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.

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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 🙌

📩 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 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.

Launching soon. Free to join.

Plug in Your Agent

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

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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?

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.

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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.

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.

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.

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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

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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.

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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

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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!

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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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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.

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.

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.

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 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

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In January 2026, USD.AI, a lending protocol that enables GPU-backed lending, approved a debt facility of up to $500 million to Sharon AI, a leading neocloud company in Australia. Nine months down, Sharon signed its inaugural GPU-backed loan of $356 million from conventional lenders at a fixed 9.95%. Its public disclosures don’t tell us anything about receiving any money from USD.AI.

That’s not a one-off story. It’s part of a larger trend we see in how GPU-backed lending is evolving. The initial lenders to any new asset always charge extra. They want to cover the highly uncertain risk that comes with an industry that few understand. The AI-backed capital markets are getting more predictable and driving out the premium that those who lent to the industry could once command.

Nvidia now guarantees some borrowers’ income, big banks are lending, and chip rents are becoming a traded price. This is changing where GPU-backed lenders make their buck from and how they do business. USD.AI has started selling its loans to investors instead of holding them through maturity.

In this piece, I explain why the premium existed once, what is killing it, and if USD.AI’s new strategy will work for the industry.

On to the story…

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The Knowledge Premium

In GPU-backed lending, an AI cloud company, or a neocloud, first buys Nvidia chips and rents them out by the hour or multi-year contracts. But since the chips are expensive to start with, the neocloud borrows. The same chips act as collateral for the lenders. These chips are owned in the books of a newly created special purpose vehicle. The neocloud services the loan using the rental contracts that fetch its income. If the borrower fails, the lender takes both.

Lending like this is uneconomical without scale. A lender has to check the chips, the data centre lease, the customer contracts, and the legal structure. That work costs roughly the same whether the loan is $20 million or $500 million. Approvals often take three to six months. Chips cannot afford to wait that long.

So mid-sized borrowers got a thin market. Morgan Stanley estimates $2.9 trillion of global data centre spending between 2025 and 2028, of which hyperscaler cash flows cover about only $1.4 trillion. The rest has to be borrowed or raised elsewhere, and mid-sized operators were nobody’s first call.

That gap is filled by protocols like USD.AI. It took stablecoin deposits from crypto investors and lent those digital dollars against GPUs, typically in deals of $50 million to $250 million.

Well-structured loans in this segment carry the same protections as deals backed by investment-grade customers. Yet they pay 400 to 600 bps extra in interest. That’s the premium to bridge the gap between people who understand compute and people with capital.

But this lack of awareness is temporary. As people get more aware of an industry, the premium charged to bet on it compresses.

Even the solar energy went through a similar cycle. In November 2013, SolarCity sold the first securitisation of rooftop solar contracts. The deal was inked for $54.4 million at 4.8%, rated BBB+. Less than two years later, its Class A notes were priced at 4.18%. The product was the same old panels But lot around the panels industry had changed. Once investors could see homeowners pay their bills year after year, they stopped charging for not knowing.

This anecdote is directionally helpful, except that GPUs are much more time-sensitive. A solar panel can last 25 years, but the GPUs can’t afford to wait for a decade to learn this lesson. So the industry bridged the knowledge gap and compressed the premium on its loans sooner.

In 2023, CoreWeave, the largest neocloud, borrowed $2.3 billion against its chips at a floating rate of around 15%. In March 2026, it closed an $8.5 billion loan at SOFR plus 2.25%, roughly 5.9% fixed. Moody’s rated it A3, making it the first investment-grade loan secured by GPUs.

What flipped in the last three years? The chips business became safer as customers became more reliable. Borrowers are now backing their loans using contracts with giants like Meta. So, the lenders lending to CoreWeave price the deal with Meta based on the latter’s reputation, and not the former’s chips.

But not all compute operators have a Meta. Nvidia decided to fill that gap for the small operators. In mid-2026, the chipmaker began guaranteeing a minimum rental income for selected neoclouds, renting back unused capacity at a fixed price in exchange for a share of revenue above the floor. Sharon AI was its first partner, for up to 40,000 GB300 chips over six years.

In the past few months, Wall Street has followed suit by bridging the knowledge gap gradually.

In August this year, Nvidia announced a financing platform with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR, targeting more than $500 billion. Nvidia retained an option to guarantee up to $125 billion.

CME Group listed futures on hourly H100 and B200 rental prices, scheduled to start trading on October 5. A lender valuing a three-year loan can now look at a three-year price curve to hedge their position better.

But there could be other factors that influence risk. Let’s go back to CoreWeave’s deals. In May this year, it borrowed $3.1 billion at SOFR plus 4.50%. A couple of months later, in August, it borrowed $2.6 billion at SOFR plus 5.50%, with the same ratings. Here, the market was pricing the risk higher. Why?

The difference was that the customer contracts behind the August loan averaged about three years, against a loan of about five. Lenders charged an extra percentage point for the chance that those contracts might not renew.

This happens across the industry. Nebius, an AI-native cloud and infrastructure provider, says its long contracts earn $20 million to $25 million per megawatt a year, just half of what their short deals of up to six months can fetch.

So, the chip industry is not witnessing a straightforward haircut in the premium on lending. Instead, the premium is being split and pulled in different directions by the factors influencing it. The premium paid for lack of awareness about the industry is vanishing as the ecosystem around it matures. But the part that prices renewal risk and resale value of the chips is still around.

Even as we speak about all this, the guarantee coming from the top is also wobbling. In under a month after announcing its financing platform, Nvidia reportedly paused parts of its backstop programme.

The squeeze on the premium also comes from the stablecoin holders. They seek yield above what on-chain money markets pay, and GPU loans have been one of the few places where they can find that. Bullish, the NYSE-listed crypto exchange, committed $100 million to USD.AI in August. As more lenders chase the same borrowers, they compress the spreads further.

From Lender to Broker

For a lender in this position, holding loans is no longer the most optimal proposition. They earn a shrinking premium for knowledge, retain the risk on their books, and hope it doesn’t materialise in the three years when they strive to get their money back.

Protocols like USD.AI have another challenge. Its depositors can ask for their money on a 30-day cycle. So there’s an asset-liability mismatch. Its loans run for three years, while the money could be called back in 30 days. Celsius’s case serves as a cautionary tale, where the idea to borrow short and lend long came back to bite the company.

USD.AI manages the gap with a redemption queue and refuses to dump loans to meet withdrawals. Yet, a queue is a buffer and not a foolproof solution.

Its deposit history adds pressure. Much of the early money came to farm points for USD.AI’s token airdrop. Data shows deposits peaked near ~$700 million when barely $1 million was lent out. The capital that stayed now funds about $311 million of GPU deployments: $177 million in active loans and $134 million in escrow, waiting for chips.

On October 1, USD.AI launched the GPU Loan Exchange (GLX) and a model ‘season-and-sell’. In this, depositors fund a loan while the servers are built, shipped, and installed. Once the chips are running and payments flow, the loan is sold to an institution that wants a steady multi-year asset. The cash returns to depositors for the next loan.

Crypto money funds the short-term funding requirement, while the patience and institutional capital is tapped into for the long-haul capital requirements. It’s an interesting idea because it mimics what made mortgage lending work for decades.

