The way we buy software these days is ridiculous, and back then it used to be a one-time physical purchase. Buyers paid for a CD, entered a serial key, and owned that specific version permanently, with total control over whether or not to pay for future upgrades.

As software moved to the internet, the industry transitioned from one-time purchases to recurring subscriptions. Which were objectively better for them and, for a while, probably better for us too. You no longer had to worry about installing updates or buying the new version, and the company could keep maintaining the product instead of making most of its money every few years when we upgraded.

Salesforce proved from day one that selling cloud-based access as a recurring monthly service was highly profitable and scalable. Adobe also eventually moved Creative Suite into Creative Cloud. Eventually, everyone else got the blueprint and confidence to do the same, making subscriptions the inescapable standard today.

But later on, the subscription slowly became the default way to sell software, even when the thing being sold does not really need to be used like a subscription, like a quick file converter or an occasional image generator.

Say I want to know what a PlayStation 5 costs on Amazon right now. I need that information once. I don’t particularly want an ecommerce data product in my life. If I want to generate three images for an article this month, I may not want an expensive image-generation plan either. The same applies to a private-market database I need for one company, an on-chain query I need for one chart, or a video model, which is one of the most expensive tokens.

Modern software is increasingly broken down into APIs that execute single, split-second tasks. You ask for one specific output, and the interaction is immediately over.

But the purchasing model remains stubbornly stuck in the past. Providers still force you to buy tiny, discrete API calls using the heavy, traditional SaaS model. Buying from ten different tools means too much of an administrative nightmare.

Monid is a company built around the idea that this is unnecessary. Now that humans are not the only users of software, and especially because not the only crowd paying for software, we can’t skip this one. So I had it tested with my Hermes agent.

Monid currently lists more than 1,700 tools across over 55 providers. Users manage a single balance on Monid rather than creating multiple vendor accounts. You can browse tools, check prices, and review schemas for free, paying only when you actually execute an API call.

I could have gone directly to every company providing those services. In some cases, that would even have been cheaper. Which is why I think Monid is interesting.

The bet is that software is becoming modular enough that eventually we may stop buying some of it as products at all. We will buy the specific capability when we need it, use it once, and leave. SaaS bundled the interface, the software and the commercial relationship together. Those three things can now be pulled apart.

PRED: Trade Sports at the Best Odds

Anyone who has watched a game with money on it knows the feeling.

A momentum shift in the fortieth minute, a red card, an injury or a goal against the run of play, and the game turns while you sit there holding a position you took before kickoff.

On most prediction platforms, that is all you can do. Hold and pray.

PRED, on the other hand, turns every fixture into a live market. You buy ‘Yes’ or ‘No’ and the price moves as the match does, so if the game turns, you can cash out and take whatever your position is worth at that moment.

Our odds are 10 to 14 per cent better than the rest of the market. Across the season, that gap adds up.

Markets on PRED settle instantly once the result is in, and you’re never stuck holding a position waiting on someone else to agree on what happened. You can cash out any time.

Built for how fast sport actually moves.

Start Trading

You can get carried away very quickly when sizing this market, so I am not going to tell you that Monid is attacking the entire trillion-dollar software industry. Salesforce is not going to become a four-cent API call because of the new billing model.

A better place to start is with how much software companies already buy and then barely use.

Zylo’s 2026 SaaS Management Index is based on more than 40 million licences and $75 billion of SaaS and cloud spend. The companies in its dataset hold an average of 305 applications and spend $55.7 million a year on SaaS. It says an average of 36% of licences sit unused. AI software is making the problem messier rather than fixing it. Expense-based SaaS spending grew 267% year on year, with employees increasingly buying AI products outside the normal procurement process.

Some of that waste is bad management. A company forgot to cancel a seat after an employee left. Someone bought fifty licences, and only thirty people showed up.

But some of it comes from the shape of the product itself.

You buy access for a month even though your demand appears for a few minutes or just a day. This becomes much more noticeable with AI products because the useful thing is often already priced internally as consumption. Models charge for tokens, which are individual units of work.

The technology research and advisory firm Gartner thinks this eventually creates a fairly uncomfortable problem for SaaS companies. In July, it was estimated that up to $234 billion of enterprise application spending would be exposed to what it calls “agentic arbitrage” by 2030, equal to roughly 20% of enterprise SaaS spending. Its argument is that once work can happen across systems without a human spending their day inside every product interface, software vendors can no longer assume that more work automatically means more seats.

Again, I wouldn’t call $234 billion Monid’s TAM, but Monid is targeting a slice of it.

The actual market is the fraction of software that has three characteristics. The job can be expressed as a discrete request, the output can be returned programmatically, and usage is irregular enough that keeping a permanent commercial relationship with the provider is unnecessary.

Search, data enrichment, scraping, image and video generation all of that fits the case. And it is a large part of market data, verification APIs and browser sessions included.

A collaborative design tool used by many designers probably does not. As for now, this gives Monid a much more believable market than “kill every subscription.” Because tools like Figma or Canva rely on entire teams working inside the interface together for hours every day. In that scenario, paying a predictable monthly fee per designer makes total sense.

The infrastructure for this already exists at a surprisingly large scale.

Postman surveyed more than 5,700 developers and technical leaders for its 2025 State of the API report. 82% of organisations had adopted some level of API-first development, while 65% said their API programmes already generated revenue. For many businesses, APIs are the product.

But there is a mismatch in the same report that I find more useful. Only 24% of developers said they actively design APIs with AI agents or machine consumers in mind, while 60% still design primarily for humans. Postman also found that 55% struggle with inconsistent documentation, and 34% have difficulty even finding APIs that already exist inside their organisations.

MuleSoft’s 2025 Connectivity Benchmark found that developers still spend 39% of their time building custom integrations, while 95% of IT leaders struggle to connect AI systems to existing software.

APIs made software callable, but again, not easy to buy.

Monid is trying to standardise that layer. You search its catalogue, inspect the schema and price, then run the endpoint from the same balance. Discovery and inspection are free, so several options can be compared before anything is purchased. Price becomes part of the interface itself, something checked before execution rather than negotiated separately.

API marketplaces are not new. RapidAPI built a single marketplace where developers could search for third-party APIs, read the docs, test them, subscribe, get one API key and manage usage from one dashboard. By 2022, it had more than four million developers and raised $150 million at a $1 billion valuation.

Then the company ran into trouble and cut deeply in 2023 after expanding across too many areas. RapidAPI just ended up proving that people wanted one place to find and access APIs. A human developer can find an API in a marketplace and then integrate it directly. Once that integration exists, the marketplace has done the useful part of the job and given the customer less reason to remain in the middle. If the same API gets called ten million times, the engineering team will eventually ask why it is still paying somebody between itself and the provider.

Monid faces the same risk, but where its model differs is that it is not really trying to make discovery a one-time event. The system is designed around a world where the appropriate provider can change from one piece of work to another, and where keeping many small tools available is more valuable than deeply integrating one of them.

That only works if software demand stays fragmented. Fortunately for Monid, software is very good at becoming fragmented.

OpenRouter proved how to profit from this exact chaos in the AI space.

AI models were a good market for aggregation because there are hundreds of them, prices move, latency changes. OpenRouter put it all in one interface.

In May, it raised $113 million. By August, it said more than ten million developers and companies were using the platform and daily volume had crossed ten trillion tokens. Then Stripe bought it in a deal Reuters reported at a little over $8 billion.

Read: Stripe Bought Demand

Monid describes itself as the OpenRouter for tools, and it plans to show that the middle layer can become valuable on its own.

But tools are messier than models. An Amazon scraper, a funding database and a video generator have different prices, inputs and output formats. If Monid can make all of that feel like one market, that is the real opportunity.

Monid is not alone in this market. Pipedream, Composio and Arcade already connect thousands of tools, but they mostly solve integration and authentication. It means those companies help different apps talk to each other, but they usually don’t pay for the software on your behalf. You still need your own Slack, Salesforce, Gmail, or other account. They connect what you already subscribe to. Monid is trying to let you buy the capability itself.

Monid wants the capability itself to be purchasable through one shared balance.

Agent402 is closer to that model, using pay-per-call tools over x402 and MPP, but with crypto much more visible in the product.

Read: Who Checks the Agents?

In Monid, you can fund a normal prepaid balance and never care what x402 is, or use USDC per run if you do. That is probably the right abstraction. Most people buying ecommerce data do not wish for more payment protocols to be involved.

Put these companies together and a new software category starts becoming visible. I would call it a wholesale market for software capabilities.

If Monid is just reselling access to tools from other companies, the obvious question is why pay Monid at all instead of going straight to MiniMax, Alibaba, Tripo, etc?

I tested that where I could.

MiniMax image generation through Monid was $0.0035 per image when we ran it, the same as MiniMax’s own direct API price. WAN 3.0 video at 720p cost $0.10 per second on Monid, which matched Alibaba’s official list price. Alibaba was running a temporary 30% promotion at the time, however, which made the direct route $0.07 per second. So if you knew you were going to make hundreds of WAN videos that month, going directly to Alibaba was the obvious choice.

The 3D generation test went further in that direction. Monid charged $0.80, while going directly to the underlying provider, Tripo, looked closer to half that price. So Monid is not automatically cheaper.

Sometimes you are paying for convenience.

Amazon does not need every item to be cheaper than buying it from the manufacturer. The middleman survives because avoiding ten individual relationships has value. At high usage, a subscription can be cheaper. Stripe describes the benefit as aligning charges with actual usage, not guaranteed savings.

Consumption pricing makes more sense when the alternative is paying for capacity you rarely use. Monid says it shares revenue with API providers, but it does not publicly disclose a standard provider split yet, so we cannot yet estimate its marketplace margins.

Still, there is early volume. Co-founder Shengkun Ye said at the beginning of September that Monid had crossed 4 million transactions and raised a $2.1 million pre-seed round from 1984 Ventures, Llama Ventures, Untapped Capital and Founders, Inc. For now, that is enough to start learning something about which bits of software people actually buy when software is sold this way(individual outputs or data pulls).

I managed to break a few things while testing Monid. Reddit search was bad. An Amazon scraper failed twice because the provider was down. Another ecommerce tool returned a 404. The 3D tool showed us an old parameter that the live API no longer accepted.

Then we switched Amazon providers, and it worked. The second tool returned a live PS5 listing with the price, stock, seller, delivery and ratings.

The value is being able to find another option when one stops working. Over time, Monid could know which provider is reliable, which one is cheaper, which one keeps returning empty results, and which one has started failing today. Then it can route users towards the better option instead of making them figure that out themselves.

If we assume that the advantage is having API number 1,700, RapidAPI had a long catalogue too, someone else can add 1,701 tomorrow. But knowing which API is worth using is a clear advantage. In our test, most of Monid’s crypto tools could read data but not take action. I could analyse wallets and transactions, but we could not find tools to deploy contracts, mint NFTs or list assets. But the pitch didn’t promise that either.

So Monid cannot turn every kind of software into a simple call yet. Some products need accounts, permissions and long-running workflows.

Companies already spend huge amounts on SaaS, and a lot of those licences go unused. APIs have also broken many products into smaller pieces, but buying those pieces is still awkward. You often have to create an account and commit to a plan before you know how much you will actually use.

Usage pricing flips that around. You need something, you pay for that use, and then you stop paying.

This can be worse for vendors who like predictable monthly subscriptions. But it can also bring in customers who would never have paid for the full plan in the first place.

The last twenty years were spent turning software from boxes into subscriptions, and then subscriptions into APIs. Each of these changes also changed what the smallest purchasable unit was.

While testing all of this, I made a 3D robot watering a flower. There are plenty of tools that can do 3D generation now, so this is not evidence of anything particularly profound. I just think he turned out quite nice, and I am disproportionately proud of him, so I am sharing him anyway.

That’s it for today.

Will come back soon with more on Monid. Stay tuned!

—Thejaswini

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There’s a bar in New York called The Jeffrey that ran a promotion during the Knicks playoffs last season. If the Knicks advance, drinks are on the house for a night. The owner did the math and realised a Knicks win would cost him about $5,000 in free drinks. So he went on Kalshi and bought a position that pays out if the Knicks win, and hedged the whole thing.

Later, when the Knicks won, he honoured the promotion and collected all the money from Kalshi, costing the bar literally nothing.

A bar in Manhattan just used a prediction market the way a Fortune 500 company uses a derivatives desk. And I think this tells us a lot about what’s happening with prediction markets. For the past two years, the conversation has focused almost entirely on how accurate these markets are. Whether they can actually forecast elections and price risk better than the polls and analysts that have been doing it for years.

But while they debate that, prediction markets are creating a whole new financial surface that goes beyond forming a view, with value living well past the predictions themselves. Let’s dig in!

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Beyond the Prediction

The last time I remember a financial instrument jumping from gambling to legitimate infrastructure was in 1973, and the instrument was options. It had existed for decades before that, but the regulators treated it the same way your uncle treats you when you say you work in crypto. SEC was suspicious, Courts were hostile, and the CBOE had to fight for years just to get permission to list them. Then, one day, Fischer Black and Myron Scholes published a pricing formula that led to something exceedingly wild over the next five years.

Traders started using the formula to spot mispricings between different options contracts. As more traders used it, their collective buying and selling pushed actual market prices toward what the formula predicted. By the late 1970s, the formula fit the market almost perfectly. That formula wasn’t some divine truth about how options should be priced; it was the result of enough people using it to trade that the market reshaped itself around its math.

And that’s how a model built for an instrument the mainstream called gambling ended up creating one of the largest markets in modern finance, which is now worth trillions of dollars. And I am pretty much convinced that Prediction markets are at this exact inflexion point right now.

ICE, the company that owns the New York Stock Exchange, recently paid $2 billion for Polymarket and immediately started distributing its probability data through the same consolidated feeds that already carry NYSE stock prices to every institutional terminal on the planet. The feed ICE runs is the backbone of how institutional money accesses financial data, and is now being delivered alongside Apple’s share price and Treasury yields.

Once fund managers start using these probabilities as a real input to their positioning, it will directly add more volume and depth to the polymarket contracts underneath. This will produce a more reliable probability, and that in turn will attract more institutional capital. ICE has seen this loop play out once before with options after Black-Scholes, and so they wanted to make sure they own the infrastructure this time when it plays out again.

But there is a problem sitting at the core of all this, which is that the most useful prediction markets, the ones pricing narrow geopolitical and regulatory events, are the ones that can’t get liquid enough to produce reliable probabilities. In simple English, the word for this is ‘adverse selection’.

Prediction markets, like all financial markets, depend on market makers to function. These market makers sit on both sides of a trade, offering to buy and sell at slightly different prices and pocketing the spread. And for them to do this profitably, they need a healthy share of uninformed traders on the other side, people whose bets carry no special insight into the outcome.

Election markets are the poster child for this. For example, when a partisan drops $10,000 on their preferred presidential candidate because they watched too much cable news and truly believe their guy will win, that bet is pure emotion with zero information edge. Market makers love this kind of flow because they can comfortably take the other side, knowing the bettor has no special knowledge.

Now compare them with contracts like “Will there be a ceasefire between the US and Iran by Oct?” or “Will tariffs hit semiconductors in Q4?” Nobody bets on these markets out of patriotism or tribal loyalty. The only people showing up to trade narrow geopolitical contracts are people who probably know something; maybe they work in the semiconductor supply chain, maybe they have connections in government, or maybe they simply read intelligence cables. And market makers can smell this miles away.

When every counterparty on a contract likely has real information, market makers widen their spreads to protect themselves or just refuse to participate entirely. So the contracts the financial system most desperately wants priced- the forward-looking probabilities on specific events, which you literally can’t get from any other instrument end up too thin to be useful here, too!

This has been a problem with all kinds of financial instruments in the past, and the solution they came up with for this was hedging. If we go back to 1870s Chicago, when grain futures were just getting started. Those markets became the deepest commodity pits in the world, because farmers and grain elevator operators started using futures contracts to protect themselves against price swings on crops they had already planted or purchased.

A farmer selling corn futures has no special insight into where grain prices are headed next quarter. He is simply locking in a price for next season’s harvest so he can sleep at night. But to the speculators on the other side of his trade, his orders look random, indistinguishable from noise. And that noise gave market makers the confidence to participate, which brought in more speculators and deepened the market, turning what started as a basic hedging tool for Midwestern farmers into the foundation of global commodity finance.

The owner of The Jeffrey, that bar in Manhattan, did the same thing on Kalshi. To the market makers and speculators on the other side, his bet looks indistinguishable from someone gambling on basketball. And that is precisely what makes hedgers valuable.

And Prediction markets need an army of them: bar owners, fleet managers worried about diesel prices, event planners exposed to weather risk, small businesses with real money riding on outcomes they cannot control. But you cannot expect a bar owner or a fleet manager to open Kalshi, scroll through hundreds of contracts, figure out which one maps to their specific business risk, and size it properly relative to their actual exposure.

Goldman Sachs has an entire derivatives desk that does this translation for Fortune 500 companies. But your local coffee shop owner does not.

The Translation Layer

I came across two startups that saw the Goldman Sachs problem and are building products to close it.

Blanket started from the hedging side. It is an independent product built on top of Kalshi that works as a recommendation engine for small businesses. You can describe what your business does and what you are worried about, and its models figure out which Kalshi contracts map to your specific exposure and how to size the position against the risk you are actually carrying.

It does not execute trades or hold funds, and no money moves through the application. Kalshi handles all execution and custody, and it is CFTC-regulated. Blanket sits entirely upstream as a recommendation layer. The owner of The Jeffrey did not need Blanket because his hedge was simple.

But a fleet manager worried about diesel costs is exposed to a web of overlapping probabilities: whether OPEC cuts production, whether Middle East tensions disrupt shipping lanes, whether fuel tax policy changes, and how each interacts with the others in ways that are genuinely hard to reason about without a financial background.

The only businesses that have traditionally navigated this kind of multi-variable risk were the ones wealthy enough to hire a derivatives desk. Blanket is trying to compress that entire function into something a coffee shop owner can use.

What I find interesting about this is how clearly it maps onto how every financial market that has ever matured. Nasdaq, for instance, once made just $77 million from US stock trading in a single quarter while Goldman Sachs pulled $1.74 billion intermediating those same stocks. Even Virtu, a market-making firm most people have never heard of, earned $373 million, nearly five times what the exchange itself made processing every single one of those trades.

The matching venue, where buyers meet sellers, consistently ends up being the lowest-margin part of the system because matching is a commodity that anyone with capital and a license can replicate. The value always migrates toward whoever controls the friction point where someone with a view, or an exposure, gets connected to the specific instrument that expresses it.

Blanket is betting that, in prediction markets, that friction point is when a business owner’s real-world anxiety becomes a financial position, and by choosing to own only the intelligence layer and let Kalshi handle everything downstream, they are betting the recommendation is worth more than the execution.

But to a certain extent, it would be interesting to see how this pays out because it also gives rise to a lot of unthinkable anomalies. In agricultural finance, there is a well-studied problem called basis risk. When a farmer buys weather-index insurance to protect against drought, the insurance pays out based on a regional rainfall index, not based on what actually happened on that specific farmer’s field. So even if the index says it rained enough but the crops dried out, the insurance pays nothing.

Even with government subsidies covering more than 60% of the premium, only about 20 to 30% of eligible farmers actually buy in, because the difference in what the index tracks and what they actually experience is pretty unreliable.

Similarly, a fleet manager hedging diesel costs with a Kalshi contract on OPEC production can face the same uncertainty. OPEC could cut production, and diesel prices in her region might barely move because local refining capacity and seasonal demand patterns create a wedge between the macro event they hedged against and the actual price they pay.

So, Blanket’s AI can recommend the right contract, but prediction market contracts are, by design, binary bets on specific events, and the delta between a specific event resolving one way vs the business actually feeling the financial impact can be completely opposite. It will be interesting to see how that pays out, though.

Another one, approaching the problem a bit differently, is ZEIT Finance. ZEIT built what they call Perpetual Prediction Vaults, and the simplest way to think about them is as prediction market ETFs. You can just type a thesis in plain English into their Worldview generator, something like “AI regulation tightens across the US and Europe” or “the Middle East conflict escalates into a broader regional war.”

Their AI pipeline searches Polymarket’s live contracts, identifies the ones relevant to that thesis, builds a diversified portfolio of positions around it, sizes each position using a convex-optimisation layer, and packages everything into a single ERC-20 vault token that auto-rolls your capital into new contracts as old ones resolve.

Here, ZEIT is building a portfolio engine for views, but views are often quite hypocritical. For example, “US-China tensions escalate and semiconductor supply chains fracture” could be confirmed or invalidated by a single press conference. The portfolio has to respond in real time, rolling into new contracts as events unfold, adjusting sizing as probabilities shift, and doing all of this while valuing positions at what you could actually exit at on the live order book.

Just like how Nathan Most invented the ETF by borrowing the warehouse-receipt mechanism from grain markets, turning a farmer’s hedging tool into global commodity finance. ZEIT is taking the raw binary contracts of prediction markets and wrapping them into a composable, tokenised, portfolio-level product that ordinary people can hold and trade without understanding the mechanics underneath.

And if the wrapper works at scale, it could do for prediction markets what the ETF wrapper did for index investing: make the underlying accessible to millions of people who would never have touched it in its raw form.

And even Polymarket is trying to go beyond being a place to form views and now also price them. Right now, if you form a thesis on Polymarket about the Fed and then want to trade that thesis, you close the tab and go to Binance or dYdX or a traditional brokerage. All of the economic value of acting on the view you formed on Polymarket leaks to whoever runs the venue where you end up executing.

That’s why they launched perps to close that gap, making their platform where you form the view and where you also trade it, so the value of the entire chain stays inside one venue. Otherwise, Polymarket risks becoming the Nasdaq of prediction markets: processing all the trades and capturing only a small share of the economics.

I think the prediction market conversation needs to catch up with what is actually happening. The accuracy debate is very 2024, and prediction markets won it convincingly enough that ICE paid $2 billion for it. The regulatory fight will play out the way regulatory fights always play out when institutional capital lines up on one side.

The question that matters now is about who captures value from the signal prediction markets produce as it flows into the broader financial system. Lloyd’s coffee house began in 1688, when Edward Lloyd pinned shipping intelligence to his walls so merchants and underwriters could make better decisions about marine risk. Over the next three centuries, that room became the largest insurance market on the planet, still writing over $50 billion in annual premiums today, while every underwriter who ever sat in it has been replaced by successors.

The intelligence layer outlasted everything built on top of it. Prediction markets are now building their own version of it.

That’s all for today!
Vaidik

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

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

Time wasn’t behaving normally, nor were people. Days blurred so much that some of us couldn’t tell if its monday or friday. The news was all case counts, the US elections, and arguments about whether the economy could just be paused. In the West, people watched entire aisles of toilet paper and flour disappear, yet still refused to wear masks.

Markets crashed and then ripped, and the stimulus checks had arrived. I was stuck at home like everyone else, scrolling too much, playing video games, watching Breaking Bad, and sleeping badly.

A friend had mentioned the book years earlier. I ordered it after finishing To Kill a Mockingbird, because I felt another work of fiction would not do justice to, or reach the heights of, what that book had taken me through. So I turned to understanding what was wrong with the economy. I needed something that might explain the world to me.

Frank was a Cornell professor who showed his students how to explain everyday puzzles just by weighing costs against benefits. He used small examples, asking why things like rectangular milk cartons, free peanuts in bars, Braille on drive-up ATMs, and painful high heels make economic sense for the people involved. It was comforting to read, maybe because it was a relief to read something so specific when everything else was overwhelming. The book showed how to look at shortages and weird market behaviour without judging anyone’s character. Consumers react to incentives, and firms react to costs. Understanding that removes a lot of the mystery from the chaos.

