxiety about the debt financing the AI buildout drew investors towards government bonds, pulling yields back down while stocks remained under pressure. Renewed attacks around the Strait of Hormuz kept oil-supply concerns alive.

What We Covered This Week

At The Token Dispatch, we attempted to answer two connected questions: what makes a financial business difficult to replace, and who carries the risk when money finds new uses?

I explored the first in Money’s Utility as Moat, looking at how NG.CASH and Lemon build around local habits, licences and customer relationships. Stablecoins can provide shared infrastructure while the businesses offering useful ways to save, borrow and spend own the customer.

In Why Chip Lenders Now Sell Their Loans, I examined USD.AI’s shift towards originating GPU loans and selling them once they have a payment record. The model recycles capital, but still needs willing buyers and someone to bear the risk before the chips start earning.

Thejaswini explored Give the Bot a Loan, asking how AI agents might borrow to fund work before customers pay them. Her examination of Priors and other early projects comes back to familiar lending questions: is the income real, can the borrower repay, and who absorbs a default?

She also examined State of Hooks, on the contracts that customise how on-chain markets operate. Changing fees, sharing arbitrage profits and updating trading rules creates room for experimentation — and gives whoever controls those updates considerable power.

In Financial Nihilism, Vaidik connected the appetite for shorter, more speculative bets with fading faith in patient wealth-building and an increasingly uncertain future. His essay asks whether apparent recklessness also reflects a loss of confidence in the conventional route to financial security.

We have got more lined up for you in the coming weeks. Until then, we’ve got some recommendations that will keep you in good company.

On to this week’s reading list.

Regulation & Policy

• Don’t Bet Against the Sports Bets: Matt Levine examines the fight over regulating Kalshi’s sports contracts and the absence of a market on whether those contracts will remain legal. (Paywall)

Crypto & Tech

• Conditional Markets Are Beginning to Blossom: Galaxy maps the projects moving beyond predicting events to pricing their effects on assets and using markets to make binding decisions.

• Active Liquidity On Chain: Evidence from PropAMMs Across Chains: Category Labs examines how proprietary market makers outperform passive liquidity pools through frequent repricing, counterparty-sensitive quotes and other trading advantages.

• App-Controlled Sequencing, in a Box: Keone Hon introduces Typewriter, an open-source framework that lets crypto applications define their rules and control the order in which transactions run.

• Paper Trade: Game Theory, Strategy, and Outlook: Shaunda Devens examines Papertrade, a synthetic perpetual-futures platform on HyperEVM offering highly leveraged trading in Bitcoin and Ether.

• Making Options Easier to Use: Tacticalls previews an options-trading interface built around copying trades, following traders and hedging existing wallet positions.

• The Big Business of Spare Capacity: Byron Gilliam connects the historical trade in New England ice with Grass’s attempt to turn spare internet bandwidth into a business serving AI’s demand for data.

• How LLMs Might Make Crypto Mining Useful: Byron Gilliam explores Pearl’s proposal to combine blockchain security with AI computation, including why its mining work need not actually be useful.

Finance & Economy

• Private Markets Are the New AI Markets: Matt Levine asks whether investors are increasingly paying private-market fees for exposure to an AI boom that already dominates the broader economy. (Paywall)

• The Compute Credit Cycle Just Turned: Stanley Lee argues that lenders are beginning to distinguish between compute operators, making financing choices and customer diversification more consequential.

AI & Innovation

• AI Companies Play Cat-and-Mouse With Secret-Stealing Spies: The Wall Street Journal reports on AI companies’ efforts to protect corporate secrets from espionage, including approaches to employees and insider threats. (Paywall)

• Where AI Chip Architecture Meets Economics: Kevin Immanuel Gubbi explores the relationship between compute, memory and chip design, including why memory matters for running AI locally.

• The State of Crypto Inference Providers: Blockworks Advisory surveys crypto inference businesses competing across price, latency, privacy and hardware, with differing dependence on token incentives.

• Microsoft’s Bigger Bet on Its Own Silicon: Satya Nadella outlines plans to scale Microsoft’s Maia chips into the primary inference system for its own products and services.

• How to Be More Ambitious: A Tutorial for Humans with Models: Felix Rieseberg offers a practical framework for using AI to pursue larger goals, investigate problems more deeply and draw on unfamiliar disciplines.

• Anthropic’s AI Worm Research Raises New Fears About Smart Device Security: Scientific American examines research into AI spreading through connected gadgets, alongside cybersecurity experts’ assessment of how novel those threats really are.

