Earlier this week, I used $10 from my MetaMask wallet to buy AI credits on an inference market. It gave me $25 back in Claude, GPT, and Gemini usage that I could spend across 400 models until I used it all up. I didn’t have to commit to a monthly or annual subscription.

But where did the extra $15 worth of compute come from? Someone who already held $25 worth of compute, but didn’t want to use it, sold it to me at a 60% discount. They pocketed all my $10 for idle credit, and I walked away with more than double the compute power for my money’s worth.

The inference market is filled with demand for cheap compute. While the compute suppliers sell it like a subscription, most people don’t use it all the time. Most people have experienced this with gym memberships. We pay for the entire year and barely turn up. But what if you were offered to buy cheap single day passes to access the gym? The compute market is living through a similar moment.

On to the story…

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The Most Natural Trade

If an airline takes off with an empty seat, it is leaving money on the table. It’s a bad trade. So an airline will sell that seat at almost any price up to departure. It explains that one time you got a sudden upgrade to a business-class seat while the airline gave away your economy seat to someone from the overbooked reserve of economy passengers.

The seat is perishable, and not selling it is inefficient. Disciplined pricing of perishable inventory can yield big rewards. In 1992, American Airlines estimated the quantifiable benefit at $1.4 billion over the last three years and expected an annual revenue contribution of over $500 million to continue into the future. Hotels do the same with empty rooms for the day. Power grids do something similar with electricity. Since they cannot store it, they trade electricity away on spot markets.

When inventory expires with time, it commands an alternative market. And if the product has a mass market, someone will always hold more than they consume, and someone else will want it cheap. This drives the price to find a middle ground.

AI inference falls in the same category of products. LLM subscriptions reset every month, and unused allowance expires. Prepaid API credits expire annually, and you don’t even get a refund. Even a rented GPU will have to be paid for whether you use it or not.

Not everyone wants all-round access to compute power throughout the month or a year. Sometimes people don’t even know in advance how often and when they’d need compute power.

For all such users, a parallel market where AI inference capacity is sold in much smaller and cheaper credits is forming.

The Need for a Spot Market

Let’s dive into my experiment with a secondary market for compute.

Orbio.so is a marketplace for AI inference. It runs on two tokens. $ORBIO is the volatile one you stake, while $CREDIT is the product. Each token of $CREDIT equals one dollar of AI inference usage across 400-odd models. When you stake ORBIO, you earn fresh CREDIT tokens every hour. Orbio funds this with half of the trading fees it generates on its platform. You can then swap your AI inference credits on an order book, transfer them like a token, or activate and use them.

My $10 bought me AI inference worth $25 on Orbio. Why would someone sell their inference for less than they paid for it?

Because they paid nothing for this compute. The seller did not buy $25 of compute and let it rot. They earned the credits by staking ORBIO and were funded by Orbio’s trading-fee income. To them, a dollar of credit sold for 40 cents gives them a pure gain of 40 cents.

This contrasts with the subscription model that expects you to pay for the whole month or year, whether or not you use it. That model suits a heavy, steady user whose usage is recurring daily and justifies a flat fee. But for a freelancer who needs a burst of inference for one project and then nothing until the next project starts, a subscription model could cost a lot. Even a developer testing an idea on weekends wouldn’t need an exclusive subscription beyond what their enterprise provided.

Orbio’s demand-based inference credit model is the only native way for the agents of the emerging agentic economy to pay and buy credits based on the compute their tasks require.

A coding agent that works overtime on a couple of days of the week and then goes cold has no use for a monthly seat. Orbio’s agent design lets an agent buy and activate credits in a single call, sign once for its API key, and top itself up against its budget with no human in the loop.

Let’s strip away the tokens and look at what a buyer gets. One key gives access to 400+ models without any subscription. You can start at $5 and go up to $2000 worth of credits with no monthly minimum or renewal needed.

All you need to do is migrate the key with two lines of code. Just change the base URL, change the API key, and streaming, tool calls, and vision keep working exactly as with any other LLM. Your request goes to the model you named and comes back with the provider’s own model ID on it.

The Liquidity Challenge

But Orbio will be tested by how deep its liquidity is.

When I looked, the order book held about $11,852 of credit for sale. That is not much. A single agent running on frontier models with continuous workload could burn through tens of dollars an hour. A serious developer could drain a meaningful slice in one session alone. To manage this, Orbio has set a tiered discount across that pool.

Smaller amounts of compute purchases get larger discounts. When I last checked, Orbio was offering $20 worth of AI inference for just $5 (75% off) but $50 worth of inference at ~$30 (40% off).

The tiered discount is designed to incentivise smaller lots of purchases. It also meets the kind of audience it wishes to cater to - agents that need to make micro-sized payments for their recurring compute tasks or developers or users who don’t want a consistent, recurring access to monthly or annual subscripti

The recent-buys feed tells us the demand-side story. 15 of the last 20 trades were less than $50. It serves a freelancer or agent, but isn’t enough to fulfil a user who needs consistent compute power above those levels.

Can It Meet the Compute Needs?

Every discount on Orbio is funded from half of its trading fees. Every hour it converts half of its fees into credits and hands them back to stakers. So the entire engine works as a function of trading volume on a volatile token which Orbio cannot control.

So the depth of the discount a buyer enjoys is a result of the speculative churn in Orbio’s token that has nothing to do with compute. As long as ORBIO trades heavily, the discounts keep flowing. When trading cools, less credits are minted per hour, the book thins and sellers hold out their compute for better prices.

This is the challenge the protocol needs to survive. A subscription business is sustained by its customers paying for the service. Orbio’s discount is sustained by traders speculating on a token beside the service.

A footnote on Orbio’s homepage also says as much: credit is “a promotional grant of product access, not an investment return.” A hobbyist spending $10 has nothing to worry about.

The only way Orbio can outlive the subsidy is by making the other half of the product indispensable, where it offers the buyer of the credits a single key to tap into 400+ models.

The Future of Compute

When you zoom out from Orbio and look at the secondary market for AI inference, you see a pattern that’s repeated across history. For over two decades, we did something similar with software. We bought access to a software for every month. We picked a plan that met our requirements and paid for it whether or not we used it. Think of subscriptions to antivirus, pagemaking and designing tools by Adobe or Microsoft’s Office suite. They all worked the same way. The seller banked on the buyer’s under-use. The model fit a world of humans with routines who logged into the same system every morning and used the same set of tools steadily.

But that world is ending. The buyer of inference can be a person with routine or someone who just needed some compute power for a rare, non-recurring task. The latter kind of buyers don’t need a subscription plan. They want to just buy what is needed for their task, pay for it and stop.

Orbio is an early, imperfect answer to that. The discount may not survive if the trading volume moves towards zero. Orbio has a difficult path to fix this. Orbio already collects fees beyond ORBIO swaps. It collects creator fees on tokenised Nvidia stock and routes them into the same payout engine. If more of the subsidy comes from real, recurring activity rather than speculative churn, the less the discount is at the mercy of trading volume of a speculative token. However that part is negligible today.

Nevertheless, the convenience Orbio’s model offers answers a lot of demand needs. Buying inference by the dollar from any model without provisioning a thing is the perfect shape for demand that no longer consistent across usage hours.

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

Until next time, stay curious,
Prathik

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Stocks have been part of internet culture for longer than crypto would want to admit. The GameStop saga proved that a subreddit could move a stock by 2,400% on pure collective delusion, and finwit has since turned every ticker into a tribal identity. The cultural energy around equities is massive, and has been, for years.

But it has always been weirdly disconnected from the capital itself. The memes were on Twitter (X), the trades settled on NASDAQ, and the culture around a stock could make or break a company’s public perception without ever touching its cap table. Stocks have been internet-native content for a long time, but they never became internet-native as assets.

Then recently, Robinhood built its own blockchain and launched tokenised equities on it. And people did something really unexpectedly interesting- they started launching memecoins paired against those stock tokens. A coin called $BONER, paired against Hims, cornered 81% of all tokenised HIMS supply in its pools. And somehow, the stock tokens actually got liquid. I think Robinhood has accidentally built a wealth-effect machine. The chain is pulling two-thirds of all Ethereum L2 revenue right now and has brought in more liquidity to tokenised stocks than all institutions.

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The Speculators always get there first

Tokenised equities are not new. People have been trying to put stocks on the blockchain since at least 2018, when security token offerings were supposed to be the next ICO. tZEO had raised $134 million back then for this. Securitise got backing from every blue-chip name in finance. Binance, FTX - they all gave it a fair shot, but Mirror Protocol on Terra was one of the most complete attempts. They built fully synthetic tokens for Apple, Tesla, Google, and a bunch of other stocks, all tradable 24/7 by anyone in the world with no broker and no KYC.

And it worked really well for about a year before the SEC sued Terraform Labs partly over it, and then the whole chain collapsed. But the interesting thing about Mirror is that even when it was live and fully functional, its liquidity was terrible. You could buy synthetic Apple, but almost nobody bothered, because why would you when the real Apple stock sat right there on NASDAQ with better spreads, actual ownership rights, and a brokerage app that let you buy fractional shares for free?

The assumption was always that if you build the right infrastructure and make the assets accessible, traders will show up. But accessibility has never actually been the thing that brings people into a new market.

Joseph de la Vega wrote the first known book about a stock market back in 1688, about the Amsterdam exchange. And one of his most surprising observations was that the exchange drew in investors and gamblers in roughly equal measure. The Dutch East India Company ran real-time ship activity and trade routes, but the people who actually made its shares liquid weren’t the ones studying shipping manifests.

They were the ones placing bets on rumours about what cargo might be on a vessel that hadn’t docked yet, trading on secondhand gossip from sailors at the harbour, buying and selling based on how they felt about the weather.

The Chicago Board of Trade observed this again in 1865 when it built grain futures for farmers and found that the market only worked once speculators showed up to take the other side. This pattern goes back to financial markets themselves.

The reason is that if every participant in a market is rational and well-informed, they mostly agree on what something is worth, and when everyone agrees on price, there’s little reason for anyone to trade. You need people trading on sentiment, narrative, vibes, and the sheer excitement of watching a number go up, because they are willing to take one side of the trade. Take out the noise, and you end up with a dead market. The people who look like they are just gambling are actually performing a very important function in a new market; they are showing up before there is any rational reason to.