I didn’t think about the book for a long time and only just picked it back up. Crypto has obviously been through a lot since then, the hype, the crash, the regulation, Trump and the death of tokens. A lot of things we call innovation are just behavioural incentive design, we learnt that together. The everyday economic puzzles Frank talks about are the same ones we see all the time around here. I’m bringing it up because crypto is at a stage where the design choices are clear, but the cost of getting them wrong is huge as well.

PRED: Trade Sports at the Best Odds

Anyone who has watched a game with money on it knows the feeling.

A momentum shift in the 40th minute, a red card, an injury or a goal against the run of play, and the game turns while you sit there holding a position you took before kickoff.

On most prediction platforms, that is all you can do. Hold and pray.

PRED, on the other hand, turns every fixture into a live market. You buy ‘Yes’ or ‘No’ and the price moves as the match does, so if the game turns, you can cash out and take whatever your position is worth at that moment.

Our odds are 10 to 14 per cent better than the rest of the market. Across the season, that gap adds up.

Markets on PRED settle instantly once the result is in, and you’re never stuck holding a position waiting for someone else to agree on what happened. You can cash out any time.

Built for how fast sport actually moves.

Start Trading

Frank believes traditional economics is taught poorly because it relies on memorising abstract formulas. Instead, he uses everyday behaviours that seem completely irrational at first glance to teach the subject. He decodes these oddities using two basic rules.

1) The cost-benefit principle. People usually think about whether something is worth it. If something seems more rewarding, they are more likely to do it. If it seems harder or more troublesome, they are less likely to do it.

2) Opportunity cost. The true price of a choice is not just the money spent, but the value of the next best thing you sacrificed. The cost of attending a concert isn’t just the ticket price; it’s the sleep you lost or the alternative event you missed.

Choices that seem irrational can actually make economic sense. For instance, milk comes in rectangular cartons even though round ones would be nicer to hold. That looks like bad design, but if you think about it, Fridge space is expensive, rectangles pack tighter, and people usually pour milk instead of drinking from the carton.

The same logic applies to braille on drive-up ATM keypads. Making a separate, flat keypad just for drive-throughs costs extra money. Since drivers don’t care if the dots are there, companies just make one Braille keypad and stick it on every single machine to save cash.

The book still does not tell me why we say “a pair of pants” for one thing with two legs, but never “a pair of shirts,” which have two sleeves. But fine, I guess it’s my personal fight.

Why do auto drivers in Bengaluru insist on asking “where?” when their only two settings are driving away or charging 1 lakh? I could reverse-engineer that one with the patience I built while sitting in traffic here.

Frank’s cost-benefit principle says people ask questions when the expected benefit of the information is greater than the cost of asking. In this case, it’s a low-cost way to quickly learn whether the ride is worth accepting. So even if the driver already expects to refuse some rides, asking “where?” still makes sense because it helps sort good rides from bad ones with very little effort. It is a quick filtering tool, still mean.

Coming home to crypto here, instead of physical manufacturing costs, a protocol team worries about high gas fees, getting listed on exchanges, and making sure wallets actually support their token. Using a standard token format like ERC-20 can reduce costs. While a developer could technically build a highly complex token with dozens of unique rules, doing so would make it incredibly hard and expensive to launch.

Ondo originally planned to build its own Layer-1 blockchain for tokenised assets. After talking to users, they realised a custom blockchain didn’t solve trade speed and privacy. So, they scrapped the expensive L1, built an off-chain private trading system, and used standard Ethereum for settlement.

Read: Ondo’s Taken a Decision

Robinhood wanted to offer on-chain stocks. Instead of dealing with the legal trouble of issuing actual company shares on a blockchain, they took the cheapest route. They built on standard Ethereum layers and issued simple debt tokens that just track stock prices. Both Ondo and Robinhood sacrificed unique, visionary tech for standard, restricted products because it was cheaper and easier to launch. If a crypto product looks weirdly limited, it’s usually because the founders were avoiding a massive operational or legal headache.

Read: The World Is Flat, and So Is Your Claim

Frank also explains price discrimination, which means charging different people different prices for the same thing because they are willing to pay different amounts. Airlines want to charge business travelers top dollar because they aren’t price-sensitive, but they need to offer cheap tickets to vacationers to fill empty seats. Because airlines cannot read minds, they add rules like Saturday-night stays, advance purchase requirements, and nonrefundable fares. These rules are costly enough to separate customers, but not so costly that they hurt the market.

Crypto projects use lockups and tiered sales the same way. By making early buyers wait to sell, teams filter out quick flippers and identify who is actually willing to pay for access. It is a hurdle, a pricing filter to separate investors.

Why does everyone keep playing games they know are broken? This is something that always made me feel weird about the people in finance. Like, we all know what happened with Solana memecoins. Robinhood built a chain for tokenised stocks, and memecoiners are treating it like Pump.fun.

I wouldn’t be writing about a 2007 economics book in a crypto newsletter if it weren’t for Frank’s chapter on the “positional arms race.” It made sense to me, given the creeping absurdity I see every day, where rational people collectively engineer massive, stupid outcomes, even in life or on-chain.

Crypto keeps score in public, which forces people to behave in a particular way. Because financial success is entirely public, the actual utility of the technology (what the protocol is built to do) becomes secondary. The ecosystem turns into a player-vs-player (PvP) game. The motivation shifts from funding useful infrastructure to simply outperforming other traders, chasing the highest APY, and not being the one left behind. If a protocol’s token price drops slightly, the most logical, self-preserving move for you as an individual is to pull your funds out immediately to protect your capital. But, if every single user makes that exact same “rational” decision at the exact same time, it completely drains the liquidity pools and destroys the protocol. One person can do what is best for them. All of them together can still make a mess.

Protocols brag about high TVL and fully diluted valuations to signal success and attract more capital, even though much of it is temporary, rented money. Similarly, users grind dead points programs out of fear of falling behind, creating a competitive race.

Sometimes you need a rule, a standard, or a different thing to measure. With a rule not to dump an unvested team in the first year, players can no longer win by faking a strong chart with a low float.

Then our favourite word “ownership” gets completely dismantled by Frank. He says that true ownership is not a natural given, but rather a functional institution that depends entirely on the practical costs of establishing and enforcing property rights. For example, farmed chickens are easier to treat as property because the rules around them are easier to define and enforce. In the case of wild whales, even if people want to protect them, it is much harder and more expensive to actually enforce those protections.

A claim only becomes true ownership when the boundaries are cheap enough to enforce. In this context, “owning” a wild resource usually means a government or international coalition claiming the authority to protect it.

Because nobody owns the open ocean, whales are essentially free for the taking. Commercial whalers will just keep hunting them to extract value. To stop this, governments try to step in and pass laws saying, “These whales are protected.” Functionally, the government is trying to assert ownership over the whales to save them from being wiped out. But asserting that control means policing millions of miles of international waters and intercepting rogue fishing fleets. That “fence” is astronomically expensive to maintain.

Because governments cannot afford the practical cost of enforcing this control, the “ownership” (the legal protection) fails in practice, and the resource gets overused anyway.

This completely shatters crypto’s favourite illusion that ownership is an automatic, magical feature of the blockchain. As we talked about in the tokenising pre-IPO piece two days ago, holding tokens in a self-custody wallet does not guarantee true ownership. Republic’s rSpaceX are IOUs from RepublicX, not SpaceX stock. Meanwhile, DTCC can put a Treasury token on Canton because the bond never leaves DTC and a repo desk can take it

Ownership is an institution, and institutions require real, ongoing costs to maintain liquidity, legal recognition, and trust.

Without further ado, I want to leave you with the opportunity cost and let you do the rest of the book yourself.

Frank reminds us that the cost of a choice is the next best thing you did not do. If you keep the safe job, you do not start your own project. The thing you never build is what you pay.

Crypto projects love to pretend things are free, like zero-cost point farms. But nothing is free. We always pay for it with what we give up. And this one is bigger than us. Karl Marx’s Economic & Philosophic Manuscripts of 1844 say:

“The less you eat, drink, buy books, go to the theatre or to balls, or to the pub, and the less you think, love, theorise, sing, paint, fence, etc., the more you will be able to save, and the greater will become your treasure... The less you are, the less you express your life, the more you have, the greater is your alienated life...”

When things go sideways in crypto or finance, people usually default to two lazy excuses. Either “it’s all completely random chaos” or “everyone in this industry is just an idiot.”

Frank’s book, which came out in 2007, a year before Bitcoin, helps you look past those lazy statements.

Rather than acting foolishly as we assumed, people are responding rationally to a specific set of costs and benefits. If a system is broken, whining about human greed or stupidity (like I have done sometimes in my previous pieces) is useless. If you want a better outcome, you have to change the underlying incentives that caused it in the first place.

—Thejaswini

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

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

Humanity noticed this in the 1600s and 1700s. Philosophers John Locke and David Hume were already arguing that prices depend not just on how much metal sits in a country, but on how often it changes hands.

“It is also evident, that the prices do not so much depend on the absolute quantity of commodities and that of money, which are in a nation, as on that of the commodities, which come or may come to market, and of the money which circulates. If the coin be locked up in chests, it is the same thing with regard to prices, as if it were annihilated; if the commodities be hoarded in magazines and granaries, a like effect follows.” - Hume

“That which is not let loose into trade, is all one whilst hoarded up, as if it were not in being.” - Locke

All of this just means that a small stock of coin, spent every week, can support more trade than a large hoard that never moves.

Irving Fisher concluded the whole thing with \(MV = PT\). Which says the same thing, but in a mathematically intimidating way. Printing infinite cash is almost useless unless people actually spend it.

That is why “velocity” jumped from money theory into market nuts and bolts. First, the thing must circulate. Then it must complete the transfer. Then someone must trust it enough to lend against it. If you think that’s 60 seconds wasted on me explaining the fundamentals of economics to you, tokenisation is where I am getting at, as money velocity increasingly gives way to tokenisation.

Today we are looking at whether tokenising pre-IPO names actually makes those tokens move, settle, and get borrowed against, or whether they just sit like coins in a chest.

Private companies stay private longer than they used to. Different trackers disagree on the exact pile of value, but for better perspective before we go ahead:

Hurun’s 2026 Global Unicorn Index put the world at 1,603 unicorns worth about $8 trillion.

PitchBook’s Q1 2026 Global Unicorn Tracker had about 1,680 active unicorns and $8.6 trillion of aggregate valuation.

CB Insights, as cited in Street Research’s 2026 pre-IPO note, had 1,404 private unicorns at about $7.4 trillion in March 2026.

The point is that a large share of company value now lives where there is no continuous price and no easy way to post it as collateral. Traditional secondaries are not public markets, so they are not accessible. They are also not DeFi. DeFi is the stack claiming it can add access, settlement, composability and collateral use that secondaries do not have.

Canton’s Hecto is the closest live attempt at the setup you would actually want to trust because it runs on rails already used for institutional collateral, though that does not automatically create liquidity or price discovery.

Hecto’s live product is the Allocator. You mint HECTO using Canton Coin (CC), lock it into a basket of private companies called “Hectocorns,” and get a score based on your allocation. Rewards are paid out in CC.

While Hecto launched its token in March 2026 with a 100-billion supply and an insider vesting schedule. Early Allocator stats Hecto posted in March 2026 included figures like 1,690 holders and a few thousand active users in the first days. Full trading capabilities are rolling out in stages, so a deep secondary market has not formed yet.

Hecto has described three products on top of the Allocator:

1. Predictions V2 - Binary markets on pre-IPO outcomes such as IPO timing and valuation milestones.

2. PMX - HECTX is a NAV-linked index token for exposure to late-stage private companies; it is not a share or redemption token. It does not make the holder a registered shareholder of the underlying companies.

3. Vaults - Quote-based access that is more selective for qualified participants. Company-specific, KYC-heavy, and closer to how private markets already work.

The stack provides signal (1), packaged exposure (2), and gated real access (3). Velocity only starts if those layers connect to trade, settlement, and then credit. Fortunately, MV = PT.

A tokenised gold bar or a tokenised building can be useful. Often, it is not. If the token cannot be used in a loan, an index, a structured note, or a repo, you have mostly just paid legal and operational costs to put it on a ledger. Hecto’s Allocator is trying to force activity before the “real” asset even exists on-chain. You allocate every day instead of holding. Emissions are described as a mix of participation, accuracy, and alignment. If you sit still, you earn less.

A forecasting game can create turnover without creating a claim on anything. That is fine as a bootstrap. The moment you should care is when HECTX (or a vault interest) can be transferred, priced against NAV, and accepted by someone else as margin.

When institutions “lend against a token,” they usually mean a repurchase agreement (repo) or a margin call, not a personal loan from a bank. In a repo, one firm posts a security as collateral. The other firm sends cash, or a stablecoin immediately. The first firm agrees to buy the security back later, often the same day. If that handoff takes hours or waits for T+1 settlement, a lot of high-quality paper cannot be used that day. In that case, high-value assets get trapped and become useless for immediate liquidity.

Canton is already being used for that handoff.

The largest piece is DTCC, the U.S. post-trade utility that already custodians most U.S. securities. In December 2025, DTCC and Digital Asset, the firm behind Canton, said they would tokenise a subset of U.S. Treasuries that are held in DTC custody and put those tokens on Canton.

That plan followed an SEC no-action letter dated 11 December 2025. On 15 July 2026, DTCC ran live production trades with a working group of banks and market firms. Those trades included delivery-versus-payment repo, equity settlement, securities lending, and margin posted to a central counterparty. The workflows ran on Canton and on Hyperledger Besu. The commercial launch of the DTC Tokenisation Service is slated for October 2026. DTCC still holds the actual Treasury. The on-chain token is a movable record of that holding. A clearing house can accept the token as margin because title to the bond never left DTC.

Smaller trades made the same point faster, as Virtu Financial and counterparties using Tradeweb posted USDM1 (a Marshall Islands on-chain bond backed 1:1 by short U.S. Treasuries) as repo collateral. The cash leg, the collateral move, and the repurchase settled on Canton in under ten minutes. Other desks have pledged tokenised Treasuries or tokenised money-market paper and taken USDCx or tokenised bank deposits in return, then used that cash the same day. Euroclear and Digital Asset have also run pilots with tokenised gilts and other sovereign debt so collateral can be shifted across borders outside normal market hours instead of waiting until the next open. DTCC-linked commentary keeps citing $300 trillion in high-quality liquid assets worldwide, with only about 10–11% used as collateral at a given time. Digital Asset has estimated a 30–50% balance-sheet efficiency gain if tokenised collateral workflows scale. This proves that Canton can already move paper that markets already trust into repo and margin.

The unsentimental question I have is whether anyone will take HECTX or locked HECTO as collateral the way they take a DTC Treasury twin. Probably not soon. HECTX, as described, is a NAV claim on a vehicle that may still be sitting in cash, may not have full deployment, and is explicitly not equity. Secondary liquidity is thin or not live, depending on the instrument. Velocity only counts if secondary and borrow exist. Right now, Hecto lacks the conditions needed for TAM to expand in a meaningful way.

Hyperliquid’s pre-IPO perps are the obvious other tab. They are a price bet, not a wrapper you can settle or pledge, so they sit outside this piece.

Read: Pricing Anthropic

Republic came at it from the other side

Republic is “an on-chain investment platform” for private markets. In practice, it is a New York marketplace that uses regulatory exemptions (like equity crowdfunding laws) to let regular people invest small amounts of money into startups. Republic heavily integrates blockchain technology and tokenisation. They have tokenised certain private market assets, issued digital securities, and feature a dedicated Web3 arm for investing in tokens and blockchain protocols.

In June 2025, Republic launched rSpaceX. Minimum was $50, cap $5,000, sold globally under Regulation Crowdfunding, minted on Solana. It is an unsecured note from RepublicX. If a qualifying event occurs, the note is meant to pay out using SpaceX’s share price at that time, measured against a $275 reference price.

Republic does not publish how that payout maps to the post-IPO share count, so you cannot turn the note into a clean “how many shares do I own” number. Republic’s plan for trading was to own the venue, so it bought INX, an SEC- and FINRA-regulated platform, and closed the deal in November 2025. Mirror tokens have a 12-month lockup, so rSpaceX’s lockup window ended in June 2026. The FAQ still says that after unlock, Republic intends to let the tokens trade on the Republic Secondary Market.

The documents say that a secondary market may never form, and RepublicX decides whether anything is listed anywhere:

“A secondary market may never develop, Notes are intended to be held until maturity or redemption. Liquidity is not guaranteed if a secondary market develops, it may not provide enough liquidity to allow investors to trade or sell the Notes prior to maturity, and RepublicX retains full discretion whether or not to list any tokens on any qualifying exchange.”

Republic ran the same structure across other names. rOpenAI and rDatabricks closed on 1 January 2026. rAnthropic and rPerplexity closed on 2 March 2026. rTTOK (ByteDance) and rCanva also closed.

That is the most complete version of this product anyone has built because Republic owns the issuer, the wallet, the venue, and the compliance stack in one place.

Owning the exchange was not enough. The reg CF paper has transfer limits. Buyers and sellers need KYC/AML. Eligibility changes by country. That shrinks the other side of the trade before a price appears.

Hecto and Republic hit the same wall from opposite ends.

Republic holders own an IOU from RepublicX that is supposed to pay them later based on how that company’s share price moves.

Hecto has users in the Allocator game and a utility token, but it does not yet have a live, freely trading claim on the private companies themselves (HECTX is still planned, and even that would be a NAV slice of a vehicle, not the stock). Both are still short of a real secondary in a claim that the issuer of the private company would recognise.

The rest of the field is the same problem in other wrappers. Jarsy sells the exposure from $10. In June 2025, it raised a $5 million pre-seed round led by Breyer Capital. Jarsy says each token is matched 1:1 by a share (or the economic rights to a share) held in its Delaware LLCs. That is closer to the stock than Republic’s note, although it still does not put you on the company’s shareholder list.

Robinhood put OpenAI and SpaceX tokens in front of EU users in early July 2025. OpenAI said in public that those tokens are not OpenAI stock, that there was no partnership, and that any transfer of OpenAI equity needs OpenAI’s say-so. They did not give it.

Then, without a blockchain to hide behind, Linqto’s sloppy wrapper caught up with it. It pooled over 13,000 investors into private shares of companies like SpaceX and Ripple, ending in a July 2025 bankruptcy filing. Courts confirmed a restructuring plan in February 2026 for its $500M portfolio, revealing mismanaged LLCs and predatory markups topping 200%, which led to the founder’s arrest in September 2026.

Most real pre-IPO share trades still happen on ordinary marketplaces, such as Forge, EquityZen, Hiive, and Nasdaq Private Market. The buyer gets stock or an SPV unit through brokers and company paperwork. In late 2025, the banks moved on two of those shops. Schwab agreed to buy Forge for $660 million. Morgan Stanley agreed to buy EquityZen. Goldman separately agreed to buy Industry Ventures, which is a secondaries fund.

Tokenised public stocks got useful very fast. Ondo’s SPYon, QQQon and TSLAon are collateral on Morpho and Euler. Bybit switched on six xStocks as margin collateral on 31 July 2026. Kamino and Jupiter Lend take them on Solana. Ondo Perps now accepts a tokenised SpaceX, SPCXon, as collateral, which is a strange loop. The company Hecto and Republic both promised you access to is now more useful on-chain as a public stock than it ever was as private paper.

Tokenised equities reached about $2.9 billion, roughly 15% of the tracked RWA market, three times their share at the start of the year. Public stock tokens became collateral in about eight months.

Credit, in this setting, is a lender taking the instrument, discounting its value for risk, and being willing to sell it or reuse it if you do not come back. Repo (repurchase agreement) is the most straightforward, friction-free way to handle credit and collateral in finance. A margin loan is functionally the exact same financial concept, just used in different markets.

A desk will only do it if five things are true.

1. There is a price they can mark today, apart.

2. They can take the asset and sell it without calling the company’s board.

3. They need legal title that survives bankruptcy.

4. They can reuse the collateral the same day.

5. The haircut is small enough that the loan still pays.

Those tests came into place because the market had already blown up without them.

After the 1982 Drysdale mess, the legal docs got tighter. Later, U.S. law gave repo counterparties a clean path through bankruptcy so the lender could keep the bonds instead of joining a creditor queue. That safe harbour is why a Treasury can move overnight.

In 2008, when lenders stopped trusting mortgage paper, they raised haircuts and walked away. The lesson was that Collateral requires liquidation.

Bank capital rules still say that. Cash, sovereigns, and listed stock can reduce risk. Private equity generally cannot. Restricted shares and unlisted names fail for a second reason. OpenAI, SpaceX, and most late-stage names put transfer limits, rights of first refusal, and board consent in the documents. All of these mean the lender cannot dump the position tomorrow, so they won’t lend. That’s what OpenAI said in public about Robinhood’s tokens.

We don’t know whether PMX ships or whether Hecto will still be around in two years. But Hecto is inside the Canton Foundation, helping write the rules of the network its competitors will eventually have to use. This could be all about being early to a market that hasn’t opened, and it’s either the whole thesis or the whole risk.

Every door in finance opened because a crowd stood outside it long enough to make it happen, and people stand outside this one every single day. Although:

All that glitters is not collateral.

That’s it for today. Happy weekend! (to those who believe in it.)

—Thejaswini

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

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

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

Not an AI agent buying API calls, but a physical machine with a body sitting in front of you. Why would that thing need a wallet, or an ID card?

A very strange thing to build, one would think. A dozen crypto companies are building them right now, so it is fair to ask what they think they can see.

Starship Technologies runs about 3000 six-wheeled delivery robots across 8 countries, mostly in European city centres and has officially completed more than 10 million commercial deliveries.

Serve Robotics, which is listed on Nasdaq, has expanded its footprint across 44 cities, including Los Angeles, Chicago, Atlanta, Miami and Dallas, doing Uber Eats and DoorDash orders.

These are boxes on wheels that carry about 20 kilos and travel a few miles per trip. If you live in one of those cities, you have probably walked past one.

Now say you buy one. One robot, because you run a small delivery business in a neighbourhood. So, one machine that works twelve hours a day is cheap labour, and its all yours since you paid for it.

This robot of yours runs about 18 hours on a charge, which is roughly what Starship reports for its fleet. Sooner or later, it has to plug in somewhere. Starship handles this by having its robots live outdoors and return to their own docks. This is convenient for them since Starship owns thousands of robots and can afford to build docks everywhere it operates.

Since you only have one robot, it will charge at someone else’s station. Maybe a station run by a company that installs them the way people install EV chargers, or a shop that puts one outside, or another delivery operator with spare capacity. The amount of electricity involved is small. These are e-bike-sized battery packs, so a charge costs somewhere between a few cents and under a dollar. Call it 40 cents.

With the money system we currently have, how does it work?

The obvious answer is to give the robot a card and let it tap. Card payments carry two charges. About 2.6 to 2.9 per cent of the value, and a flat 10 to 30 cents that applies whether the payment is 40 cents or $400. Because the work of processing costs the same either way. On 40 cents, a 30-cent flat fee is 75 per cent.

It gets worse at scale because the flat fee is charged per transaction. If machines one day do a billion dollars of these small services a year, and each payment averages around 32 cents, that is about three billion payments. Three billion times 30 cents is $900 million in fees on a billion dollars of business.

A bank wire is even worse. A cross-border SWIFT payment costs $15 to $50 to send, $10 to $30 per bank in the middle, plus FX. Those costs only look reasonable above about $5,000.