USD.AI has already completed its first execution under the season-and-sell model. The company had lent $98.1 million to Duos Edge AI in June for 2,304 B300 chips. On September 30, SEC filings show Axe Compute bought the cluster and repaid about $87.8 million of its debt.

For USD.AI, almost its entire capital came back in under four months.

Who Gets Paid Now

Season-and-sell splits a GPU loan into two different layers.

At the top sits Nvidia - the chipmaker. It makes its primary revenue by making and selling the chip. Additionally, wherever it backstops a neocloud’s revenue, it also takes a cut of what the chip earns after it steps outside the manufacturing floor.

In SemiAnalysis’s model, that cut averages about 18% of the neocloud’s rental revenue under a 40% revenue share.

Along with being a supplier, Nvidia now also gets to earn a rent by insuring the fate of its downstream customers.

One layer below this, the neoclouds capture the value. How much value depends on the contracts they sign. Nebius’s short deals fetch $40 million to $50 million per megawatt a year, and long ones half that. But short contracts cost more to finance. CoreWeave paid a full percentage point extra when its contracts ran shorter than its loan.

Further below the neoclouds are the lenders. Now, even this stack is split in two.

The first lender funds the initial build. In USD.AI’s case, depositors’ money earns around 6.5% to 7% in escrow while servers ship and get installed. It steps up once the loan starts earning, and USD.AI’s active loans average about 11%. This is the riskiest period in the life of a loan, when it hasn’t yet proven to be worth anything yet.

The second lender buys the seasoned loan. By then, it is already a performing, multi-year asset with a payment record. Insurers, private credit funds and banks love this kind of steady income.

The gap between what the build-phase lender earns and what the final buyer accepts is where the new stream of value is created. Whoever sources the borrower, must check the chips, write the legal structure and sell the loan. That first lender then earns from that gap and the fees in facilitating the transaction. All these new layers, where value accrues, make holding loans an unattractive business proposition.

A holder of the loan earns a single spread for three years while carrying the resale risk. But the loan originator can turn the same capital over several times a year and earn on every round of originating the loan, albeit a smaller ticket value.

Figure Technology has shown how this plays out in home-equity lending. Its blockchain marketplace handled 65% of its second-quarter volume. Although it earns less per dollar passing through its system, it still ties up far less of its own capital in that process.

What USD.AI’s New Model Achieves

USD.AI wants to operate where GPU loans are made and sold, and not where they simply hold these loans on their books. That changes how the protocol makes money. A lender that holds a loan ties up each dollar for three years (or the tenure of the loan) and earns a single spread. On the Duos deal, USD.AI’s money came back in under four months. At this pace, the same dollar can fund three loans a year and earn USD.AI a fee on each of the loans. It makes the capital work more efficiently.

At a time when USD.AI claims to have more than $25 billion of inbound loan demand against the $311 million deployed in active loans currently, recycling the same capital is the only way to close that gap.

Selling loans also makes USD.AI a pricing venue for GPU loans, something it could never be by holding these loans. Every loan that trades on GLX shows what a performing GPU loan is worth for a given chip, borrower, and contract length. An “exchange” quite literally. Those who own the data on the pricing of GPU loans will end up setting a market for everyone.

Buyers get access to a market they couldn’t reach otherwise. Insurers and credit funds want steady multi-year income without a full-fledged investment team that could vet tens of millions of dollars in loans to small cloud operators. GLX lets them skip both the vetting and the riskiest months, and buy a loan with a payment record.

For borrowers, a lender that gets its money back in months instead of years can say yes to the loan more often.

It also creates jobs that only exist when loans trade. Someone has to collect payments and enforce the loan after it’s sold. In mortgages, that is a large business of its own, called servicing. Someone has to value the chips when the loan ends, and the Silicon Data index and CME’s rental futures are the first tools for that.

The season-and-sell strategy also enables pooling. When hundreds of seasoned loans across dozens of operators are bundled into one bond, it can create another asset class of pooled GPU loans.

The fact that all these loans and transactions will be recorded on-chain ensures that an entire repository of how GPU loans are made, repaid and priced is open for any buyer, rater or lender to check.

While tokenised private credit already makes up 54% of on-chain real-world assets, most of it wraps loans that lenders like Apollo made first. Crypto ends up buying what was born on Wall Street. USD.AI reverses this. Stablecoins originate the loan, earn the fees, and then sell it to the same institutions that have been around for decades, if not more.

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.

By the middle of 2026, Americans owed $18.8 trillion across mortgages, credit cards, car loans, student loans and other debt. 4.7%, roughly $880 billion of that debt was in some stage of delinquency.

In the second quarter, about 7% of credit card debt was becoming seriously overdue, with payments at least 90 days late. More than 23 million Americans also had old unpaid card debt that lenders had classified as a loss.

Banks look at years of payment history. In fact, they know an absurd amount about us.

They know whether we paid the last card. They can see the mortgages. How much credit other banks already gave us and whether we missed those payments too. For larger loans, they can ask about income, employment and assets. That’s why companies collect this history, give us scores, and sell them to lenders.

Even with all of that, 581,570 Americans filed non-business bankruptcies in the twelve months ending June 2026, up 12% from the previous year.

Credit doesn’t require perfect borrowers. A bank can separate them, then limit how much they can borrow and what they charge.

Now that AI agents are holding money and spending it too, can we do the same for them?

What would a machine have to do before it is credible enough for somebody to give it a loan? What could happen when it’s lent money. I’m looking at that through Priors, and others that are trying to build that bridge.

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When I put $5 dollars into an AgentCash wallet for Hermes, the agent spent, and when the balance fell, and I send more. That’s how its designed. We can load it up with stablecoins and the agent pay for API calls one by one through x402.

But if I want my agents to take paid jobs on their own, they need to buy data and model time today when the work may only pay next week.

The top-up is annoying in that kind of work, especially when it comes in bundles.

That’s why credit for AI agents can become a thing. So if my agent needs $10 now to build something that earns it after a week, a short loan covers the days in between.

There is a second reason. Agents are starting to deal with agents they’ve never met. ERC-8004, the Ethereum standard for agent identity, gives each agent an on-chain ID also some kind of feedback, but feedback can be written, and we don’t have any record of credibility.

A loan is always a way of putting a price on trust. Long before credit scores, that bet was based on reputation. In medieval Venice, merchants with a good track record found it easier to raise money for trading voyages since they had a better chance of getting money for their next voyage.

Even in medieval English trade, credit was closely tied to a merchant’s “name”. Once someone became known for not paying debts, other merchants had a reason to stop extending credit.

Priors is building a lending market around AI Agents. It lends USDG, a dollar stablecoin, on Robinhood Chain.

It is still in early access, with Google sign-ins opened in waves. But the protocol itself is quiet transparent about how lending, backing and credit scoring work.

The owner first creates an agent and gives it an identity with ERC-8004.

Then the owner first puts down a refundable $5 deposit. Priors holds it as a guarantee and returns it later if the agent meets the required repayment conditions. Then the treasury gives the agent a separate $5 credit line.

Each loan can last from one day to 30 days. Priors charges 1% for a full 30-day loan, with the fee reduced for shorter periods. So borrowing $50 for one week costs about 12 cents, while borrowing the same $50 for a full month costs 50 cents.