Crypto, of all, understands this better than anyone. Uniswap got its first real liquidity from yield-farming degeneracy in DeFi summer, and not from institutional allocators doing due diligence on its AMM design. Pump.fun tokens mostly die within a day, but the protocol still pulled in over $600 million in revenue last year because all that frantic activity generates infrastructure underneath it.

Hyperliquid launched as a perps exchange and let people trade memecoins on it, and now tokenised stocks and RWA make up 67% of its HIP 3 Volume. Every time something in crypto has tried to skip the degen phase and go straight to serious institutional adoption, it has ended up with a compliant and well-built but completely empty order book.

How a memecoin ate the float

So Robinhood did the one thing every other chain got backwards. Every blockchain ecosystem you know has followed roughly the same growth arc: launch DeFi protocols, attract builders with grants, wait for memes to show up organically, and then eventually try to bring real-world assets onchain at the very end.

But Robinhood launched with tokenised stocks as the foundation from day one and was fairly quiet for a few weeks; then a launchpad called long.xyz gained traction and started letting people create memecoins paired against the stock tokens instead of ETH or stablecoins.

That’s where it gets interesting, because the pairing changes where the liquidity actually resides. On Pump.fun, when you buy a memecoin, the other side of your trade is SOL, and the memecoin and the idea it references have no economic relationship beyond sharing a cultural joke.

But on Long, when you buy a memecoin paired against tokenised Nvidia, the router has to acquire NVDA first to fill your order. At the protocol level, every memecoin purchase becomes a buy order for the stock token on the other side. The equity exposure gets locked inside the liquidity pool as the reserve asset that prices the joke. If you have ever looked at how Olympus DAO’s bonding programme worked in 2021, this should feel familiar.

OHM sold tokens at a discount in exchange for LP tokens, and the high-quality asset on the other side went into the protocol’s own treasury. The protocol owned its liquidity, and OHM’s multi-billion-dollar market cap was denominated against a reserve it had itself absorbed.

You can see something similar with $BONER, paired against tokenised Hims & Hers. The total tokenised HIMS float on Robinhood Chain was around 73,685 tokens, and $BONER’s pools were holding 39,136 of them, roughly 53% of the entire on-chain supply. When Wall Street closed for the weekend and nobody could mint new stock tokens, the memecoin’s demand kept running while supply was frozen, and on-chain HIMS started trading at a 37% premium to its NYSE close.

After the squeeze, Robinhood’s Jersey entity expanded the HIMS float by 4.8x to relieve the pressure. But 53.1% of the new supply ended up right back inside memecoin pools. The supply response did not reduce the memecoin’s grip on the float. It just gave it more liquidity to absorb. For 11 out of all 17 tickers on the Robinhood chain, more than 40% of the wrapper’s total trading volume is a side effect of someone trading a joke.

A meme is basically a carryless cultural asset: you hold it, and your outcome depends entirely on the meme’s momentum. But pair it against a real underlying, and the cultural asset starts accumulating something outside of itself. The meme acquires carry. Finance has recycled the same primitives for centuries, but what we are starting to attach them to has never looked like this.

The accidental flywheel

Once carry exists, things start compounding in directions that are hard to predict. For example, with Artificial Inu, which is paired against tokenised Nvidia. When you buy $AI, 80% of the buy fee goes into a permanent NVDA vault that can only grow and can never be withdrawn from. When you sell, the fee splits between burning $AI tokens and locking even more NVDA permanently.

Every single trade, buy or sell, ratchets up the stock exposure in one direction. Roughly $3 million in NVDA has been permanently removed from circulation through this mechanism alone, and no human consciously decided to accumulate Nvidia stock. The memecoin’s own trading churn did it automatically.

And then Long took it a step further with LongX, which takes a 3x leveraged perpetual position on NVDA, wraps it into an ERC-20 token, and then uses that token as the quote asset for another memecoin. You now have a share being tracked by a debt security issued out of Jersey, wrapped into a leveraged perp on a DEX, tokenised into an ERC-20, and sitting as collateral inside a joke token’s pool.

That’s...Four layers of financial wrapping on top of a single Nvidia share. At one point, this whole thing held 16% of all NVDA open interest on its exchange. That’s what makes it a wealth-effect machine. A BNB Chain study found that users who entered through speculative memecoin trading converted to holding tokenised equities at 8.6%, compared to 0.6% for users offered equities directly.

Speculation was 14 times more effective at turning people into equity holders than simply showing them the equity.

Robinhood Chain is doing $4 million in daily revenue two months after launch, roughly double Hyperliquid and about two-thirds of all Ethereum L2 revenue combined. Pons alone is the second-highest fee earner across all of DeFi right now. A memecoin launchpad, outearning almost every protocol in the ecosystem.

But there are also things worth watching. Robinhood is running a 90-day gas subsidy for its own wallet users that expires around late September, and nobody knows what happens to transaction volumes when those users start paying gas for the first time. The permanent vaults also haven’t been tested at the scale where things tend to break. And the weekend premium phenomenon, where on-chain prices decouple from the NYSE because issuers cannot mint new tokens over the weekend, might be a feature right now, but it could start looking like a problem to a regulator who has never heard of a bonding curve.

Regardless, it still does matter beyond just Robinhood. Every memecoin that pairs against a stock token locks up tokenised equity inside its pool. Before this, the money always flowed away from the meme. You made the joke on Twitter, you traded the ticker on Robinhood, and the value ended up with the platform or the exchange. The meme itself never held onto anything.

What we are watching right now is a joke token accumulating a balance sheet of tokenised equities through its own trading activity. Blockchain gives it a common programmable environment, where some arbitrary things can start having economic relationships with other arbitrary things.

A meme can be paired against a stock. A leveraged derivative can become the collateral for a cultural bet. A weekend supply freeze can create an arbitrage that funds a sequencer. And none of these was deliberately designed by a product team. They emerged because the primitives were composable and early adopters found them first.

That’s it for today!

Vaidik

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Dario Amodei, who runs Anthropic, said that the companies building the most powerful AI models should deliberately slow down. After a few hours, Sam Altman, Elon Musk and Demis Hassabis had broadly lined up behind the idea. This bothered me only because Elon Musk agreeing with this particular gang is an event on its own.

Essentially, the people pushing AI forward the fastest were now suggesting that perhaps everyone should slow down. Including, presumably, themselves?

Donald Trump was not convinced. Speaking in Ireland, he downplayed the warnings and returned to the geopolitical argument that has followed every attempt to regulate AI in America.

China is not going to stop, so why should the United States? His administration’s position remains that losing the AI race would be worse than moving too quickly.

Amodei is worried about models becoming capable faster than humans can understand or control them. OpenAI has already temporarily slowed parts of its own training while strengthening safeguards around models it now considers capable of serious cyber work. The company’s chief scientist, Jakub Pachocki, wrote last week that he expects AI systems to increasingly contribute to their own development and called the situation one requiring “extreme caution.”

The internet and I naturally arrived at the more entertaining possibility that all these people had seen something terrible and were politely trying not to tell us.

Maybe. But can AI end the human race? No one has a non-diplomatic answer, and we have no precedent to support either view.

As of now, we know AI agents are getting access to things.

Gmail, GitHub, Slack, calendars, company databases, cloud servers, social accounts, payment methods, CRMs, the ability to generate media, buy software, and publish content that other people can see.

So now, when you ask what this model is allowed to do, that becomes one of the larger infrastructure markets created by agents. The industry has been improving intelligence. Now companies have to build the layer around intelligence that decides where it is allowed to go.

So, this is the best monday to talk about Aident, one of the companies making that bet.

Not the possible apocalypse, we probably can’t stop that. But I have been testing a small version of the problem with Aident Loadout, which gives an AI agent access to outside tools while putting credentials, approvals, spending controls and an audit trail around what it does. Let’s see what it tells us about the broader AI permission problem and how the Aident Loadout experiment puts it to the test.

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In 2006, Twitter had a similar headache.

Third-party developers wanted their apps to use Twitter accounts, but the obvious way to make that work was terrible. A user would give another application their Twitter username and password, and that application would hold the credentials indefinitely.

Blaine Cook, then Twitter’s chief architect, started discussing a better system with people working on OpenID. What emerged in 2007 was OAuth, an open standard for delegated access. Instead of handing a photo-printing website a user’s entire Flickr password, for example, they could give it permission to access the photos it needs. The service got a limited token, and the user kept the master key.

That decision became part of the plumbing of the modern internet. Whenever Google asks whether you want to allow some app to access your calendar, or GitHub asks whether an application may read your repositories, you are living inside the solution to that old problem.

AI agents make this awkward again. OAuth is very good at answering one thing. Does an app have permission to access Gmail? It was designed for a time when apps did one clear, predictable thing. A calendar app had calendar features. A photo printer printed photos. We knew what we wanted out of the software. An agent is different because its useful feature is precisely that you have not programmed every action in advance.

For example, when you tell it to organise a launch, it decides this requires researching competitors, generating images, writing posts, opening X, publishing something, and perhaps updating a spreadsheet afterward. Tomorrow, you ask for something different, and the same agent discovers another set of tools.

So “has access to X” is no longer a sufficient security model.

Should it publish this particular post?

Can it delete one?

Can it message customers?

Can it spend $2 on research without asking? Can it use the same Gmail connection when you switch from Claude to another agent next month?

This is now permission because software is no longer that predictable. Agents are moving into companies faster than the systems controlling them.

Every time a company wants one agent to work across software owned by several different vendors, somebody needs to handle authentication, tools, policy and records of what happened. The more models become interchangeable, the stranger it becomes to rebuild those connections for every new agent.

Okta found that 81% of CISOs and security executives were concerned about AI agents having excessive access, while fewer than half were confident they could identify every agent in their organisation or centrally control what each one could do. Microsoft has responded by extending Entra identity infrastructure to agents, arguing that every agent should have its own identity, scoped permissions and audit trail.

