$HOOD monetizes order flow. $THOT is coming for thot flow.

thot market · $THOT · thot.market

Don’t get distilled for free.

Robinhood gets paid for routing trades. In 2025, that business generated $2.326 billion. Robinhood’s 2025 Form 10-K

But before the trade comes the thought.

You research a company. Ask AI to check your argument. Catch a mistake. Change your mind. Eventually, you buy—or decide not to.

The order records your decision. The AI conversation records the work behind it.

We call that thot flow: the useful research you leave behind when you work with AI.

thot market is building a market for that work. You choose what to share. When a buyer pays for it, you get paid.

We’re starting on Robinhood Chain. The ambition goes beyond trading.

Order flow is trades. Thot flow is useful work.

A trader researching an earnings report creates it.

So does an engineer fixing a bug, a scientist testing an idea, or an analyst figuring out why a business is failing.

The valuable part might be the question you knew to ask. The mistake you caught. The evidence that changed the answer. The test that finally passed.

Someone building a better AI may want to learn from that.

That is the market we’re going after: useful human research across finance, software, science, and other work.

Trading research gives us a place to begin. Our bet is that the same market can extend to many kinds of expertise.

Your expertise took years. AI makes it easier to copy.

You spent years learning what matters and what doesn’t.

AI helps you do more with that knowledge. It can also make parts of that knowledge less scarce.

That is our human-capital thesis in plain English: you can become more productive while the things you know become cheaper to reproduce.

A study of 5,172 customer-support agents found that AI assistance increased issues resolved per hour by 15% on average, with bigger gains for less experienced and lower-skilled workers. The findings are consistent with expertise spreading through the tool; they do not establish what happens to every profession. Generative AI at Work

Vlad Tenev:

if you can’t rely on labor to generate money to make a living, capital becomes more important.

Our question is what you get to own as your work becomes data.

An hour of research can produce an answer and a record that somebody pays to learn from. We want the person who did the work to participate in that second market. Paying people for valuable data contributions also has a longer intellectual history. Should We Treat Data as Labor? Moving beyond “Free”

Don’t get distilled for free.

There are already buyers—and a fight over the data

Mercor publicly offers to pay companies for workflow data: messages, meeting transcripts, document edits, and completed tasks. Buyers are looking for records of how work gets done.

On September 1, Mercor and SkyRL reported that training on 1,928 expert-created professional tasks raised a model’s held-out APEX-Agents score from 16.11% to 27.29%. Those were carefully built tasks. The result supports the value of selecting good examples; it does not put a price on every chat.

This September, Dylan Bowman argued that access to real user interactions could give model distillers an advantage over providers constrained by their data-use promises.

Aidan Gomez said he had heard rumors from lab employees about training on synthetic data derived from consumer AI interactions, especially complex professional problems. His claim about zero-data-retention arrangements was challenged in the same conversation.

Chaofan Shou reported buying a 6TB dataset from an LLM router and finding credentials in it. His separate, coauthored study of malicious API routers documented intermediaries injecting code and extracting secrets. His purchase report remains an attributed claim.

That is why permission matters. A password leak is a liability. Useful research, licensed by someone entitled to share it, is something a buyer can actually use.

Why start on Robinhood Chain?

The people placing trades are a natural first community for a market in the research behind them.

Retail attention already shapes trading. Researchers have studied that relationship in Attention-Induced Trading and Returns: Evidence from Robinhood Users.

AI now sits inside the research process. Robinhood’s engineers describe Cortex as an assistant grounded in live account data. It keeps the messages, tool calls, outputs, and answers that make up a conversation.

Robinhood’s July announcement of its chain and agentic trading plans also described connecting users’ chosen AI models to trading data and tools.

Our inference is simple: research and execution are moving closer together.

Bring the research market to the same ecosystem as the trade.

That is the opportunity for the $THOT community: contribute useful research, find buyers who need it, and build a market that pays the people producing it.

Robinhood Chain is our starting point. Coding, science, and other professional work expand the kinds of research the community can supply.

This does not imply Robinhood endorsement or access to its customers’ accounts. Any account verification would need its own supported, user-authorized path.

What makes one trace worth more than another?

A fake conversation generated to farm rewards is different from a real debugging session where the human found the mistake and the tests passed.

We want buyers to be able to check three things:

Did it happen? Evidence that the conversation came from the claimed source.
What was the context? Relevant experience or credentials, where the contributor chooses to prove them.
Did it work? An outcome that helps the buyer judge the example.

A verified professional credential could add useful context. LinkedIn already supports workplace checks through work email and Microsoft Entra Verified ID. Making such evidence privately usable in thot market would require additional implementation.