Culture & Beyond

• The Ladder of Knowledge: Alexander V Gheorghiu traces debates over mathematical truth and argues that Gödel’s incompleteness need not impose a final ceiling on human knowledge.

• Deskilling the Body: Melanie Challenger examines how outsourcing navigation, memory and attention to technology can erode the abilities we develop by exercising them.

• Plants Move to Get Away from Their Decaying Brethren: Scientific American reports on research into how plants move away from decaying material to protect themselves from its microbes.

That’s all for this week. I’ll be back next Saturday.

Until then, happy reading!
Prathik

P.S.: Read, watched or listened to something worth sharing? Send it my way for a future edition.

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Claus splits its trading fees between buybacks, burns, liquidity and rewards for NFT holders.

This was a coin that had already given the weather a say in its finances. Claus divides trading fees between buybacks, burns, liquidity and rewards for NFT holders. For a time, signed weather reports from London Heathrow helped determine how much went towards destroying tokens and how much went back into the pool to support trading. Rain favoured burns. Dry weather favoured liquidity.

If the reports are not arriving, the allocation returns to an equal split.

The weather experiment was one of several functions attached to its trading pool. On 6 October, the project reported removing an arbitrage function while keeping the token address, pool and other features in place. This way, the holders did not have to exchange their tokens for a replacement.

This happened because the Uniswap v4 pool uses an upgradeable hook.

A hook is a smart contract that adds instructions around trading. Other developers use hooks to adjust fees or put liquidity to additional use. This lets people build different kinds of markets inside an existing exchange, without having to launch a DEX and persuade everyone to move there.

So by October 8, if you checked Uniswap’s public hook registry, the contract running all the fee stuff for Claus was now pointing at this new piece of code called FeePrivacyHook instead of the old FeeWeatherHook.

The team says that the coin is its own agent with the token as its body and an upgradeable Uniswap v4 hook as its mind. A token’s rules were always written once into the contract, and every exchange treated it that way forever.

Hooks pull those rules out of the token and stick them in the market where it trades. Sometimes they even let someone rewrite the rules later.

An industry is forming around this, and that opens the door to a bunch of interesting products and also some fresh ways to lose money.

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What are the hooks?

If ETH costs $2,000 in one pool and $2,020 in another. A trading bot can buy from the cheaper pool and sell into the more expensive one, keeping whatever profit remains after fees and gas as arbitrage. Those trades help bring the two prices closer together. Usually, the bot keeps the profit while the pools earn trading fees.

A normal liquidity pool already has rules for calculating trades and charging fees. But a developer might want it to capture some of that arbitrage profit for its users. A hook is a separate smart contract that runs extra instructions at specific points during a trade. Some hooks use those instructions to check for a price gap after a swap and execute profitable trades to close it, all within the same transaction. They can then share the captured profit with liquidity providers, the trader and the project running the hook.

In Uniswap v4, a shared contract called the PoolManager handles swaps and tracks each pool’s balances. Every v4 pool on the same chain uses that PoolManager, but each pool still has its own liquidity and settings.

If you swap ETH for CLAUS, the PoolManager processes your trade using the ETH/CLAUS pool. If that pool has a hook, it also checks when the hook is supposed to run.

A hook can contain a function called beforeSwap. That means “run these instructions before processing the trade”. For example, it could calculate the fee for your swap or check whether you are allowed to trade. Another function, afterSwap, means “run these instructions after processing the trade”. It might trigger a buyback or record the updated price. A hook can use either function, both, or other functions connected to adding and removing liquidity.

These instructions do not have to change every time. A hook could follow the same rule for years, such as allocating a fixed share of fees to rewards.

A pool can also work without a hook. In that case, the PoolManager processes trades using v4’s standard pool behaviour. Hooks are there for developers who want to customise it.

The complication comes when a developer wants to replace the instructions later. When the pool is created, its settings include the hook contract’s address. That is how the PoolManager knows which contract to call. Uniswap v4 makes that address a permanent part of the pool’s identity. Deploying a different hook at a new address does not reconnect the existing pool to it.

Without an upgrade mechanism, using that new hook would mean creating another pool and moving liquidity into it.

Claus uses an upgradeable setup to avoid that. Its hook has two parts. One contract keeps the permanent address that the pool calls. A second contract contains the instructions that the first contract runs. The first contract is called a proxy.

When Claus’s authorised controller wants to change the rules, they deploy a new version of the instructions and tell the proxy to use that version. The pool continues calling the same hook address, but the hook now runs different code.