That makes identity and access management a more useful neighbouring market than the entire AI industry. Traditional IAM already handles who can enter which system. Agents need another layer that decides what they can do once inside, like read or write, spend or not spend, delete or ask first.

Aident calls its product Loadout. The easiest way I’ve found to think about it is as a capability and control layer placed between an agent and the software it wants to use.

Aident says Loadout already connects to more than 1,000 apps and gives agents access to 27,000 different actions.

Instead of giving your passwords or API keys directly to the AI agent, you can store them inside Aident Vault. Connect an app once, and you can then use the same connection across tools like Claude Code, Codex, Hermes, ChatGPT, and Cursor.

Aident also keeps a record of what the agent actually did. You can see which action it ran, whether it succeeded, and how much it cost.

But first, let’s clarify what Aident means by “safety.”

Aident does not make an AI model aligned. It cannot guarantee that Claude, Hermes or some future agent will never make a stupid decision. What Aident tries to do is limit what happens after that mistake.

Your credentials can stay hidden from the agent. You can restrict what an app connection is allowed to do. You can also check an action before the agent runs it.

Aident splits agent control into two checks. Spending approval asks only whether the agent may spend money. You can let it auto-spend small amounts, like $10 on research, without asking you each time.

Action approval asks whether it may perform a specific act. That check applies even when the act costs nothing. Sending an email, deleting a file, publishing a post, or changing a customer record can still cause real harm. The two risks are different. Money risk is about the budget. Behaviour risk is about irreversible or high-impact actions. So an agent might spend $5 on its own, yet still need your OK before it emails anyone or deletes anything.

Finally, Aident’s audit trail keeps its own record of what happened. So instead of asking the agent if it has send that email, you can check an independent log to see whether the action actually ran.

So I gave it something ridiculous to do

I connected Aident to Hermes and asked it to create an online pet company.

The eventual brand was Blorb & Co. I wanted ten digital pets, all with specific personality, rendered in 3D. The agent had to research similar products and conversations on X, choose an audience, develop the positioning and

tone, create a logo, posters and campaign copy, animate a couple of characters, give all ten voices, build a quiz and website, and then actually publish an organic campaign.

Through Aident, Hermes used research tools such as Exa and SerpAPI and generated the characters and creative assets through Fal.

Then it produced voice lines through ElevenLabs, made two animated clips, created trading cards, and built a small meme system that was so stupid, but maybe it can be fixed with a bit more cleaning. I eventually connected my X account, and the agent published six posts.

Later I connected to Vercel and it deployed the interactive pet site.

The whole experiment cost roughly $3 in Aident usage.

The agent was not working through a workflow I had designed beforehand.

It searched the available capabilities, inspected what a tool expected, checked prices and then used the tool. When RedFoxHub’s X search broke, it moved to SerpAPI instead. The route changed while the work was happening.

A few things broke during the experiment. The text-to-speech tool expected a different input than the agent was sending and later ran into rate limits.

Vercel was the biggest headache. File uploads did not work properly, and some deployments looked successful even though the files were missing. I eventually got around this by putting the entire website into a single HTML file.

@adoptablorb.vercel.app

OAuth also required me to manually approve account access. That is actually a good security feature, although it was not always obvious what I needed to approve or where.

A permission layer can make outside software available to an agent, but it still inherits the unreliability, schemas, rate limits and strange decisions of all that outside software.

Aident’s product looked to me as an attempt to put one controlled surface over a very messy internet.

Btw, figure out which pet you are ( I am Nugget) -

Which Pet Are You?

The next question is how Aident makes money if much of what it connects to belongs to somebody else. Its current model gives us a rough answer. Loadout supports pay-as-you-go usage, paid Pro and Team plans, and additional credit purchases. For some built-in services, Aident supplies the underlying provider access and deducts the cost from a user’s Aident balance.

For other applications, you connect your own provider account or API key, in which case that provider can bill you directly.

That creates two businesses sitting on top of each other. The first resembles a software marketplace. Aident aggregates demand, buys or routes access to outside capabilities and gets paid as those capabilities are consumed. More agents doing more work should mean more transactions. This is the obvious revenue line, and it is the one I experienced while watching a few cents disappear every time Hermes generated something or ran a search.

But I would not want that to be the whole company. If Aident’s value were simply adding a margin to somebody else’s API, a customer making ten million calls would eventually work out that it can negotiate directly with the provider. Aident itself already supports BYOK, where users bring their own provider credentials and Aident’s deduction for the external action can fall to zero.

That business is the control plane. A company may happily bring its own OpenAI account, Salesforce contract, GitHub organisation and cloud bill while still paying Aident to decide which agents can reach them, store the credentials, apply approval rules, enforce spending policy, maintain the integrations and keep an audit record. At enterprise scale, governance and security can become the thing being sold rather than the underlying API call. Aident’s public pricing already points in this direction with paid plans and an enterprise tier around security, governance and customised deployment.

That is probably the healthier economics if Aident succeeds. Marketplace revenue grows with the amount of work agents perform. Subscription and enterprise revenue grow as Aident becomes more important to the organisation. One monetises traffic. The other monetises trust.

Aident is early to an opportunity that other companies have clearly noticed too. Composio helps AI agents connect to apps like Gmail, Slack and GitHub without developers having to build every connection from scratch. It supports more than 1,500 apps and is especially useful for companies building AI products for lots of users because each user can keep their own accounts and permissions separate.

Arcade is more focused on big companies. It lets businesses control exactly what an AI agent is allowed to do, keeps login details away from the agent and records every action. Those records can also plug into the company’s existing security systems.

Zapier is the giant in this market. Its MCP product lets AI assistants use more than 9,000 apps and 40,000 actions. Zapier says users have already created more than 450,000 MCP servers and made 18.5 million tool calls through them.

Zapier already owns a gigantic map of how business software talks to other business software.

The distinction is becoming smaller as Zapier moves deeper into agents, so saying that Zapier only runs fixed workflows would now be unfair. But its historical centre of gravity is still automation. Someone knows the workflow, connects step A to B to C, and wants it repeated reliably.

Aident starts from a slightly different assumption. The agent may not know which tool it needs until it starts the job. It discovers capabilities while working, inspects the current schema, preflights an exact action, checks the cost and then executes it. The wedge is making tool access, spending, and permissions feel native to an agent that decides its route as it goes. Even Aident’s own comparison concedes that Zapier remains difficult to beat for repeatable business workflows.

That also tells you who Aident is for right now.

If you want Gmail to copy every attachment into Drive, this is probably unnecessary. Zapier is excellent at those things, which is an underrated quality in infrastructure.

Aident becomes more interesting for developers building agents, teams using several different agent clients, and companies where agents are expected to move between research, communication, coding, media and outside APIs without a human rebuilding the integration every time.

I would say that the AI model itself may turn out to be the most replaceable part of an agent stack. A company might use Claude for one team today, move a workflow to ChatGPT next month, give developers Cursor or Codex, and switch again when a better model shows up a few months later.

Since the models are improving so quickly, committing too deeply to one of them may not make much sense.

Once agents start doing real work, companies still have to decide where the line is. Maybe an agent can draft a customer email but not send it. And if something goes wrong, someone needs to know exactly what it did.

Those decisions are just company rules, like the ones normal employees follow, but now software has to understand and follow them. Access and spending rules are currently fragmented across disconnected consoles, tokens, cards, and manual offboarding checklists.

As the market matures, the permission layer becomes an enforceable job description defining what an agent can see, spend, alter, publish, and escalate.

That is why I think Aident is sitting in an unusually good pocket of opportunity, even if the company itself is still early and parts of the infrastructure were rough in our test. Governments will probably spend years arguing over how powerful the models themselves should be allowed to become.

If agents become normal parts of how companies operate, that rulebook has to live somewhere outside the model itself. Which makes the opportunity slightly different from the race to build the smartest agent. The valuable position may be owning the layer a company trusts enough to let all of those agents through.

—Thejaswini

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📩 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.

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

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

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

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

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

On to Mike’ story,
Prathik

Follow Schema on X

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

The Rate behind DeFi Returns

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

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

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

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

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

Two Rate Markets, Two Different Risk Drivers

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

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

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

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

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

Who Needs to Trade Lending Rates

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

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

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

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

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

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

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

Follow Schema on X

How DeFi Currently Creates Fixed Rates

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

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

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

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

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

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

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

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

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

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

XCCY as Infrastructure for Predictable Yield

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

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

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

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

Case study 1: Fixed sUSDe loop

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

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

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

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

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

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

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

Case study 2 : Locking Yield Before Rates Normalise

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

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

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

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

What Makes a Lending-Rate Market Work

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

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

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

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

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

Conclusion

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

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

That’s it for today.

Follow Schema on X

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

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

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

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

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

Welcome to Edition 17 of The Reading List — your fortnightly antidote to the noise. Every two weeks, I share my favourite reads across crypto, finance, technology, culture, and beyond. I also share what I’m watching and reading.

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The Fortnight That Was

Over the last two weeks, oil (WTI) is up almost 17%. Everything else is flat or down: the S&P and bitcoin barely moved (−0.3%), the dollar softened (−0.5%), and gold and silver fell the hardest, down 2.4% and 2.7%.

The trigger was the Strait of Hormuz. US and Iranian forces traded strikes on oil tankers, Saudi Aramco’s Jizan refinery was hit for the second time in a month, and traffic through the chokepoint that carries a fifth of the world’s crude thinned to roughly ten ships a day. Brent pushed toward $97, US pump prices to $4.15 a gallon, diesel to a record.

An oil shock is an inflation shock, and an inflation shock changes what the Fed can do. Coming off a strong August jobs report, with 162,000 added against a 56,000 forecast, and unemployment steady at 4.1%, the market started pricing a rate hike. JPMorgan flipped from “no moves in 2026” to a 25bp increase at the September 15–16 meeting, and the odds of a hike crossed 60%.

That’s why the metals broke. Gold and silver had been the momentum trade all year; a hawkish repricing knocked the legs out, silver falling 6% in a single session. The tell of this fortnight is that the classic havens didn’t act like havens. Oil was the only asset honestly pricing the war. Bitcoin and equities just managed to hold.