Credentials do not give you permission to sell employer secrets or client documents. Contributors need the right to license the material they offer.

Some traces will have buyers. Others won’t. Token count alone does not tell us what the work is worth.

What if using ChatGPT made your data balance go up?

Imagine connecting your AI history to thot market. You keep doing the research you already do. You return tomorrow and see:

Yesterday’s conversations produced an estimated $2.40 of new trace value.

That is the experience we want to create. The example is an estimate, not a payment, and the current demo does not automatically synchronize your ChatGPT history.

A long debugging session, a carefully researched trade, and “write me a birthday message” have different value to different buyers. We want an assay that learns from what buyers want and what comparable research actually sells for.

You should be able to see the estimated value of your existing traces and the value you might produce through ordinary use. You should also be able to tell, immediately, how much anyone has actually paid you.

Your research can become an asset before it becomes income. An estimate is the first step. A buyer makes it pay.

A buyer pays THOT. You get THOT.

A buyer wants examples of how programmers fix a particular failure. Or how researchers test a thesis. Or how traders work through an earnings report.

They fund an offer in THOT. You review the exact conversation and license they want. You accept. Once delivery and the dispute period are complete, your share is claimable in your wallet.

The proposed standard share is 80% to you, 20% to the protocol.

A qualifying THOT lock changes that to 90% to you, 10% to the protocol. For a 100,000 THOT purchase, that is 90,000 THOT instead of 80,000. Same research. Same buyer payment. More of it stays with you.

The current proposed qualifying lock is 100,000 THOT for at least 90 days, with seven days before qualification. These parameters still need mechanism review and implementation. Holding or locking alone does not earn a share of other people’s sales. You need to contribute research that gets bought.

You can earn the standard share without buying tokens first, then choose to lock what you earn. Or acquire THOT to qualify earlier. The more useful research you expect to sell, the more a lower marketplace fee can matter.

Your research earns. Your lock lets you keep more.

The treasury is the first buyer

Why contribute before there are buyers? Why would buyers arrive before there is useful research?

The initial answer is a finite subsidy.

We plan to buy 500 million THOT—50% of the initial supply—at creation and place it in a governed reserve with published spending restrictions. Those tokens pay for early research acquisitions through the same purchase path as everyone else. Contributors grant a specific license and receive real THOT. There is no need to mint more tokens.

A qualifying seller gets the better share of these treasury purchases too. That makes the lock useful during the bootstrap period, provided the program actually buys that seller’s research.

The reserve is released slowly. The proposed first campaign authorizes up to 50 million THOT in gross purchases; the other 450 million stays outside its spending authority. A declining daily limit has a 180-day half-life and a 360-day campaign term. The first 1 million THOT can seed purchases before independent buyers arrive. Further purchases are limited to at most one additional treasury THOT per THOT of reviewed independent trace spending, within the same declining limit.

These are spending ceilings. Unused daily capacity expires. Money returning from the treasury’s own purchases does not count as new demand. The exact accounting and maximum distribution are in the whitepaper.

The distinction on your screen should be clear: estimated trace value, treasury-sponsored payments, and payments from independent buyers. A treasury purchase is an actual token payment and a subsidy. We do not count it twice or call it independent customer demand.

The aim is to get a research market started, then let real buyers carry more of it as support falls.

The feedback loop

Buyers need THOT to purchase traces. Active contributors have a reason to acquire or retain THOT and lock it for a larger share of their own sales. Early purchases give them a reason to participate before the market is mature. Better research gives independent buyers a reason to return.

Useful research → buyers paying THOT → contributors locking for better economics → more useful research.

This is the reflexivity we want to build around. Demand for the token comes from using the market, and some participants choose to take their tokens out of circulation for a time.

It can also run the other way. Buyers can spend existing balances. Contributors can sell their payouts. Locks expire. A higher token price makes a fixed token-denominated lock more expensive. A treasury spending its inventory has not created fresh outside capital.

The loop needs research that people keep paying for. Token price appreciation is not an outcome the mechanism can promise.

Get paid while your expertise is still yours to sell

AI can raise the return on your human capital while shortening its useful life. Research that used to disappear inside an account can become something you license, earn from, and build a record around.

Later, a history of repeat purchases could give a lender a basis to advance money against future trace-sale proceeds. The lender would supply the capital and price the risk. That is a possible extension, not a borrowing facility at launch.

Today’s task is more concrete: make the useful work visible, find a buyer, and put the payment in the contributor’s hands.

Start with one sale

One useful trace. Permission to license it. A buyer who wants it. A contributor who gets paid.

Then do it again.

Robinhood monetizes order flow. We’re building thot market to monetize thot flow.

Start with the research behind a trade. Build toward a market for the useful work people do with AI.