$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

Before the trade comes the thought.

You read the earnings report. Ask Claude to tear apart your thesis. Catch a mistake. Find the number everyone else missed. Buy—or decide the whole thing was a terrible idea.

Robinhood sees the order. The AI conversation contains the research that got you there.

We call that thot flow. And we think the person doing the thinking should have something to sell.

thot market lets you offer useful AI research to buyers, on terms you choose, and get paid in THOT when it sells.

Trading is the first community. The same thing happens when a programmer fixes a miserable bug, a scientist tests an idea, or an analyst works out why a company is failing. The market we want to build is for useful work, wherever it happens with AI.

The market is already forming around you

This is what the last few weeks looked like.

September 1: Mercor and SkyRL reported training a 397B model on 1,928 expert-created professional tasks. Its held-out APEX-Agents score rose from 16.11% to 27.29%. These were carefully constructed tasks with evaluation environments, not a random dump of chat history. Selecting the right work mattered.

Native-resolution heading from Mercor and SkyRL’s announcement, captured September 15. The result measures an expert-task training intervention, not the value of arbitrary personal chats.

Mercor also publicly offers to pay businesses for their workflow data: messages, document edits, meeting transcripts, completed tasks. There is already a buyer-facing business around acquiring records of how people work.

September 8: Dylan Bowman raised the possibility that model distillers could gain an advantage through routers that see real user interactions. One reply got straight to the consumer proposition:

Offer discounts for queries if they can train on it.

— FoolishPig, September 8. An independent suggestion, not a statement about thot market.

Original Dylan Bowman post, captured September 15. Tap the image for the full-resolution capture.

September 9: Aidan Gomez said he had heard rumors from lab employees about training on synthetic data derived from consumer AI interactions, particularly hard professional problems. His claim about zero-data-retention arrangements was challenged in the same conversation. That exchange is evidence of the argument happening in public, not proof of the alleged practices.

Aidan’s original post, captured September 15. Read Will Depue’s challenge alongside it.

September 11: Chaofan Shou reported buying 6TB of data from an LLM router, including exposed credentials.

Opening excerpt of Shou’s September 11 post, captured September 15. The purchase and contents are his report; this screenshot is not independent verification of the dataset. The credential image is deliberately omitted.

The opportunity and the failure are sitting next to each other. People want the research. People also put passwords, client information and private documents into the same conversations.

A data market has to distinguish those things. It has to ask permission. And it has to pay the person who can actually license the work.

AI is compressing the useful life of your human capital

Your expertise took years to build. AI lets you apply it faster. The same process can make parts of it easier for someone else to reproduce.

The return on your expertise can rise while the time you have to earn from its scarcity gets shorter.

That is the tension behind thot market. If your research helps make expertise cheaper, you should be able to participate in the transaction.

This is more than a metaphor. A study of 5,172 customer-support agents found AI assistance increased issues resolved per hour by 15% on average, with larger gains for less experienced and lower-skilled workers. The authors discuss the tool spreading practices associated with stronger workers. That is one setting, not a forecast for every job. But it makes the economic question concrete: who captures the value when know-how becomes reproducible?

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. The idea of paying people for valuable data contributions has a longer intellectual history. We want to make one narrow version practical: a market for the research you choose to contribute, with a buyer, a license and a payment receipt.

Don’t get distilled for free.

Why Robinhood Chain?

Because research and execution are getting closer together.

On August 25, Robinhood’s engineers described the architecture behind Cortex: an assistant grounded in live account data, with a history of the conversation, tool calls, tool outputs and answers. Robinhood’s July announcement of its chain and agentic trading plans also described connecting a trader’s chosen AI model to its data and tools.

The research is becoming machine-readable before the order reaches the market. Our inference is that a market for that research belongs near the people making the trades.

Robinhood monetizes order flow. thot market wants to monetize the work upstream of it.

That is the reason to start here. Coding, science and other professional work give the community more kinds of research to supply. We are making a bet about how broad this market could become, not claiming a measured TAM larger than Robinhood.

thot market is independent of Robinhood. Running on its chain does not give us access to brokerage accounts or establish an endorsement.

What if using ChatGPT made your data balance go up?

Imagine connecting your AI history to thot market. You don’t fill out surveys or invent tasks to farm points. 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 what similar research has sold for before your own trace has sold. The comparison shows a gross price range, before your seller share, and how much real evidence sits behind it. If there are too few comparable sales, it should say so. Treasury sampling prices and independent buyer prices stay separate. Neither a comparable sale nor an estimate guarantees someone will buy your trace.

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.

Bring your work. Keep your tools.