What We Covered Last Fortnight

At The Token Dispatch, we spent the fortnight on covering structural issues. Two movements ran through the coverage: crypto reaching outward into the rest of finance, and a machine economy quietly wiring itself up alongside.

I opened with From Tokens to Everything Else, on protocols converting their fees into real-world assets, like stocks, dividends, tokenised equities, so that a protocol’s market is no longer measured only by the value of crypto. I stayed with structure in The Unbundled Fund, where near-zero custody costs let strategy, rebalancing and issuance come apart into separate, competing layers.

Vaidik then argued in From Bookie to Bloomberg that prediction markets are becoming risk infrastructure, ICE’s $2 billion move on Polymarket is about owning the data layer, much as Lloyd’s coffee house once did for insurance. I picked up credit in Troubleshooting On-chain Credit, making the case that on-chain lending only works once it stops pretending it can skip identity, reputation and legal recourse. Vaidik closed the finance thread with Crypto Options: Is the Time Now?, on why options are finally overtaking perps after October’s crash exposed how punishing path-dependent leverage can be.

Thejaswini took us into the machine economy. Software by the Sip looked at pay-per-call software markets built for AI agents that buy tools one API call at a time, while Who Checks the Agents? asked who verifies, pays and polices those agents, and found the trust layer lagging badly behind the payment rails.

Regulation & Policy

• AI Could Make Markets Unintelligible: Byron Gilliam on economist Markus Brunnermeier’s pitch for a regulator-protected “human-only” trading lane, as insurance against markets where AI’s informational edge grows too wide for people to price.

Crypto & Tech

• Robinhood Chain’s Reverse Playbook: 100y on how Robinhood Chain inverts the usual crypto playbook: start with tokenised stocks and proven DeFi rails, then bolt on stock-paired memecoins to pull in speculative liquidity.

Finance & Economy

• Why Would Anyone Sell a Good Asset?: Oister on why secondaries are becoming central to India’s maturing private markets: selling a good asset isn’t a red flag, just two investors with different time horizons.

• Interest Rates and Stock Prices: An Old Debate Resurfaces!: Aswath Damodaran dismantles the reflex that higher rates mean lower stocks; rates move discount rates and cash flows at once, so the net effect splits by company and sector.

• An Ode to Counter-Positioning: Packy McCormick on counter-positioning: how startups win by adopting a model incumbents can’t copy without gutting their own revenue, and why that window shuts fast.

• The Rise of Compute Markets: Jay Yu on whether GPU compute becomes a traded commodity like electricity, and why fragmented hardware, locations and pricing make clean benchmarks and derivatives so hard to build.

• Hedging Compute Prices: Ronit Jain’s primer on cash-settled compute forwards and options, and how buyers, operators and lenders could finally put a price on future GPU cost.

• Dallas Gets Some Equities: On the Texas Stock Exchange moving into equities and what a Dallas venue built to court issuers means for the NYSE/Nasdaq duopoly.

• AAA AI: On how the AI data-centre buildout is quietly becoming a debt story, and the credit machinery — ratings, structured finance — assembling around it.

AI & Innovation

• Software Is About to Eat the World Much Faster: Marc Andreessen argues coding agents move software production from the speed of human labour to the speed of compute, multiplying what a single engineer can ship.

• The AI Transformation Loop: Mark Ajzenstadt on dropping AI into private-equity portfolio companies through short, measurable workflow experiments rather than year-long, consultant-led transformation programmes.

• How AI Is Breaking the Internet Economy: Shoal Research on AI crawlers dismantling the web’s content-for-referral bargain, and the pay-per-request infrastructure publishers will need to charge machines directly.

• My Prediction on AI in the Next Five Years: Ben Cera sees models becoming invisible commodities under outcome-priced apps, agents forming their own economy, and human edge shifting to trust, taste and distribution.

• Will AI Soon Lead to Double-Digit Growth?: Ben Moll and Alex Imas, self-described AI bulls, explain why a capability explosion isn’t a GDP explosion; five heroic assumptions all have to hold, and probably won’t.

• Investing in AI: Diego Lora maps the AI value chain across nine layers, with an uncomfortable takeaway for retail: most of the good exposure is private, foreign, or already richly priced.

• The Existential-Risk Story Might Have Some Plot Holes: Byron Gilliam pokes at AI doom forecasts: earnest, internally coherent, and resting on precisely zero historical data points.

Culture & Beyond

• How Do People Get New Ideas?: Isaac Asimov’s long-unpublished essay on creativity: new ideas come from connecting unrelated ones, which needs informal, low-pressure rooms where people can risk sounding foolish.

• The “Learn to Code” Era Is Dead: John Burn-Murdoch reads falling computer-science enrolment as young people betting that “learn to code” no longer guarantees a career in an AI-shaped labour market.

• The Group Chat Theory of Everything: Ben Roy on the group chat as the new private social unit quietly shaping what we buy, believe and vote for.

• Doomscrolling Ourselves to Death: Ed West on James Marriott’s The New Dark Ages: the collapse of reading since its mid-century peak, and what a post-literate public does to democratic life.

• The Forest Is Always Dark: Michael Dempsey on deciding under uncertainty: how much certainty you actually need before acting, and why a longer time horizon lets you ignore what short-term players obsess over.

• It’s Not the Thought That Counts: Jords makes the case for wilful, selective ignorance: one finished local good deed beats endless paralysed awareness of problems you can’t touch.

• One Big Web: A Few Ways the World Works: Morgan Housel on borrowing truths across disciplines: lessons from kittens, genetics and ecology that quietly govern business and behaviour.

• The Curio Cabinet of Medieval Mental Health: Katherine Harvey on medieval medicine’s holistic view of the mind — gardens, music, company and animals as prescriptions that eerily anticipate today’s “social prescribing.”

• Tolkien’s Secret to a Lasting Marriage: Tolkien’s 1941 letter to his son, framing marriage as sustained sacrifice rather than soulmate-hunting — a pointed rebuke to leave-if-unsatisfied culture.

• Have We Finally Found Dark Matter? The LUX-ZEPLIN detector logged a collision that fits neither known backgrounds nor standard predictions: maybe a crack in a 40-year search, maybe noise.

That’s all for this fortnight. Catch you in the next edition.

Until then, happy reading!
Prathik

P.S.: Want to recommend interesting literature? Write to me, and I shall include them in the next edition 👇🏾

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

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

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

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

Consider a business that rewards its customers only with coupons they can spend inside the same business. That’s how most brands plan their loyalty programmes. And they do so for good business reasons. As long as people keep coming back, this arrangement works.

The idea is to make customers spend money, hand them coupons, and make them come back to spend those coupons again. The coupon-issuing brand can make these coupons scarcer over time by giving them an expiry date. But the entire system works only if customers feel the desire to earn these coupons.

For much of crypto’s history, its economic systems also have worked similarly.

Protocols created tokens and dangled them to attract users. They then rewarded those users with even more tokens. When that model made tokens inflationary, the industry found an alternative. Instead of printing new tokens, protocols began charging fees, buying back their own tokens from the market and destroying them from the circulating supply.

This mechanism worked better than the earlier one, but the money was still circulating inside crypto. That is beginning to change.

A new crop of projects is taking money generated inside crypto markets and using it to buy traditional assets. This is one of the most significant signs of crypto coming of age. The industry is beginning to connect its endogenous economy of fees, tokens and trading activity created inside its markets with the much larger exogenous asset universe of traditional finance.

On to the story…

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.

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On July 1, Robinhood launched its blockchain as a permissionless network, letting anyone build applications on it. By early September, nine native tokens with live products on its chain had crossed a circulating market capitalisation of $10 million: Pons, The Index, Orbio, StonkBroker, Hookr, Delta, Statics Protocol, Down to Finance and Pare.

Although most are too young for valuation to tell us much, what they do with the money moving through them hints at crypto’s collective trajectory.

Pons, the token launchpad, routes roughly 80% of protocol fees to buy and burn PONS. Even Hookr, another token launchpad, uses collected fees to buy back and burn HOOKR. Both follow the same mechanism as Hyperliquid: routing exchange trading fees to buy HYPE through its Assistance Fund.

While this crypto system mimics the share buyback mechanism from traditional markets, it’s only as good as the business supporting the buyback.

Pump.fun helps us understand this. By the end of April 2026, it had burned roughly 36% of its maximum token supply and dedicated 50% of its revenue to buybacks and burns in the following year. Yet its native token, PUMP, still traded around record lows for almost 10 months, from September 2025 to June 2026, when launchpad activity weakened.

While the buyback-and-burn mechanism did what it was designed to do, it can’t manufacture demand.

Some protocols on the Robinhood Chain are opting for a different model.

The Index’s stock distribution model contrasts this by not using the 3% fee it earns from all the trades it facilitates to buy back its native token, INDEX. Instead, the protocol converts part of its fee income into a basket of roughly 18 Robinhood Stock Tokens, including Apple, Nvidia and Google, and distributes those assets to INDEX holders.

Although these are not conventional shares registered in the holder’s name, the stock tokens provide economic exposure to the underlying equities held in custody. Holders still don’t have legal ownership or the voting rights of ordinary shareholders. But not everyone wants to hold tokens for voting rights and legal ownership.

Read: A Token Can Be the Asset

This pitch connects the two financial ecosystems by converting revenue generated within the crypto world into rewards that give holders exposure to real-world assets like publicly listed company stocks.

For years, crypto has moved capital efficiently within its own ecosystem. A token could be deposited as collateral to borrow another, which could then be used to earn yield in the form of another token. Trading fees could buy a token, then burn it to support the same economy. The industry built sophisticated financial loops, but most ended at another crypto asset.

But this wall is gradually coming down. Both the ecosystems have begun feeding off each other’s moats and captive audiences to expand their revenue streams or bases.

This changes crypto’s customer-acquisition problem.

Until now, most protocols have effectively had to sell a user the idea of entering crypto, and then the idea of owning a new token. That limits the audience to people already willing to speculate on crypto assets. An Apple stock, an Nvidia dividend or an ETF-like basket does not need the same introduction. People already understand why they may want them. If crypto can become the infrastructure through which those familiar assets are bought, packaged and distributed, the asset itself becomes the acquisition funnel. The user does not need to come for crypto. They can come for Apple and end up using crypto underneath.