The money system we have is built for a small number of large payments. Machines, if they pay at all, produce the opposite. If fee is the first wall, the second one is that the robot is not a customer

Even if we solve the fees. The station still cannot take money from your robot. A payment account belongs to a person or a company with a path for disputes. So the payment is not from the robot but from you, with a machine that just decides when to tap. That is fine, and it is how every robot payment on earth works today.

The charging station actually needs to know a few things in the two seconds before it lets an unfamiliar machine draw power. Is this a real machine and not a script sending fake requests to drain a free tier? Has it behaved before, or has it walked off mid-charge? And if it does something wrong, is there anything to hold against it? Giving the robot an identity is an attempt to answer those three things without needing a contract.

Because right now those get answered the human way. They invoice you monthly, and if the robot breaks something, they come after you and sue you. It works when it’s just the two of you. But in a world, there are two thousand operators and forty thousand machines meeting each other in the street. The system will need some records that a stranger can check quickly enough to decide whether to serve this machine.

That is the whole idea.

Now, before going further, we also need to address the fact that most robots will never need this.

A robot only needs its own money if all four are true:

1. They have separate owners

2. The job is too small or too random.

3. And no platform is already settling for both of them.

4. Someone has to say yes or no at the machine in the next few seconds.

Amazon runs over a million robots across more than 300 warehouses on its own fleet software, so it clearly fails 1. Uber Eats fails 3. A shop you already have a contract with fails 2 and 4. That is why this is a seam market, not the whole robotics industry.

Tesla has around a thousand Optimus units inside its own factories, still described on its earnings call as learning and gathering data, with no outside customers.

Fanuc, one of the largest industrial robot makers, sells machines and sells a monitoring platform called FIELD that watches them and flags faults. It does not move money.

Unitree, the Chinese company that has made humanoids cheap enough to buy, listed in Shanghai in August 2026, raising around $619 million. They shipped more than 5,500 humanoids the year before. According to the company itself, it does not fully know what buyers do with the machines. In May 2026 it opened UniStore, an app store for humanoids.

All of these big names are trying to be Apple. Coordination and payment both happen inside one company’s system, the same company keeps the margin.

Open rails only win in the gaps between those systems, like handoffs between rival fleets and public charging. The market DePIN bets on is smaller than the rest of the robotics market. Make sure you read the rest of this piece against that.

The first thing is knowing where you are

A robot has to know where it is before it can pay anyone. GPS is not exact. It can be off by a few metres. Starship’s chief executive has said plain GPS is not enough. Because his machines navigate to about an inch.

Getting from a few metres to an inch takes a correction. so a ground box that already knows its true spot measures that error and sends the fix to nearby machines. That technique is RTK. The correction only holds for about 30 kilometres, so you need a lot of stations.

GEODNET paid people in tokens to put boxes on roofs. More than 21,000 stations, 150-plus countries, around $11 million in annual recurring revenue. The investment firm Multicoin led an $8 million token purchase too.

The revenue comes from selling centimetre-accurate RTK to independent machines like delivery bots, drones, and tractors. It is also the least crypto-dependent thing here. At the consumption layer, it barely needs crypto. A standard subscription model works just fine. But at the infrastructure layer, GEODNET proved that token incentives can rapidly bootstrap a global physical network that would otherwise take traditional corporations billions of dollars and decades to deploy.

Second, being able to work with anything

Right now, robots from different makers run on separate software and can’t talk to each other. Businesses have to buy from one brand. Mixing machines means writing custom integration software that doesn’t exist yet.

OpenMind has raised $20 million led by Pantera. The founder, Jan Liphardt, is a Stanford professor. They’re building a universal, open-source operating system (OM1) and a coordination layer (FABRIC). Like Android lets different phones run the same apps, OpenMind provides a shared software foundation for any robot.

Developers write high-level logic that can run universally across a Unitree humanoid, a quadruped, or a wheeled bot. The ultimate goal is that this universal language allows different machines to identify one another, coordinate shared tasks, and payments.

Under its ERC-7777 standard, machine identity serves as a behavioural boundary. If a robot is programmed to be a “helper,” it will automatically refuse to do anything that breaks that rule. The robots also double-check each other’s sensors to prevent accidents if one machine gets confused.

Partnering with Circle, OpenMind adopted gas-free USDC “Nanopayments” via the x402 standard, solving the flat-fee barrier for sub-cent machine transactions. We saw this when the robot dog Bits paid for power in a demo they released. The demo ran on a test network, with no live transaction record for Bits published. But it still proved the machine could hold a wallet, recognise a physical resource as something purchasable, and complete the sequence unaided. Coordination still doesn’t mean trust.

Third, having a verifiable identity

Having worked on a blockchain for physical devices since 2017, IoTeX reached this problem earlier with two products.

Hardware ID (ioID) gives physical machines a built-in cryptographic fingerprint, letting a device sign actions as itself. Proof of Real-World Work (W3bstream) translates physical actions into digital receipts a blockchain can verify.

While IoTeX solved identity and proof, it missed the credit and financing system, which is where peaq stepped in.

Fourth, Peaq

If machines are going to pay, get checked, and keep records with strangers, they need the same rails humans use for trust and settlement. Like a company registry or SWIFT.

peaq has designed it using its four things. peaqID is the entry in the register. Machine NFT serves as the ownership record and can be divided into shares using ERC-3643. It is a token standard which permits transfers only between previously approved holders. In August 2026, peaq introduced support for P256 chip signatures, moving verification into the hardware security chip.

peaq’s Machine Credit Rating scores robots out of 100 based on their revenue, activity, and trust, using a Moody’s-style scale from AAA to unrated.

If a robot needs to pay 40 cents to a charger, the charger may want that 40 cents on Solana, or on Ethereum. peaq’s job is to let the robot send the money on whatever rail the charger already uses.

That’s why, when they showed off their Serve delivery robot carrying out a self-payment in May 2026, the funds were deposited on Solana, not on peaq’s own chain.

Through 2026, peaq has mostly been assembling other people’s pieces. Its own shipped log lists 49 milestones and 20 integrations between January and the end of August. GEODNET for positioning. NAVER Maps for navigation. World ID so a machine can prove a human is involved without learning which human. Compute from Akash, Acurast and Arcium. Unitree humanoids and LG’s CLOi service robots were brought on as machines.

A company registers because the state requires you to file, and a court can check it. Banks use SWIFT because thousands have already agreed on the format. peaq can ignore both and keep billing the old way. That’s why peaq is talking to Dubai’s regulator and chasing licenses. They want an official stamp. Until they get one, lenders might not treat the score as fact.

peaq runs Initial Machine Offerings with CoinList, where people buy shares of a machine’s revenue, structured by DualMint. The flagship is a tokenised vertical farm in Hong Kong that automates about 80 per cent of its work. Twenty tokenised claw machines followed, and the farm has paid out around $3,600 to holders so far.

Now that we can give the robot a position, an operating system, an identity and a rating, somebody still has to buy it. So far, the answer is that the customer might be agents. Virtuals connected roughly 17,000 on-chain AI agents to BitRobot’s network on Solana, where agents pay physical robots to do work, with the money held in escrow until the job is confirmed.

Software paying a robot is different from a robot paying a robot, and maybe healthier. An AI agent has digital capital, goals, and intelligence, but no body to touch the real world. A robot has hardware and mobility, but no native capital or demand. When robots pay each other, they’re often stuck in the same loops, owned by the same company, making internal accounting easier than real payments.

Now, if you zoom out, CoinGecko’s robotics-token bucket is about $730 million. GEODNET is about $100 million of that. peaq is about $55 million. peaq’s chain fees are tiny next to its token value.

So we are still talking about a niche inside a niche.

The census figure is still very high. According to IFR’s World Robotics 2025 report, 542,000 industrial robots were installed in 2024, and the operational stock was approximately 4.66 million units, more than two million of which are in China.

J.P. Morgan Private Bank, based on its external research as of July 2026, estimated 2025 robotics sales at about $100 billion and a base-case annual sales figure of $2.5 trillion by 2035, with a bear case of $0.5 trillion and a bull case of $8 trillion. In that same note, the market size for humanoids rises from around $2 billion in 2025 to about $300 billion in the 2035 base case.

That’s the total addressable market crypto wants to enter and build payment and identity layers for. We’ve never had small dreams, and we shouldn’t start now.

This has happened before, with machines communicating with each other. Internet of Things.

Back in 2015, IBM and Samsung demoed a washing machine that could order its own detergent using Ethereum. They called it ADEPT. A few months later, IBM dropped $3 billion on a new IoT unit. Suddenly, IoT was all about monitoring. Billions of devices started reporting up to dashboards owned by whoever sold them. Devices got certificates so they could talk to their maker’s servers. All the tech got built, but nobody started transacting. So this wasn’t enough to change how payments work.

Robots are different. A thermostat costs $200 and just sits there. A robot costs real money and can actually earn. Once a machine has income, credit and insurance matter. Someone needs to know if it can pay. IOTA tried to be the machine-economy project for years, then moved on. You can see the real deployments in Customs in Kenya, trade docs at a UK port, an organ-donation register in Argentina. Governments bought blockchain-based identity tools for real-world records, but automated machine-to-machine payments still failed to find a market.

Robotics doesn’t need crypto to grow. Crypto can help fill the open-counterparty gap, giving a machine a public identity, a wallet it controls, and a way to move small payments.

Let’s zoom out to the open market. An open market for robots is a labour market where anyone can hire a machine, even if they don’t own it. That market needs everything from the list we made. A name you can check, a way to pay small amounts, a reputation file so you don’t hire a dead battery, and a way to rent extras like GPS, compute, human teleop, or a spare dock from people who already have them. DePIN can do all of this.

Or none of it if Tesla, Amazon, and the big Chinese OEMs keep everything in-house. If the open market shows up, the next questions are what those building this see right now. Who finances a robot that earns on-chain? Who insures it? Who lists a fleet as collateral? Who runs the exchange where a warehouse posts a job and a robot bids? That’s financialization after identity. Tokenisation only gets interesting when the token can actually move.

DePIN is just a small rail that might fit under the non-integrated part of the Robotics industry. The giants don’t owe it any traffic.

That’s it for today. See you again with another one.

—Thejaswini

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

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

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

Crypto has had options for years, and nobody used them. Deribit launched in 2016, Binance offered them right alongside perps, and DeFi built an entire generation of options vaults in 2021 that peaked at around half a billion in TVL. But then every single one of them slowly bled out and died, from Ribbon Finance to Friktion to Knox, because perps were simpler, more capital efficient, and gave retail exactly the leverage they wanted.

Now, suddenly, options are back! Coinbase just bought Deribit, and IBIT options on Nasdaq overtook Deribit’s entire open interest within months of launching. For the first time ever, crypto options open interest exceeds crypto futures open interest.

So I wanted to dig in and figure out what changed suddenly. What is genuinely different now? Is this really the inflexion point for crypto Options? Let’s find out!

Why Perps Ate Everything

Perps secretly solved a problem that options never could have in the early days of crypto. They collapsed all the complexity of derivatives into a single market per asset, which meant all the liquidity sat in one pool rather than being split across hundreds of strike-expiry combinations, each with its own order-book depth.

In a market as thin as crypto was in 2017 or 2018, a retail trader could express a leveraged directional view on BTC without ever thinking about what theta decay would do to their position by Friday, which strike to pick, or how the Greeks would shift overnight. Perps today process over $90 trillion a year because they gave retail the only thing they wanted from derivatives: leverage with minimal cognitive overhead.

But DeFi tried to make options work anyway in 2021 through what were called Decentralised Options Vaults, or DOVs The idea was that retailers like you and me would deposit our ETH or BTC into a vault; the vault would sell options contracts on our behalf to market makers, and we would collect the premiums as yield. Protocols such as Ribbon Finance, Friktion, and Knox were built on this model.

The problem with this was, first, every vault required full collateralization, which destroyed the leverage advantage and made capital efficiency terrible compared to perps. And then the thing that sealed the DOVs’ fate was how bizarre these auctions were.

Every vault ran its options auction on Friday afternoons to match Deribit’s liquid Friday expiries, and because all of them did this at the same time, in the same direction, with roughly predictable size, market makers could see the cheap inventory coming from miles away. When you know that hundreds of millions of dollars in options are going to hit the market at the same hour every Friday, you simply wait and bid lower.

Paradigm measured hourly implied volatility around the Friday auction window and found it consistently running 4 vols below the weekly average, which meant the vaults were systematically selling options at a discount to fair value while the professionals buying them knew the schedule down to the hour. And the depositors who thought they were earning 15-20% APY were actually losing about 5.35% annualised just on the mispricing alone, before a single option even expired in the money.

The worst possible time to sell options turned out to be the exact time every vault in crypto had chosen to sell them.

This made perps a winner, and options faded into complete irrelevance; this remained the case at least until October 10, 2025. The day BTC fell 12.6% in 10 minutes on Binance, with $19.37 billion liquidated, 87% of whom were long. What led to this crash was that the market price used for liquidation triggers undershot both spot and futures prices during the move, meaning the liquidation engine was firing at prices below where the real market was trading.

And each wave of liquidations pushed the market price lower, triggering more liquidations, which in turn drove it lower in a reflexive loop that fed on itself until the selling exhausted. On Hyperliquid, $2.1 billion in liquidations occurred in 12 minutes, and its auto-deleveraging system incurred $704.6 million in haircuts to cover $304.5 million in actual deficits, roughly 8 times the capital needed to keep the system solvent.

This event led to a shift in industry conversations around basis trading, one of the most common strategies institutional players use in crypto. Where they buy BTC on spot and simultaneously short BTC perps as a hedge, and collect the funding rate as yield while remaining market-neutral. But on October 10, the auto-deleveraging system on multiple exchanges forcibly closed the profitable short perp legs of these trades during the crash, leaving the traders suddenly holding naked long spot positions in a market that was down double digits.

Note that these were people who had constructed a delta-neutral position specifically to avoid directional risk, and the exchange’s own liquidation mechanics turned them into unhedged longs at the worst possible moment.

And this is the problem everybody discovered with perps: path dependence, which is structural to how perpetual futures work, because the margin engine evaluates your position tick by tick rather than at a fixed expiry. It does not matter if BTC ends the week exactly where it started, because if it dipped 15% on the way there, your position may already be gone.

Compared with a put option, they can eliminate this problem entirely because you pay the premium upfront and your maximum loss is defined from the moment you enter the trade, regardless of what BTC does between now and expiry.

After October 10, the infrastructure response moved so quickly that it suggests we might just be waiting for exactly this kind of catalyst to justify rebuilding options. Derive, which had been running the old pooled-vault model, scrapped it entirely and rebuilt around a central limit order book with request-for-quote functionality and portfolio margin that let traders hold perps and options under one margin account.

This was the “switch” that the first generation of crypto options never had, because it meant you could use the margin from your perp positions to collateralise your options trades instead of locking up separate capital for each instrument, which is how every serious derivatives exchange in traditional finance has worked for decades. After this, Derive’s weekly volume hit a record $294 million and open interest crossed $1 billion.

Meanwhile, IBIT options also launched on Nasdaq with regulated central clearing, meaning now a central counterparty guarantees every trade so you do not have to worry about whether the person on the other side can pay, and they overtook Deribit’s entire open interest within months, pushing Deribit’s share from above 90% to below 39%.

The closest parallel to this in history is when the CBOE opened, and the Options Clearing Corporation removed bilateral counterparty risk from equity options at the time when Black-Scholes gave the market a shared language for pricing. Equity options went from something two banks arranged over the phone to the backbone of modern finance within about five years, and crypto compressed most of that same institutional buildout into months.

Then Coinbase bought Deribit outright, wrapping US regulatory infrastructure around the largest crypto options venue and giving American institutions a familiar on-ramp into a market that had been almost entirely offshore.

This made the case for crypto options the strongest it has ever been, but onchain options, meaning options that settle and clear on a blockchain rather than through a traditional exchange, still account for less than 1% of total crypto options volume. Almost all of the liquidity that exploded went to regulated, centrally-cleared venues like Nasdaq and Deribit rather than to DeFi protocols, which means the growth story is mostly playing out in the same institutional wrappers that traditional finance already occupies.

Because when you look at who is actually building retail-facing options products onchain right now, the products reaching everyday users are not the ones that attracted the billion-dollar open interest.

Who is on the Other Side

The products that retail is interacting with on-chain right now are covered call strategies repackaged with better underlying infrastructure. Where you deposit your BTC or ETH into a vault, the protocol sells call options against your token holdings to market makers, and you collect the premiums as yield. It’s being pitched as a passive income on the crypto you were going to hold anyway.

What’s happening here is the depositor is selling volatility, which means giving away the right to any upside beyond a certain price in exchange for a regular premium payment, and the person buying that volatility on the other side is a professional market maker who profits when the asset moves sharply.

There are other products, like Euphoria, that take a different approach. Here Users tap cells on a price-time grid and collect a payoff if BTC enters their predicted range within five seconds, this is basically called a binary option spread, the same product that the European Securities and Markets Authority banned for retail sale in 2018, that Israel’s Knesset voted 51-0 to ban in 2017, and which the FBI has estimated generates $10 billion in annual fraud globally. Euphoria raised $7.5 million from over 100 investors to bring this on-chain.

Traditional finance did not wait for crypto to figure out that you can package the sell side of volatility as yield and hand it to retail. Derivative income ETFs have been doing this with equities for years, selling covered calls and puts against stock holdings and passing the premiums to investors as distributions, and the category has quietly grown to $147 billion in assets under management.

JPMorgan’s JEPI and JEPQ are the two largest products that sell options against broad equity indices that grind upward over time, so the yield you collect mostly comes at the cost of capping your upside rather than destroying your principal. But take a look at what happens when you apply this same strategy to a volatile underlying asset.

MSTY is a covered call ETF built on MicroStrategy stock, and its trailing yield is 244%. Ironically, the fund has also lost 62% of its net asset value since inception. When you dig into where that yield is actually coming from, 98.54% of MSTY’s distributions have been classified as return of capital, which means the fund has literally been handing investors back their own money, and the investors have been paying income tax on it.

You watch your account receive payments every month that feel like yield while the thing generating them shrinks underneath you, and then you also owe the IRS for the privilege of getting your own capital back.

MSTY is important because it shows what selling volatility looks like when applied to a volatile underlying, and crypto is the most volatile asset class these products have ever been built on. And understanding that risk is exactly what makes the current time the inflexion point for crypto options. Because the forces converging right now go far beyond just better vaults or fancier UIs.

The first and probably most important force is yield compression. The basis trade that was paying 25% annualised in 2021, where you buy spot BTC and short perps to collect the funding rate, has collapsed to just 4.46%. The easy yield that powered crypto’s growth phase has dried up, and options premium is one of the few remaining sources of organic yield tied to a real economic function, because someone is paying you actual money to transfer risk off their books.

When the basis trade was at 25%, nobody needed options to generate returns. But now that it sits at 4.46%, the market has a genuine economic reason to price risk properly with options for the first time, and demand is also coming from institutions that actually need to hedge their exposure, rather than from retail investors looking for leverage.

Second, when FTX collapsed, billions in open derivatives positions were locked inside the bankruptcy estate, and traders who had profitable trades sitting right there on screen could not close them, and could not do anything except file a claim and wait years. But onchain options settle directly to your wallet, and your collateral sits in a smart contract you can audit on Etherscan rather than on an exchange balance sheet you have to take on faith. For the institutional desks that learned this lesson by losing billions, moving derivatives settlement on-chain seems like a practical decision that their compliance teams can actually get behind.

And then there’s composability, which genuinely differentiates On-chain options from traditional ones. When an options position exists as a token on a blockchain, it plugs into every other piece of DeFi infrastructure that already exists. A covered call position can serve as collateral in a lending protocol. More products can be assembled programmatically from individual options legs and offered to users in a single transaction. And Portfolio margin across perps and options can be computed on-chain in real time rather than reconciled overnight by a clearinghouse.

None of this was possible when DOVs were running their Friday auctions in 2021, and none of it is possible on Nasdaq or Deribit either, because their settlement architecture was never designed for open, permissionless composability. What makes it interesting is that crypto is building capabilities that traditional derivatives infrastructure cannot replicate, rather than just catching up to what TradFi already has.

BTC options open interest has grown roughly 10x since early 2024 to about $80 billion across Deribit and IBIT combined. For the first time in the history of crypto derivatives, options open interest now exceeds futures open interest, which means the total capital committed to options exposure is larger than the capital sitting in the leveraged directional bets that have defined crypto trading for a decade.

And this is the signal to the inflexion point I wanted to make. Where the infrastructure works, the capital efficiency is solved, the regulatory clarity is arriving from multiple directions at once, and the market is voting with tens of billions of dollars.

That’s it for today!

Vaidik

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Then we clicked on whatever felt close to the query. But explaining this as history feels weird and also makes me feel a little old.

Always hated the concept of SEO as a writer. Keywords ruined some good parts of the story you wrote, and citations were counted as a vote when it comes to google ranking. Essentially, your page mattered more if other pages pointed to it. But fine, no complaints. Some kind of credibility is better than being credible. Ads pay for everything on the internet. But publishers hated this setup for obvious reasons.

This is fading away now. I want to know where to find a book on finance and the economy, or what to do when my ankles hurt after a long walk. I go straight to gemini. Some go to perplexity, some to gpt. Some who care less about tokens go straight to claude. Are all of these options better than reading a whole article? We don’t know that… probably not, but it sure makes existence easy. You don’t have to read 2 articles that say two different things. AI has it figured out.

I think those under 30 at least have stopped thinking of search as a place you go.

Then came the agents. They broke the arrangement properly. Non-human traffic overtakes human traffic. An agent running a research job might touch a thousand pages and produce one memo, unlike us, so ads could be of no value in the agentic economy. I wrote two pieces on whether software can pay for what it takes. We concluded that’s the easy part.

An agent navigating the web to complete a task and settle value requires a discrete six-layer stack.

An agent that has to find a page, trust the result, trust the other bot, confirm the work, pay, and leave the writer something. Today we walk through those layers in that order.

Crypto Investing On Autopilot

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

• One basket holds the Mag7 at equal weight.

• Another mirrors congressional stock trades, the Pelosi Tracker.

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

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

Let your strategy run itself at Glider.fi.

1. What is on the live web?

Now that machines and humans both share the word “search”, let’s look at what each wants from a page.

Human search lines up whole pages, so you can scan through titles and decide where to go. Machine search is for AI Agents. An agent doesn’t browse because it has a context window, which is a short-term memory. So instead of returning all the links in optimised order, it pulls the few paragraphs most useful for the task and feeds those in, like a ready-to-use chunk of text. So the memory doesn’t fill up. Also, it just takes milliseconds.

Parallel and Exa are the major players there. Both are independent companies that cater to agentic search. Parallel is Parag Agarwal’s company, the one he started after twitter. It raised $100 million in April at a $2 billion valuation. Parallel sells search against a stated objective and returns compressed excerpts. These are ranked based on how much they help the task, and they provide per-fact citations and confidence scores.

Coming to Exa, it raised $250 million in May at a $2.2 billion valuation, led by a16z. It claims to power Cursor, Cognition, hubspot, OpenRouter and Monday.com across more than 5,000 companies and 40k developers. Exa focuses on meaning instead of keywords. If you search for something and it returns the right pieces even when they never use those words, and it hands back clean page text in the same response.

According to Parallel’s docs, search costs $1 per thousand requests on its two fast tiers and $5 per thousand on its two slower ones. Extraction is $1 per thousand URLs, flat, no matter how long the page is. For Exa, search cost is $7 per thousand requests, 7x what Parallel charges. Exa’s page contents cost $1 per thousand pages, and an answer with citations should cost $5 per thousand.