That is how an existing pool can gain new behaviour without making holders move to a new token. Whoever controls the upgrade has power over how that pool behaves, which is part of what users trust.

Things that were harder before

Before hooks, developers could already make tokens behave differently or do unusual things. The difficulty was getting those features to work with the rest of DeFi.

The first was to write the rules into the token itself. On Ethereum, that gave us “tax tokens,” ERC-20s which deduct a charge when tokens move between addresses. If you send 100 tokens to pay for a swap but the token deducts two along the way, only 98 reach the pool. The swap can fail because the exchange’s contract expects the full 100.

Uniswap’s v3 update says these tokens do not work with its standard router contracts. Developers need a workaround, such as a wrapper or a customised router.The tax raises money for rewards, but it also makes the token harder to trade because some exchanges cannot handle that deduction.

Developers could also build separate contracts around a pool, or modify an exchange’s code and deploy their own version. But the amount of work to build that infrastructure is bigger than the feature itself.

Hooks give developers a place to put rules that belong specifically to trading. It made it possible to:

• Fund buybacks or rewards from trades without taxing ordinary transfers between wallets.

• Adjust trading fees automatically, for example when prices become more volatile.

• Share captured arbitrage profits with traders and liquidity providers.

• Check who can trade, allowing a pool to accept only approved wallets.

• Connect trading to lending, so liquidity can earn interest when it is not needed for swaps.

• Build these features inside an existing exchange, instead of launching and maintaining a separate DEX.

Hooks also creates room for businesses whose main product is the behaviour of the pool.

On 14 October, Uniswap introduced VolatilePair, a hook that recalculates separate fees for buying and selling, starting with ETH/USDC on Ethereum. It uses recent price movements to decide how to charge each side. According to the announcement, StablePair, its earlier hook for stable pairs, had handled $3.1 billion in four weeks.

Other teams are adapting fees to the assets being traded.

Fables, a DEX on Robinhood Chain, is taking the first approach. It builds markets for assets including tokenised stocks, using hooks to adjust fees based on signals such as time and volatility. A stock token can keep trading while its underlying market is closed, when reliable prices and opportunities to hedge may be harder to find.

The argument for a changing fee is that supplying liquidity at those times is a different job from supplying it during a busy trading session.

Fables adjusts fees around stock-market hours. But a tokenised fund may only allow approved investors to hold it.

Uniswap Labs introduced Permissioned Pools in July, with Superstate, Securitize and Dowgo among its launch partners. The design uses hooks to check whether a wallet is allowed to trade or provide liquidity.

Its DualPool hook lends out a market maker’s idle tokens until a swap needs them, and Spark moved $150 million of stablecoins to v4 to use it. Its auction hook sells a new token over time and then opens a pool at the price buyers settled on, which is how Aztec raised about $59 million from more than 17,000 bidders.

And people are using it. v4 is doing $39.7 billion in volume over the last 30 days, about half of all Uniswap trading, and taking 64% of Uniswap’s fees. A lot of that comes from Robinhood Chain. When Bitquery counted pools there in early September, only about a third of v4 pools had a hook, but those pools handled 74.5% of the trades.

The Ethereum side

If hooks are a new place to keep a token’s rules, the business is in selling those rules ready-made. That’s Programmable. It calls itself open launch-model infrastructure for Uniswap v4, which is a mouthful for a toolkit that launches tokens with their rules already set up, on Ethereum and Robinhood Chain. If you pick its Classic launch and you get a token with buy and sell fees you set and an ETH pool that’s locked forever.

If that sounds like a tax token to me and it pretty much is one, except the fee now lives on the pool where routers can see it coming. The Custom launch, done through an API, is for teams that want their own hook setup. Programmable’s default for new custom launches on Robinhood Chain sends a 0.2% fee to a published recipient, and its README is upfront that the contracts haven’t had an outside audit or a public security contest.

Claus launched through Programmable. Buybacks, NFT rewards and weather-based allocations can all become parts of the experience around trading one token. Claus can change what happens to trading fees without launching a new token. But holders need to watch those changes, because the rules they agreed to when they bought may change later.

Hookr is a similar kit on Robinhood Chain, with 22 “Hook Blocks” a creator can tick, like anti-sniping or auto-burns. One block, from a startup called What The Hook, catches price gaps between a token’s pools before the bots do and shares the profit with traders and liquidity providers. Hookr includes What the Hook’s service as an option in its token-launch toolkit.

For third-party pools integrating WTH directly, its published default gives 45% to LPs and 5% to the trader, while the treasury receives 40% and a referral recipient gets 10% - these are shares of captured profit. What The Hook, the only one of the kits that shares its numbers, has distributed about $68,000 in total, mostly to its own treasury.