The agent can do this automatically through Priors’ x402 client. If it needs to pay for an API call and its current balance is not enough, it can borrow only the missing amount, as long as it stays within the limits the owner set. In practice, it works a bit like an overdraft.

What exactly happens when the agent wants to borrow more than the first $5?

Someone has to put more money at risk behind it. The owner can do that by locking Priors’ own token, PRIORS, behind the agent. That can open a larger credit line, starting around $25 to $50. The amount locked has to be worth more than the credit line itself. So if the agent is allowed to borrow $50, the owner needs to have more than $50 worth of PRIORS sitting behind it. Priors requires an even larger cushion when the token has been volatile.

If the agent keeps repaying on time, the borrowing limit can rise in stages, eventually reaching $500. It takes at least ten weeks of successful loans to get all the way there.

If the agent defaults, that backing gets hit first. A missed payment can burn 75% of the PRIORS stake. For the first $5 line, the owner can lose the original $5 deposit.

Half of each loan fee goes to the USDG lending pool. A quarter goes to the agent’s backer, and the rest goes to a reserve. If the agent misses the due date by more than three days, anyone can mark the loan as defaulted. Once marked, the agent’s score drops to zero for good.

Priors also accepts other assets as collateral, but it does not lend their full value. Riskier assets get lower borrowing limits.

As of October 13, Priors showed $271,910 of loans written and $270,180 repaid across 36,387 repayments. It listed 272 agents. The pool has recorded two defaults so far. The relevant backers absorbed both defaults, so the lender pool recorded zero losses.

Priors originally gave repayment history more power than it does now.

In the first version, an agent could increase its own borrowing limit simply by repaying on time. The line could grow by up to $25 a week, with a maximum of $250, and Priors’ reserve fund covered the extra risk if there was no outside backer.

That changed with Pool v2 on September 24. Repayment still improves the agent’s record, but someone has to put their money behind it.

A repayment only matters if the money really came from outside. Otherwise, the owner may just be moving their own money around. Priors gives more weight to loans where someone other than the agent’s owner put real money at risk. Because nothing stops you from creating several agents and making them transact with each other.

The same logic carries into income. Priors runs its own x402 facilitator, and the top rung in Score v2 is called “income-backed.” It looks at whether an agent is actually getting paid by outside parties, rather than just moving money around its own wallets.

Now let’s say nobody sponsors the loss. There are still options for an agent to get a loan.

Clawloan, built for OpenClaw agents, is the most permissive.

Verified agents can borrow USDC without collateral and without a human approving each loan. New agents are supposed tp start around $10 to $50, move to $100 to $500 after at least five successful repayments, and eventually get $1,000 or more if they become trusted.

This shifts the loss to the lenders, which makes the credit score much more important. The owner still sets an agent’s maximum borrowing permission, while a CreditScoring contract enforces limits based on repayment history. Clawloan live market is tiny, with only $360 TVL and $33 borrowed so far.

Another lender called Bank of Bots came out of stealth on April 1 and said it had issued the first loan to an AI agent.

The agent did most of the borrowing process itself. It submitted the loan request, signed the agreement with its cryptographic key and received USDC in a Base wallet.

Since there was no previous agent-loan history to look at, Bank of Bots says it partly judged the borrower from its existing onchain activity, including things like transaction volume, account age and how many different counterparties it had interacted with. The human operating the agent still had to complete KYC first. Bank of Bots has not published evidence of much lending activity since.

That leaves Priors entering a market that is still strangely empty. This will help it define some of the early rules before there is much competition around them.

Are we too early with an agentic credit system?

By October 13, x402 had processed roughly 192 million transactions. Chainalysis found that transactions worth $1 or more accounted for 95% of the value transferred on x402 on Base by early 2026, up from 49% earlier. Still small compared with normal payments, but the shape of the problem is already there. The numbers are still tiny beside normal payments, but there is economic activity for credit to sit on top of. An agent that keeps earning from unrelated customers and repaying loans could gradually get access to more capital even if its creator is not rich.

We are also heading to agentic commerce, where agents buy things for you. Muse can shop for a user, while Rye gives agents a way to place orders across thousands of stores without the customer doing the checkout themselves.

Read: AI Goes Shopping

Today a person sees the price, the instalment offer and the interest rate, then decides whether to borrow. An agent could compare those too. Maybe it decides paying $800 today is worse than taking a three-month loan because the cash is needed somewhere else.

The human may still be the one legally responsible for the debt, but the agent could decide when to borrow and which lender to use.

We already know that putting credit directly inside a purchase changes behaviour. Buy Now, Pay Later use in the US rose from 10% of adults in 2021 to 16% in 2025.

If I tell an agent to buy a $900 laptop and keep as much cash in my account as possible, it might compare paying now with many different financing offers and pick one itself. Lenders would then have to make their terms attractive to whatever rules the agent is using.

Also, if a lender can pay an agent or the platform behind it to be preferred, we have basically recreated sponsored placement inside credit. The CFPB has already worried about BNPL companies using payment data and shopping apps to steer consumers toward particular merchants.

The agent may make the loan choice before the human ever sees the options.

So how do you advertise to agents? That’s for another day.

Stay tuned.

—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.

Last week, I argued that remittance won’t have a global winner. Stablecoins help move money faster, but they can’t make sending money cheaper on their own. I wrote about how those who own licences, exclusive banking relationships, and the last-mile ramps will capture all the value arising from the evolving remittance stack.

More often than not, those who are building with these moats will be on either side of the blockchains and will have little to do with on-chain infrastructure.

Read: Remittance Won’t Have a Global Winner

In today’s piece, I take that argument one step further. With the help of some case studies in the LatAm region, I will explain why the logic holds for everything people do with money. Be it lending, borrowing, spending, saving, or investing.

Stablecoins are becoming finance’s common denominator just as fiat currency has been for decades. Nobody gets to capture much value just for supplying a common denominator.

Today, I will explain why money will be made a layer above, by local businesses who fit dollars around how people in their regions use such money.

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

Stablecoins are a Commodity

In June this year, more than 140 firms, including Visa, Mastercard, and Stripe, announced OpenUSD stablecoin and promised free minting and shared reserve income among its partners. Circle, the issuer of the second-largest circulating stablecoin, already pays ~58% of its revenue to distributors.

This shows that the stablecoin itself is being commoditised and is leaving little value on the table for others to capture.

So if the dollar isn’t the moat, what is?

Building around Local Habits

I think a local business needs three things to capture value in the evolving stack of moving money. First, a way to reach customers that a global player can’t copy. Second, a product that solves a money problem that is specific to that market. Lastly, the ability to provide further utility to the money they already transport, by way of investing, borrowing, saving, and so on.

Brazil found a fix to make payments more efficient. They did so even before stablecoins existed. Pix, the central bank’s instant-payment system, processed 67.25 billion transactions worth approximately Brazilian R$31 trillion (about ~US$6.22 trillion) in the nine months ending September 30, 2026.

Making payments in Brazil is already instant and free. So a stablecoin has nothing to add to a Brazilian payment.

But there’s a gap here. Brazilian minors have struggled to open bank accounts in their names. Founded in 2021, NG.CASH let the Brazilian GenZ population open an account with Pix, gave them a customisable prepaid Mastercard and savings tools, with a parent’s approval.