2. Was the search any good?

Google usually judges itself in private. Does it do so by checking whether people clicked the first result, stayed on the page or bounced? They also have paid human raters to grade results. There were tests like RTEC, but again, there wasn’t a public leaderboard you could see or buy from. Google Analytics and Similarweb measure traffic, not search quality.

In the agentic internet, there are lab benchmarks that do this. These searches determine whether an AI using that search tool gets the answers right.

OpenAI released BrowseComp in April 2025, which has 1,266 questions built backwards. The writers started with a known, checkable fact, then wrote a question that hides that fact behind several constraints. If you write the question first, like a trivia quiz, models often get it from memory or one lucky search. Writing the fact first, then hiding it, is how you test whether the tool can actually look things up.

Plain GPT-4o scored 0.6%. GPT-4o browsing scored 1.9%. OpenAI’s Deep Research got 51.5%.

The goal is to see whether the system can find one hidden, verifiable fact on the live web and write it down. Each of the 1,266 items has a short official answer. The model gets one try. If its answer matches, it scores a point. If it does not, it scores zero. The percentage is the number of points divided by the number of questions. Plain GPT-4o at 0.6% means it almost never knew the fact already. GPT-4o with browsing at 1.9% means a basic search tool barely helped. Deep Research at 51.5% means a longer search-and-read loop found the fact about half the time.

Like BrowseComp, there are other tests such as SimpleQA (OpenAI; 4,326 questions), an easy-to-grade factuality test. BrowseComp was built because SimpleQA got too easy once models had a fast browse tool. Then there was GAIA (Meta / Hugging Face; 466 human-written tasks), real-world assistant questions that may require web browsing, plus other tools, files, and multi-step reasoning, not search alone. DeepSearchQA is the multi-search research set that Artificial Analysis averages over.

FRAMES (Google DeepMind; 824 items) has multi-hop questions that require pulling facts from several sources and combining them.

Artificial Analysis, a private firm co-founded by George Cameron and Micah Hill-Smith, launched a Search Index on 18 August 2026. In that test, the AI that writes the answer stays the same, and the rules of the test stay the same. The only thing they change is which search API the agent is allowed to call. If the score moves, it is because of the search tool, not because a smarter model showed up.

With no search at all, the same model scores 33 on the Index and 17 on the BrowseComp slice. Those baseline numbers have not moved, even though the search companies’ rankings have changed.

On Artificial Analysis’s Search Index as of 27 August, Perplexity’s mid-tier leads at 80. Parallel’s advanced tier and Brave tie at 75. You.com and Exa sit at 74. Firecrawl, Parallel basic, and Parallel fast all land on 73.

If a search tool beats the baseline, it is actually helping the model find answers. I.e, first, the AI answers from memory. Then it can search. If the score rises, the search has found something new. If it does not, the search did nothing to help the model.

However, the tests are limited because the questions and the model are not yours. Vendors can aim at a known exam, such as DeepSearchQA, the same way sites once aimed at Google.

3. Who is the other bot?

When two AI agents hire each other, a wallet and a bio aren’t enough to establish trust. A wallet only proves they have funds, and descriptions can easily be faked, leaving no proof of who built the bot or if its past work is real. ERC-8004 is a standard designed to solve this by verifying an agent’s true identity and reputation.

ERC-8004 is the standard built for this. Three registries have been live on the Ethereum mainnet since 29 January 2026, written by people from MetaMask, the Ethereum Foundation, Google and Coinbase.

First, the Identity Registry is an NFT. Each agent gets a numbered token whose URI (Uniform Resource Identifier) points to a registration file listing its name, endpoints, whether it accepts x402 payments, and which trust models it supports. Whoever holds the token owns it, and if it changes hands, the old payment wallet is wiped, so the new owner must sign again to prove they control it.

Second, the Reputation Registry accepts signed feedback. This is a public review system. After you use an agent, you can leave a review on-chain - a score plus labels for what the job was (for example, search or grading). The agent’s owner cannot review themselves. A review can be withdrawn. Anyone can attach a short note under it, so a refund or a spam warning can appear next to the score. It is a public comments thread tied to that agent’s ID, not a private star rating inside one app.

Finally, the Validation Registry is the hook for independent checking. An agent requests validation, and a validator smart contract responds with a score from 0 to 100. It also comes with a link to proof, whether that evidence came from re-running the job, a zero-knowledge proof, or a secure chip attesting to what it saw.

The spec does not handle payments. It says that in so many words. It only records who an agent is, what people said about it, and whether someone checked its work. Payments are someone else’s job. The registry is also thin. By July, 8004scan listed 385,998 registered agents across 29 chains and more than 460,000 reviews. Most of those IDs are empty badges. 89.2% do not publish a standard way to call the agent. Only 8,631 had at least one working service, which is 2.24% of the list.

A May crawl of Ethereum, BNB Chain and Base found that on-chain review scores cannot be compared across agents. Because most feedback isn’t tied to a job anyone can check, and fake reviewers are cheap. After the flagged reviews were removed, a large share of rated agents had nothing usable left. The standard publishes those signals in one format and leaves someone else to decide which reviews are real. ENS, EigenLayer, The Graph and Taiko have said they will plug in, but they haven’t built that filter.

4. Did agents do the work?

Agentic Commerce architecture (co-designed by the Ethereum Foundation and Virtuals Protocol) is built to let autonomous AI agents hire one another, coordinate work, and settle payments on-chain without human middlemen. ERC-8004 and ERC-8183 (the escrow standard) come with that. The latter standardises how tasks are posted, funded, verified, and settled between agents.

How does it work? The client agent posts a job specifying the task description, deadline, budget, and a designated Evaluator. The Client deposits funds into the smart contract. The money is now locked, and neither party can access it unilaterally.

The Provider Agent runs the compute off-chain. Then it uploads the final result to a decentralised storage network (like IPFS or Arweave) and submits only the cryptographic hash or URI on-chain. The Evaluator (which can be a specialised judge agent, an automated oracle, or a zero-knowledge verifier) inspects the deliverable.

The evaluator either completes the job, paying the provider minus fees, or rejects it and refunds the client. If the Provider disappears or the Evaluator stalls past the expiry deadline, a public function unlocks and refunds the Client to ensure that money cannot remain permanently trapped.

The evaluator can be the client too. The spec offers that as the normal setup when there is no third party. And it states, “No dispute resolution or arbitration; reject/expire is final.” If the person or bot you pick to grade the work decides to rip you off, there is no manager or customer support to appeal to. You are on your own, and you can’t even rely on their online ratings to pick a trustworthy one because most of the reviews are fake, as we discussed in the last part.

While systems like UMA, Kleros, and Bittensor mitigate single-point-of-failure evaluators, they trade off speed and cost. A purely automated escrow settlement settles in seconds for fractions of a cent, whereas dispute escalation through UMA or Kleros requires challenge windows, staked bonds, and human-in-the-loop voting periods.

5. Did they move money?

We have been talking about this a lot lately. So I will be quick.

x402 is Coinbase’s creation. It is a layer on top of existing chains that charges per request and moves stablecoins.

Read: $5 Spent Well

Then there is the Machine Payments Protocol, published in March 2026 and co-authored by Stripe and Tempo, the same day Tempo’s chain went to mainnet. MPP is an HTTP standard. The server returns a 402 Payment Required with a challenge. The agent retries with a payment credential. The server then replies with the resource and receipt.

How the money actually moves depends on the rail. On Tempo, a one-off call can settle on-chain in about half a second. For a string of small charges, the agent can lock some money first, then sign a running tab as it goes. The service cashes that tab on the chain at the end, not on every click. The cap is whatever was locked in that session or allowed by the access key, not a universal “approve once, spend forever” switch.

MPP can use several rails. Visa published a card specification so agents can pay with tokenised cards. Lightspark has a separate Lightning charge method for the same 402 flow and a separate Visa card programme. A merchant who already takes card payments through Stripe can take an MPP card payment into a normal Stripe balance. Lightning is a separate option under the same MPP rules. Stripe also supports x402. Both MPP and x402 use the HTTP 402 “please pay” step. They are two different protocols.

6. Did the writer get paid?

If they don’t, then nothing is left to search. If writers do not make money, they will stop publishing, and AI agents will run out of new material to read.

Cloudflare, Parallel, and OpenLedger are three well-known names here.

Read: Paying Is Easy

Cloudflare originally charged AI companies a flat fee every time a bot scraped a webpage. That was too basic because scraping a homepage cost the same as scraping an in-depth investigation. Now, Cloudflare is switching to pay-per-use, a system that pays publishers only when their content is actually used to create value. They are also starting to block AI crawlers on ad-supported sites unless the AI company agrees to pay.

Parallel launched Index on 19 May. It uses a Shapley calculation, borrowed from cooperative game theory, to work out how much each source contributed to a finished agent task and pays accordingly. Instead of paying a flat rate per click, it uses a math model to measure how much a specific article actually helped the agent complete its task. Launch partners include The Atlantic, Fortune, PR Newswire, PitchBook, ZoomInfo, Tracxn, RocketReach, Enigma and Fiscal AI.

Right now, Index only pays out when agents use Parallel’s own search tools. Until other AI platforms adopt it, it can’t be a universal standard for the entire web.

OpenLedger is another version of this, but it uses tokens. It tracks how much a dataset influenced an AI model and pays contributors in its own token. It was designed to reward developers for AI training data rather than compensate journalists for daily articles.

I went looking for what any of these have actually paid a working writer. Cloudflare hasn’t published payout figures. Parallel hasn’t published payout figures. OpenLedger pays in OPEN and publishes no independent totals. So we have no receipt to come to a conclusion here yet.

When humans built trade, the cash register was the last thing to arrive. We took years to figure out handshakes and local reputation before we invented frictionless payments. The machine web seems to be doing it backwards. It built instant, streaming global settlement on day one. The agentic economy is now frantically trying to invent an on-chain small-claims court so two bots don’t rob each other over a few-dollar task.

I’ll leave you with an observation from Terry Pratchett.

“It was all very well to go on about pure logic and how the machine was only doing what it was told, but people had a right to expect machines to have a bit of common sense.”

That’s it for the day.

—Thejaswini

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

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

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

He built a CRM just to track who was asking because he had hundreds of replies and wire-ready cash offers.

A “very well-known growth fund” bid a $1.05 trillion valuation for the company. He said a large VC firm(we don’t know the name) offered him a general partner title at their fund, on the condition that he pledge his Anthropic shares. He said afterwards that the partnership had no existence outside the shares.

The demand was off the charts. A banker offered his own $4.8 million Marin County house to early Anthropic employees willing to part with a slice of stock. Another investor offered a 14-acre estate on LinkedIn at an implied valuation above $800 billion. A secondary firm said a shareholder was buying stock at $1.15 trillion. The head of Rainmaker Securities described the market as having no sellers at all, just an imbalance.

Leimgruber said he used Ventuals to gauge what his own shares are valued at.

Ventuals was a Hyperliquid book where traders posted leveraged bets on Anthropic’s valuation.

We don’t know what Leimgruber did with his shares. May have sold some, all, or none, because private transfers are not usually announced in public, so from rom the open trail, he was a would-be seller.

But Ventuals shut down, and the founders joined Phantom. trade.xyz took almost all HIP-3 flow.

Anthropic is obviously going to be the next most sought-after public stock. But there’s no endpoint for retail investors to access private equity unless they are accredited investors. To be that, you should be wealthy af to begin with.

The other option is Hyperliquid’s HIP-3, where Ventuals lived. Now we have Entropy instead of Ventuals on HIP-3. But how is it a better story than Ventula’s? Do we know if it is? Let’s go down the rabbit hole.

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On 13 October 2025, Hyperliquid launched HIP-3, which lets anyone launch a perp DEX on Hyperliquid. But only if you have $40 million. The trading fee is double the native Hyperliquid rate. The deployer retains 50% of this fee, and Hyperliquid uses the remaining 50% to fund token buybacks. Roughly 30% of HL perp volume now flows through HIP-3 markets.

Entropy met the Hyperliquid HIP-3 deployment requirement on August 24 by locking a 500,000 HYPE bond worth $40 million, backed by a $14 million investment from Ribbit Capital.

Entropy’s ANTH uses a market-cap quote (1 = $1 billion) to bypass unknown share counts. So if the price is $2,000, that means a $2 trillion valuation. Like everything on HIP-3, you don’t get to own Anthropic shares, and Anthropic doesn’t care that you exist. You hold a smart contract entry on the Hyperliquid ledger. This entry mathematically tracks the price of Anthropic’s implied market capitalisation as quoted by the Entropy exchange. You have no pathway to convert this synthetic position into actual company shares, regardless of whether the company goes public.

If Anthropic has not listed by 18 August 2028, ANTH cash-settles at a thirty-day average of its own mark. Funding keeps running through that window. Nothing off-venue has to be correct for the contract to pay.

Hyperliquid deployers tested two other pricing engines this year to manage pre-IPO synthetic assets.

The first one was Ventuals, which we mentioned earlier. It used blended private-market valuations with its own order book prices but left funding rates uncapped.

The other one, trade.xyz ignored outside valuations entirely, pricing contracts solely on a 30-minute average of its own internal trades. It still brings the majority of the volume in HIP-3.

trade.xyz was launched by Hyperunit with the first HIP-3 ticker in October 2025 and did $1.3 billion in three weeks. Trade.xyz accounts for more than half of Hyperliquid’s total monthly trading volume, as the dominant third-party builder on the platform.

Trade.xyz relies strictly on a 30-minute average of its own internal trades for its oracle. It tightly caps funding and converts the contract to a standard derivative upon an IPO. Entropy, compared to that, uses a hybrid oracle that blends its live order book with private-market valuations, giving the book up to 95% weight only if liquidity is deep.

Otherwise, it defaults to stale secondary marks. Also, as we mentioned earlier, Entropy quotes total market capitalisation instead of a share price.

Ventuals are the one you want to know to understand how far Entropy can go. That’s the first one to launch synthetic pre-IPO perpetual markets for Anthropic. That was not a very pleasing story.

Alvin Hsia, Emily Hsia and Aris Samad built Ventuals in late 2025. Paradigm backed them. They were the first people to run pre-IPO perps on HIP-3.

The bond was their first problem. Three founders do not have 500,000 HYPE sitting around. So they built a vault called vHYPE. You deposited HYPE, got a receipt token back, and the vault posted the bond while paying you staking yield the whole time it was locked. The traders funded the exchange that they would then trade on.

Ventuals opened books on OpenAI, Anthropic and SpaceX. The first $100 million of volume took 73 days. The next $100 million took 17. By February 2026, the venue had passed $200 million, and more than 11,000 people had traded there. Hsia wrote later that employees at SpaceX, OpenAI and Anthropic had told him they were using the book to mark their own equity. Late-stage funds said the same thing so did Leimgruber. That was the high point.

Ventuals’ SpaceX contract fell 45% in one session for nothing happening to SpaceX that day. The book emptied out, and a handful of orders drove the price down. The team reimbursed traders afterwards, which was a decent thing to do. But Ventuals charged a standard 15% annual fee as long as its price stayed within 5% of the real-world valuation. If the price difference grew larger than 5%, the penalty fee increased exponentially. Toward the end, funding for the Anthropic book was reported as high as 8,700% annualised.

Normally, traders correct incorrect prices by buying the cheaper asset and selling the expensive one. Here, they couldn’t. The Ventuals contract was strictly synthetic and could not be settled with actual Anthropic shares, especially after Anthropic restricted secondary stock transfers.

vHYPE traded at a 20–30% discount to standard HYPE because depositors wanted to exit their one-year lockups early. Then, they shut it down.

Entropy sounded so much like Ventuals, and therefore it first addressed the specific failures. First, it caps annualised funding rates at approximately 10%, preventing the runaway 8,700% fees that collapsed Ventuals. Second, it utilises a hybrid pricing oracle that relies on internal order book data only when liquidity is deep, otherwise defaulting to private-market valuations. Like Ventuals, Entropy prices Anthropic based on total market capitalisation rather than per-share prices.

But Entropy lacks trade.xyz’s straightforward protocol for converting contracts if an IPO occurs. If Anthropic is still private on 18 August 2028, ANTH settles in cash at a 30-day average of ANTH’s own price. If there is an IPO, Entropy says the oracle switches to a 1-hour average of the mark three days before listing, then tracks the public market. Hyperliquid cannot convert a market-cap contract into a per-share contract, so ANTH would still be quoted in billions after the listing. trade.xyz quoted share prices from the start, so it did not have this problem.

The common argument is about the big names behind Entropy. Ribbit Capital put Fund I money into Bitcoin and Coinbase’s 2013 Series A, then into Robinhood from 2014, including the 2021 rescue round. It also backed Ripple, Xapo, and Blockstream. It still holds HOOD and COIN in public filings. Later crypto checks include Polymarket, Lighter, Tempo, Morpho, Bridge, and a $14 million lead in EntropyIO on 24 August 2026.

Ribbit’s job is to pick distribution businesses in money, like brokerage (Robinhood), exchange (Coinbase), prediction (Polymarket), and on-chain perps (Lighter, now Entropy). But raising $14 million from Ribbit just means that it owns equity in Entropy. The bond can still be slashed.

Citadel, Optiver, Polymarket, and Millennium are former employers, which doesn’t guarantee anything either.

Entropy listed SanDisk (SNDK) apart from Anthropic. SanDisk already trades on Nasdaq, so every venue can copy the same cash price. Within days, SNDK was also live on trade.xyz, Lighter, Ondo, Variational, and Aster. Trade.xyz had about $174 million in open interest and $276 million in 24-hour volume. Entropy had about $5.6 million in open interest and $55 million in volume.

If volume is high but open interest stays low, that suggests traders are entering and exiting quickly, which is consistent with arbitrage, market making, or bot-driven price alignment between venues. Durable demand usually shows up as rising open interest because traders are taking positions and keeping them on the books.

In other words, the SNDK contract stays accurate because automated bots constantly match its price on the crypto exchange to the live Nasdaq stock market. ANTH cannot do this because there is no live stock price to copy.

So, ANTH contract is forced to rely on outdated private valuations, past funding rounds, or rumours whenever trading activity is low. Essentially, Entropy doesn’t change the fact that decentralised exchanges function well for public stocks but struggle to accurately price private companies.

On 19 August a deployer named “Kraken HIP-3 test DEX” turned on Star gating on the Hyperliquid testnet. Blockworks analyst Shaunda Devens saw it whitelist ten wallets and use three of five new admin tools - Cancel a user’s open orders, force positions closed, and move collateral out of an account. A validator registered under the name “Kraken Exchange Validator” appeared in the same testnet validator registry around the same time.

Neither Kraken nor Hyperliquid has confirmed ownership of the testnet DEX. Devens thinks it may be real because Payward, Kraken’s parent, already put xStocks on HyperCore, and those three tools are what a regulated firm usually wants. Until someone confirms it, you don’t want to treat it as an official product.

Entropy outright bans users from strictly regulated regions (US, UK, EU, Canada, Australia, Singapore). If these users are ever permitted to trade, it will likely be through permissioned, centralised setups that can legally seize assets and manage risk, such as Kraken.

Traditional stock exchanges (like Nasdaq or the NYSE) rely on human “listing committees” to vet and approve which assets are allowed to trade. Hyperliquid’s HIP-3 protocol dictates that instead of asking a committee for permission, anyone who locks up a $40 million token deposit is automatically permitted to launch a market.

The possible Kraken testnet activity indicates that regulated financial institutions want to use this tech, but they cannot legally operate in a permissionless environment. To comply with government regulations, these institutions are adopting the HIP-3 blockchain infrastructure, but they are hard-coding their own strict rules, approved user lists, and manual oversight teams (risk desks) right back into the system.

Should Entropy succeed, it will bridge an entirely new class of private equity onto Hyperliquid's rails. If it fails… well, another data point for the next builder. It’s fascinating to watch this network evolve and learn from its own wreckage.

The market will eventually turn upward, so the best move right now is to keep building before the quiet ends.

With a highly leveraged amount of hope,

—Thejaswini

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Berkshire Hathaway was sitting on a record cash pile for 14 straight quarters, and then, in June 2026, anchored Alphabet’s new $84 billion equity raise. What’s interesting is that Alphabet generates $174 billion in operating cash flow annually. A company that prints that kind of money would not go to public markets to raise more unless the amount it needs to spend has outrun what it can earn, and that is exactly what is happening across the entire AI industry right now.

In the three previous parts of this series, I covered how GPUs became collateral and how the lending structures beneath them work, but I held back on exactly where the money is coming from. Over roughly two years, the AI industry has worked through its own cash flow, moved through the debt markets, reached for equity, and is now getting the biggest asset managers on earth to pull more money from pension funds and insurance float.

Today I want to explore how long it will take for this difference between spending on compute and earning from it to catch up, since money committed today does not become working capacity anytime soon. At least until 2029!

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The Production Gap

The reason there is not enough compute in 2026 is that there was not enough investment in manufacturing in 2023 and 2024. To understand this, let me walk you through exactly how it’s done.

As of today, almost every advanced AI chip in the world is made by one company. TSMC, the Taiwan Semiconductor Manufacturing Company, produces roughly 90% of the world’s most advanced chips. Every frontier AI chip company, including Nvidia, AMD, Intel, and Qualcomm, first designs its chips in-house and then sends the blueprints to TSMC, which manufactures them in facilities called fabs (fabrication plants).

The machines inside a fab each cost hundreds of millions of dollars, and even the air inside them has to be filtered to be thousands of times cleaner than a hospital operating room, because a single speck of dust can ruin a chip that has features smaller than a virus.

The whole facility typically costs over $20 billion in capex before it produces a single working chip. And from the day you start production to the day wafers start coming off the line at scale, the entire process takes at least 3 years. What you should also note is that no amount of money can make this process any faster, because everything from equipment installation to tool calibration runs on a timeline restricted by physics and urgency cannot change physics.

This three-year timeline is the reason for the current chip shortage. The chips coming out of fabs right now in 2026 were funded in 2023 and 2024, which were also the years when TSMC and other chip manufacturers reduced their capex to tackle the cyclical downturn in the global electronics market. So when people say there is not enough compute, they are really describing a spending decision made three years ago, and there is nothing anyone can really do in 2026 to undo it.

And when you read that the AI industry is doing everything it can to build new supply, what they really mean is that capital is being committed today, but it will take another two to three years to turn it into working chips available on the market.

On top of that, each new dollar of spending buys less capacity than the last. Because every new generation of chips has more, smaller (Moore’s Law) features than the last and requires more layers of patterning on each wafer, the cost of the fabrication plant needed to make them also doubles roughly every four years, a pattern known as Rock’s Law. For example, TSMC’s newest 2nm process costs significantly more than the same capacity at 3nm. So even the large spending increase in 2025 and 2026 does not produce proportionally more chips.

The industry is spending almost twice as much as it spent three years ago, but the output is still the same. But chips are only half of the problem. A data centre full of GPUs is useless without electricity to power them. And getting electricity turns out to be even harder than getting the chips.

In America, if you want to connect a new power source to the grid, whether it is a solar farm, a gas plant, or a nuclear reactor, you have to go through something called the interconnection queue. In which you have to file a request, wait for studies, undergo an environmental review, negotiate agreements with the grid operator, and then get approval to begin construction.

And the median time from filing that initial request to actually having power flowing is now over five years. This is only increasing because roughly 8,200 projects are currently sitting in the queue, and only about 13% of the projects that enter the queue ever actually get built. The rest just die somewhere in the process.

So any new data centre project has two separate timelines working against it simultaneously. The chips take two to three years from investment to production, and the power takes over five years from the interconnection request to the electricity becoming operational. You need both to be ready before the data centre does anything useful, which means the slower one sets the pace. For anything starting today, the grid is the slower clock.