Read: Distribution Trumps Asset Class

Stablecoins were the first large breach in that wall. A blockchain token could represent a claim on dollars and US Treasuries sitting in the traditional financial system. Tokenised stocks extend the same idea to equities. The Index uses internally generated crypto activity to acquire those external assets and hand them to users.

Nvidia’s value does not depend on whether people keep trading INDEX. It sells chips and computing infrastructure to customers who need compute power. Apple sells phones and services that have little to do with the crypto ecosystem. But the aspiration to own assets outside the crypto ecosystem can drive activity within it.

StonkBroker, a marketplace for 4,444 NFTs, converts fees into stock tokens, like Tesla, Amazon, Nvidia, or Palantir, and airdrops them into the wallets held by NFT holders. When the NFT changes hands, the tokenised equities inside the wallets also move with it. The protocol turns a simple NFT wrapper into a portable financial container that can carry value across into an exogenous world of traditional finance.

Pare lets users split a stock token into two pieces: one representing the stock’s principal value and another representing the dividend stream it generates until maturity.

Traditional finance has done this for decades with instruments such as Treasury STRIPS, which separate principal from interest. Pare applies this logic to tokenised equities. Once an external asset becomes programmable, you can separate its cash flows and trade them in ways that were previously cumbersome.

Statics Protocol and Down to Finance do the reverse by bundling assets together.

Statics can create redeemable baskets containing several assets, including tokenised equities. Users can create fixed baskets of up to 16 assets, including tokenised stocks, and issue the basket as a single redeemable token.

Think of it as something closer to building your own index fund: rather than buying and managing Apple, Nvidia and a handful of crypto assets separately. For someone coming from traditional finance, this is a much more familiar proposition than navigating individual tokens, liquidity pools and wallets. The basket does not need to remain entirely crypto-native. A protocol can combine assets whose value originates in public companies with assets and financial activity that originate on-chain.

Down to Finance (DTF) packages tokenised stocks, lending positions and other assets into products it describes as decentralised ETFs.

Its baskets can span tokenised stocks and real-world assets alongside crypto-native positions such as Morpho lending and Uniswap liquidity pools, all wrapped into a single token. A traditional investor is already comfortable with the idea that one ETF can contain dozens of securities; DTF applies the same abstraction to assets that normally sit in completely different financial systems.

A non-crypto investor should not have to become a DeFi user before getting any benefit from DeFi. Someone buying an ETF does not need to understand how its market maker hedges inventory or how its custodian settles securities. In the same way, an investor should be able to buy a basket combining equities and on-chain yield without first learning Morpho, Uniswap or wallet mechanics. If Statics and DTF get this right, blockchain becomes invisible infrastructure underneath a familiar investment product. That is a much larger market than asking millions of traditional investors to become crypto natives.

Pare, Statics and DTF collectively show that bringing stocks on-chain is only the first step. Once traditional assets share infrastructure with crypto assets, they can be split, bundled and combined with entirely different sources of return. A stock dividend can become its own instrument. Several equities can become one basket. That basket can sit alongside income generated by an on-chain lending market.

For years, crypto’s composability was economically constrained. It could endlessly rearrange crypto collateral, crypto yield and crypto risk, but the total pie was still largely bounded by capital already willing to enter crypto. Connecting the same machinery to equities, credit and other productive assets changes the ceiling. Crypto protocols stop fighting only for a larger share of crypto’s market and start competing for activity that today belongs to brokers, exchanges and asset managers. The opportunity is no longer simply to grow crypto’s own pool of capital. It is to intermediate a small piece of the vastly larger pool of capital already invested elsewhere.

In some cases, the external asset doesn’t even have to be a financial instrument. Orbio converts part of its fees into OpenRouter credits for AI inference.

But none of this guarantees that the protocol will become a sustainable business. Paying someone with an external asset or tying a crypto token to a publicly listed company share isn’t a magic pill.

For now, the rewards have escaped the circular economy. But the protocols still need to find a way to keep user activity up.

The Index is only half a bridge. Its payout has become exogenous, but its revenue engine is still endogenous. It has replaced the coupon with Nvidia, but people still have to keep trading the coupon to fund the Nvidia.

The real graduation happens when both sides cross over: the protocol distributes value originating outside its own token and earns the money to fund that distribution from demand that would exist even if its token stopped trading tomorrow.

For instance, Hyperliquid pays value back through HYPE, an endogenous asset, but its revenue comes from traders using the exchange to trade crypto and real-world assets alike. This is a stronger foundation than a business whose revenue rises mainly because speculators keep trading the same token that finances its rewards.

This shows that exogenous rewards alone aren’t enough. External rewards work better when external demand finances them. Coca-Cola pays dividends because people buy its drinks, and not because people keep trading its shares.

This is ultimately why these tiny Robinhood Chain experiments matter beyond their current market caps. Connecting crypto to external assets changes the size and quality of the market crypto can serve. The person buying an equity basket may not care about crypto at all. The investor buying a dividend stream may simply want income. Someone using an on-chain fund may care about diversification rather than decentralisation. Crypto does not need to convert all these people into believers. It only needs to become a better rail for the financial product they already want.

If that happens, the relevant market for crypto protocols is no longer measured only by the value of crypto assets. It begins to include the trading, packaging, lending and distribution of assets from the much larger traditional financial system.

Crypto coming of age is about breaking the assumption that money entering crypto must ultimately end up in another crypto asset. The Index, Pare, Statics and DTF are early attempts to punch holes through that wall. Some of them might fail. But the direction is more important than the individual products: crypto is moving from building a financial economy beside traditional finance to building infrastructure that can connect to it.

The moment that connection becomes two-way with outside assets flowing in, outside demand generating fees, and crypto infrastructure sitting invisibly in the middle, crypto’s addressable market outgrows crypto.

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

Until next time, stay curious,
Prathik

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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

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.

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.

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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

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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

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For almost a century, we handed our money to a professional and trusted them with it. We wired our assets into a fund and received a receipt establishing our claim on a pool of funds. We didn’t own the company’s shares directly.

These fund houses charged a fee to hold our assets and rebalance them whenever asset weights drifted.

This packaged deal of ownership and management had a few problems. First, the investors couldn’t split them apart and claim custody of underlying assets. Second, the fund houses rebalanced them at set intervals and placed constraints on how investors could use them as collateral.

Blockchains make unbundling the fund possible and change how people hold their assets, while still letting fund houses rebalance the investor portfolios, albeit differently.

In today’s piece, I will explain how this works and who captures value in this unbundled work of fund management.

On to the story…

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The Fault in Our System

Consider an investor in Mumbai who wants exposure to the Magnificent 7 stocks. Because these are among the most liquid on Earth, it may seem like an easy thing to do. But reality is complicated.

The investor would need a US brokerage account that allows Indians to participate, or a local broker that charges a commission on every trade, plus a currency spread each time she converts her Indian rupee to US dollar. The real challenge is accessibility.

Most fund management houses take custody of your funds and manage your portfolio. But some let you keep your assets in a separately managed account, where the manager trades using the account without taking custody of your funds. But this option is gated behind minimum thresholds.

So an average investor picked a managed fund that came with a surrendered ownership model. That brought with it two constraints.

The first is how the fund rebalances the investor portfolios. When the market moves and holdings drift from their target weights, the fund trades the whole pool on its schedule. Some rebalance quarterly, others do it semi-annually. You don’t get to choose the cadence, see the trades or hold a position for a little longer - if you wanted to. Some funds rebalance daily, but those tend to have a problem (more on that later).

The second constraint in this model is the utility of what you hold. These funds let you borrow against your portfolio, but the loan runs through the broker or bank that custodies your shares. The rate and terms depend on their approval. Open-end fund units that are rebalanced daily are often refused as collateral or heavily discounted.

The bundled fund was a reasonable equilibrium in a world where custody remained expensive and specialised funds had high entry barriers. Paying extra fees to access an exclusively managed fund for self-custody might not be affordable to everyone.

But blockchains let you unbundle the entire model.

Unbundling the Fund

Blockchains cut the incremental cost of holding your assets in your wallet. So, irrespective of how much a wallet holds, $50 or $5 million, the cost of holding that wallet remains the same. That is why fund management is being unbundled at this layer of the stack.

Two weeks ago, Bitwise, Coinbase and Glider came together to do just this.

As part of this partnership, Bitwise would design the strategy and set the weights. Coinbase would issue each holding as a tokenised US stock, backed 1:1 by a real share.

Meanwhile, Glider’s automated on-chain tool rebalances your holdings back to Bitwise’s weights. They do this by buying and selling every time the prices drift, even as your tokens stay in your wallet all the time. The startup, backed by a16z, Coinbase Ventures, and Uniswap Ventures, calls this tool Automated Token Portfolio (ATP). It recently launched its first one that holds the Magnificent 7 plus SpaceX.

Unlike the traditional fund houses, Glider doesn’t rebalance your wallet using a fixed ratio. It rebalances daily toward the target weights whenever prices pull the portfolio away. Using a pre-approved session key, the ATP trades only when needed and charges 0.30% on the volume of those trades.

Investors can hand over the pre-approved session key as a limited-time authority signature that Glider can use to trade inside your wallet. The key is programmed to prevent Glider from withdrawing any funds from the wallet. This retains custody. The key is also programmed to act like a switch that can be revoked at any time by turning it off. This stops all future rebalancing instantly.

The flexibility such an automated platform affords is what sets it apart from a traditional fund house, which charges a premium for the same or similar features.

Rebalancing bots are not new in crypto. We have had them for years; think Stoic and Shrimpy. But these bots run through API keys on our exchange account. The exchange still holds the assets. Glider rebalances tokenised stocks held in your self-custodied wallet. The assets don’t have to move.

But why care so much about custody?

Since you hold the tokens rather than a claim on a pool of assets, Glider also changes the way you use your portfolio. When you had to borrow against your traditional fund, the broker had to approve your collateral, set terms, and, worse, could refuse your units if they were priced once a day because of liquidation fears. Tokenised stock in the wallet doesn’t need a fund house to approve any of this. You can post your position as collateral in any lending protocol directly, or even sell one position to set off a loss against gains elsewhere.