The Solana Side

Uniswap’s protocol doesn’t run on Solana, and trading there is split across separate exchanges like Raydium, Orca and Meteora. With no single PoolManager to attach a hook to, Solana puts the hook inside the token itself.

Its newer token standard, Token-2022, lets a creator attach a transfer hook. This is a small program that runs every time the token moves. That covers buys and sells, and also transfers. This is called a transfer hook. It can check rules, such as whether the recipient is approved to hold the token, and stop the transfer if those rules are not met.

Hooked is a Solana launchpad where every token comes with one of these transfer hooks. Creators pick from 39 ready-made rules, like Hot potato or one that makes buyers wait 15 minutes before they can sell, or ask an AI builder to write a custom one. Each rule comes with an easy English reading, and some tokens let the creator, or a holder vote, rewrite the rules later. Every trade pays a 1% fee, and most of it buys back and burns Hooked’s own token.

The catch is that Raydium won’t create pools for transfer-hook tokens unless an admin approves them. Meteora takes them on its bonding curve, the starter price formula new tokens trade on. But it strips the hook off once a token graduates to its main pools. Hooked’s workaround is to keep tokens on the curve forever, so the rule never switches off.

On Uniswap v4, the extra rules belong to a pool.

The same token can trade in a pool with a hook and another without one. You can choose where to trade, or let a router choose for you.

Solana’s transfer hooks work differently. Their rules belong to the token and apply whenever it moves, including transfers between wallets. In both cases, the surrounding businesses still matter. Routers decide which pools to route trades through. Exchanges decide which token features they will accommodate.

Developers have more ways to design a market, but their choices affect where that market can find users and liquidity.

What upgradeable rules could change

I think the upgradeable part matters more than the hook itself. A fixed hook is a better tax token. That makes the market around a token behave a little more like an app that receives updates.

Until now, fixing a token meant migrating it. The team launches a new contract, asks every holder to swap, begs exchanges to relist and watches liquidity split between the old version and the new one. When Polygon replaced MATIC with POL in September 2024, every holder had to convert one for one, and exchanges had to run the switch for their users.

In these cases, some holders always get left behind. An upgradeable hook lets a project change what its token does without that exodus, and that changes who can afford to experiment.

The most useful home for this may be far from memecoins. I am thinking, the tokenised stocks because Real-world assets come with trading rules that keep moving. A fund might expand to investors in another country and need a different eligibility check. An upgradeable hook could accommodate that change while keeping the existing pool. It would not rewrite the investor’s legal rights or remove restrictions in the token contract, but it could help the trading venue keep up. For a market meant to operate for years, that seems more useful than permanently committing to every decision made at launch.

A fund might need to update its trading rules, while a memecoin community might want rules its creator cannot touch. Both have good reasons for their choice. With an upgradeable hook, I’d want to know exactly what the person controlling it can change. Being allowed to update who qualifies to trade is one thing. Being able to change fees or redirect rewards gives them much more control over the people using that pool.

I’d want time to review an update before it affects my money, and a chance to withdraw if I disagree. A project can build that notice period into its contracts and restrict what its developers can change. Giving up upgrade powers altogether would prevent later revisions, including fixes users might want. These are terms I’d check before depositing, alongside the fees and expected returns.

Claus calls its hook its mind. A team could let an AI agent update that hook as trading conditions change. For example, the agent could adjust fees within limits the team sets and explain each change. I can see the appeal of letting software handle those adjustments. But I would want to know who can set its limit and also stop it if it makes a bad decision.

Money has always had opinions.

In 1932, during the Great Depression, the Austrian town of Wörgl introduced its own local money. To keep a note valid, whoever held it at the end of each month had to buy a stamp costing 1% of its face value. Who would want money that was shrinking? That gave people a reason to spend the notes rather than keep them.

The town used the scheme to fund public works and accepted the notes for taxes, including overdue payments. The experiment ended in 1933 once Austria’s central bank intervened.

In that world, the money opinions belonged to governments, and you couldn’t see them. Governments still set rules that shape how people use money. Crypto gives developers their own means of making such choices for individual tokens and markets, with buyers able to select among arrangements that serve different purposes.

Choosing a token is you deciding whether that arrangement works for you.

—Thejaswini

Token Dispatch is a daily crypto newsletter handpicked and crafted with love by human bots. If you want to reach out to 165,000 subscriber community of the Token Dispatch, you can explore the partnership opportunities with us 🙌

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

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