About 70% of its users are still under 18. Most come from middle- and lower-income households, and for many, NG.CASH is the first financial account anyone in their family has opened.

NG.CASH found its initial customers almost entirely on YouTube, TikTok, Instagram, and Twitch. Co-founder Mario Augusto Sá ran a Minecraft YouTube channel with about 20 million subscribers before he built a bank.

This helped the company inherit a distribution that few can command.

NG.CASH’s other advantage is the stack that it has built to enable various utilities of money for its users.

Once teenagers started using NG.CASH app, the company kept adding more features and uses of money. After allowing them to open an account and card, they let them save their money and earn interest on it like a savings account in a bank. Then followed consórcio (a Brazilian group-buying EMI scheme to pay for phones, games, or motorbikes in instalments), insurance, fixed-income investments, and crypto trading products.

As the customer base grew, it added more features. Last month, the company expanded its financial services and credit offerings in Brazil after raising $15 million in a funding round led by Blockchain Capital.

The push for the credit product followed the demand from users. About 30% of NG.CASH’s 10 million-plus accounts now belong to adults. That could mean at least 3 million employees. Formal jobs speed up the adoption of credit products since the employees are left with disposable incomes and aspirational goals.

In March 2025, Brazil extended payroll-deductible loans to every formally employed private-sector worker. Repayments of up to 35% of a salary are deducted straight from the paycheck, which makes these loans far safer for lenders than ordinary personal loans.

New loans under the programme jumped 184% to R$54.5 billion in 2025. By March 2026, the programme had lent over R$117 billion across 20.9 million contracts, at an average interest rate of 3.67% a month.

This year, NG.CASH launched its own payroll loan, Crédito CLT. The company expects credit to make up more than half of its revenue next year. It aims to lend R$1 billion by the end of 2027.

What helps NG.CASH capture value across all these products is that no other global players can boast of the licences the company has. It has been an authorised payment institution since May 2025 and has bought a direct-lending licence so it can lend from its own balance sheet. It has also applied for Brazil’s new crypto licence. The company spent significant time and capital to acquire each licence, and a foreign entrant would have to spend much more.

For every $100 USDC in circulation, Circle keeps roughly $1.60 a year after paying its distributors. That’s a net margin of 1.6% a year on every USDC in circulation. A single R$4,500 payroll loan at the programme’s average rate earns about R$165 in interest in its first month alone. That’s an average of 3.67% a month, which works out to about 54% (compounded) a year. Defaults and funding costs will eat into that, but a lender still starts with more than 30 times the issuer’s cut on each unit of money.

But where do stablecoins fit in NG.CASH’s stack? Brazil’s central bank governor has said around 90% of the country’s crypto flows are linked to stablecoins. NG.CASH plans to offer stablecoin accounts. When that happens, a dollar balance will become another way for the users to move, store, and spend their money. It will be available in the same app that its users have been trusting all this time, alongside the card, savings, and loan products.

For NG.CASH, the stablecoin will just be an enabler that will let it cross-sell multiple uses of money to its users.

Using the Chain to Own the Customer

Across Latin America, the businesses capturing value from stablecoins use on-chain tools without building on-chain infrastructure.

Take Argentina. For the Argentines, protection from the devaluation of the peso is more important than seeking credit. In 2002, the government forcibly converted dollar deposits into pesos, wiping out about three-quarters of their value. In 2019, it capped dollar purchases at $200 a month. For those in Argentina, the option to save in dollars is a lifeboat.

Lemon, one of Argentina’s largest crypto apps with more than 4 million users, built its first product around that lesson. Users convert their peso salaries into stablecoins and spend them with a Visa card that converts back to pesos at the checkout counter.

Lemon has also added yield on dollar balances through Morpho, a lending protocol running in the background. It then added a peso credit card backed by Bitcoin and a virtual US bank account. Tokenised stocks are on the way, too.

These apps are sticky. Once users get habituated to using these products, they seldom leave, even if the original factors that led to the adoption of the app cease to exist. For instance, Lemon’s downloads kept rising every quarter, even as monthly inflation fell from 25.5% to 2.1%.

Here, Circle and Tether issue the digital dollars, Morpho runs the lending market, and Visa runs the card network. Yet, it’s the local apps like Lemon and NG.CASH that own the customer, and decide which use of money they should offer next on their app.

We see something similar even with Mercado Pago, Latin America’s largest fintech. In Argentina, it addressed the demand for US dollars with a dollar account that pays 1.8% a year.

There is a global winner in this picture, but it is the common denominator that sits underneath all this. Morpho’s lending vaults now hold more than $17 billion. Coinbase and Robinhood each passed $500 million in their Morpho-powered earn products in September, and Lemon’s yield product runs on the same engine. Visa and Mastercard process the card swipes for Lemon and NG.CASH alike, and both joined the Open USD consortium. The infrastructure is becoming shared, interchangeable, and global.

But the customer relationship will continue to remain the exclusive moat. Chainalysis found that 71.2% of crypto balances in Latin America now sit with service providers rather than in personal wallets, above the global figure of 68%. So, customers want to receive, hold, convert, spend, and access financial products from the same venue.

That’s why these businesses don’t work equally well across different geographies.

In October 2025, Lemon raised a $20 million in Series B, and said it would double its users to 10 million and expand to Chile, Colombia, Brazil, and Mexico. Last month, it told its Brazilian customers it was leaving. Four years after entering Brazil, Lemon had about 15,000 customers holding balances there. It said getting licensed under Brazil’s new crypto rules would require capital out of proportion to an operation of that size.

What let Lemon down in Brazil wasn’t their product. It had already solved Argentina’s problem using the same product. The concern was that Brazilians already use Pix for payments, USDC rewards inside Nubank, and dollar access through Mercado Pago. Lemon’s unique selling point, which was a way to escape a collapsing currency, had little pull in a country where people don’t feel the same urge to escape the Brazilian real. So Brazilians had little reason to move their money to Lemon. After four years, only about 15,000 of them held balances in the app, too few to justify the capital a licence required.

Lemon launched in Brazil without the local distribution and licence it was willing to pay for. That sets it back by two important moats that help local businesses win in the evolving payment stack.

Meanwhile, Lemon is doing well in Peru, where it has more than 1 million validated users. This shows that the product grows where people share the money habits of Argentines.

The direction is the same across remittance or savings. As stablecoins become cheaper and easier to issue, Circle and its rivals will keep handing more of their reserve income to whoever holds the customer. In Brazil, that’s NG.CASH, with its licences, its creator-led reach among teenagers, and its payroll loans. In Argentina, it’s Lemon, with dollar savings that Argentines have learned to trust.

Stablecoins have already cut the complications by making the dollar within reach of anyone with a phone. What people do with that dollar depends on the money habits of the people in that geography. Those who can build moats like licences and distribution in the geographies they operate in will capture the most value.

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.

South Korea opened single-stock leveraged ETFs in May 2026, and within weeks, an entire generation of twentysomethings turned them into the world’s biggest collective bet on AI. These are kids who can’t afford a Seoul apartment on fifteen years of their graduate salary, so when someone handed them a way to go 2x leveraged into Samsung and SK Hynix, they went all in.