The Effect of Unbundling

The traditional fund bundled asset selection, holding, and investor retention. It offered all of this in a single wrapped product. Now this bundle is being split into a token, a rebalancing engine, and a strategy, with each layer handled by a different player.

The payoff from unbundling the fund mirrors unbundling many other stacks in the financial system. We explained how each layer got cheaper and more contestable when the IPO stack and private-company cap table unbundled.

In each case, value migrated to the layer that was hardest to replace. The same logic turned integrated brokerages like BlackRock and Fidelity into interchangeable parts. But now, with blockchains bringing universal accessibility and flexible rebalancing, this value is migrating again.

Giving investors the flexibility to manage their portfolios at the click of a button seems like the hardest part. So, that’s where the value is now accruing.

But there are no free lunches.

Consider establishing a $10,000 position using the unbundled model. You’d pay between $45 and $60 in fees, depending on whether you use Glider’s 0.30% or Bitwise’s 0.15%. Compare that with Vanguard’s S&P 500 ETF, which charges 0.03% a year. That’d come up to just $3, 1/15th of the new approach.

So why care about the unbundled model?

The unbundled model isn’t a utopian fix to replace the traditional funds. It brings accessibility and flexibility to those who value these features, albeit at a higher cost. An investor with a good brokerage account and simple index needs should still opt for a traditional ETF bundle.

The unbundled model is a better alternative compared with the balance-based fees of robo-advisors and licensed managers. Some investment platforms still charge 0.15% to 0.60% a year, every year, whether or not anything trades.

Even those who want to manage their portfolios without giving up their keys would adopt an unbundled model.

Glider can benefit from this adoption in two ways. It has already built the consumer layer that lets users grant revocable permission and make rebalancing discretionary. But the bigger takeaway is that it doubles down as a B2B interface for any wallet, exchange or fintech that wants to plug into it.

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

Until next time, stay curious,
Prathik

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

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

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

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

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

On to Aleks’ story,
Prathik

Follow Aleks on X

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

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

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

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

Containerisation Enabled the Modern Global Supply Chain

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

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

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

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

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

Tokens are Financial Containers

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

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

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

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

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

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

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

Follow Aleks on X

Capital Markets Will Reorganise around Tokens

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

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

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

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

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

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

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

The World’s Balance Sheet Will Come Onchain

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

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

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

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

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

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

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

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

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

That’s it for today.

Follow Aleks on X

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

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

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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

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Credit is one of the hardest problems in finance. Every loan is a bet on a person or organisation’s future behaviour and needs a system to ensure the bet is well measured and risk is minimised. Lenders need to verify identity, check past records and put in place a recourse if things go wrong.

On the demand side, credit helps money reach the people who need it the most. This keeps the liquidity moving in the economy. But here, too, the cost of borrowing needs to be set in a way that makes credit inclusion in nature.

These problems are difficult but unavoidable.

For almost a decade, on-chain lending has struggled with solving many of these problems. One of the biggest is overcollateralisation, where you have to deposit $150 to borrow $100. This defeats the purpose for someone who needs $100 and doesn’t have it.

Every once in a while, we see attempts to fix the on-chain lending landscape. Many who dared faced the same sad fate. Yet few others persist. Today, I analyse some recent attempts to address on-chain credit.

On to the story…

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Ever since decentralised finance began offering credit, it has relied on overcollateralisation. Pseudonymity and privacy on-chain left no trace of borrower identity. For an industry built on decentralisation, this left no option but to resort to overcollateralisation to avoid bad debts. But collateral that requires you to deposit more than what you need doesn’t make sense. Yet over 90% of on-chain lending is overcollateralised today. This makes credit exclusionary for those who need it most.

But who needs on-chain credit anyway?

If you have a bank account and a decent credit score, on-chain credit offers nothing new. Your credit card already offers an unsecured, revolving line of credit. The card also includes fraud protection and chargebacks that on-chain credit doesn’t offer. At first glance, borrowing is also more expensive on-chain. 3Jane pays its depositors 7.2% to 13.42%, so it charges borrowers more than that.

But some groups of people still aren’t served by the current credit system.

The World Bank’s Global Findex 2025 found that roughly 1.3 billion adults lack financial accounts. Without on-chain credit, this group’s alternatives are to borrow from local usurers or do nothing. Another group with wide unmet credit demand is small businesses. The IFC estimates the financing gap for micro, small and medium enterprises at $5.7 trillion.

For all of them, the mainstream credit from the banking infrastructure is missing or insufficient. That’s because the economics don’t work for the banks. A crop of crypto projects attempted to fix this problem, but they failed.

A Bitter Pill from the Past

One core pillar of a reliable credit system is measuring trust. Credit scaled historically when the industry built systems that standardised and measured trust. We verified identity, shared records, and used courts to support both.

The world of DeFi lenders skipped this entire chapter.

Maple Finance, once the largest lending protocol, had replaced collateral with “pool delegates”. These credit professionals decided who could borrow and who couldn’t. That was the biggest vulnerability.

In late 2022, one borrower, Orthogonal Trading, defaulted on $36 million across eight loans after misrepresenting its FTX exposure to the pool delegate. Maple ended the cycle with about $54 million in bad debt. It survived by abandoning unsecured lending altogether and pivoting to overcollateralised institutional loans.

Goldfinch also faltered, making a contrasting play. It lent over $100 million to real-world lenders in emerging markets, underwritten by a decentralised community. About $18 million went bad.

Both of these failures had similar problems. They removed collateral without replacing it with another mechanism that ensured enforcement.

Newer on-chain projects are rebuilding the credit stack by focusing on elements the older ones ignored. These are the same ones that made Tappan’s system work — verified identity, a shared record of trust, and courts.

Rebuilding the Stack

One ingredient in a healthy credit system is tracking identity. On a blockchain, identities are free. A defaulter can open a fresh wallet and borrow again as a stranger. No credit system can survive this.

Divine Research fixes this with its product, Credit, which runs inside the World App. Using its scanning technology, Divine lends only to humans verified by an iris scan. It offers unsecured loans of up to $1,000 through a ‘progressive trust’ mechanism. You start with a small limit, repay on time, and grow your borrowing limit toward $1,000.

This directly addresses overcollateralisation. Borrowers need to submit zero collateral, but the trade-off is their privacy via iris scans.

Since December 2024, it has issued almost $4 million in loans to almost a million borrowers. These loans were taken out predominantly by individuals in Argentina, Nigeria, and Colombia, mostly for groceries, medicine, and transport. This is the excluded group that I referred to earlier in the piece.

But the model lacks a solid recourse. Nothing stops a verified human from keeping the money. The system is built to disincentivise defaults and not avoid them altogether. The borrower can take $80 today and walk away with it or build toward a $1,000 credit limit gradually.

While it serves excluded groups, the high cost of doing business without collateral is a hard ceiling. If it can’t lend above $1,000 without collateral, it means its interest income is also capped and scales slowly.

The second ingredient in a healthy credit system is a good trust record.

Ethos Network is rebuilding this layer as a portable, public reputation score built from reviews and vouches. It lets users stake their own ETH to support a person’s credit reputation. This staked ETH is slashable if that person misbehaves. Random reviews are gameable. But when people have skin in the game by investing their money, they are less likely to manipulate.

Credifi, a lender on Base, has attached money to Ethos’ reputation scores. An Ethos score above 1,800 unlocks up to $3,000. No need for collateral or KYC.

If someone defaults on a loan, their credit reputation score on Ethos Network gets permanently downgraded. This hurts their chances of accessing credit across the Web-3 ecosystem. The person who vouched for the defaulter’s credit reputation also faces a downgrade in their score and a possible penalty deduction from their staked ETH.

Credifi also runs a second lending track where U.S. borrowers can borrow by verifying their bank accounts. The reputation track builds their presence in the informal, non-US, and on-chain markets. The bank-led approach helps it scale faster because of lower default risk and existing risk systems.

The third ingredient is recourse.

A year ago, 3Jane pitched unsecured credit lines to individuals. Back then, it underwrote the borrower’s on-chain history and extracted your VantageScore and bank data through cryptographic proofs that verify without exposing details.

Its infrastructure ensured defaulted loans were sold to licensed US collections agencies and losses hit a junior tranche before senior capital. It solved recourse with courts.

This recourse-first design helped 3Jane pivot from issuing unsecured retail loans to giving out direct credit lines. 3Jane’s recourse-first design helped it originate about $8 million in loans in just seven months to 60 U.S.-based prime/superprime yield farmers at 376 bps over Aave borrow rates. The pool achieved 100% in monthly payments, zero defaults and zero principal impairment to the staked capital. The pool ran clean through the October 10 liquidation event, some of the worst stretch of the DeFi market in recent times. Careful underwriting of individuals might not scale fast, but helps build a disciplined customer base.

In May, 3Jane launched Fintech Credit Conduits, a standing funding infrastructure that channels its stablecoin deposits into short-duration Small- and Medium-sized Business (SMB) and consumer loans. Other U.S. fintech lenders originate these loans and already have borrowers and underwriting models but can’t access securitisation markets.

The individual credit lines stay live on one track, while these conduits, with a $60 million pipeline, become the growth engine to scale. The model is a strategic marriage between the best of both worlds. On-chain lending provides the capital, settlement speed, and a transparent loan book. Off-chain brings in borrowers, underwriting, and the courts. It is the least crypto-native of the three, yet the one I’m most bullish on.

Can They Troubleshoot On-Chain Credit?

For credit to work, all the pillars that hold it upright need to be stable. A lender needs to know who the borrower is, what their credit history is and how to keep the bad actors at bay.

On-chain-native builders in the credit industry need to understand that blockchains don’t replace off-chain credit; they just make credit more efficient. The chain moves capital faster, settles instantly, and keeps the loan book transparent. But the underwriting itself — verifying who is borrowing, judging whether they’ll repay, and enforcing when they don’t — is work the off-chain system spent two centuries perfecting. A complete on-chain system that targets the underserved has to merge the two: blockchain infrastructure for capital and settlement, and proven credit practices for underwriting.