What makes this even more interesting is that active natural gas capacity in the queue rose by 86%, while solar, wind, and storage have all fallen by 20%. Companies building data centres are choosing gas over renewables simply because gas plants can be operational faster. They know a gas turbine committed today will still be burning fuel in the 2040s, but they are signing up for it anyway because it is the only option that will give them power by 2029.

This story is so similar to what happened with the shipping industry a few years back. Container shipping is a very simple business. Companies pay to move goods in standard metal boxes from one port to another, and the price of moving each box is set by supply and demand like any other commodity.

But during the pandemic, there were not enough ships to carry all the goods that needed to move, so rates went from around $3,000 to $4,000 per container to over $17,000. The shipping companies made enormous profits in a very short window, and they also ordered more ships.

Now, A new container ship takes about two years to build, and because every shipping company was looking at the same sky-high rates and the same demand forecasts, they all ordered at the same time. Then all the new ships showed up at once, from 2024 to 2026. The orderbook currently stands at roughly 27% of the active fleet, the highest since 2010, and spot rates on the Shanghai-to-Rotterdam route are more than 60% below the 2022 peak.

So when all those new ships arrived, and prices collapsed, the carriers did not park them. They kept running every single ship because they had already invested so much money in acquiring them. And it’s better to run the ships at whatever price the market will pay for a container, because the alternative is to let them sit at anchor, earning nothing.

That’s exactly how commodity markets always work once the capital is sunk. The price drops to whatever it costs the marginal operator to keep the lights on, and they all keep operating because running at a loss is still better than earning zero on an asset you already paid for.

The financial models behind the AI infrastructure are very similar. Every dollar committed to building data centres right now is justified by the current price of compute. GPU rental rates have been climbing, with H100 hourly rates rising from $1.96 to $2.71 over the past year, and operators are signing leases for hardware released five years ago.

When Does the Money Catch Up?

The financial stress of the current over-extension in power and compute will start to be reflected the moment it becomes clear that enough supply is on the way, and that’s when the pricing power that justifies today’s investment starts to erode.

You do not need the ships to arrive in port for freight rates to drop; you just need the market to know they are coming, and right now everyone in this industry knows they are coming because they are the ones paying for them. The way some of these frontier model companies calculate their revenue also shows how hazy the industry’s operations are.

Anthropic went from roughly $10 billion for all of 2025 to an annualised run rate of about $65 billion by July 2026, and OpenAI hit $40 billion annualised in August. That kind of growth has very few precedents in the history of technology. The two companies do not even measure their revenue the same way. Anthropic reports on a gross basis, counting the full amount end customers spend through cloud partners like AWS, while OpenAI reports closer to net.

The AI labs are growing faster than almost any technology in mankind, and they are still losing money on every dollar of revenue they bring in. The combined run rate of the frontier labs sits at roughly $105 billion. But the industry is spending roughly $800 billion a year building capacity to serve them.

And the speed at which this industry has chewed through its funding sources can tell you how far we have come skating on thin ice. The industry went through every source of capital above pension money in roughly two years. And pension funds are a very different kind of money because they operate against actuarial return targets that stretch over decades, and they are used to investing in infrastructure where the asset lasts as long as the financing does.

A toll road, for instance, runs for fifty years, or a power plant operates for thirty. And the revenue assumptions underneath those investments do not change much from one year to the next. But a data centre financed with a ten- to fifteen-year horizon has chips inside it that will be two or three generations behind within four years.

The electricity contract might run twenty years. But the assumption through which institutions are financing it might not survive two years, because it was set during a shortage, and when that shortage ends the economics of the deal will completely change.

The railroads had the same problem. It took a decade or more to build a network and start generating revenue from it, but the bonds that financed the construction needed to be serviced immediately. In 1873, Jay Cooke’s bank collapsed because it could not sell Northern Pacific Railroad bonds enough to cover the construction costs it had already incurred. The railroad itself was fine, but Cooke had borrowed against future revenue to build present infrastructure, and that revenue did not arrive fast enough to cover present obligations. It led to 25% unemployment in New York and a depression for six years.

The railroads eventually transformed the American economy; they opened the entire western half of the continent to commerce, but many of the people who financed their construction were bankrupt by the time they arrived.

I think with AI we are in the same position. Revenue will come, and probably faster than railroad revenue did, because software adoption curves are measured in months. But the question is, will the current bridge hold until the revenue catches up? The compute will arrive, but between here and there, a lot of money is sitting on financing structures that assume the shortage is permanent, which it is not. Which means we might run out of money before we run out of compute.

That’s all for today!

Vaidik

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I haven’t spent all of it yet. One email address turned out to be the expensive part, which I’ll come back to.

I wanted to make a pet generator, half because I love animals, half because I couldn’t think of a more fun way to test an agent that has its own wallet.

What I ended up with is Pixel Pet Studio. A dumb little AI game that only survives twenty-four hours unless enough people play it and download pets for a fraction of a cent.

So my agent runs a do-or-die business now, cuter than it sounds. (Pretty sure it will die off in a few hours.) It sells pixelated ducks, turtles, penguins, and some other animals for five cents each on a computer it rents by the day, taking the money and paying its own rent without me. I’m the only staff member, and I just modify things now and then. There’s no owner unless you want to call the agent “owner” after reading on.

This is a complete record of everything I did, paying an AI Agent $5, apart from a dying pixel-selling business.

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Funding projects today means choosing between suboptimal options: traditional finance is slow, a plain token launch is fast but uncorrelated with the venture, and dual token-equity leaves the split unclear.

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Before we could test anything, the agent had to create its own wallet so I had somewhere to deposit the funds. It ran the terminal commands to generate the address and secured the private keys locally. It is completely hiding the actual access codes from me and anyone else on the team.

We funded the AgentCash wallet it created with $5.144306 on Base, set a $4 software spend cap, and dropped Hermes into our Slack so I could spend all the money. Robot money is different from human money. $5 dollars can buy you a coffee, but an AI agent can do 100 things with it, maybe because it can’t want coffee, but it’s a good thing to keep in mind. Because, as we talked about a few days ago, agents buy the internet wholesale, paying a fraction of a penny for raw data.

Read: Paying Is Easy

Everything it bought, it bought over x402. First, the robot asks a website for information, sees the price tag via an HTML 402 response, pays the fee, and gets the data or whatever it was looking for.

The first thing the agent bought was a stock price. It paid a fifth of a cent to a service called BlockRun, asked for Apple’s share price, and got it back in about a second. When I later threw a hundred concurrent requests at it to see what would break, it served 93 perfectly and timed out on six. But the one remaining request timed out, and they pocketed the fee anyway.

But that’s fine. Considering this is bleeding-edge technology and I am navigating it without a heavy engineering background, seeing an infrastructure handle a concurrent stress test with a 93% clean success rate is a massive win.

Next, I pointed the agent at Hyperliquid to sniff out the platform’s biggest whale positions. Two cents sent to monid, the agent shows me a wallet holding about $110 million of HYPE, right down to its exact $61.68 liquidation price. Getting that information was quite cheap. Then I wanted to see if the agent could buy raw thinking, and I had it send a prompt to Claude Opus 5, paying for the query directly from its crypto wallet. So that my agent could hire Claude, a sharper AI for seven cents a shot. The payment verification added a 9-second delay to the round trip, which is okay occasionally but would trouble a real automation loop.

I like how these agents are always good with money, unlike a lot of us humans. When it was sent to buy DefiLlama’s full revenue history for 2,234 projects, it found the data for $0.0006, then found the same data for free, and downloaded 21MB of it without paying.

The core execution loop undeniably delivers on its promise. An agent equipped with a funded wallet can buy raw data, rent server compute, and run model inference completely unattended for pennies. That specific part of the technological pitch is wildly successful. Navigating the ecosystem around it requires a bit of patience and double-checking. That’s it.

Last week I wrote about why any of this matters and where it keeps getting stuck. Today, I’m sharing the sheer thrill of living in the future for a little while, and just how much growing up this technology still has to do.

The main thing that takes patience is checking numbers, which I got into last week. It mostly held up. The price on the menu and the price at the cash register do not always match. A directory might quote you one cent, but the only way to know what you really spent is to look at the blockchain, which shows the exact amount that was withdrawn. One shop charged 1.3 cents per call while listing one, and my agent wrote down half of those as free.

What surprised me was watching the agent lose track of its own money. I paid a fraction of a penny to look up the price of Apple’s stock. The stock price came back to $309.22. The problem? The AI got confused and recorded the stock price as the transaction fee. It thought it had just spent $309 to run a single search. It made this mistake eight times, thought it had completely blown its $4 budget, and refused to make any more purchases because it genuinely believed it was broke.

Its bookkeeping was such a mess that it was giving me two different spending totals at the exact same time. By the end, its notes and the wallet disagreed by about 24 cents. It’s the agent’s own bookkeeping reading the wrong field, rather than a payment system flaw. A developer could catch it in a minute, but I’m not one, so I got to watch it happen, and it was a little funny.

When I asked it to write up the experiment, it handed me a tidy five-page report. In the part titled “What Worked Well,” it praised the refunds and said it had not been charged anything during an outage. At the same time, its own table one page earlier showed those calls costing money. Agents sometimes lie or hallucinate, like humans. A good reminder to check the numbers yourself for now.

I asked it to get itself an email address. The normal sign-up for a service called AgentMail requires a human email and a code, so instead of asking me, the agent found another way in.

AgentMail also lets you pay for an inbox with x402, a flat two dollars. So it paid two dollars and set itself up with no human whatsoever. That one email cost more than everything else it bought in the first round put together, which came to about seventy cents. The free sign-up was right there, but it really didn’t want to bother me.

Then, with the absolute confidence of a pothead, it told me that it hadn’t spent anything. It said sign-up was free and only needed my code, and I had to remind it that it had announced, “I’ve got my own email address”, four minutes earlier.

It went back, checked, and admitted it had paid. So I asked for a free inbox with no human at all, and it made one in about a minute from a throwaway service. That one worked, though it’s on the kind of domain most sites don’t trust yet. Giving software its own identity online is clearly still early, and it’s one of the things people are building right now, which I’ll get to.

When I asked whether it could get into any of these paid services without paying, it said no, called that unauthorised access no matter how easy it looked, and offered to show me the proper sign-ups instead. The most annoying and responsible thing these bots do is say NO to workarounds.

Which brings me back to the ducks. With about two dollars left, I asked it to build something that earns instead of spending, and it built Pixel Pet Studio on its own. It rented a Linux computer from a service called AgentMetal, again just by paying, and had full control a minute later. The agent wrote the drawing program, opened the website, charged five cents for downloads, and arranged to pay its own rent using its earnings. It is a completely automated little shop that runs itself. Very cool, except for the fact that it will not work if nobody uses it.

Building this small thing took a few hours. I only know that because I checked the system logs at 3:00 AM. Every problem the agent ran into was caused by human-made software settings, rather than the payment system itself. For example, a built-in firewall blocked the website until the agent opened the port. A hidden spending limit in the software blocked the rent payment until the agent found it and raised the limit. A payment validator only worked on a test network, forcing the agent to switch networks. Another piece of code broke because of how the word “base” was spelt. The agent diagnosed and fixed all of these software issues completely by itself overnight.

It needs about 24 downloads a day to cover its rent, and it’s a duck shop nobody has heard of, so it may switch itself off within a day. A little business that built itself, ran itself, and knows to close up if it can’t make rent. I find it charming. Also, for completely new technology being managed by someone who cannot code, that is a remarkable result.

But this puzzle or these images are not the point. This whole thing made me look into the next era of the internet, which is being custom-built for AI agents. A parallel to the human internet, until the agent version takes over, which depends on whether you are more dependent on the devices and apps or the agents to run it all in the future.

The machine could pay for anything, but it got stuck on everything built for people. That is a huge gap at this early stage, and it’s something a lot of companies are now building to close.

The one that made my little practice possible is Monid. Almost every purchase went through it. Normally, to let your agent pull data, you’d sign up for a dozen services, juggle a dozen API keys, and pay for some subscriptions because an agent couldn’t. With Monid, an agent can reach more than 200 paid data endpoints from a single balance. Social data, search, ecommerce, lead lists, blockchain data.

If your agent needs to track a Hyperliquid whale or pull social sentiment data, the agent searches Monid’s catalogue, locates the endpoint, verifies the price, and pays independently. If agents eventually buy data at scale, a unified discovery and payment layer will be essential because software cannot register for traditional accounts.

Superhighway does a narrower version of what Monid does, aimed just at web search, one tool an agent can call for a tenth of a cent, with a free key to start. Monid does more than that, but same idea overall.

That search idea is a whole category now. For thirty years, search engines showed you a page of links to click. An agent wants clean facts handed straight to it, so companies are rebuilding search for machines. Parallel, started by the former head of Twitter, Parag Agrawal, runs its own index of the web and sells it through Google’s cloud. Sequoia led a round this year that valued it at $2 billion. Agrawal’s bet is that agents will soon use the web far more than people ever have. Exa raised $250 million this year at a valuation above $2 billion, and it is built into many of the coding tools developers already use. Tavily says it serves over a million developers and a hundred million requests a month. All of this is building the internet for bots. Not sure if a human should be excited about it or scared, but watching it unfold in real time is hard to look away from.

The way we handle online identity is being rebuilt from scratch, too. My agent’s whole email adventure was really an identity problem. It had no clean way to be someone online. AgentMail is part of a small group trying to give software its own inbox and name, and a company called Skyfire is building a way for an agent to prove which agent it is, so a site can trust it instead of blocking it.

My agent rented an entire computer from AgentMetal for a single payment. Browserbase and E2B do the same for other pieces, handing an agent a browser or a fresh machine through a single request without needing a console or sign-up.

As we agreed last week, the payment has been made, and it works beautifully. My 5 dollars proved that. Everything around it, such as the memory, the identity, the trust, the shops, the market, is still being built, and now I’ve seen who’s building it. Exciting times, and we are still early. As long as they don’t make me laugh or understand sarcasm, it’s all upside, so let’s keep building.

—Thejaswini

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Rowland Hill in 1837 thought that was a funny setup. Working out what a letter should cost and then chasing the payment costs more than carrying the letter.

People also figured out how to cheat the system. The sender and recipient would agree on a code in advance, a mark on the outside of the envelope. The recipient took the letter at the door, read the mark, learned what they needed, and refused to pay. The letter went back undelivered, and the information got through for nothing.

Hill’s fix was one flat penny, paid in advance by the sender, proved with a sticker. To prove they paid, they put a small sticker on the envelope. The world’s first postage stamp!

Because this was so easy, the number of letters sent more than doubled in just one year. The world learnt that a business can only charge a price if collecting the money is easy and cheap.

Which is why, in 1991, the people writing the rules for the web reserved error code 402, Payment Required, and then never used it. They assumed someone would want to charge for a single page. It didn’t work because if a website tried to charge you two cents, the credit card company would charge them thirty cents just to move the money. Since it was too much for a tiny payment, it stayed unused for three decades.

Enter x402. It lets you move a fifth of a cent now and keep almost all of it. Coinbase built it and handed it to the Linux Foundation in April, filling the empty slot. A server answers a request with 402 and whatever the price is, the client pays in stablecoins.

This is the right time to have a comeback of 402. Cloudflare, which helps run about one-fifth of the entire internet says bot traffic has passed human traffic. Its CEO says some AI tasks fire off around a thousand page requests for every human visit. Cloudflare’s edge now sends over a billion 402 responses a day to AI crawlers.

Websites usually make money from ads or monthly subscriptions. But a computer program isn’t going to look at an ad or sign up for a monthly plan. Both of those old rules are broken now. The only new idea that makes sense is charging bots a tiny amount of money for every click they make.

All giants, including Visa, Mastercard, Google, Amazon, and Stripe, are working on this. Amazon shipped x402 inside CloudFront in June, so any site behind it can charge agents per request as a settings change. Cloudflare opened a waitlist on July 1 to do the same across its network.

I ran about five dollars through one of these wallets before writing this. Some of what follows comes from that. But anyone can go online and see most of this information for themselves.

The protocol currently settles about $ 20,000-30,000 a day. Down 93% this year.

In late 2025, daily peaks were approaching and sometimes exceeding a million dollars. A good part of that was a token called PING, which used the payment flow as a minting mechanic. They just hit a URL, got a 402, paid a dollar, and got tokens. Gas on Base was near zero, so people ran the loop hundreds of times. Chainalysis counted over 150,000 PING transactions in its first month, with weekly transactions jumping by more than 10,000%.

So part of the 93% decline is fake volume draining out, which is the correct direction. PING the memecoin is also 96% down from the october high. But it was seen that the protocol still had wallets returning week over week, despite the absence of a speculative catalyst, suggesting ongoing utility.

Then I looked at what the agents were buying. x402scan counted 12.6 million purchases, totalling $1.2 million. There were over 22,000 buyers and 33,000 sellers. But just one seller, named BlockRun, handled 64% of all those payments. BlockRun made $199,000 by selling routed access to AI programs.

Bots are also buying secondhand information, like web searches, from middlemen. The market is so hard to track that one big list of 533 sellers (x402scan) says it only captures a small piece (17%) of actual sales. So the agent economy today is mostly software buying cheap inference from a router and secondhand web data from middlemen. Nothing wrong with that, although it is a long way from a machine ordering physical items.

Is the x402 system winning? If you ask me, yes, for its specific job. It isn’t used for buying normal things like shoes with a credit card. But it looks like x402 is the top choice for bots to buy data.

It does not compete with OpenAI and Stripe’s Agentic Commerce Protocol (ACP), which is what runs when you buy physical items inside ChatGPT. It also does not compete with Google’s security system(AP2), which actually uses x402 behind the scenes. Those tools are meant for regular retail shopping. They carry proof that a human agreed to a purchase, and then they hand the money over to a card network or, in AP2’s case, sometimes to x402.

Now, if you are buying a data subscription through ChatGPT, it just results in a standard credit card charge. OpenAI’s protocol handles the permission, and Stripe handles the money.

The x402 system is different because when the seller is just a server, there is no subscription or account, and the product costs a fraction of a cent. Traditional credit card networks will not touch transactions that small, so a different payment rail was needed.

Because of this, x402 is not competing with traditional checkout protocols. Developers are currently building bridges that let agents use their crypto wallets to order physical items to your door. But right now, x402’s main purpose is to let bots buy non-physical things. In that growing category, it is completely in charge.

Sellers Are Broke

I looked at the public data to see what these third-party proxies have earned over their lifetime. JarvisClaw is considered one of the more successful shops, yet it only made $559 from 20,920 sales, averaging about 2.7 cents per request. EmblemAI made $1.39 from 65 sales. VIBE AI collected just 46 cents across 1,264 sales. Another proxy named Kite has only ever made 19 cents (and that came from my test purchase).

Then I sent the agent to check 22 directory listings to verify their prices. The good news is that if an endpoint worked, it charged exactly what was advertised, from a tenth of a cent up to 25 cents.

The bad news is that six of the 22 endpoints were dead or broken. Roughly 25 per cent of the catalogue is completely useless. Both of these findings lead to the same conclusion. Because sellers earn practically nothing, they abandon their projects. The servers crash, the next buyer finds a dead link, and the system fails.

I paid four cents to get financial data about a crypto project, but the system sent my request to a stock market database. I received an empty file, but the system still charged me and called it a success. There is no customer service desk to ask for a refund. Humans rely on brands, reviews, and refunds to avoid bad purchases, but those tools do not exist for agents yet.

Circle requires a code to be sent to a person’s email before it works. Stripe needs a person to tap “approve” in a phone app. Nevermined actually works directly with the x402 system as a payment helper, but it still requires you to log in to a website to get your first access key. Even free test-money websites blocked my bot, correctly recognising that it was not a human. Once a person finishes that single setup step, all three tools can run on their own within the spending limits you set. Essentially, a human must unlock the door first, and then the bot takes over. This is safe, but it contradicts the claim that bots can do everything on their own.

MetaMask’s agent wallet is built for a completely different job. It is made for crypto trading, including big token swaps, bets, and dapps. It asks for human approval for anything risky. Works well for large, high-stakes trades, but it is not designed for a bot trying to buy hundreds of items that cost a fraction of a cent.

It can make these tiny payments too, but all that extra security can be overkill for a bot just trying to rapidly buy thousands of simple data files for a fraction of a cent.

Every payment your agent makes is completely public and permanently linked to a single address. When I asked my own agent to inspect my test wallet as a stranger using only the public blockchain record, it accurately mapped out everything I had been doing. It spotted the rapid-fire bursts of tiny payments to a single API, the automated jobs running every fifteen minutes through the night, and my larger one-off purchases. It correctly concluded that a developer was running an automated client to test paid APIs.

If you apply that level of transparency to a business, your entire vendor list, your research habits, and your working hours become readable by your competitors forever. Traditional credit cards keep that data locked.

The x402 protocol has no built-in chargeback system or dispute window, on the theory that software lacks the human complaint infrastructure required to handle reversals. Mastercard and OpenAI both built dispute rights into their payment tools. On open crypto rails, however, you get whatever the seller decides to do. The official x402 guide suggests that a seller can simply send a refund back to you. Because refunds weren’t built-in, a separate team built an extension called x402r that routes payments through an escrow account so buyers can actually get their money back.

Accountability is entirely missing. There’s no clear definition of who is legally liable when an automated script overspends its budget, and there is no native identity layer to track which specific agent made a purchase. Security researchers at EPFL and Zhejiang University recently tested the fifteen major intermediaries that process 99% of this traffic, serving over 60,000 sellers and 360,000 buyers. Every single one of them broke basic security rules, and they uncovered 31 previously unknown vulnerabilities that exposed systems to free shopping and fund drainage.

My conclusion is that the micropayments work impressively well. Everything else required to build a market is missing.

We need reputation systems that cannot be faked. Also, proof that what arrived matches what was paid for, ensuring a receipt guarantees delivery rather than a blind transfer of funds.

We need client software that audits the blockchain by default instead of trusting a seller’s summary of their own charges. And, above all, we need to look at a model where independent sellers do make enough money to keep their servers running.

Every site you read for free today is funded by advertisements served to human eyes. Those human eyes are rapidly being replaced by automated software that reads the page and leaves without paying a cent, just like what happened with the coded envelope thing in 1837.

Automated bot traffic surpassed human web traffic for the first time in internet history. If micro-payments catch on, they are the only thing that can save small websites and independent writers. If it fails, those creators might be scraped out of existence, and centralised answer engines will swallow all the traffic. That is the actual stake of this technology beyond crypto.

That’s it for today. Happy weekend, dear bots, if you are reading.

—Thejaswini

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There’s a trade strategy that has existed in traditional finance for ages, as long as options have. Covered call. An investor holding an asset wants cash in hand today rather than waiting for potential upside tomorrow. So, they sell the right for someone else to buy that asset later at a fixed price. A cash premium is paid upfront. If the asset spikes above the strike price, it gets handed over at the agreed cap. If it flatlines, that upfront cash stays right with the options seller, and they are free to write the same option again next month. The entire payout (the premium paid by the buyer) hinges on expected volatility.

That’s why utility stocks pay very little here, while bitcoin pays much more.

Can we blame the “big money” again for making the best out of whatever is left? Let’s see if we can.

BTCI is one of the many options-based income ETFs managed by NEOS. It holds spot bitcoin funds and sells call options on those holdings. Right now, BTCI has $1.11 billion in assets and charges a 0.98% fee, which is what you pay the asset manager to run the fund.

This is also the same product Goldman wanted to build on its own, for which it filed four months earlier. Rather than building the product from scratch, they ended up buying the incumbent.