But then the AI trade snapped, and the country margin-called roughly 3% of its own adult population in a single month. Sixty-two per cent of those liquidated accounts belonged to people under 30.

Korea looked like a one-off until I started pulling the numbers for every other market underneath. Two-thirds of all S&P 500 options now expire the same day they’re created, 90% of crypto trading volume runs through perpetual futures that never settle, and prediction markets have started running contracts that resolve in five minutes. The average holding period for a stock went from eight years in the 1950s to about five and a half months, and it keeps shrinking. Nobody wants to own anything past tomorrow.

So I went deep into what’s actually driving this, and the story turned out to be quite strange. 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

The Unpriceable Future

The easiest explanation for all the financial nihilism that we are experiencing is smartphones. Trading apps have removed all friction from access, our attention spans are now shorter than a goldfish’s, and a whole generation raised on TikTok is bringing its dopamine habits into financial markets.

But something about that story never added up for me. If phone apps and retail traders were the whole reason, you’d see it in retail accounts and nowhere else. But Institutional options desks running money for pension funds and hedge funds have moved to the same ultra-short timeframes on their own. Crypto derivatives markets built by a dozen quants in a Singapore WeWork have done the same.

Not only that, but Prediction markets operating under completely different rulebooks are racing to compress contracts down to five minutes. These groups barely overlap, and neither do their regulations, yet they all independently decided within a few years to stop pricing anything past the next few hours.

So why did every part of finance, at roughly the same time, lose the ability to think past tomorrow?

I think it’s pretty similar to what Frank Knight figured out in 1921. He split “not knowing what comes next” into two completely different problems. He called the first problem risk. That is, you don’t know exactly what may happen, but you know the menu of possibilities and can attach rough odds to each one. For example, A company might miss earnings by five per cent or beat by three, or an interest rate decision might go one way or the other.

In all of these cases, you can draw boundaries around what could happen, assign probabilities to each outcome, and build a strategy around it. You can model that exposure, hedge it, sell it to somebody running a different model, and let the market figure out who was closer. That’s what Black-Scholes, or value-at-risk, does.

The financial engineering revolution of the last fifty years was built to take messy, noisy, complicated reality and turn it into probability distributions traders could optimise against. It always worked perfectly, until now, because real-world outcomes stayed clustered tightly enough that even rough models gave you something to steer by. Your model didn’t need to be right. It just needed to be roughly in the neighbourhood, and the law of large numbers would do the rest.

The second problem Knight identified is when you can’t even write down the list of outcomes you’d assign probabilities to. Basically, you can’t even price the risk. For example, Will AI eliminate 80% of white-collar jobs within ten years, or will it plateau next year into fancier autocomplete? Both futures have serious people with real credentials backing them, and neither side has data. You can’t build a probability distribution here because nobody can agree on what the possible states of the world even are.

For most of economic history, this second problem of not-knowing stayed in containable pockets. A pharmaceutical company runs a drug trial and has no idea whether the molecule works. Nobody can assign a probability because the biology is too complex and every compound is different. That’s true uncertainty in the Knightian sense.

But it sits inside one company’s balance sheet. If the trial fails, Pfizer’s stock may drop a few per cent, and the rest of the market won’t even blink. Similarly, A startup building a new kind of battery has no idea if the chemistry will scale, and neither does anyone funding it. The founders can’t model success because the thing they’re building has never existed before.

That’s what venture capital was invented for. VC works precisely because it absorbs these concentrated pockets of real uncertainty and walls them off from the rest of the financial system. A venture fund makes fifty bets knowing that forty will fail completely, and the model still works because each bet is independent and the failures don’t cascade into anything else.

Meanwhile, the broader economy also moved slowly enough that you could model around it. A company selling toothpaste in 1985 could project ten-year cash flows with reasonable confidence because the forces that shaped its business, like population growth, consumer spending patterns, and retail distribution, used to change on timescales of decades, and not months.

Pension funds committed capital for thirty and forty years because the range of plausible futures was narrow enough to build a portfolio around. The world in 2025 would look recognisably like the world in 1995, give or take a recession or two. You could fight about the numbers and still agree on the shape of the future. The models weren’t perfect, but they didn’t need to be. They just needed to be roughly right, and roughly right was good enough to run a pension fund or price a thirty-year bond.

But AI wrecked that, and not on its own. Several forces hit the world at once, each of which, on its own, would have been manageable the old way. A handful of fabs in Taiwan and South Korea produce half the world’s advanced chips. One policy decision or one military escalation could sever those supply chains overnight and reshape the global economy in ways nobody has a model for.

Climate tipping points sit somewhere inside a thirty-year window that the best atmospheric scientists on the planet cannot narrow, and each one triggers feedback loops that make the next one harder to predict. These are big problems on their own. But they’re the kind of big problems that institutions had slowly started to develop frameworks around.

And then, on top of all that, a technology landed whose impact ranges from “bigger than electricity” to “overpriced autocomplete”, depending on which equally credentialed expert you ask. AI didn’t just add one more hard-to-model variable. It also made every other variable harder to model because AI’s trajectory determines how fast supply chains restructure, how quickly climate adaptation happens, how many jobs exist in five years, and how much any given company’s revenue is worth.

It’s an uncertainty about uncertainty, what decision theorists call deep uncertainty, the point where you can’t even define the set of outcomes you’d try to assign probabilities to. You’re not arguing about which future is most likely. You’re arguing about what futures are even possible.

This showed up outside of finance first: Homeowner insurers in Florida and California have started pulling out of those states. These are not impulsive companies. Insurance is the most mathematically conservative industry on the planet. Actuaries built the entire modern framework for pricing future risk. They’ve done it for centuries, through world wars, pandemics, and every kind of natural disaster. When they looked at climate models and loss distributions, they decided they couldn’t price a policy anymore. The historical data they’d always relied on to predict future claims had stopped being predictive. Because now a hundred-year flood was showing up every decade. Wildfire seasons that used to follow recognisable patterns were burning through neighbourhoods that had never been considered high-risk.

AI is doing the same thing to the financial markets. Right now, AI uncertainty lives mostly in the tech sector. If you’re worried about Nvidia’s valuation or Microsoft’s AI spending, you can hedge by owning healthcare, industrials or consumer staples, sectors that AI hasn’t disrupted yet. Thats Portfolio construction’s core idea, you blend assets that don’t move together, and you can get better returns for less risk.

But as AI spreads, uncertainty spreads with it. Once AI touches healthcare, you can’t hedge your tech exposure with healthcare anymore because healthcare now carries the same unknowable AI future. Once it reaches manufacturing, logistics or finance, every sector carries it. Diversification, the thing that made the whole portfolio work, fails. You can’t hedge sector A with sector B when both face the same unknowable future. And when investors can no longer diversify away that risk, they need compensation for bearing it. That means the equity risk premium across the whole market has to rise. For the premium to rise, prices have to fall.

A fund manager in 2000 could look at Pets.com and know with reasonable confidence that a company burning cash to ship dog food at a loss wasn’t going to be worth anything in five years. The dot-com bubble had a built-in resolution mechanism for its own absurdity.