Verifying identity, building a tested trust record, and putting reliable enforcement in place are the most obvious and inevitable ingredients of building a healthy credit market. Once that is achieved, borrowers wouldn’t care less if the lending platform is completely on-chain or off-chain.

Along with those three ingredients, builders in this industry need to track defaults closely in the first few months and have a business plan that earns money in the medium- to long-term.

The measures the above companies have adopted, including the reputation score and slashing of staked ETH, are merely conduits for discipline. While they may not solve the over-collateralisation problem from day one, I believe they can kick off a directory of reputations for credit-seeking participants in on-chain markets. We might still be years away from a reliable directory the entire on-chain lending ecosystem can rely on. But it is still a start that the industry absolutely needs and cannot do without.

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

Until next time, stay curious,
Prathik

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

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

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

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Although we see a “Transaction Successful” message and hear a beep, nothing moves from our bank account when we tap our card. At that instant, the only thing that happens is a bank promising a merchant it will settle the payment later.

The bank charges a fee for facilitating this trust layer that vouches for someone’s ability to pay. From our neighbourhood shopkeeper who lets us run a tab to the banks that vouch for thousands of businesses to honour a payment, they all run on this trust layer. It is one of the most valuable parts of the financial system, yet the least visible.

Without the trust layer, no kind of commerce or financial system can function. I wrote about this last week, on how companies are building the trust and identity layer even for machines transacting in the agentic economy.

Read: The Business of Trust

Crypto rails, coupled with stablecoins, remove intermediaries from the monetary system - be it for holding money, moving it or earning more through compounding it. We now have crypto cards that let us use our digital assets to pay for day-to-day expenses. Here, too, this new form of payment requires guaranteeing a wallet holder’s ability to honour the payment.

But since crypto payments settle much faster than bank-routed card payments, we need a faster vouching mechanism for this wallet holder’s ability to settle payments. In today’s piece, I explore this.

On to the story…

The Anatomy of a Card Payment

When you dissect the anatomy of a card payment, you find five parties and a moment of trust. There’s you - the buyer, a merchant (the seller), the merchant’s bank, the card network infrastructure (Visa or Mastercard) and your bank (issuing bank).

When you tap, a payment request moves from the POS terminal, through the merchant’s bank, across the network, to your bank, and back. All of this happens in seconds.

Your bank (issuing bank) checks whether you have the money, ensures the transaction isn’t fraudulent, and gives the green signal. It then places a hold on your balance to ensure the merchant gets paid before money moves from your account. The money eventually changes hands on a one-to-two-day delay.

Until then, the merchant has only the bank’s certainty that the buyer will honour the payment. The issuing bank fills the gap between certainty and settlement by bearing the risk that the buyer fails to honour the payment. For this, it charges a handsome fee.

In the United States alone, merchants handed over a record $198 billion in card fees in 2025. The largest slice of these fees (interchange fees) flowed to the banks that vouched for the transaction, as shown in the chart below.

Here, the bank that vouched for the buyer’s ability to pay, and not the card network (Visa/Mastercard) whose infrastructure facilitated the transaction, captures the lion’s share of the fees. This is because it is the most scarce layer in the payment stack. Vouching requires the bank to employ its resources - technology, systems and humans- to ensure that the buyer has the liquidity and that liquidity is blocked for honouring the payment.

Too Fast to Vouch

For more than half a century, this system has been working. But crypto rails challenged this by moving money faster and cheaper.

A stablecoin transfer can settle in seconds, for a fraction of the cost banks charge, and doesn’t care about geographical borders, time, or day. For businesses that pay suppliers regularly across borders or pay employees, blockchains offer major time and cost savings.

This preference to choose crypto rails to move money is only picking up pace.

Roughly $33 trillion in stablecoins moved in 2025, about a third ($10.8 trillion) of which was adjusted transaction volume after filtering activity from automated smart contracts and bots. This year, the adjusted stablecoin transaction volume has surpassed $11 trillion in just eight months.

Companies are enabling this movement by introducing crypto cards that let stablecoin holders spend the digital currencies in their wallets.

But this faster, better payment infrastructure creates new problems. The existing vouching system in card payments can afford to be slow because settlement occurs with a one-to-two-day delay. Blockchains collapse settlement to a few seconds.

If the vouching for crypto card transactions still takes days, then the entire point of choosing a faster payment infrastructure is defeated. The merchant who prefers blockchain-enabled payments over the centralised banking route does so for faster and cheaper settlement. But a self-custody wallet, whose entire purpose is that no institution stands between you and your money, has no banking authority in the position to give the merchant the guarantee of honouring a payment.

But friction in new technologies and systems also creates opportunities to capture value by building solutions that solve it.

The Race to Vouch Faster

Crypto card companies are approaching this problem in different ways.

Gnosis Pay settles the payment on-chain without requiring your money to ever leave your wallet. It achieves this by integrating Gnosis Safe, a smart-contract wallet deployed on Gnosis Chain, with two modules.

The Roles Module restricts what the card can do. This lets users set rules to limit spending to only one approved stablecoin, set a daily spend limit and add a whitelist of authorised settlement addresses. The Delay Module places a three-minute buffer on any transaction you initiate.

When you tap a Gnosis Pay card, it receives the authorisation request from Visa, checks your balance in the Gnosis Safe account and replies with a ‘yes’ or ‘no’ immediately. If yes, it simultaneously moves your stablecoin to the issuer’s account through the Roles Module, and the issuer settles with Visa off-chain at the end of the business day. The three-minute delay on your withdrawals ensures the user can’t pull the funds that Gnosis just approved for a Visa transaction out from under it before that on-chain debit clears.

Merchant settlement still takes about 24 hours, but the non-custodial wallet lets the buyer keep control of their funds and earn yield.

Gnosis captures value like a bank by reserving settlement liquidity and taking a slice of every transaction it guarantees.

Ether.fi lets you borrow against your staked ether and spend the loan so that the underlying asset can keep earning yield. It uses Rain’s underlying stablecoin issuing and settlement infrastructure for its Cash programme to issue a non-custodial, cash-back credit card for its users.

While Ether.fi built the borrowing engine, it uses Rain’s infrastructure to vouch for settlement, letting Rain capture the value it creates.

Bleap also keeps your money in your own wallet until the instant you tap. When you pay, its issuer, Unlimit, pulls the exact amount straight from your wallet and converts it to euros in real time. Tria does the same across many chains, and its cards run through Rain, the same firm that supports Ether.fi.

In both these cases, your money stays with you in a non-custodial wallet until the moment you tap. When your money sits in a bank, the bank holds it, lends it out, and keeps most of the earnings. A savings account may pay you back a little, but the bank sets that rate and pockets the spread, and you carry the risk if the bank fails. In a crypto non-custodial wallet, you hold your money. The edge here is that until the money leaves your wallet, it is unfreezable and untouched by anyone else.

All four of these cards — Gnosis, Ether.fi, Bleap, Tria — keep the money in your wallet and still let a licensed firm vouch for your payments the moment you tap.

However, these approaches only solve the buyer’s side. Payment is instant the moment the stablecoin leaves your wallet. The money reaches the merchant’s account based on what they accept. If they accept stablecoin, it can reach in seconds. But if they only accept fiat, it still moves on Visa’s clock.

Then there are the biggest players, KAST and RedotPay, who are resolving the vouching problem by replacing it altogether.

They do this by holding your money. You load your cash into their account first, and only then can you spend. Once a firm holds your balance, it can vouch instantly. It is simpler and faster, but the trade-off is losing self-custody. But it works nevertheless.

RedotPay alone accounted for roughly half of all crypto card spending in mid-2026. People don’t care whether the money is in their custody or with a fiduciary. As long as the payment and settlement happen with the least friction, they are willing to pay a cost.

Show Me the Money

When you track money through all of this, it drifts away from all the commoditised layers. Stablecoin issuers make nothing from the card payment stack.

The wallet owns the distribution of users, but still rents out everything underneath - including the licence, the guarantee and the settlement. This limits the margins a wallet can make out of the vouching process.

The value settles with the licensed firms doing the unenviable, risky and regulated work of vouching. They cannot go wrong with this job. Rain holds the card-network membership, settles volume in stablecoins, and provides the actual guarantee for Ether.fi, Tria, and others. Rain, the infrastructure provider, raised at a valuation near $2 billion, much more than what several of the apps (Bleap, ether.fi, Tria) built on top of it were valued at.

Above them all looms the incumbent. Visa continues to run stablecoin settlement at a multi-billion-dollar annual pace and pockets a spread that might seem inconsequential but adds up quickly with the volume it processes.

This is a pattern you can see whenever crypto merges with fintech. All the value does not accrue at the front end. Instead, it accumulates at the infrastructure layer. It’s where irreplaceable, non-commoditised layers form, calling for specialised solutions from builders to solve existing or emerging problems.

In the traditional banking system, settlement is slow because banks moved money in overnight batches through a chain of intermediaries. This slow process also has a benefit. If an incorrect payment is made or if the customer doesn’t receive the goods or services, they can claim a chargeback. Since the payments are controlled by the banks, they could could always reverse it after sufficient inquiry. Crypto made settlement fast and irreversible. It traded off guardrails for speed. This faster settlement made vouching more valuable because it has to happen instantly and is more susceptible to failures and risks. This necessitates a licensed, well-capitalised firm to do that. Crypto compressed and commoditised the settlement layer by making it faster and cheaper to move money. But in the process, it created more value to capture at the vouching layer.

Those who can combine the scarce job of instant vouching with self-custody will capture the most value.

Both Rain and RedotPay are approaching this from different ends. Rain holds the licence, settlement liquidity, and the guarantee, and earns by renting this infrastructure to everyone else. RedotPay gave up self-custody entirely, holds the balance itself, and bets on its distribution edge. It knows most people care about friction, not custody, and is capitalising on that by making users pay to avoid friction and get a user-friendly interface to move and manage their money.

Every wave of financial technology promises to cut out the intermediary. But each attempt has inevitably introduced new friction, attracting more players to capture the value created by solving it. In the card payment ecosystem, that friction occurs at the vouching layer. Whoever removes it inherits the money that was stuck behind it.