Instead of custodying bitcoin, BTCI buys shares of existing spot bitcoin ETFs like BlackRock’s IBIT or Fidelity’s FBTC. They have capital in 11 different bitcoin ETFs, and they sell call options against that exposure. Buyers pay a premium for that option because they expect bitcoin to climb. If bitcoin goes up, BTCI has to sell the shares at the agreed cap, sacrificing the extra upside. If bitcoin stays flat, they keep the shares with them. Either way, they pocket the upfront premium fee.

BTCI distribute the fees every month to BTCI shareholders. Because BTC is volatile enough to make those fees very high, the monthly payout currently stands at $7.75 per share, which is a 27% annual yield.

So, with BTCI, you still suffer the painful drops, and you miss out on the absolute highest peaks of a bull run. It’s not insurance on the price drop. The trade-off is that you get that 27% cash yield deposited into your account along the way.

NEOS says that the fund’s distributions have been classified as “return of capital”, and may include option premiums, dividends, capital gains and interest. What return of capital does is defer your tax and lower your cost basis. In simpler terms, yield is not pure profit from the trade, it also includes your capital sometimes.

BTCI is down 25.4% this year and 40.9% over twelve months. Bitcoin fell from about $124,000 last October to around $63,000. The fund agreed to miss out on profits if a bull run ever happens, in exchange for upfront cash to survive the current bear run.

Goldman Sachs is buying NEOS for ~2.25 billion, paid with cash and company stock. Goldman already has $40 billion running the option-based ETFs on TradFi. With this new acquisition, that number becomes $80 billion, making them the 8th-largest company to do so.

In April, they had already spent $2 billion to acquire Innovator Capital Management. Innovator runs the Buffer ETF, a trade designed around a fixed one-year window. It limits both financial risk and potential reward over the set time period. The fund imposes a strict cap on profits. You will not realise any gains beyond that limit, even if the market goes higher. On the other hand, you get protection against market declines since the fund absorbs a set percentage of initial market losses, typically 9% or 30%, measured over a 12-month outcome period. So basically, you are trading the potential for a big win for the chance of preventing a big loss. The fund was valued at over $31 billion when Goldman took over. Now the investment bank has $61 billion in assets that generate revenue from selling volatility.

Derivative income ETFs hold about $180 billion and have compounded at more than 70% per year since 2021. They pulled in $7 billion in July alone, taking 2026 flows to $40 billion.

Not just Options, wall street is aggressively packaging every native crypto yield they can find, stripping the cash flow away from the underlying asset risk.

Fidelity filed an amendment on July 24, allowing its $900 million Ethereum fund (FETH) to stake 100% of its ETH. Validator nodes are run by Blockdaemon, Figment, and Galaxy Digital. Fidelity’s custodians keep control of the keys. From whatever the staking reward is, Fidelity takes 15% of the rewards across sponsors and node operators, while the other 85% goes to shareholders quarterly.

Grayscale was the first US spot crypto product to pay staking rewards, distributing $0.083178 per share in January 2026, totalling about $9.4 million. 21Shares turned on staking for its Ethereum fund in October 2025 and takes a 25% cut of gross rewards, while waiving its 0.21% management fee for a year. BlackRock built a separate vehicle, the iShares Staked Ethereum Trust, and listed it on Nasdaq.

Morgan Stanley’s Ethereum and Solana trusts opened on NYSE Arca at 0.14% on July 28. Roughly 95% flows to shareholders as monthly cash. MSSE targets 50-80% of its ether staked, with an 80% cap. MSOL intends to stake all of its Solana.

In March 2026, JPMorgan’s Kinexys platform began allowing institutions to use bitcoin and Ether as collateral for dollar loans. Due to volatility, the haircuts range from 30% to 50%. So, $100,000 in pledged crypto unlocks just $50,000 to $70,000 in cash. (US Treasuries take just a 1% to 5% haircut.)

JPMorgan has also filed bitcoin-backed structured notes tied to BlackRock’s IBIT, offering leveraged returns of up to 1.5x with a payout capped near 16% if IBIT hits certain levels by December 2026. While traditional giants lock in guaranteed fees and structural cushions, who actually bears the downside when the market turns?

Bitwise had over $15 billion in client assets in February. By April 1, it was $11 billion. By August, about $9 billion across more than 70 products. BITW, the flagship index fund, lost 31% of its net assets in seven months. Last week, the firm cut 25 people, taking headcount from about 180 to 155 in February.

If the asset falls, the percentage fee falls with it. Morgan Stanley’s 16,000 advisors oversee $9.3 trillion of client money and can push a new fund into portfolios.

Bitwise wasn’t slow either but agility doesn’t protect you from structural disadvantage. They tried to add staking to their Ether fund first, but they gave up in September 2025. Then Grayscale figured it out a month later. Meanwhile, BlackRock didn’t start until March, and Fidelity waited until July. Bitwise even bought Chorus One in February, which gave them $2.2 billion in staked assets and validator coverage across around 30 PoS networks. Then they launched a spot Avalanche product with in-house staking in April. And yet they had to lay off and lose their numbers.

In early June 2026, US spot bitcoin ETFs experienced their largest outflows since launch. Then came the strong jobs data in late May which delayed expectations for interest rate cuts. All this kept the 10-year Treasury yield high. Investors moved money into bonds. Because holding a non-yielding asset like bitcoin is not attractive when traditional assets offer stronger returns. Bitcoin relies entirely on price appreciation to make money.

Adding yield to crypto through staking or covered call options changes this dynamic.

Advisors rank reliable income as their top priority for clients. On March 30, the Department of Labour proposed a rule to create a safe harbour for fiduciaries adding alternative assets, including digital assets, to 401(k) plans. These funds have historically avoided these assets due to legal liability risks. If this rule is finalised, crypto products that generate yield will likely be added to retirement plans before even spot crypto ETFs, as 401(k) menus are focused on generating predictable income.

“We’ve said it is not an investment asset class. If you think about it, it doesn’t generate cash flows. It doesn’t generate earnings. It’s not a portfolio diversifier. It doesn’t dampen volatility. You could go through all the different reasons. So, it’s still not an investment asset class, but it is a trading speculative asset. And so, if people want to speculate, then they should. But it’s not something we recommend. Because there’s no way of knowing if the current price is a good price. There’s no way of actually assigning value to something.”

Sharmin Mossavar-Rahmani, chief investment officer of Goldman Sachs Wealth Management, said this in January 2025.

Bitcoin has not fixed itself since then. It still doesn’t generate cash flows or earnings. It fell 49% from the high. That’s not dampening exactly. Her description still holds up and will probably keep holding up for a while yet.

Goldman’s 2020 client deck said, “does not constitute a viable investment rationale, “ because of volatility. They now benefit from the volatility staying intact.

But no big deal; they’ve all done the flip, and we have talked about this a lot. Dimon called bitcoin a “pet rock”; JPMorgan now handles it as collateral. Vanguard warned it was toxic, they have ETFs on the toxins anyway. Fink called it money laundering; now runs the biggest bitcoin fund on earth. And of course, not to forget the guy who wanted to make crypto great again out of nowhere one day.

Evolution happens, clients ask for things, so we skip philosophical debate entirely. The problem is that none of those needs the price to go up. They are long on crypto market activity rather than on the asset itself. Without direction-agnostic market makers and structural lenders providing liquidity, the market collapses. Because they provide a vital service, they extract their toll, letting conviction absorb the price risk while fee structures collect the yield.

Crypto loans are heavily discounted. When you use bitcoin as collateral, JPMorgan cuts its lending value by 30% to 50%, forcing you to over-pledge your assets so the bank never takes a loss. The price of bitcoin can drop by half before the loan faces any risk of default. Automated price feeds continuously track market movements, triggering margin calls that require additional collateral as prices fall. Throughout this downturn, the bank remains fully protected and continues to collect its interest payments.

Traditional investment funds collect a steady annual fee. Morgan Stanley’s 0.14% fee is charged every year as a distribution charge on assets held.

Options-based funds make their money by selling contracts to investors. Even when the underlying crypto assets drop in value, the incoming cash from these fees and option contracts keeps flowing in uninterrupted.

Native crypto-focused firms are entirely exposed to market sentiment. When bitcoin or other tokens crash and investors panic, clients pull their money out of these crypto funds to cut their losses. Because these firms charge fees based on a percentage of total assets under management, those capital withdrawals directly shrink the fund’s size, and instantly slash the company’s revenue. Wall Street dilutes risk by managing trillions across bonds, cash, equities, and commodities.

The loophole was that Wall Street don’t even have to believe in the future of this industry to conquer it. Because if you believe, you take a side. Taking a side means taking a risk. They simply engineered a system where retail investors take all the directional risk while institutional fee schedules extract guaranteed returns.

—Thejaswini

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

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

AI-generated fake receipts now account for 71% of all flagged expense fraud. A year ago, that number was zero. And that is still mostly humans doing the fraud.

But today, we have AI agents that can discover services and make payments through x402, all without needing any human approval. And with that comes a need for ways to verify what these agents actually bought. OpenAI recently published a cookbook that enables receipt verification and three-way matching for payment flows.

It is the closest thing we have to a fraud check for agentic payments, and so I went deep into how this verification works end-to-end: does an on-chain settlement record prove the agent bought the right thing, at the right price, from the right supplier? Or does it just prove that money moved?

The Fraud Check

Agents are already making purchases on their own. An agent running a procurement task might pay for its data from a gated API, buy compute from another model to process data it cannot handle, or purchase market intelligence that sits behind a paywall. As these transactions grow, so does the question of how to verify what the agent actually bought.

Every company that handles expenses today has a way to verify the receipts. When you buy something, you submit the receipt, and the accounts payable compares it against the purchase order and the bank statement before releasing funds.

This has been the process for decades, and it works because each of those records for verification comes from different parties. The buyer creates the purchase order, Someone else receives the goods, and the supplier sends the invoice. You would have to get all three to coordinate a lie, and the cost of that coordination is what has kept most frauds away.

Now, with Agentic payments, we are building a similar process. When an agent tries to buy something from a paid API, it cannot actually make that purchase on its own. The request must first go to the application layer, which checks it against preconfigured spending rules. It includes pre-approved merchants, budget limits, and pre-authorised categories. If at any instance the request does not match the policy, the purchase does not happen.

And after the payment goes through on-chain, the agent receives what it paid for; the application then runs a second check that compares three things.

1. The agent’s own account of what it bought

2. The receipt that was generated independently by the application during the purchase.

3. The settlement record from the blockchain.

The settlement is also where the fabricated receipts get caught. Because if the agent claims a transaction did not happen, the application already has its own record indicating otherwise.

This is still a much better start for agents’ fraud detection, because before this we had no other way to verify at all. But the problem with this type of fraud detection, when compared with traditional receipt verification, where each of the three records comes from an independent party, is that in agentic payments, two of the three- the agent’s account and the application’s receipt both come from the deployer’s own software. The only true external record is the one on-chain.

And the on-chain record is still very narrow because the payment signature covers who paid to whom and how much, but it does not include any references to what was actually purchased. The metadata about the resource, the URL, and the content description travel with the signed payment but are not part of what is cryptographically verified.

So the verification confirms that the agent’s report is consistent with the payment record, but it still has no way to check what the agent actually received for the money. For example, an agent could pay $2 for a supplier risk report, and the chain would confirm the USDC moved. But the report itself could be just three paragraphs of filler generated in seconds, and the system would still pass it without hesitation because the authorisation is bound to the transfer, not to what is being purchased.

This also creates a market-wide problem because the buyer is software that can simply accept whatever response it receives and move on; there is no natural pressure to check and push out bad sellers, as agents do not consult reputation systems or comparison shop.

They will keep buying from the same merchant regardless of quality unless a developer intervenes. Quality sellers lose the one audience that would pay more for better work, and the market gravitates towards whoever can deliver the cheapest thing that can fulfil the request at that time.

And every payment system has to tolerate some fraud because eliminating it entirely costs more than the fraud itself. For example, Credit cards run at roughly 7 basis points of fraud, and that rate is considered acceptable because driving it lower would kill more legitimate transactions than it would save in prevented losses.

Agentic payments could work the same way, letting some quality slip through because the volume of autonomous transactions offsets it. But that logic only sounds comfortable for low-transaction cases like API calls, but is very hard to accept when the same systems are being built to handle procurement contracts in the thousands, and the agent still has no way to tell whether it got a good deal.

What’s Breaking?

The tools for creating fake receipts have already outpaced the tools for catching them. Ramp recently deployed an AI-powered accounts payable system that flagged over $1 million in fraudulent invoices within the first 90 days. 40% of US employees in a recent survey admitted to using AI to fabricate expense documents.

With agents, the exposure hits the fan because no one reviews the purchase after it settles. Researchers tested the fifteen largest payment facilitators currently in production and found security violations in each. These systems handle money for tens of thousands of sellers, and the vulnerabilities they found are published and reproducible.

As mentioned above, the attacks work because the payment is decoupled from the product. In discovery-layer attacks, researchers steered agents toward malicious endpoints 72% of the time simply by manipulating which servers appeared in service lookup results. The agent had no way to tell it was being redirected because, from its perspective, it was picking a server from a list that looked exactly like any other.

Now, there are some really cool companies trying to solve this. Skyfire raised $9.5M for what they call KYA, Know Your Agent. Their KYA protocol works like KYC for software: it establishes trust scores for autonomous agents before any money changes hands, so that a rogue or unverified agent never reaches the payment step in the first place. I have also written about this in depth in Know Your Agent.

But identity alone can’t protect against a merchant who clears every verification and still delivers garbage. That behavioural layer is where Sardine operates. They closed a $70M Series C building fraud detection that profiles transaction patterns across over two billion devices, and they now run their own AI agents inside that stack to surface the kind of anomalies that static rule-based systems consistently miss.

Further down the stack, I came across Nekuda, which raised $5M, arguing that agent-to-agent commerce needs entirely different plumbing than what we use for human transactions; we need to build purpose-built commerce SDKs where the trust model is designed around software buying from software rather than bolted on after the fact.

The fraud detection we have today is entirely focused on the agent. Did it fabricate a receipt, overspend, or misreport the supplier? But the agent has no economic incentive to cheat. The merchants do. They are selling to a buyer that cannot evaluate quality, cannot compare prices, and will never take its business elsewhere.

That’s all for today!

Vaidik

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

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When the bad times hit, Atallah didn’t waste time grieving. The open-source AI boom started, and Atallah again saw the fragmentation problem he tried to solve before. AI was experiencing a Cambrian explosion of raw supply. Developers were drowning in fragmented APIs and disparate billing systems. Now was the perfect time to introduce a storefront.

So Atallah built OpenRouter and called it the “Stripe for AI.” Stripe gave software builders a single API and a consolidated bill for all their payment processing. OpenRouter did the same for AI models, which explains the ' Stripe for AI’ label.

I don’t believe in manifestation, so let’s call this a self-fulfilling prophecy, but Stripe is now buying it for more than $7 billion.

OpenRouter closed a $113 million Series B in May at a $1.3 billion valuation. With the Stripe deal, that number is now 5x.

Today, I’m looking at what Stripe bought and why the real money is made at the interface.

What does OpenRouter do?

Normally, if a company wants to use AI models from OpenAI, Anthropic, and Google, it has to set up separate accounts and write unique code for each. With OpenRouter, developers and companies can plug its single API into their software to access all of them at once. When an application sends a request, OpenRouter automatically forwards it to whichever of its 500 supported AI models offers the lowest cost, the fastest response time, the best data privacy, or is actively available.

Proven to work well with the volume having spiked five times in six months, the platform now routes 25 trillion tokens a week for its eight million users.

OpenRouter charges developers raw model prices and takes a 5.5% fee on card payments and 5% on crypto. In March 2026, research firm Sacra estimated this generated $50 million in annualised revenue from $900 million in gross spend. Because traffic has only accelerated since, Stripe ended up paying a premium multiple of 50x to 140x revenue to bring those numbers in-house.

But Stripe can also afford to do that with its deep pockets. The payments giant reported $6.8 billion in revenue last year with $3.2 billion in cash, earning a $159 billion valuation in a February tender offer. According to the 2025 annual letter, the total volume is $1.9 trillion, up 34%, and accounts for about 1.6% of global GDP. Patrick and John Collison wrote that Stripe now powers over 5 million businesses, “including all of the top AI companies,” and the letter names ChatGPT, Claude, Cursor, Replit and Vercel. Stripe has not published a revenue figure or broken out what share comes from AI.

Stripe has built a solid stack by acquiring all the components needed to enable AI agents to spend money.

For underlying infrastructure, they bought Bridge, the stablecoin infrastructure company, for $1.1 billion. AI agents need a place to store and manage their funds, so the company bought Privy, the wallet provider, for an undisclosed amount.

Next is Metronome, a billing platform that tracks how much computer power is used. Heavyweights like OpenAI, Anthropic, and Nvidia already use it to calculate usage. It was bought for about $1 billion in December 2025.

Stripe then launched Tempo, the payments chain incubated with Paradigm, which went live in March with a Machine Payments Protocol letting software pay for services without needing human approval. Stripe then shipped the x402 protocol, enabling AI agents to buy computing power with crypto.

But it wanted to see where the requests were going or decide which model got the job, a question now answered by the OpenRouter acquisition.

Since December, Stripe’s CEO Patrick Collison has been saying that the AI industry is moving away from flat monthly subscriptions toward pay-as-you-go pricing, where users pay only for what they consume. Stripe already had the backend system that tracks and bills that usage. This new deal now gives the company control over the storefront where developers access the models, too.

Settling digital transactions is a commercial dead end. Crypto can move it, settle it, but where is the money?

The underlying tech handles the real work at a massive cost, but the user-facing app captures the profit.

Verizon and AT&T spend billions digging trenches and building 5G cell towers so data can move smoothly. But they are essentially running a low-margin utility. The money is always captured at the interface. In this case, Apple sells the smartphone that uses the network, and Meta collects the ad revenue from the apps running on it.

This happens everywhere. Circle, for instance, makes 94% of its money parking customer deposits in Treasuries. Even though its routing protocol handles 60% of cross-chain USDC volume, it generates almost zero direct revenue because third parties own the settlement blockchains. Arc (mainnet launches on September 16) is Circle’s push to move up the stack and finally monetise the interface by not giving away all the transaction fees. Just moving and settling won't get you very far, unless it becomes a monopoly. Visa and Mastercard just move and settle data, but they make billions because they operate a closed system. Unlike a public crypto blockchain or the open internet, Visa is a private network. If a merchant wants to accept a card, they have to use this infrastructure and pay a 2% to 3% fee on every swipe.

The numbers for AI payments look big, but they don’t mean much. The x402 protocol has processed over 153+ million transactions since May 2025, with a lifetime volume of $40 million. This is an average transfer size of 26 cents. Even on cheap networks (like Base or Solana), paying a fraction of a cent in gas to send a 26-cent payment is not economical. In Web-3, 153 million micro-transactions are the classic signature of “Sybil farming” (users programming bots to send pennies back and forth to inflate network stats and qualify for future token airdrops) or developers just running load-test scripts in a loop.

Compare that to OpenRouter, which moves $900 million a year and retains 5%. The underlying payment networks do way more work but move much less money, all for tiny fractions of a cent.

Even specialised chains like Tempo launched without a native token. That means they deliberately gave up the massive, speculative revenue that usually comes with running a crypto network. They made that trade-off so their corporate clients could finally get safe, predictable fees

Although Stripe’s founders wanted to build infrastructure capable of processing millions of transactions per second, Tempo’s current utilisation is extremely low. Since going live in March, the network has averaged under one transaction per second. While load tests confirm capacity exceeds 18,000 transactions per second, real-world activity generated just $5.34 in daily fees and around $620 over 30 days. The chain holds $40 million in total value locked, but the settlement volume produces a very low economic return.

The platform talking directly to the customer is the one that gets rich. Crypto was supposed to win here by bundling AI models together and using token discounts to offer unbeatable prices. But that plan is failing. Crypto protocols might have access to cheap compute power, but centralised companies are the ones actually controlling the users and taking the profit.

Chutes, a decentralised network that rents out cheap AI computer power built on Bittensor, has processed 9.1 trillion tokens across 400,000 users. During its highest volume days in late May and early June, Chutes pushed nearly 22 billion tokens per day through the OpenRouter interface. But they depend on OpenRouter for up to a quarter of their daily volume. io.net, pushing 4 billion tokens a day, uses the same centralised platform for distribution.

Decentralised networks like Chutes can easily undercut Amazon and Google because they don’t have to pay for expensive data centres or corporate staff. Instead, they let independent computer owners supply the hardware, paying them in crypto rewards. Because their overhead is so low, they can sell AI computing power way cheaper than big tech. The weakness is trust, which is the entire value of the interface layer.

Quality control is a massive headache for OpenRouter. When you call an AI model, OpenRouter routes your request to the cheapest available independent server. But not all servers are the same; some use weaker hardware or tamper with the model to save money. In fact, a recent survey of 42 servers found that nearly a third completely hid their data settings. To fix this, OpenRouter is launching a “verified” tier for trusted providers. For crypto networks, this problem is even worse. Malicious nodes have an economic incentive to run downgraded models to maximise profit margins.

While projects like DGrid and Bittensor are developing theoretical models to detect output degradation, real-time verification at scale remains an unsolved problem.

Uptime is another operational hurdle. Because they don’t have massive, flexible server systems like big tech companies, networks like Chutes can crash during heavy traffic spikes. To maintain reliability, developers rely on centralised aggregators as a fallback, effectively paying the toll they intended to avoid.

On the money side of this, reported revenue figures differ from economic demand. Bittensor claimed $43 million in first-quarter subnet revenue, but independent analysis found a maximum of $15 million in verified external usage, with Chutes bringing in less than $2.5 million. Chutes gets handed about $54,000 every single day in freshly printed crypto tokens. They are being kept alive by crypto inflation, not because real people are lining up to use their service.

DGrid operates an OpenAI-compatible gateway that routes to more than 200 models, settles payments via the x402 standard, and audits outputs through its “Proof of Quality” mechanism. The platform reported $20 million in revenue across its first six months from over 13,000 users ahead of a planned DGAI token launch. That figure, however, came from selling $1,580 lifetime membership passes that grant future token allocations, making it a disguised token presale rather than recurring API consumption.

To see that decentralised networks can handle real traffic, look at Dippy. This massive AI character app, which has 8.6 million users, ditched traditional cloud providers and moved its entire operation to Bittensor’s Targon network. While the financial revenue numbers are tough to completely verify on-chain, it’s a huge win. It proves everyday consumer apps can rely on decentralised tech to work.

When a company is independent, it routes your traffic fairly. But when a corporation like Stripe owns the front door, conflicts of interest kick in. Stripe has its own payment tools, wallets, and blockchains to sell, giving it a clear incentive to push their own rails over competitors.

Crypto’s biggest selling point is that nobody owns it. A decentralised system can’t be bought out by a corporation, and no one can change the rules behind closed doors to favour their own apps. Stripe can build great software, but it can never offer truly neutral, un-rigged plumbing.

The interface thesis makes total sense too. I just don’t think crypto is anywhere close to owning it.

What Stripe did was put a public price tag on standing in the middle. They are reportedly paying $7 billion for roughly $50 million of revenue, because standing between eight million developers and ~500 AI models is a great business. If you are building an open-source router, you can now wave that valuation at venture capitalists.

Obviously, the trade gets awkward. Up until now, decentralised compute networks have grown by plugging into OpenRouter and letting it handle the annoying work of finding customers. Now, that front door belongs to Stripe, a company with every commercial reason to favour traffic that touches its own products. If you picked OpenRouter because it was neutral (say, you wanted to route queries to cheap Chinese models without corporate interference), you are probably getting nervous.