AI doesn’t have that. We may be sitting inside a bubble while the thing being bet on keeps producing real results every quarter. A robotics company just hit a $39 billion valuation with barely any revenue. From 2026, that valuation looks insane. From 2040, if humanoid robots are doing warehouse logistics and elderly care and construction, it probably looks cheap. Both can be true at the same time.

A decade ago, when people imagined monetary collapse, they mostly imagined hyperbitcoinization. Bitcoin wins, becomes the global currency, and fiat turns into toilet paper; a bright-orange monetary system replaces the old one.

I think that’s still possible. What’s changed is that it feels almost quaintly linear. Maybe we don’t replace dollars with Bitcoin. Maybe money itself becomes utterly meaningless. Maybe you buy your eggs with government-allotted compute credits cryptographically signed by your DNA. The range of plausible futures has grown so wide that even people who’ve spent years thinking about the end of money can’t narrow it to a single scenario anymore.

The promise capitalism made to the middle class always rested on two things at once. Wages had to grow fast enough relative to asset prices that ordinary people could accumulate capital over a working lifetime. And the future had to stay predictable enough for long-term compounding to work. Both of those have fallen apart at the same time today. Wages flattened against asset prices starting in the 1980s, and the crack has widened every decade since. Which makes the future impossible to price.

Financial nihilism gets framed as a generational attitude problem, as if millions of young people across dozens of countries just independently decided to be reckless for fun, and that’s a very biased way to think about it.

When patient accumulation can’t produce wealth on any timeline that matters to your actual life, you compress the timeline and swing hard. A 5% chance of making it beats a 0% chance through the approved channels.

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.

Until the late 1970s, shampoo was sold mostly in bottles in India. Many couldn’t afford a whole bottle, so they didn’t buy it at all. Then a small company in Tamil Nadu started selling shampoo in a single-use sachet for under a rupee. That created a whole new economy for the underserved.

The unit cost of shampoo in a sachet was far more than that of a bottle. But people bought sachets nevertheless. India had crores of people who could not buy a month’s supply of shampoo at once. Although sections of people still bought bottles, the sachet turned shampoo into an everyday product.

Today, the AI compute industry has its ‘bottle buyers’. Frontier labs, like OpenAI and Anthropic, and big cloud companies can lock up GPUs in long private deals financed with enormous amounts of debt. For them, compute is a strategic asset. Here, compute won’t trade like a commodity, no matter how big the market gets.

The sachet buyers of compute are in open-source AI. There, the price of a token changes constantly. But there’s a problem here. The GPUs underneath are still sold in bottles, mostly on three-year contracts that only a handful of buyers can sign. Someone has to buy the bottle and sell it in sachets. This part needs a lender who is willing to back short contracts.

In today’s guest essay, Vishwa Naik, co-founder of Anera, a market maker in compute, explains how this splits the entire compute industry into two different ones.

On to Vishwa’s story…
Prathik

Follow Vishwa on X

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

Over the last 18 months, I’ve been trying to answer one question: will compute trade as a commodity, like oil or electricity?

For all the venture activity in the space, the answer so far has been no. At Anera, we were one of the only market makers willing to price cash-settled compute derivatives. In March 2026, we did a pilot trade with FalconX on Polymarket Institutional. After that, very little. In particular, we saw almost no demand from the counterparties that matter: businesses with compute as a core input or output.

I know what real commercial demand looks like. Nine years ago, I traded oil futures and FX at the world’s most complex oil refinery, and saw firsthand how commodity finance and derivatives make the core business better. (For anyone who thinks compute has too many SKUs to standardise, it’s worth looking at the oil complex.) Commercial hedging takes hold when two things are true. The market is liquid, with enough counterparties that no small group can set the price. And the hedgers are exposed to a price that actually moves.

Compute passes the eye test: there are natural buyers and sellers. But it has two markets. In the first, compute is a strategic good, priced bilaterally and financed through credit, and it will likely never trade like a commodity. In the second, the open-source market, the characteristics of a real market are already showing up, and tokens are already discovering their price. A freely traded market for compute is coming, just not in the market everyone keeps looking at. To see why, start with where the risk lies in each.

Market One: Compute as a Strategic Good

Most of the infrastructure in this market was built to serve the tokens of two AI labs, by the hyperscalers and a handful of neoclouds purpose-built to serve those labs. Two labs, four hyperscalers, and a handful of neoclouds hardly form a market. There is no price discovery. OpenAI and Anthropic set their prices. The hyperscalers get privileged access to those weights and procure compute against very long-term agreements at fixed prices. For those familiar with the story of Marc Rich, this should sound eerily familiar to the seven sisters in oil. If you are not well-versed, I strongly recommend reading about it here.

The first market is built on hyperscaler credit and cash flows. Here, the counterparties are investment grade, financing runs through large bond issuances, and credit risk trades in the CDS market. Financial markets, particularly derivative contracts, are built as an instrument of risk transfer. For this market, Credit Default Swaps (CDS) are undoubtedly the correct instrument. In fact, CDS trading activity is hitting new highs every week as more data center and AI names race to the bond market to build the infrastructure needed to serve this market.

In this market, tokens have fixed prices, compute is procured on very long-term leases, and the stakes are now at a national-interest scale. There is no floating price for anyone to hedge. When I spoke to some of the neoclouds last year, most of them categorically didn’t need derivatives. This might change as the market matures. However, at the moment, serving the market for OpenAI, Anthropic, and the Hyperscalers is a gigawatt conversation and not a handful of megawatts. When scale is the product, the underlying infrastructure loses its commodity-like properties.

OpenAI and Anthropic are upstarts in a market dominated by the largest tech companies in the world. They have poured ever more capital into building the market for AI, and they sit inside partnerships that span the whole stack: model, application, chip, and cloud. Their partners bring decades of cash flow and public-market trust.

The rule of this game is simple: stay at the frontier. In the race to superintelligence, compute is not a commodity. Demand for it is price-inelastic, because falling behind the frontier costs more than any premium paid for compute. The model weights become the crown jewels, and they need ever more compute, data, and capital to stay ahead.

Anyone hoping the lab IPOs will ease pressure on liquidity should expect the opposite. They will draw on public-market capital at a scale we haven’t seen. When they do, they will put even more pressure on the bond market.

Bold prediction: the end state for this market is chips and nationalisation. The US government is already contemplating investing in AI companies, with OpenAI and Anthropic atop those lists. Staying at the frontier means relentless competition for compute and data, at multi-trillion-dollar valuations financed with unprecedented amounts of debt. Margins are thin, and getting thinner as open-weight models erode the value-add of proprietary ones. There is almost no margin for error. A market that large, levered, and strategically important doesn’t get to fail. When the room for error runs out, the state becomes the backstop of last resort. Increasingly, staying at the frontier has become a national security subject. Particularly with heavy Chinese state subsidisation and a multi-gigawatt headstart, America would not be able to afford to see these two labs fail. Upon listing, they will become structurally more important to the broader US economy and a bastion not just of the US capital markets, but also its intelligence apparatus.

In this market, compute is a strategic resource, unlikely to ever trade as a commodity like oil or electricity.

Market Two: Open-Source AI

Until about a year ago, it looked as if only the proprietary labs could push the frontier. Then, open-weight models closed most of the gap with far better unit economics. Add the ability to own your model and keep your data out of someone else’s training set, and open weights became a credible default from a just an alternative.