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

Until next time, stay curious,
Prathik

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

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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.

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

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

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

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

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

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

On to Ana’s story,
Prathik

Follow Ana on X

Crypto Investing On Autopilot

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

• One basket holds the Mag7 at equal weight.

• Another mirrors congressional stock trades, the Pelosi Tracker.

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

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

Let your strategy run itself at Glider.fi.

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

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

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

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

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

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

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

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

1. Overcollateralised lending solved a crypto problem

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

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

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

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

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

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

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

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

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

2. Cashea: underwriting through local behaviour

One of the most instructive examples is Cashea in Venezuela.

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

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

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

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

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

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

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

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

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

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

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

That separation is important.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Follow Ana on X

5. Cap: separating the borrower from the risk capital

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

This is where Cap becomes relevant.

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

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

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

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

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

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

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

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

6. Blend: making the underlying account productive

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

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

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

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

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

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

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

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

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

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

7. How the pieces fit together

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

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

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

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

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

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

They should experience one coherent financial relationship.

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

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

8. Credit remains local, even when capital becomes global

None of this eliminates regulation.

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

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

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

But every participant must know what it is responsible for.

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

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

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

9. The real stablecoin killer product

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

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

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

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

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

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

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

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

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

That’s it for today.

Follow Ana on X

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

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

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

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

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

Welcome to Edition 16 of The Reading List — your fortnightly antidote to the noise. Every two weeks, I share my favourite reads across crypto, finance, technology, culture, and beyond. I also share what I’m watching and reading.

I didn’t read much last fortnight. Instead I dusted off my own personal Substack and wrote about something that’s been on my mind for a while: the pleasure is in the process, but not outcomes. Perhaps give it a read?

This fortnight, I will get back to the book I am halfway through - When Genius Failed and give you a full review into what you could take away from it in the next edition.

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.

The Fortnight That Was

Last fortnight, the bond shook crypto from its deep slumber that lasted for months. Scott Bessent, the US Treasury Secretary, announced a plan to at least double the size of the Treasury’s quarterly bond buyback operations to lower borrowing costs and manage market liquidity.

This increases the maximum size of liquidity support buyback operations for longer-dated nominal coupon securities from $2 billion to at least $4 billion per operation.

Crypto rallied in response to this. Long-end yields dipped, the dollar softened, and a market that had been leaning heavily short got run over. The market session on August 19 saw well over $1 billion in short liquidations.

Over the fortnight (Aug 16–29), Ethereum and Bitcoin shot up by almost a third and a fourth of their market caps. Everything else stayed flat.

Bitcoin remains well below both its January 2026 high and last October’s all-time peak. But for the first time in a while, the price moved in a direction that was different from sideways.

What We Wrote at The Token Dispatch

Last fortnight at The Token Dispatch, we focused a lot on what’s being built for an agentic, tokenised economy.

Vaidik opened with Agentic Payments With Fraud Check, where he examined OpenAI’s new receipt-verification cookbook that comes closest to a fraud check for autonomous agents transacting over x402. He also proved on whether an on-chain settlement record actually proves an agent bought the right thing, or merely that money moved.

TJ followed with Yield-Industrial Complex, on how Wall Street is now packaging every native crypto cash flow it can find, including BTCI’s 27% covered-call “yield,” Fidelity, Grayscale and 21Shares turning on staking in their ETFs, and stripping the income away from the underlying asset risk.

I wrote Unblocking Capital With Blockchains, on the $1.7 trillion trapped in excess working capital and the web of circular B2B debts that keeps it stuck. In this piece, I explored how blockchains can net those obligations the way Splitwise settles a group trip, or the way Wall Street solved its own paperwork crisis 60 years ago.

TJ then did a fancy experiment with AI agents in $5 Spent Well. In the field experiment, she handed an AI agent its own $5 wallet and watched it run Pixel Pet Studio, a do-or-die micro-business selling five-cent pixel animals and paying its own daily server rent - all this with no human in the loop.

Vaidik continued his series with Compute Capital Markets: Part IV. Here he traced where all the AI compute money actually comes from, including corporate cash flow, debt, equity, and now pension and insurance float. He also examined why the ~3-year lag between committing capital and getting working chips explains today’s shortage.

And I closed the fortnight with The Business of Trust, on how every era of commerce needs a new middleman to broker trust between strangers. In this piece, I explore how, in an age where agents spin up disposable identities in seconds, crypto rails (ERC-8004 identities, soulbound reputation tokens, behavioural scoring from the likes of Skyfire, RNWY and ChainAware) are racing to become the credit bureaus of the machine economy.

We are lining up a lot more interesting pieces for you in the coming weeks. Until then, here’s a recommendation list to give you good company.

Regulation & Policy

• Banking lobby’s campaign against Clarity will backfire: Columbia’s Omid Malekan argues that banks holding the Clarity Act hostage over stablecoin yield are a hugely profitable industry demanding protection it doesn’t need and inviting populist retaliation.

• The SEC sets crypto free: Byron Gilliam on the SEC’s proposed Safe Harbor rule, which gives protocols up to four years to reach a “decentralised or functioning” state and self-certify their exit from securities law.

Crypto & Tech

• Tokenised Stocks Have Arrived. Their Credit Market Hasn’t: The piece maps a 24,000x gap between the ~$461B off-chain securities-backed lending market and the ~$19M of credit drawn against tokenised equities onchain, arguing the bottleneck is legal enforceability, not price or dividends.

• Everything else is a name. Payments is a market: Alex unpacks H1 2026’s $11.2B crypto fundraising, showing the $2B “exchange” category is two mega-rounds and five licence-grab acquisitions rather than a broad venture signal.

• Why commodity traders may be the strongest vertical for stablecoin banking: Kirill Bulychev argues stablecoin neobanks win many low-volume accounts but that commodity traders, with genuinely high transaction volumes, are the segment where the horizontal model actually pays.

• A Second and Final Eulogy for Bitcoin Maximalism: Nic Carter argues Bitcoin succeeded wildly while nearly every testable maximalist prediction failed, using the Coldcard hack and the failed Filters coup as the ideology’s Great Disappointment.

Finance & Economy

• The bond market has something to say again: Byron Gilliam on the Bond Vigilantes waking from a two-decade slumber as 30-year yields hit 20-year highs, with the Fed neutered by a $40T debt load and Congress unwilling to act.

• Deep Dive: The Next Trillion-Dollar Futures Market: Chamath Palihapitiya on CME’s plan to launch compute futures, framing the financialisation of GPU-hours as AI capex ($765B) overtakes oil and gas for the first time.

• AI Refi: Matt Levine explores the consumer-finance truth that lenders profit from customers who fail to optimise, and what happens to that edge when everyone has an AI to refinance their mortgage for them.

• Predict the KPIs: Matt Levine on stocks as bets on future earnings and the strange market that emerges when the metric being predicted is itself a moving target.

AI & Innovation

• Give me 20 minutes and I’ll make you impossible to manipulate: Dan Koe lays out three levels of thinking (NPC, Main Character, Programmer) and four daily habits — question, curate, read, create — to escape the “intellectual obesity” of the algorithmic feed.

• The death of articulation: jords argues that outsourcing writing to LLMs risks a “genius on the tweets, illiterate on the streets” future, and that competent human communication is becoming a scarce, valuable edge.

• What Does It Mean To Be Human, Now? Highlights and reading list from Cosmos founder Brendan McCord’s Aspen seminar, tracing an arc from AI’s “dethronement” of human function to what remains irreplaceable.

• Human in the Loop: Nic Carter’s short story about a “responsibly aligned” future where mandatory human oversight turns every automated system into a bureaucratic farce — as a man’s ceiling slowly collapses.

• The humanoid robot ‘Olympics’ is as ridiculous as it is impressive: Two robots beat Usain Bolt’s 100m record at Beijing’s World Humanoid Robot Games — then crashed into a wall (one caught fire), a neat illustration of narrow feats versus real-world competence.

Culture & Beyond

• Twitter Brain: jords writes, as a self-described survivor, that the mind is shaped by what it consumes, and that a week of one-thing-at-a-time attention can rebuild a scattered attention span faster than you’d think.

• Internet Overexposure Syndrome: Katherine Dee proposes that “Borderline,” “Autist,” and “Schizo” online archetypes are rational adaptations to the internet’s environment rather than the psychiatric conditions they resemble.

• Empire of flight (Why birds don’t buy Bentleys): Antone Martinho-Truswell argues birds never developed complex culture because flight is an “evolutionary black hole” that removed the selection pressure that pushed humans toward it.

• What’s this buzz about bees having culture? An Aeon/Nature video on Alice Bridges’ experiment showing untrained bumblebees learning a two-step puzzle by watching others — the first socially-learned problem-solving seen in an invertebrate population.

• Where are all the aliens? A new book explores possible answers: Astrobiologist Seven Rasmussen on the Drake equation, the Fermi paradox, and the “cosmic mirror” idea that the technosignatures we hunt for reflect our own moment in technology.

• Why your ability to cope with stress changes with time: Behavioural scientist Masha Remskar on how seasonal, circadian, and social rhythms stack to make resilience a dynamic balance rather than a fixed trait.

• Some Thoughts on the Real World: Bill Watterson’s 1990 Kenyon commencement address on play, refusing to sell out, and inventing your own life’s meaning against a culture that measures worth by salary and title.

• The Principles of Better Decisions: Hades argues better decisions come from better mental models and not more information. In this piece, Hades walks us through first-principles thinking and opportunity cost as tools for seeing what others miss.

• Kulfi: Life After Swipe: Co-founder Garvit Agarwal on building Kulfi, a games app exclusively for couples that’s raced up India’s charts by owning the 9 pm-to-midnight bedtime slot, on a “content-as-engineering” playbook of 20+ in-house posts a day and zero paid marketing.

That’s all for this fortnight. Catch you in the next edition.

Until then, happy reading!
Prathik

P.S.: Want to recommend interesting literature? Write to me, and I shall include them in the next edition 👇🏾

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

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

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

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

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.

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.

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

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.