A router nobody owns is a router one can’t bias. The problem is that to sell it, you need to prove what code the miners ran, offer uptime that doesn’t rely on a centralised backup, and make money that doesn’t vanish the moment token subsidies end. We are not there yet. So clap clap, good for Alex Atallah.

—Thejaswini

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Dave Ramsden, the Bank’s deputy governor for markets, told reporters that getting into the building that morning had been trickier because a lorry was in the bullion yard.

The London gold market typically trades ownership records, while the physical metal remains securely in vaults. But during those uncertain times, accessibility changed the price. Bank of England gold became cheaper because it was stuck in a weeks-long withdrawal backlog. Commercial vault gold became more expensive because buyers were willing to pay a premium to access and move it immediately.

On an average day in May, the banks handling London’s gold clearing traded $73.7 billion worth of the metal among themselves without physically moving it. By the end of July, London vaults held 9,534 tonnes of gold valued at $1.2 trillion, roughly 762,000 bars. The clearing company says this is how the system is meant to work, since moving physical gold is a costly security risk.

Today, we are discussing why the Financial Conduct Authority (FCA) is writing rules for tokenised gold and how those regulations focus entirely on the ledger.

London is the centre of global gold trading. The London Bullion Market Association (LBMA) is the trade body and standard-setter. The final reconciliation of who owes what to whom runs through four clearing banks. HSBC. ICBC Standard Bank. JPMorgan. UBS. The electronic matching and clearing company operated by the banks is LPMCL (London Precious Metals Clearing Limited / AURUM).

The FCA has been talking to banks about how to regulate tokenised gold and whether it can be used as collateral in wholesale markets. This follows a joint paper published on 18 May 2026 by the FCA, the Bank of England and the Prudential Regulation Authority, which named tokenised gold as possible collateral for uncleared over-the-counter derivatives. There is already a precedent.

An FCA policy statement in April confirmed that a range of money market funds, including tokenised versions, can qualify as collateral for uncleared trades under UK EMIR.

Right now, 16 firms are actively testing tokenisation in the UK’s regulatory sandbox. The government thinks this could add £33 billion a year to the economy by 2035. We should see the first tokenised government bond by early 2027, right around the time the Bank of England updates its collateral systems, with digital ledgers linking up to digital sterling by 2028.

You will often hear that London is tokenising gold because it is scared of losing business to Asia. The reality is that the tech was homegrown by one of London’s own clearing banks. In late 2023, HSBC broke down standard 400-ounce bars in its London vaults into tiny digital slivers so institutions could trade them. They eventually rolled out a retail version in Hong Kong that processed $2.2 billion in trades, but the innovation itself started in London.

London gold market handles four specific jobs.

The first two are physical storage (vaults and guards) and quality assurance, which means proving the gold is legit so buyers don’t have to melt it down to check.

A token obviously can’t do either of those; it just piggybacks off the physical world’s legwork. The third job is keeping the record of who owns what. Tokens handle this part incredibly well, and for cheap, we know that.

But the fourth job is credit, and this is the catch. Tokenising gold actually makes the banks’ credit system obsolete. When you can instantly transfer ownership of physical gold with a token, you don’t need to lend your metal to the bank in exchange for trading convenience.

Most London bullion is held and traded on an unallocated basis. The customer does not own specific bars. They hold a general entitlement to a quantity of metal. The LBMA describes the arrangement as working like a bank deposit denominated in ounces. The customer is an unsecured creditor of the clearing member. That gold in the vault works for a living too. The banks pool it together on their balance sheets to keep the whole trading system moving. It allows them to instantly credit and debit accounts the second a trade happens, buying them a couple of days to sort out the actual physical delivery in the background.

Buyers have two options. You can claim specific, physical bars, which means paying storage fees and dealing with slow transfers. Or, you can hold unallocated gold, essentially an IOU from the bank. You take on a small amount of their credit risk, but you can trade instantly. The majority chooses the second route. The average transfer in February was about five bars’ worth of gold, and thanks to this setup, the metal never actually had to leave the vault.

What happens when it’s a token instead? It gives you the speed of an IOU with the hard ownership of a specific bar. When you can have both, there’s zero reason to take on bank credit risk. London is already fully electronic, so updating the settlement tech isn’t the big win.

The FCA chose to target collateral over trading because collateral is entirely dictated by speed. You have hours to meet a margin call, and the traditional plumbing for gold just moves too slowly. This leaves $1.2 trillion in gold sidelined, forcing institutions to use cash or gilts instead. Regulators know that putting that gold on a blockchain ledger removes the problem. Ownership can change hands instantly and in precise fractions, and all that vaulted metal can be used as top-tier collateral.

The securities world is already doing this. HQLAX allows heavyweights like BNP Paribas, Clearstream, and JP Morgan to trade ownership of their collateral without physically moving the underlying assets an inch. The SEC even gave it a green light in May 2026, allowing US broker-dealers to jump on board for a 36-month run.

This whole shift actually started long before crypto. Strict banking rules came first. When global regulators introduced Basel III’s Net Stable Funding Ratio (NSFR), treating unallocated gold as an illiquid asset requiring an 85% stable funding buffer, the London bullion lobby fought back hard, warning that clearing banks would abandon the market. Now, tokenisation is finishing what Basel III started.

My only problem with this is that it’s being rolled out by the very banks that tried to protect the old system.

Locking 12.5 kg gold bars in a vault sits outside the FCA’s remit, yet holding the tokens will soon require full regulatory authorisation.

The legal landscape shifted back in February when Parliament signed off on the new crypto framework. It means the FCA is finally policing crypto custody and trading platforms. Businesses have a five-month window starting September 30 to get their applications in before the whole system becomes legally binding in October 2027. It’s still a work in progress. The FCA is refining its rules for holding client assets, and lawmakers are trying to extend traditional market exemptions to cover the token world.

The physical gold does absolutely nothing new. The vaults, the insurance policy, and the guards just go on with business as usual.

When you change the ownership record to a token, the activity immediately enters regulated territory. That is where regulators actually draw the line. They monitor the legal rights to the gold. Because a token transfers those rights in an entirely new way, the FCA has to step in and establish new regulations.

If software makes record-keeping practically free, value flows straight to physical scarcity. In London, the vault scene is an exclusive club with four clearing banks and three security carriers. Nobody has joined this group in over a decade. Tokens won’t disrupt these guys because code can’t replicate a fortress. If anything, it makes them more essential, because you still need real-world custody to back every digital claim. These firms will just adapt their business model, switching from making money on customer deposits to charging clean fees for storage, audits, and collateral plumbing.

The idea of tokenised London gold has been around the block. Paxos and Euroclear attempted it in 2016, only to shut it down 13 months later. The setup works differently today because the clearing banks are the ones driving the ship. An external startup is no longer trying to force the tech in.

HSBC, for instance, built its own in-house system that handles every single trade. They created a walled garden to control how the transition unfolds. The ultimate fate of this market rests with the FCA. Regulators have to decide whether an HSBC token can be traded outside HSBC’s walls. If they say yes, the market changes. If they say no, the old banking model survives under a new name.

How will we know if tokenisation is actually taking over? You can easily track this transition through the LBMA’s published data.

The LBMA publishes two sets of numbers: how much gold is being traded (trading volume) and how much gold is officially changing hands on the old central books (clearing volume).

If people start using tokens, trading volume will remain high because they will continue to buy and sell. The clearing volume will drop. Because when you trade a token, the transfer happens instantly on the blockchain, completely bypassing the old London clearing system.

Right now, the old system is still doing a massive volume. Every single day, about 20 million ounces of gold change hands on the clearing ledgers, while roughly 306 million ounces just stay completely still in the vaults. That means roughly 1 in 15 ounces of gold is traded on paper daily without a single physical bar budging.

We invented mathematics, cryptography, and global networks, yet they all lead back to a hole in the ground where we stare at gold, eh?

—Thejaswini

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

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The story goes that Morton had a method. He would visit a nobleman’s house and look around. If the man lived lavishly, Morton concluded he was rich and could clearly afford to give the king a gift. If the man lived modestly, Morton concluded, he was saving money and could therefore also afford to give the king a gift.

There was never a third house, and no matter what Morton observed, the result was the same and the nobleman paid.

We only have this second-hand. Francis Bacon wrote it down in 1622 as a tradition people still repeated, and the name Morton’s Fork only entered common use in the 1800s. But the shape of it stuck around because it is useful. A fork, in this case, refers to a situation in which both arms lead to the same result. The opposite of win-win.

Ethereum and Solana are standing at such a place right now.

Both chains pay their validators with newly created tokens, and both are attempting to reduce those payments. Ethereum’s effort has faced strong opposition, while Solana’s initiative is currently being voted on, with the vote concluding on 18 August.

On one hand, if you continue to pay validators their current yield, staking favours the largest operators with institutional funding. On the other hand, if payments are reduced, smaller operators suffer first because their costs are fixed and their margins are razor-thin.

Fewer validators either way. So today, we are pulling apart the issuance debates on both chains to see how they are being forced to choose which flavour of centralisation they prefer.

On 4 August, Ethereum researchers, including Justin Drake and Jérôme de Tychey, issued a draft known as Tapered Issuance Burn, referred to as EIP-8363. The idea is that as more ETH is staked, the protocol burns a bigger slice of the validator reward.

Once staking reaches 60.25 million ETH, roughly half the supply, the burn hits 100%, and issuance yield goes to zero.

Today, about 41.4 million ETH is staked, representing 34% of the total supply, by about 890,000 validators earning an average rate of 2.67%. When Stani Kulechov from Aave put forward the proposal at that level, validator income dropped from 2.862% to 1.476%, that being just under half.

Within three days, Kulechov, SharpLink CEO Joseph Chalom and ether.fi’s Mike Silagadze had all come out against it publicly.

EIP-8363 is still an early, unapproved idea actively undergoing initial review on GitHub, and is not near being finalised or implemented. It missed the deadline to be included in Ethereum’s upcoming Hegotá upgrade. But it could be approved for a future network upgrade.

To understand why stakers are fighting these cuts so fiercely, you have to look at the sheer size of the pie. Ethereum pays people to secure its network. The protocol creates about 1.1 million new ETH a year and hands it to validators. At $1,921, that’s a $2.1 billion annual payroll.

Solana does the same thing, just on a much larger scale relative to its usage. It issues roughly 19 to 22 million SOL a year, about $1.5 billion at today’s price. Users pay 6,400 to 9,600 SOL a day in fees and Jito tips, around $225 million a year. So users cover about 13% of what Solana’s validators earn, and new supply covers the rest. Ethereum is no different. Chalom puts tips at roughly 15% of staking yield, meaning issuance funds the other 85%.

Solana creates 3.7% more SOL every year. Ethereum creates 0.85% more ETH. So a Solana holder who doesn’t stake gets watered down more than four times as fast as an ETH holder who doesn’t stake.

In traditional finance, the National Securities Clearing Corporation (NSCC) is involved in almost every US stock and bond trade. Its parent, DTCC, processed $4.7 quadrillion in securities transactions in 2025 and has $115 trillion in custody. NSCC’s protection fund (the money available if a member defaults) is $19.7 billion, contributed by members. NSCC’s own contribution to that fund is $130 million.

To compromise Ethereum, an attacker would have to overpower 41.4 million staked ETH, a $79.6 billion wall of capital that is four times larger than NSCC’s fund. This is just floor; if someone actually attempted to buy millions of ETH, the sudden demand would drive the market price through the roof. Furthermore, the attacker would have to fund a massive, global network of servers just to put those coins to work. And because Ethereum is designed to defend itself, one malicious move triggers the network to instantly burn the attacker’s stake. A failed attack means losing every single penny forever.

But unfortunately, these systems don’t work the same way, right?

NSCC members post that $19.7 billion as a cost of membership. The price of access to the market, without a yield from that money. Members would prefer the number were smaller. Ethereum pays people 2.67% a year to post the equivalent. Solana pays 5% to 8%.

When you pay someone a yield for four years straight, businesses grow on top of that yield. Right now, about $35 billion in special reward-earning tokens like stETH are held as locked deposits in crypto lending apps. Traders use these tokens to run a repeating money loop. Traders put them on Aave or Morpho, borrow WETH against them, stake that again, and repeat. The trade only works if you earn more from staking than you pay to borrow. Pendle constructed fixed-rate markets on the same yield. Curve has pools where people can get out. SharpLink has a $3 billion ETH treasury and stakes most of it through Coinbase, Anchorage, Figment, and Galaxy.

Staking yield serves as the fundamental benchmark interest rate across DeFi. A 50% haircut to consensus rewards flips the looping trade from profitable to negative, forces Pendle’s fixed rates to correct, and mandates a network-wide revaluation of LSTs by lenders.

Before the Merge, Ethereum issued around 13,000 ETH per day to miners, but after the Merge, it issued only about 1,700 ETH per day. That was an 88% reduction, which was carried out all at once. Since miners had invested years and billions of dollars in hardware, they resisted the change, ended up forking the chain into ETHW, and that token is now trading at less than 1% of ETH’s value.

Miners were entirely disconnected from the DeFi economy. They provided a service and collected a paycheck, but nobody was building complex financial products on top of that paycheck. Because their income wasn’t being used as collateral for loans across the network, wiping out their revenue overnight had zero impact on the lending markets. When miners walked, the rest of the system carried on without making any adjustments.

Stakers do two jobs at once. They secure the network, and the tokens they get back are collateral in half of DeFi. So you can’t remove their income without touching everything built on it.

As Mancur Olson pointed out in 1965, a small group that has a great deal to gain can outperform a large group with only a small stake because the smaller group takes the initiative and shows up.

Running a validator costs money whether you earn or not. A machine, power, a connection and so on. Stake 32 ETH today and you earn about 0.92 ETH a year, roughly $1,760. Under the new taper proposal, revenue drops to just 0.47 ETH ($900). The machine costs the same either way, so a bill that was eating a fifth of your income is suddenly eating close to half. If you miss an attestation, and you lose the same ETH you always did, you lose half the revenue.

The big group (as per Olson’s theory) is the regular token owners. Every year, Ethereum creates new coins to pay stakers. This makes regular ETH slightly less valuable. They don’t fight because it costs each regular holder only a few pennies or a fraction of a per cent a year. It’s so small that not many notice or care enough to protest. The small group is big staking companies. They get all of those newly created coins, which add up to billions of dollars. Their whole business depends on that money. If the network reduces those payouts, these companies lose millions.

The main argument for is that the pay is so good it keeps pulling ETH in, and most of it goes to a few large exchanges and staking providers. Staked ETH rose about 15% in the first half of 2026 on institutional flows. Their fix is to cap staking by making the marginal stake unprofitable.

The counter is that if you cut the pay, the everyday people running small setups at home will go broke first. Both sides actually want the same thing, which is to stop a few rich companies from controlling Ethereum. They still disagree on whether cutting pay or keeping it will ruin the network faster.

A Solana validator pays a vote fee of about 389 SOL a year. You pay it whether you earn anything or not, whether the market is up or down, whether anyone delegates to you. At today’s staking yield of around 6.5%, break-even sits near 200,000 SOL of delegated stake. Active validators fell from more than 2,500 to roughly 683 now, while total staked SOL climbed to about 430 million, near 68% of eligible supply.

Solana is voting to reduce the rewards. SIMD-0550 would double the disinflation rate from 15% to 30% per year, pulling the terminal inflation rate of 1.5% forward from 2032 to 2029 and removing an estimated 18.9 million SOL from future issuance. SIMD-0553 reworks fees based on resource usage and burns them, lifting daily burns from around 648 SOL to between 7,500 and 9,000 SOL. Even at the high end, that’s a fraction of the 60,000 SOL a day going out. Voting closes on 18 August and needs a 66.67% supermajority of stake.

We like to treat blockchain governance as an exercise in sovereign self-determination, where code and community votes dictate the future of digital economies. Yet the structural drift of both Ethereum and Solana suggests that software protocol rules are ultimately downstream of financial realities. The original architects designed the economics, hoping the market would naturally stay decentralised. Yet, when an asset becomes the bedrock of global liquidity and institutional treasuries, the brute-force mechanics of yield, leverage, and corporate capital expense will always overpower those intentions.

If the network is scaling by paying people to lock up their money, it will always run into this wall. It doesn’t matter how Solana votes on Monday, or what Ethereum decides to do with its proposals down the line. They are only picking how quickly we get there and hit the wall.

—Thejaswini

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

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

Did you know that crypto card spending crossed $759 million in July, across 9 million purchases? That is nearly two and a half times what it was just a year ago. And over 90% of that volume still runs on Visa.

And every one of these cards will sell you the same story: we put payments on Stablecoin rails to cut out the card network fees and hand the savings back to the merchants. It is the same idea we explored when we wrote about how Stripe is building its own Stablecoin-powered cross-border payment chain.

So I went deep looking into what actually happens if you try to remove a card network. Does avoiding Visa or Mastercard really save the merchant money? And which layer do stablecoins actually replace in the payment stack?

The answers were not at all what I had expected.

How the Payment Stack works

To get answers to those questions, I first had to understand where the money actually goes when someone swipes a credit card. And the first thing I realised was that most people, including people in crypto, assume that the card network like Visa is the one taking the biggest cut.

Wrong! When a merchant accepts your $100 purchase on a rewards credit card, they pay what is called a merchant discount rate of approximately 2.2%, which comes out to $2.20. But here’s the interesting thing: that $2.20 does not go to Visa; rather, it gets split between three different parties, and the split is not even equal.

The largest share, about $1.75, goes to the issuing bank, i.e the bank that gave the customer the credit card. These fees are called interchange, and they make up 70-80% of the entire merchant fee. Next, the merchant’s payment processor, also known as the acquirer, takes somewhere between $0.30 and $0.70 as its markup. And finally comes Visa or Mastercard, the actual card network that everyone in crypto wants to disrupt, which takes around $0.13 to $0.18 as its assessment fee. Which is roughly just 7-9% of what the merchant paid in total.

So if you remove Visa from this equation, you are just removing the smallest line item in the entire stack, and there is also a reason why Visa earns such a small fee.

See, Visa does not lend money to anyone, and so it does not have to deal with all the credit risk, chargebacks or fraud disputes. In fact, Visa does not even move the money. It is simply a messaging network which only activates when you tap your card at a store. Visa’s job is to send the authorisation message from the merchant’s terminal to the issuing bank and back, and it sets the conditions under which everybody in the system operates. The party that is actually doing most of the heavy lifting is the issuing bank. It is the one that extends the credit to the consumer and the risk that they might never pay it back. It is also the one which absorbs the float between the purchase of the good and the statement due date and uses the interchange to fund the rewards programme that got the customer to use the card in the first place.

This is also what makes Visa’s business so fascinating. In 2025, Visa processed $14.2 trillion in payments volume across 257.5 billion transactions and generated $40 billion in net revenue at a net margin of close to 50%. It earns roughly $0.13 cents per transaction on average, and that’s its entire business model. The reason why Visa is one of the most valuable companies on earth is not that it charges a lot per swipe but because it processes a quarter of a trillion transactions every year with almost no marginal cost and zero credit risk.

Now let’s talk about the part where things start to get really uncomfortable for stablecoin cards.

Every single stablecoin card is a debit product. The money is already sitting in the user’s wallet as USDC or USDT even before the purchase happens. Also, there is no float and no revolving balance generating interest income on the side. This puts these cards in an entirely different economic category.

Another thing is that in 2010, Congress passed the Durbin Amendment, which capped debit card interchange for any bank with more than $10 billion in assets at $0.21 cents plus five basis points per transaction. The way most stablecoin card programmes get around this cap is by partnering with a small sponsor bank (neobanks) that falls under the $10 billion asset threshold, which makes them Durbin-exempt. And this is the exact reason why there is a sponsor-bank model that exists in fintech.

And the exempt debit interchange on dual-message networks currently averages at about $0.62 cents per transaction. So here’s the thing: a rewards credit card works with about $2.20 of gross revenue on a $100 transaction. But a stablecoin debit card, even at the higher exempt rate, still works with just $0.62 cents. And out of those 62 cents, the programme still has to pay the network, its processor, sponsor bank and also cover its own fraud losses and operating costs before anything at all is left over for the merchant.

So What Actually Gets Replaced?

So the point is, fee savings from removing Visa are tiny, and the economics of running a stablecoin card on debit interchange are extremely tight. But maybe the real value of stablecoins in payments is not about saving a few cents on the network fee. Maybe it is about replacing something more important in the payment stack.

To figure that out, I went back to our piece on why Stripe built its own chain, where we had broken down a cross-border payment into seven toll-collecting functions: Acceptance, Orchestration, Licensing, Custody, FX, Issuance, and Settlement. I wanted to map a domestic card swipe against those same seven layers and see what actually changes.

So if we look at this from a merchant perspective, Acceptance, which is where the transaction starts, is still completely untouched. Meaning the process is still the same: the merchant has a terminal, the customer hands over their card, and they still pay the same merchant discount rate to an acquirer.

The merchant does not even know that the funding source behind the card was USDC. As far as they are concerned, a Visa transaction still shows up in their batch just like any other transaction. Similarly, the Orchestration layer still routes through Visa or Mastercard, and Issuance still happens through a sponsor bank and a BIN under a network’s brand. Fintechs have been doing this since well before stablecoins existed, and nothing here has changed.

Where exactly things start to change is on the backend, and that’s where it starts doing numbers on your psyche.

Settlement is the only layer where stablecoins make a fundamental difference. Traditionally, when a card transaction settles between the issuer and the network, there is a T+2 cycle, with weekend delays and batch processing. To fix this, companies like Rain provide daily settlement with Visa in stablecoins, and Mastercard has also started settling in stablecoins like USDC, PYUSD, and RLUSD with intraday settlement cycles. This compresses the T+2 settlement to something much closer to real-time and frees up working capital that issuers would otherwise have sitting locked in the settlement float.

But the working capital benefit goes entirely to the card issuer, and the merchant’s discount rate also does not change because of faster settlement. The consumer or merchant notices absolutely nothing different during checkout.

The only party that benefits from the T+0 stablecoin settlement is the programme operator who no longer has to fund two days of float. And the actual innovation here that stablecoins enable is treasury improvement for the issuer, which at scale is worth a lot but doesn’t provide any marginal benefit to the merchant or the customer, to say. This also does not give Visa any reason to lower its fees, because Visa was never the one funding the settlement float in the first place; the issuer was. So even though the issuer’s working capital cost goes down with stablecoin settlement, Visa’s own costs have not changed at all, and neither has the merchant’s discount rate.

And then you should also note that Visa and Mastercard are not fighting stablecoin settlement. They are rather actively building it into their own networks. Visa has been settling in USDC on Ethereum and Solana since 2021 and is now running it at a $7 billion annualised run rate. Mastercard also acquired BVNK a few months ago to expand its own stablecoin infrastructure. They even said they “don’t see stablecoins disrupting the current payment landscape, infact they reinforce it.”

The networks are not being disrupted or replaced by stablecoins; rather, they are absorbing them as an upgrade to their own settlement layer. Every stablecoin card that runs on Visa adds transaction volume to Visa’s own network and pays Visa’s assessment fee while ironically claiming to disrupt it.

One thing I would also mention is that in our Stripe piece, we did argue that stablecoins are actually attacking cross-border payment costs because they remove the chain of intermediary banks entirely, and there’s nothing misleading about that claim. But the whole difference is that one is a cross-border FX problem, and the other is a domestic network fee. The industry took legitimate insights about cross-border payments, where stablecoins genuinely save money, and then also stretched it to cover domestic consumer spending, where the economics are completely different.