Anera’s token demand indices track consumption for proprietary and open-weight models separately. The baselines are very different, but the relative growth tells a clear story: the future is at least multi-model, and a growing share of tokens will run on open weights.

This isn’t an essay about open-source AI. It’s about where the market line falls. In the first market, compute is a strategic resource, where scale is the norm and commands a premium. In the second, compute is scarce and tokens are price-sensitive, so procuring compute becomes a strategic optimisation problem: every operator is trading off access, price, and tenor to protect margin on each token it serves. This blog from Vikram Singh at Galaxy does a great job explaining the business equation for many of the participants in the market for Open Source AI. There are also enough counterparties here for real price discovery. Tokens in this market continuously reprice. Compute, as we’ll see, is another story. The infrastructure providers for this market are hardly a few years old, far from investment grade, and completely underlevered.

As open source models have closed the gap to the frontier and increasingly own the Pareto Frontier, there is a massive arms race to serve compute to this market.

The Bare-Metal Boom

The proprietary market runs on gigawatts of capacity across the hyperscalers and the large neoclouds. The open-weight market runs increasingly on a boom in bare-metal providers. Open-weight inference prefers bare metal, and all bare metal needs is colocation space and capital to procure GPUs. There are a ton of off-the-shelf cluster management software providers that have made the 0-to-1 of launching a bare metal neocloud easier than ever. As the established neocloud operators build out gigawatts for hyperscalers, they retain megawatts to compete in the market for open inference. CoreWeave, Nebius, Crusoe, and Nscale all have their own token factory businesses and have all but stopped serving other inference clouds. Therefore, there are more bare-metal providers today than ever, most of them formed to serve this demand that is growing exponentially.

The top inference clouds shape this market, often leasing at least 1000 GPUs + at a time on 3+ year leases. These major inference platforms are supplier-agnostic. They often work with 20+ suppliers (as Tuhin from Baseten mentioned in this interview) and have a procurement “guy” who is just continuously knocking on doors to find compute. I have been equal parts fortunate that I was one of those doors and unfortunate that I have had little to add other than introducing to another cloud or supplier.

These bare metal clouds offer a rather undifferentiated service. The top inference clouds know that they represent the most creditworthy counterparty in this segment of the market. Therefore, the only real lever these neoclouds have, if their goal is to only sell fixed 3Y take or pay leases, is their capital stack.

The Capital Stack

Cost of capital deserves its own piece. The capital behind open-weight compute looks nothing like the capital behind the proprietary market.

Bare-metal clusters are mostly financed by non-bank capital at a megawatt scale. Asset-backed lenders can fund up to 80% of a cluster’s bill of materials as long as an operator brings the other 20%. The condition for releasing that money is contracted cash flow from a creditworthy counterparty. No contract, no cluster. The more senior debt in the structure, the longer the contract the lender wants to see.

Operators in this market are considerably smaller in scale than the neocloud giants like CoreWeave, Nebius, Crusoe, and Nscale. These are increasingly new operators, who are entering the market with off-the-shelf software and some equity capital, hoping to capture a slice of this demand.

Typically, these operators look to fill their equity tranche of clusters BOM with a potential off-takers money. To understand this flow, I recommend visiting American Compute.

The standard practice today is for an offtaker to give up to 30% of the contract as a prepayment. This typically represents 1Y of service, and in many cases, that 1Y of service is back-loaded. This means that an offtaker gives an operator 30% up front, waits for 8-16 weeks until the cluster is ready for service (RFS), and upon getting delivery, they need to pay their monthlies up front too.

We are starting to see some cracks in prepayments, with bridge financing entering the mix. Bridge financing typically means that a financier can provide capital to the operator against a signed master service agreement (MSA) from a creditworthy counterparty. The caveat here is that their capital gets taken out upon delivery of the cluster. While this helps build trust, it does not solve the problem for the offtakers. Since there are barely 10-15 counterparties in this market who can be meaningful, creditworthy off-takers, they have begun to push back on these terms for the past three years. Some are reducing prepayments materially, while others are also starting to ask for pro rata credits every month instead of back-loading the contracts. All of this puts tremendous pressure on the capital stack of the bare metal operators.

Still, while tokens in this market reprice continuously, compute itself is sold in a 3Y credit box. This is where the promise of a liquid market is currently getting blocked.

The Tenor Mismatch

By definition, open-weight offtake could end up with the same concentration risk as the proprietary labs if the capital stack doesn’t evolve: a few large tenants absorbing almost all the financed capacity. One of my main takeaways from running a microlending business years ago was that banks and large credit asset managers have to deliver yield at scale. They are managing trillions of dollars of capital with hurdle rates in the tens of billions of dollars. To make their economics work, you need to deliver scale. As a consequence of this need for scale, you will continuously see systemic risk in the system. Blowups happen at the top, never at the bottom. This is a feature of large-scale capital markets.

We find ourselves in a similar place with compute. If compute can only be sold on fixed 3Y-5Y take-or-pay contracts, it can only be accessed by a few. Just like with credit, there is a massive, underserved market begging for compute. This market is fundamentally not creditworthy as duration increases, and equally creditworthy as duration shrinks. A startup’s credit horizon is roughly its cash runway: a company that just raised with 18 months of cash can credibly commit to a year of compute, not three. Most startups are good for a month. Anyone is good for a day.

The base of the pyramid is where most of the underserved demand sits, and it’s exactly the part today’s capital stack can’t reach. Access to compute is rationed by tenor, and the limited evidence so far shows that shorter tenor is commanding a significant premium.

The Evidence: A Tenor Premium

Though the data is thin, it’s clear that shorter-term contracts command a premium. The first thought that comes to mind is that the premium just pays for the operator’s idle capacity and remarketing risk. However, the demand we see says otherwise: short-tenor capacity isn’t sitting idle. It’s oversubscribed. The premium is what buyers pay for flexibility that the financing structure keeps in short supply. In fact, operators with the capital stack that allow it to serve the shorter duration market have considerably more demand and potential cash flow than operators who cannot.

As is clear, the data shows that shorter duration commands a considerable premium. On demand, B300s are clearing almost $9.00, with prices falling off a cliff as the duration goes from 1Y to 2Y.

Compute price indices already exist, and they are the first attempts at price discovery in this market. But the current capacity flowing through short-duration contracts is a tiny sliver of the actual traded market. Long-tenor deals are few, large, and bilateral, so they never print publicly. Short-tenor activity has the most counterparties but is spread across hundreds of providers with no common reference.

So the curve today’s prints describe should be read with a grain of salt. The indices have real issues, but they are a start. The data is thin; the logic is not.

Short Tenor Is the Easy Sale

The standard objection is remarketing risk. If the loan amortises over three years and the contracts run six months, someone holds the gap, and lenders assume the operator will struggle to re-lease the cluster.

That risk is overstated. Short-duration compute is far easier to sell than long-duration compute. At Anera, we turn away requests for one-, three-, and six-month compute every day.