One useful starting point is the developer who already pays for AI, does difficult work, and cares about the bill.

The experience we want is simple: bring your own OpenRouter key, keep using your coding client, and opt in to contributing selected research. If a buyer—or the finite treasury program—acquires it, receive THOT. You still pay for inference through your existing account. The rebate is a trace-sale payment; it is not a promise that every API token earns a token back.

The September 8 reply above captures the appeal. The difficult part is deciding which work to buy, whether it is authentic, and what the contributor has agreed to release.

Participation has three steps:

Bring your work. Import a supported history, record Codex or Claude Code sessions with the capture CLI, or connect your OpenRouter key and point an OpenAI-compatible client at the thot endpoint. Every exchange sent through the connected relay is recorded in your private vault.
Put selected research on the market. Review the release, set its THOT asking price and license, then authorize automatic sales. Include it in treasury sampling if you want the reserve to consider buying it. Recording and selling are separate choices.
A purchase pays you. A qualifying buyer funds the escrow. The authorized release is delivered, your pending proceeds appear, and an undisputed sale pays your wallet after the one-hour delivery window.

The connection setup tells you what will be recorded once; it does not interrupt every exchange. You keep paying your inference provider normally. Capturing work alone does not create a payment. Ordinary ChatGPT and Claude website histories are not automatically synchronized; the app’s connection screen identifies the available routes.

For OpenRouter research, the buyer can see which model you requested and which model OpenRouter reported returning. If the provider does not report a model, we say so. Connecting a key captures calls through the thot endpoint; it does not fetch your old OpenRouter conversations or follow calls made somewhere else. You review the recorded material and sign its listing before it becomes available for purchase.

Uploading an old conversation is useful too. It just carries a different kind of evidence from an exchange observed as it happened.

How a buyer finds something worth buying

The basic object is a funded purchase order. A buyer specifies the kind of research, the evidence it must carry, the license, the price per accepted trace and a maximum budget. For example:

Wanted: debugging research
Evidence: an accepted capture source, with sufficient provenance
Property check: coding workflow; at least 20 recorded turns
Inspection: a contributor-authorized free excerpt, if available
License: a specific permitted use and retention period
Price: 20,000 THOT per accepted trace
Limit: 10 traces; no more than 200,000 THOT total

Illustrative order terms, not a funded offer or an API request.

The contributor authorizes the exact material, license and price when enrolling it for sale. A funded match to those terms proceeds automatically, without another approval. The contributor can stop future unfunded sales; purchased rights remain in force.

But a buyer should be able to look before paying. Under the selected preview policy, contributors can authorize a free excerpt: up to 10% of the released text, capped at 1,200 characters, for conversations of at least 800 characters. The contributor sees the exact excerpt before signing. Its position is selected pseudorandomly; identical content always shows the same window, even if the buyer refreshes, changes wallets or opens another listing. Buyers with 100,000 THOT across their wallet and qualified locks can inspect ten distinct trace contents per day without spending any tokens. The permission is for purchase evaluation, not training or redistribution. Older listings need a new opt-in and signature to offer it.

The initial property assay is equally concrete: choose a workflow and a minimum number of turns. It checks the recorded workflow label and turn count and returns the result. No model reads the conversation, scores its quality or recommends a fair price in that check. The asking price is the contributor’s; comparable-sale estimates are separate. A free excerpt gives the buyer some actual research to judge before funding the full licensed release.

General buyer-supplied assayer agents require a sandbox, restricted output, limits on repeated queries and an explicit disclosure policy. That broader agent environment is a later capability. Once paid, the buyer receives the complete release that purchase specified, before the one-hour dispute window runs. The rest of the contributor’s vault stays private.

A trusted execution environment can help enforce the processing boundary. Its remote attestation lets a verifier check evidence about the machine and measured software. A useful verification chain must bind the reviewed application and its keys to the particular capture or result being checked. Merely displaying a TEE hash does not prove that an uploaded conversation came from OpenAI, that its author owns a brokerage account, or that an evaluator’s price is correct.

Three different checks matter:

Provenance: how the conversation was obtained, and what the capture actually witnessed.
Credentials: a narrow, supported statement about its contributor, backed by a named verifier.
Outcome: evidence that a result happened, under a defined measurement rule.

A brokerage-control proof is different from a verified balance or a Sharpe ratio. A claim of strong trading performance needs a defined time window, return series and calculation, as well as authenticated source data. Those stronger financial predicates are not established by the current account-control connector.

The purchase receipt should bind the trace commitment, license, buyer, seller, gross price, quoted shares and settlement result. Then the contributor can distinguish an appraisal from a funded offer, and a funded offer from a completed payment.

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.