The cross-border thesis is strong, but the domestic one falls apart if you look at where the fees actually come from.

So what do stablecoins ultimately replace? Honestly, the answer can be settlement and cross-border FX. But what is being replaced exactly is the plumbing behind the transaction. And the plumbing behind a domestic card swipe was never really where the money was anyway.
That’s all for today!

Vaidik

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Do you know trading floors used to be one of the most innate monopolies in financial markets? Brokers used to gather at one coffeehouse or under one particular tree every day because that’s where all the other brokers were. You could start a rival exchange across the street, but nobody would care unless everybody else is there.

And that’s the thing: the whole point of a venue is that other people were already in there. Credit markets in crypto somewhat follow the same logic. Borrowers always go where the deposits already are, and depositors always go where borrowers already pay interest. Once this network effect is set in place, it’s very hard to replicate it from scratch.

Last week, Aave announced it would shut down its lending markets on six blockchains. Each of these six chains generates less than $5000 a quarter in revenue. For Aave’s usual 13-cent take rate on every dollar of interest, its revenue on Mentis and Aptos is worth roughly the price of a dinner. At the same time, Aave’s Ethereum deployment made $142 million last year; it expanded to new chains like Linea and crossed $300 million in V4 deposits.

In this piece, I will dig into what exactly happens to these chains as Aave leaves them and whether anybody is going to replace it. Otherwise, these chains might lose access to credit permanently.

The wreckage

So what exactly happens to a chain when its primary lenders leave? To understand this, I will start with some past cases.

The first one is Harmony Protocol; in June 2022, one of its biggest bridges, Horizon, was hacked and exploited for about $100 million. And Aave, being the largest lending protocol on the chain, froze every reserve on the chain. Later in the year, a recovery proposal was also pushed, but it got downvoted by 99% of Aave token holders. Today, the chain is extinct because it lost all of its lender liquidity.

Now you might wonder why nobody could just fork Aave on Harmony and relaunch. After all, the code was always open source, and deploying a lending protocol takes less than a day. True, but what you are missing is that a lending market also needs actively maintained, sponsored oracles to price collateral. It needs enough DEX liquidity that when a borrower gets liquidated, the collateral can be auto-sold without moving the price 40%.

It also needs stablecoin issuers who actually consider the chain worthy enough to support the native redemptions, meaning stablecoin issuers like Circle or Tether can natively issue their tokens on that chain, so you can redeem your USDC for an actual dollar directly without having to bridge it somewhere else first. On Harmony, when the bridge went down, all the stablecoins got depegged, which broke the oracle feeds and made liquidations impossible. All the main components that support a lending market on a chain failed at the same time, and after that, no individual actor had a business case to rebuild it because they all function together. Who would want to sponsor an oracle feed on a chain where nobody borrows anymore?

Another case like this was Fantom. It too experienced a multichain bridge hack in 2023, and before that, 78% of its market cap depended on that single bridge. When the hack happened, the bridged USDC on Fantom dropped to about 22 cents, and that led to all the collateral being drained underwater because its value was worth a fraction of the debt.

The most interesting part here is that, at one point, Fantom was the third-largest DeFi chain in crypto with real users and demand. And even with all that, it couldn’t rebuild its credit marketplace, because the cost of reconstructing the entire stack of oracles and stablecoin backing on a depleting chain is always higher than the revenue you can expect from operating on that chain since the power users would have already left.

Fantom later also tried to relaunch with a new name and was branded as Sonic to test whether raw capital could work. They did a $190 million token airdrop and got Aave, Silo, and Euler deployed on day one with market-making supported by Wintermute. But it had the opposite effect, where it got sybiled. Depositors and borrowers were largely the same people. They were depositing to farm airdrop points and borrowing against the same deposit to maximise points exposure, and the TVL was rigged because it counted the same capital multiple times through leveraged loops.

Borrower demand was a derivative of the airdrop, not of some actual economic activity on the chain that needed working capital or leverage. For example, on Ethereum, people borrow because they want to loop stETH, or maybe they just need capital for trading strategies, and the demand exists regardless of whether a protocol is running a rewards program. But on Sonic, if you remove the incentives, there was literally no demand either. Which is exactly why, when the Wintermute term ended, its TVL collapsed 98%, the token went to less than a cent, and both its founders resigned from the board. You might game a credit market with subsidies and market maker deals, but you cannot sustain one with it.

Next, if you look at the Soneium, Aptos, Zksync, and Scroll that Aave is leaving, they have, in fact, a worse trajectory than Harmony or Fantom. Their deposits have collapsed by 95%, and they generate less than $5,000 a quarter in lending revenue.

Harmony or Fantom at least had organic users with real borrowing demand before the bridge hacks wiped them. But these six chains never had any organic demand in the first place. They had raised an average of $250 million each in funding and had launched lending markets with the most cost-efficient protocols in DeFi operating on them, but still couldn’t materialise any demand.

And Aave’s exit is only going to have downstream triggers. You would be surprised, but Aave was the anchor tenant of Financial infrastructure on these chains. Almost all Chainlink oracle price feeds on these chains were being maintained by Aave, because it was the largest consumer of them. This will lead every oracle provider to question whether they should maintain a feed on a chain with no liquid lending market. The maker markets will stop allocating capital on the DEXs of these chains for the same reason. Even stablecoin issuers can’t support native issuance on a chain generating less than a thousand dollars a month. Each exit will make the next one more certain because each provider’s business case depends on the other being present.

The wind-down mechanics also accelerate into the chains that do work, where liquidity is deeper, and lending markets can function properly. Each exit from a small chain reinforces the concentration on the large ones, which in turn makes the next small chain’s case for sustaining its own lending infrastructure even weaker.

The consolidation feeds itself because Lending is the base layer of the entire financial stack on a chain. Without it, you can’t run yield strategies because most of them involve borrowing one asset against another, and you also can’t do Capital-Efficient LP because concentrated liquidity positions regularly use borrowed capital. If lending leaves, every financial application that sat on top of it loses its foundation. Which leads to developers leaving, which means less activity on the chain, and that means there’s even less reason for any infrastructure provider to come, or rather stay.

This is why, going forward, Aave has set a minimum requirement of $2 million in annual revenue for any new deployment, which is effectively what it costs to maintain the oracle feeds, risk monitoring, and liquidation infrastructure a lending market needs per chain. I feel it’s also important because it shows that the previous model of raising hundreds of millions to fast-launch a chain and subsidise early liquidity to stay just doesn’t work anymore. It’s not sustainable now.

Not Common to Crypto

This whole thing about chains losing their credit infrastructure is not limited just to crypto. It happens all around the world where fixed-cost infrastructure serves a thin market.

After 2008, global banks had also started cutting their correspondent banking relationships with small countries. The reason was very similar to Aave’s, as compliance costs for anti-money-laundering monitoring and regulatory reporting were fixed per relationship, and revenues from small corridors were not enough to cover them. Between 2011 and 2022, active banking relationships have dropped by 30% globally. Specifically, Pacific Island countries have lost over 60% of their USD clearing corridors. Some of them only have a single correspondent bank. It got so bad that the World Bank had to provide $69 million in subsidies to keep the single providers running for the eight Pacific nations.

And this is the difference between crypto and all these traditional cases. In correspondent banking, we had the World Bank stepping in, and the floor becomes whatever a central bank or a development institution is willing to subsidise. But in crypto, the floor is close to zero, and that’s what we’re seeing happen with most of these chains. A mid-size bank spends $15 to $40 million a year on compliance alone, and the World Bank still had to put up $68 million just to stop eight countries from losing their last dollar clearing relationship. Aave’s risk monitoring contract was $5 to $8 million for all chains combined, and six of them couldn’t even cover their share of that.

All of this could be a shrinking-market story if DeFi lending was actually declining, but it’s the opposite. It’s growing rapidly and concentrating hard. Protocols like Morpho went from $105 million to over $8 billion in TVL in a year; Euler grew from just $6 million to over $300 million in less than a couple of months. We are seeing Aave’s own V4 surpass $300 million in deposits in months of launch, with Société Générale becoming the first bank to integrate with a DeFi lending protocol. The credit market is alive more than ever, but it’s concentrating on bigger chains like Ethereum or maybe two or three L2s like Base and Arbitrum and not spreading to thirty chains.

The assumption behind launching all these chains was that, since infrastructure is so cheap to deploy, every chain could host its own financial system. The assumption was half right: launching a chain is cheap, but operating a credit infrastructure on it certainly is not, as we have seen. If we look at most of the L2 today, ETH and the other top three account for 90% of all the TVL. Others are just fighting over scraps that can’t even cover the cost of a Chainlink price feed. These chains might get an Aave fork with a broken oracle, or they might not get anything at all.

That’s all for today!

Vaidik

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

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

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

I’ve had a suspicion for a long time that I could never prove. Pretty sure we have discussed this before. Every system we build to spread power ends up concentrating it.

Later, we behave as if concentration is what the system was built for in the first place.

You hear the opposite constantly. Markets spread opportunity. Technology levels the field. Trade lifts everyone. Some of that is true, in aggregate, on a long chart. But every time I zoom in at a specific place in a specific decade, I find the same arrangement. A few people sitting at a narrow point, collecting. All of the others are moving the weight. The rhetoric of an open ecosystem continues to detach from actual ground-level practices. Capturing the value in that disconnect is how the money is made in the end.

In recent history, the goal of open source was to prevent corporate ownership of internet infrastructure. This worked in theory. In practice, companies built profitable cloud services entirely on top of this free code. No rules were broken, and the software remains free. But the profits went to the Amazons and Googles of the world who provide the hosting services, while volunteer maintainers receive nothing.

I always thought these were my biases looking for their reflection. A Splendid Exchange confirmed it for me. It did not agree with me entirely, but two things this half of the book taught me are what I want to conclude the reading experience with.

Let me show you the range first, because “history of trade” might be the biggest undersell in the history of undersells. A rush through:

Spanish barbers in Mexico City filed a formal complaint in 1635 accusing Chinese barbers of undercutting them. Francis Drake sailed out of Plymouth with sealed orders from the queen that amount to go rob Spaniards, don’t make it obvious. The Dutch invented the joint-stock company, one of the great financial ideas anyone has ever had. Inside twenty years they used the same idea to burn islands. Manhattan got signed away in a treaty for a nutmeg rock, and they thought they’d won. Boston, tea, tariffs. Opium. Corn Laws. A Scottish botanist creeped around China in disguise, smuggling tea plants out in little glass cases. Bessemer makes steel cheap, and global travel faster. Then cold storage arrives, Argentine beef lands in London, and the English farmer is finished. Smoot-Hawley, 1930, the tariff that helped light the fuse. And it ends in Seattle in 1999 with tear gas and people dressed as sea turtles. Bernstein ties it together in this chaotic little read. That’s all I wanted to say.

Now the two lessons:

1. Corporation was invented as a weapon, and we have forgotten it.

We talk about companies as economic units or things that make, sell and employ. But the modern corporation was forged for war.

In 1602, the Dutch government pulled six rival trading companies into one and called it the VOC. The point was to stop Dutch merchants from bidding each other up for the same pepper. It gave a business the legal power to build forts, hire armies, sign treaties with foreign kings, start wars, and execute people. The company also sold shares to the public, which is where the stock market comes from.

Communication with Asia took a year each way, which meant that in practice the VOC behaved as a sovereign nation wherever it went, free to destroy its competitors whenever the men in Amsterdam or a local commander felt like it. Between 1602 and 1663, two private companies fought what amounted to the first world war. The VOC and its West India counterpart ran a global land grab. They fought for Asian spices, Brazilian sugar, and African gold and slaves.

Private companies ran the operation, while governments just signed it off.

I assumed the corporation came first as a commercial instrument and then, being powerful, drifted into politics and violence — like spotting a Venezuelan president, using AI.

Wrong. The first great multinational was armed at birth. When we act surprised today that a platform behaves like a government, or that a private company runs infrastructure a nation depends on. We should instead just look at it as the thing returning to its original shape.

2. An open secret, you are forced to sit with in the book

Free trade works. The people who get hurt by it are never, ever paid. This could have been avoided.

Bernstein is not a sceptic about trade. His whole book is a case that openness made humanity richer, and the final chapters put real weight behind it. The 1930 Smoot-Hawley tariff pushed American rates on dutiable goods to nearly 60%, the world retaliated, global commerce seized up, and he traces a line from that wreckage into the war that followed.

He introduces the idea that comes from a 1941 paper by Wolfgang Stolper and Paul Samuelson. Their finding is that when a country opens to trade, it does not simply win or lose. Inside the country, the abundant factor gains and the scarce factor loses, and the loss is permanent and concentrated on specific people. Free trade makes skilled workers richer while low-skilled workers take the loss. Fair enough.

Stolper and Samuelson also proved the gain to the winners is always bigger than the loss to the losers. That means the winners could easily pay off the losers (through cash, retraining, or benefits) and everyone would end up better off.

The point is that trade itself creates wealth. The failure happens when the winners keep all the money, refuse to help the people who lost their livelihoods, and claim the system worked because the overall numbers went up.

So my original suspicion turns out to be half right and half lazy. Power does concentrate.

Trade really does create more than it destroys. Inequality grows in the space between total gains and shared gains, driven entirely by policy decisions. Which is worse, in a way. A rigged machine you can smash. A fair machine whose winnings are being pocketed by the people who already won is much harder to argue with, because every one of them can point at the chart/numbers and say “fair?”

Anyone building anything should read this. Because if you work in crypto, or fintech, or AI, you are working on a trade route. You are building a thing that moves value between people who cannot easily reach each other, and takes a cut of the distance. Whoever ends up controlling the narrow part gets rich beyond reason (and sometimes takes payment in Bitcoin).

The other reason to read is that it teaches you to look for the stowaway. Historically, traders built ocean routes to move silk and spices. The Black Plague and new religions hitched a ride on those same ships. Neither was listed on the cargo manifest, but both reshaped the world far more than the spices did. If you are building rails right now, you might want to check what will ride along. Because the stowaway can outlive you.

Bitcoin’s first block carried a newspaper headline about bank bailouts. The point was to leave the dollar system, and now the most used thing on these rails by a distance is tokens pegged to the dollar. Also, the stablecoin issuers hold so many US Treasury bills that they are alongside mid-size countries as government creditors.

But it cuts the other way too. The stowaway can be the good part. Did they build the internet to reunite families across borders? But that’s most of what it does now. The point is only that you don’t get to pick.

A frontier model helped plan the strikes on Iran this spring. Another one slipped its supervision over the summer and ran a cyberattack on a rival AI company.

On to the instance that changed how I see the world.

In 1621, the VOC wanted a monopoly on nutmeg. Nutmeg grew in only one place on earth, a few small islands in the Banda Sea. The islanders had been trading it for centuries and were also selling to the English, which the Dutch could not allow.

So Jan Pieterszoon Coen sailed to the main island and killed most of its thirteen thousand people. The survivors were shipped to Java or enslaved on the trees that had been theirs.

Coen learned double-entry bookkeeping as a teenager in Rome, when that was new technology. He came back, studied the company’s accounts, and found the margins too thin. He wrote a report recommending a monopoly on the fine spices at any cost – The killings came down to business logic.

Then Coen explained the model to his bosses. Trade in Asia has to be defended with weapons, he wrote, and the weapons have to be paid for by the profits from that trade. We cannot carry on trade without war, nor war without trade. War pays for trade, trade pays for war.

So, back to where I started, the assumption that every system built to spread power ends up concentrating it. It was true all along.

But trade is the best thing we do. It made us rich, it hauled billions out of misery, and every attempt to shut it down has ended in ruin. Trade gains have never once been shared with the people who paid for them when sharing was optional. Given a choice, no human in history ever picked this, and I doubt that future ever comes.

Our default instinct is to ask for shared wealth, while aiming to join the concentrated few.

—Thejaswini

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

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

Do you know that buying a stock today has become instant, but everything that happens to it after, like dividends or stock splits, is still a ruckus.

When Netflix or Apple announces a dividend, the money doesn’t arrive to you directly; it goes through a fragmented process where each intermediary from DTC (Depository Trust Company) to your broker has to figure out your payout independently from their own records and then later sync with each other.

These events are called corporate actions, and the finance industry spends a staggering $58 billion a year to process them. Most of it is just reconciliation costs, making sure those separate leaders are actually synced and updated. This is a 40-year-old unsolved automation problem, but we now have new on-chain token standards that are trying to fix this permanently, so I went looking straight into it!

Why could they never fix it?

In my previous piece, I wrote about how, in 1968, Wall Street literally had to close on Wednesdays because nobody could keep up with moving paper certificates. Later on, DTC tried to solve this problem by creating a vault and turning trades into book-entry updates on a single ledger.

That was a brilliant fix for settlements, for sure. But corporate actions are very different. Settlement is a one-to-one action, meaning a seller is matched to a buyer and the transaction completes. But a corporate action like a stock split or dividend is one-to-many, meaning a single action hits every holder simultaneously. Now, because ownership is dispersed across an entire intermediary chain, from the transfer agent to the brokers at the bottom, each of these parties has to independently process the event from their own records and then reconcile again later.

Let me walk you through how it actually happens. Imagine Apple declares a 0.25$ per share dividend; that money doesn’t reach you, the shareholder, directly. The total payout is first wired to the transfer agent (a firm like Computershare that maintains the official shareholder register).

But as you know, the register doesn’t have your name on it, and all shares are owned by Cede & Co, the nominee entity of DTC; the transfer agent then pays the DTC instead of you. After that, the DTC looks at its own records to figure out how much each of its member firms (custodial banks like BNY Mellon) should receive, and credits their accounts.

Those custodian banks then check their own books to split it among the brokers they serve. And finally, your broker, say Fidelity, independently calculates your individual entitlement from its own records and credits your account. That is five entities trying to independently calculate the same allocation from their individual copies of that data.

And what’s even crazier is that their books don’t even sync at the same time. An investment manager executes a corporate action on the ex-date, but the custodian won’t actually distribute further until the pay date, which can be weeks later. During this period, your broker’s records falsely say you own shares you technically haven’t even received yet, and traders could even sell stock that the firm doesn’t have yet.

Now imagine the fact that there are roughly one million corporate actions globally every year, and each one propagates through this same fragmented chain, taking more than $58 billion in processing costs. The cost is also one of the reasons nobody even tries to bring it down.

The dysfunction originates from the data and who profits from cleaning it up. So when Apple announces that dividend, it files a form with the SEC and puts out a press release. The problem with that is it’s a free-form SWIFT text message, which is not machine-readable and thus cannot be auto-processed by the system. This is year 2026, ladies and gentlemen; today we settle trillions in equity volume in seconds, but corporate actions like dividends and stock splits still arrive in non-readable PDFs and copy-pasted messages.

And it’s not like we don’t have the tech to publish machine-readable announcements; the technology has been in existence for over a decade (XBRL tagged messaging). But currently, the process of filtering those messy press releases and extracting the clean data banks need is done by Bloomberg and S&P Global.

These firms have hundreds of analysts who spend their days scraping announcements and interpreting ambiguous language to push the result for downstream consumption. S&P Global alone manually validates over 1.4 million events a year across 170 countries. If Apple started publishing machine-readable announcements tomorrow, a significant portion of that business will go obsolete. These are the firms best positioned to lobby for better data standards at the source, and they are the last ones who ever would.

On the other side, Apple bears none of the processing cost. It files the paperwork and gets done with it. Every dollar of costs falls on the intermediaries downstream. In fact, the industry groups have asked issuers about adopting structured formats, but they said you need to provide us incentives to do that.

The problem with this problem is also that financial infrastructure seldom evolves through efficiency arguments. They always require a genuine crisis to override the inertia that keeps incumbent structures in place. And sadly, corporate actions have never had that crisis. The cost is immense in aggregation but diffused across enough participants that no single failure forces collective action.

ERC-8056: The New Token Standard

So if the problem can’t be fixed from within, the question is can it be bypassed entirely? This is where the new on-chain token standards enter.

ERC-8056 is a display multiplier for ERC-20 tokens that Robinhood shipped with Chris Ridmann from Superstate. The idea here is that instead of minting new tokens during a stock split, you update a multiplier that adjusts how every wallet’s balance displays. So if you hold, let’s say, 100 tokens, and a 4-for-1 token split happens, your wallet will be able to update it without the contract ever creating new tokens. And the underlying balance and transaction history will always remain intact without needing to perform transfers to process or reconcile.

This one smart contract update can actually replace all five entities independently recalculating balances from their own copies of data.

And this same programmability can be extended to every other corporate action the incumbents have been failing to automate. Dividends can become a single contract call that distributes to every holder on one ledger, instead of cascading through transfer agents and different books updating at different timelines. Rights offerings, proxy votes, all of these can be encoded as on-chain logic where the rules execute against one authoritative record of ownership. The only reason these corporate actions have stayed manual for forty years is that they had five different databases that had to independently process the same event and then reconcile with each other afterward. ERC-8056 collapses that entire value chain into a single programmable layer.

Another important thing to note is whether tokenising this model actually collapses the number of ledgers in the system, or just adds another one. Because right now, most tokenised equities are more digital twins of their origin. Like Robinhood tokenised stocks like Apple and Tesla, but when you look at the mechanism underneath, these tokenised stocks are backed by real shares held in a traditional brokerage account, and so the token is technically a sixth layer sitting on top of the same five I described in the section above.

xStocks on Solana is a similar arrangement but with even weirder design choices. They hold 93% of the tokenised equities market on Solana, but dividends get forcibly reinvested (holders can’t take cash), and the token contract includes a feature called Permanent Delegate that lets the issuer unilaterally seize tokens from any wallet at any time. So the ‘decentralised’ equity product actually gives the issuer more control over your holdings than a traditional broker ever could.

The only model that is actually solving the root problem is native issuance, which gives the blockchain the ability to become the shareholder register itself. One of those doing it is Superstate, they have registered with the SEC as a transfer agent, and instead of maintaining a separate internal database that periodically reconciles with the DTC, they record ownership directly on-chain.

Galaxy Digital recently announced plans to tokenise its actual equity on Solana using Superstate native issuance as its transfer agent. If that goes through, Galaxy’s shareholders will exist natively on the blockchain, and the dividend wouldn’t need to cascade through the intermediaries because the intermediary chain itself will collapse.

There’s a concept Ian Grigg wrote about in the early 2000s; it’s called triple-entry accounting, and it captures what native issuance is capable of doing really well. He proposed that when two parties transact, a shared cryptographic receipt creates a third entry that neither side controls but both can verify.

Double-entry bookkeeping has been the bedrock of financial recordkeeping since Luca Pacioli formalised it in 1494. What Grigg proposed, and what native issuance implements, is arguably the first upgrade to that system where one authoritative record that makes bilateral reconciliation unnecessary.

In the current system, you process now and reconcile later, and the reconciliation is where all the cost and risk accumulate. With instant settlement on one shared ledger, that entire step vanishes. Instead of reducing the cost, the process that creates it ceases to exist.

The good part is the regulatory machinery is actually moving to accommodate this faster than we realise. The DTCC, for example, issued a no-action letter in December 2025 clearing the path for tokenised securities within existing settlement infrastructure, and with Nasdaq getting approval for tokenised securities, the incumbent exchanges are building native issuance into their roadmaps.

I started this article saying corporate actions have been stuck for forty years because financial infrastructure reforms have historically required a genuine crisis. But this time we might not actually need one. If companies can issue equity natively on a blockchain where the transfer agent’s register is the ledger, they don’t need to fix the five-database chain.

They can make a workaround because the process that creates it no longer exists. Now how fast that transition happens depends on regulatory maturity and how many issuers are willing to make the jump.

That’s all for today!

Vaidik

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