Those requests don’t only come from startups and independent developers. Many come from existing inference operators who want to scale their fleets up and down with demand. In fact, the largest inference platforms in the world still run almost 50% of their fleet on spot/short-term compute across tens, if not hundreds of venues. Today, when an operator wins an enterprise contract, it often has to pull GPUs off its serverless inference, on OpenRouter for example, to serve that customer. It can’t add capacity for the length of the deal, so it takes supply out of the open market instead.

The outcome lenders fear, a cluster nobody wants when the contract ends, is the least likely one. The queue for short-tenor compute is longer than the queue for long-tenor compute. What’s missing isn’t demand.

Access Flows through Credit

The data on cash flows from realising the tenor premium is emerging. A new crop of resellers is setting out to serve short-tenor demand, among them SFC, Runpod, Hyperbolic, AMP, Andromeda, Ornn, Compute Desk, and Liquid Compute. Their model is to take down long-term capacity and sell it in strips: one-year, three-month, one-month, and on-demand. Some of them sell bare metal access, others with custom VMs on top. Some take on principal risk, while others take none. The essence of their work, though, is to realise the tenor premium and open up the market for compute.

They are raising equity to do it. But equity alone can’t scale to meet the demand, and without leverage, they will stay small. Leverage won’t come until a specialist underwriter emerges: one that understands the demand for compute, can prove that short-tenor compute is creditworthy, and can scale the amount of compute allocated to shorter tenors. Once that leverage arrives, these resellers will depend on price as well as utilisation, and they will need price protection on margin, and high utilisation to realise superior economics. This is how a freely trading compute market gets created.

Until then, access stays gate-kept. Compute goes to whoever can sign a three-year take-or-pay contract, and that is a handful of buyers. Compute is too essential a commodity to concentrate in the hands of a few. The path to mass democratisation of access to compute and liquid markets runs through credit.

That’s it for today.

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.

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Welcome to Edition 20 of The Reading List. Starting last week, we made this a weekly, and not fortnightly, Saturday affair. Every Saturday, I’ll keep sharing my favourite reads across crypto, finance, technology, culture, and beyond, alongside what I’m watching and reading.

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

Let’s get going…

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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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Plug in Your Agent

The Week That Was

Last week, the US economy handed the Fed another awkward combination to deal with. While businesses in the country were still expanding, hiring was subdued and price pressures persisted.

Monday’s services survey showed the ISM index easing to 54.9 in September, comfortably above the 50 mark separating expansion from contraction. But its input-price index climbed to 74, the highest since July 2022. Fuel costs and supply disruptions were making it more expensive to meet demand. An expanding economy is good for company revenues, but rising costs complicate what shareholders get to keep.

September’s weak payroll gain of 29,000, reported on 2 October, suggested employers were reluctant to hire. But Thursday’s release showed new unemployment claims falling to 197,000 for the week ending 3 October. Slow hiring had not become widespread firing.

For the Fed, that leaves room to wait without declaring victory over inflation. Minutes published on Wednesday showed most officials expected another rate increase before year-end. Christopher Waller reinforced that message on Thursday, while allowing that increases need not come at consecutive meetings. A possible October pause is still compatible with higher rates later. September’s CPI report, due on 14 October, is the next important test.

US markets finished the week with gains across major US equity indices: the S&P 500 rose 1.2%, the Nasdaq 0.6% and the Dow 0.9%. Small caps diverged, with the Russell 2000 losing 0.9%. The US 10-year Treasury yield ended around 5.24%, slightly lower over the week but still elevated. Gold futures gained 1.4%.

Bitcoin was trading around $82,750 on Saturday morning in the US.

Crypto still faces a demanding backdrop. A Fed pause could offer relief, but expensive money and persistent energy inflation limit the case for an easy liquidity-driven rally. The economy’s resilience gives investors something to buy into, and central banks a reason to remain cautious.

What We Covered This Week

At The Token Dispatch, we examined what changes when software makes more of our financial decisions, and which parts of the system remain difficult to automate.

In Everything Everywhere All at Once, I explored why we need agents that can navigate across stocks, perpetual futures and prediction markets. In the piece, I also talk about the challenges that constrain the current agentic stack.

In Remittance Won’t Have a Global Winner, I explained why the evolving remittance stack will be won by local champions who will dominate licences, banking relationships and last-mile payouts.

Thejaswini tested how much judgement we hand over in The Apps That Trade for the Clueless. Turning a hunch into a strategy is getting easier; knowing whether its backtest means anything, or whether its creator benefits from unnecessary trading, remains harder. She then went shopping for discounted intelligence in GPT 97% Off, examining AntSeed’s open marketplace for AI access and the problem of verifying what an unfamiliar seller actually delivers.

Vaidik, meanwhile, questioned the foundations in DeFi Built the Wrong Toolkit. DeFi’s early tools were shaped by volatile tokens and anonymous borrowers. He explored why the arrival of income-producing assets could require different market structures, and bring on-chain finance closer to the traditional infrastructure it set out to challenge.

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

• What Is Needed for Tokenisation to Deliver: The IMF examines the legal rights, interoperable networks and reliable settlement assets needed for tokenisation to move beyond fragmented markets.

• Asymmetry: Scott Galloway argues that cheap drones and unconventional tactics expose the weaknesses of America’s expensive military. In this piece, he also makes the case for rethinking defence spending.

Crypto & Tech

• Crypto Can Originate-and-Distribute Now: Byron Gilliam explores how USD.AI’s GPU Loan Exchange brings the banking model of originating and selling loans to on-chain infrastructure financing.

• Does the Internet Need to Be Liberated?: Grass’s business of collecting web data for AI revives an old conflict: information is easy to copy, but the people producing it still need to get paid.

• Stocks With Benefits: DCo traces how tokenised stocks evolve from price exposure into useful collateral and native on-chain shares, asking which apps and issuers capture the value.

Finance & Economy

• Consilience of Curves: Alexander Campbell connects AI acceleration, geopolitical fragmentation and Western reindustrialisation into an investment thesis grounded in energy, minerals and the cost of capital.

• Markets Are Feeling Crowded: Byron Gilliam examines whether AI’s enormous financing needs are raising borrowing costs and crowding out investment across the rest of the economy.

• Underwriting Digital Credit Liquidity: Keyrock tests whether Bitcoin-backed preferred shares can function as dependable collateral, examining stressed liquidity, exit times and reliance on issuer buybacks.

• Losing Less Between Your Best Ideas: Rhino offers a compact reminder that preserving capital between high-conviction opportunities matters as much as finding the next winning trade.

AI & Innovation

• Vibe Manufacturing Is Here: Greg Isenberg explores how AI-assisted design, desktop 3D printing and on-demand factories could make small-scale physical-product businesses easier to build.

• Honor Among Machines: Samuele Marro introduces dovetail, a research project developing verifiable rules that could let independent AI agents cooperate without a central gatekeeper.

• The Physics Nobel for Hunting Cosmic Neutrinos: Francis Halzen’s Nobel recognises the IceCube observatory and its discovery of high-energy cosmic neutrinos, opening another way to study the distant universe.

Culture & Beyond

• What If Your Kid Becomes a Nepo-Baby?: Jords questions how we judge inherited advantage, arguing that success should account for where people started and whether they enjoy the lives they build.

• How to Assemble Science: Helen Pearson explains why combining existing evidence can reveal life-saving insights that the constant pursuit of individual scientific breakthroughs overlooks.

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.

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