You already chose the conversation, license and price when you contributed it. They fund a matching purchase in THOT. You do not have to come back and click Accept. The service delivers the authorized material and your pending proceeds appear.

The selected settlement window is one hour after recorded delivery. The service then finalizes an undisputed purchase and sends your share to your wallet. You do not need to return to collect each sale; manual claiming remains available if the service is delayed. There is no additional lock on your sale proceeds; the 90-day lock applies to the THOT you chose to stake. Delayed delivery, a dispute or a delayed payment worker can extend settlement. The buyer receives the complete purchased release before the dispute window runs. A dispute pauses payout for operator review: the contract records a reason hash and resolution, while the explanation stays private. A resolution either refunds the purchase or pays the agreed split. It cannot undo a copy already downloaded.

With nothing locked, you keep 30% of your own sale. At the highest lock tier, you keep 95%.

For the same 100,000 THOT purchase, that is 30,000 THOT or 95,000 THOT in your pocket after settlement. The difference is how much THOT you have committed to the market.

Qualified THOT locked	Your share of your own sale
Below 10,000, including none	30%
10,000 or more	50%
100,000 or more	70%
1,000,000 or more	90%
5,000,000 or more	95%

The higher tiers require a 90-day lock, seven days before qualification, and ten days remaining when an offer is quoted. Newly added tokens have their own waiting period. Your principal stays yours and becomes withdrawable at its stated expiry. Holding tokens in a wallet does not earn the better seller split.

You can start without buying tokens, sell useful research at the 30% share, and choose to lock what you earn. Or acquire THOT to qualify earlier. The top tier pays about 3.17 times as much for the same completed sale. It does not guarantee a sale or a return on the locked principal. These are the selected launch terms. The same terms must be enforced by the deployed payment and lock contracts before paid launch.

Your research earns. Your commitment decides how much you keep.

Hold to buy. Lock to keep more.

Buyers also participate in the token economy. The entry requirement is 100,000 THOT held, in addition to the tokens spent on the purchase. Your own qualifying locks count toward that balance, so you do not need to hold a second 100,000 in your wallet if you already qualify through a lock.

Buying a 20,000 THOT trace with no qualifying lock therefore needs 120,000 THOT before payment: 20,000 funds the purchase, and 100,000 meets the access threshold. Buyers can browse public descriptions without holding tokens. Access to private research still requires a specific license and the contributor's consent.

Holding is enough for buyer access; locking is required for better seller terms. Buyer eligibility is checked at funding and when the automatic sale applies the contributor’s authorization. Later selling your membership balance stops you qualifying for new purchases; it does not erase a license you already bought. The protocol's disclosed acquisition vault is the sole exemption, with its reserve spending limits still enforced.

Bring someone whose research matters

Introduce a contributor, with their agreement, and you can earn a share of the marketplace fees their independent sales produce for 180 days after they accept your referral.

The referral share starts at 10% of the marketplace fee and rises to 15%, 20%, 25% and 30% at the same qualified-lock thresholds. This comes out of the protocol's cut. The contributor still receives their full quoted share.

For example: a buyer pays 100,000 THOT; the seller's qualified 1m lock gives them 90,000. If you referred that seller and have 100,000 qualified THOT locked, you receive 20% of the remaining 10,000 fee: 2,000 THOT. The protocol retains 8,000.

Register the referral when enrolling your contribution, before its first automatic sale. One contributor, one accepted referrer, one level. No reward for a signup or token purchase. No referral commissions on treasury purchases, self-dealing or reimbursed purchases. These are payments for introducing useful suppliers whose work gets bought, not a dividend for everyone holding THOT.

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 authorize a specific license and price up front, and receive THOT when a qualifying purchase settles. There is no need to mint more tokens.

The initial program can be simple: publish a sampling price, let contributors enroll eligible traces, then buy selected samples from the reserve. An operator approves the campaign or batch; a worker handles its purchases within the contract’s limits. Inspect what was bought, record a quality assessment, and use that evidence to decide what to buy next. These paid acquisitions are separate from the optional free buyer excerpt. No custom assayer agent is required to get the first research paid for. Contributors decide which excerpt to disclose and which complete release to sell.

Uploading the same conversation again does not create another reward. Exact import duplicates reuse your existing record, and the reserve worker blocks a second funded acquisition of identical released content across wallets or changed listings. Modified copies and multiple identities still need review; a content hash is not proof that someone created useful new research.

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 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 hold THOT to enter the market and pay THOT to purchase traces. Active contributors lock THOT for a much larger share of their own sales. Referrers can lock for a larger share of fees from the suppliers they introduce. Early purchases give contributors 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.