I’ve spoken to 3 founders of different companies playing this game over last 10 days. Their companies sell training data to frontier AI labs, and all of them talk the way people talk when the ground is moving under them. It goes something like this.
We started in April. First quarter we closed $30M in orders. There are open purchase orders sitting on my desk for $100M . By December we should land somewhere north of $150M
. None of it is recurring but all of it is growing. This month might end up in $20M for us. We’re less than 12 people, and maybe some interns.
Every conversation I have in this market sounds like that now. For a while I kept thinking this is a rocket ship, why are more people not talking about it? Then it dawned on me that the founders themselves are asking a better question. They know the cash is real. They know the contracts are not forever. What should you do in such a situation?
What is actually being sold
Six things.
Some companies sell hours: humans labeling images and rating chatbot answers, the assembly-line era product, already dying. Some sell judgment: doctors, lawyers, and physicists writing down how they reason, at $100 to $500 an hour, because the models exhausted what amateurs could teach them. Some sell worlds: simulated Salesforce instances, fake banks, replica hospitals where agents practice a job across millions of repetitions. The unit here is expert judgment wrapped into a task, a world to act in, a rubric that defines good, and a verifier that scores it. Some sell verdicts: benchmarks, evaluations, red teams, the referees of the race. Some sell bodies: sensor rigs, tactile gloves and camera harnesses on real workers, because robots need to watch hands. And some sell rights: licensed archives, the Reddit-style deals worth tens of millions a year, institutions converting decades of accumulated text into an annuity.
Now look at how the money actually arrives. Almost everything is a purchase order against a deliverable: a dataset accepted, a batch of tasks passed QA, an environment shipped. Nothing renews by default. The headline numbers you read are annualized, usually the best month multiplied by 12, in a business where a lab can double or zero its orders inside a quarter. And everyone inside knows gross is not net. Marketplaces pass 60-70% of billings through to the experts doing the work. The exception is structural and companies that run their delivery from lower-cost geographies keep 70-80%+ of every dollar, which is why some of the most profitable names in this market are ones the valuation lists barely track. The labs do not care where the judgment was manufactured, at least for now. The P&L of the vendor definitely does.
The accidental giants
Almost nobody at the top of this market set out to build it.
Mercor started as a marketplace matching freelance engineers to companies, with an AI interviewer doing the vetting. Micro1 started the same way, an AI recruiter named Zara. Turing spent years as a remote-developer marketplace. Handshake spent a decade as a college recruiting network and pivoted after noticing that labs were poaching PhD annotators out of its own member base. It stopped renting out its network and started selling the work itself, and went from 0 to roughly $1B in gross annualized revenue in about 16 months. Even Scale began life as an API for Mechanical Turk before finding self-driving cars.
The pattern tells you what the product really is. These companies did not win because they understood data. They won because they had already built machines for verifying strangers at scale such as who is actually a doctor, which engineer can actually code, whose judgment can be trusted without meeting them. When labs suddenly needed vetted experts by the thousand, the recruiting companies were the only ones holding supply. The data was never the product. Verified judgment was and the incumbents of verified judgment were job platforms.
Why the labs keep paying
The reason labs sign 9-figure purchase orders is a war they cannot exit. It almost looked like anthropic was ahead for a while but last two weeks have largely levelled the playing field. No lab holds a durable capability lead anymore. Nobody keeps the crown for a full season, open models trail the frontier by months, and every price tier keeps collapsing. They are on a treadmill. Data vendors sell what's needed to power that treadmill. Their revenue does not require picking a winner. It is a tax on nobody winning.
Alex Karp has spent this month accusing Silicon Valley of overselling AI, telling the public not to believe its lying eyes. The purchase orders agree with him. If models were nearly finished, labs would not be paying this much for human judgment. Every invoice in this industry is a confession about what the models still cannot do.
But the same treadmill keeps executing its own suppliers. In 2023 the product was crowd workers rating responses. Once models outgrew the raters, the ratings became noise, and 2024 belonged to credentialed experts. Then reasoning models learned to grade themselves against checkable answers, and 2025 moved the money to environments and rubrics. Each generation of models graduates past the data that trained it. The rungs below the frontier keep dissolving. The frontier keeps paying.
I spoke to a friend at a frontier lab this weekend and asked how many data vendors he works with directly. Seven, he said. All seven are tasked with producing the same type of datasets. It goes without saying that a year from now, some of them will watch that PO vanish. That is the whole market in one anecdote: enormous demand, deliberately duplicated supply, and a buyer who owns the clock.
The clock inside every contract
Researchers at Epoch AI interviewed vendors and published the price sheet - a simple website replica for agent training runs about $20k, and one lab reportedly bought hundreds of them, once, the way you buy cones for a driving school. A high-fidelity clone of an enterprise tool with expert-written tasks runs . Individual tasks price between $200 and $2k, and exclusivity multiplies everything 4-5x, because a task your rival also trains on teaches you nothing about beating them.
But here is the twist, once models pass a task about 70% of the time, the task is discarded. The product depreciates by succeeding. That guarantees repeat orders, which is why the revenue curves look vertical, and it also guarantees that nothing annuitizes on its own. Everything must be rebuilt harder, forever. In a way the vendors are running on a weaker treadmill too just next to the frontier labs.
I’ve a sense that the founders in this space are bullish about the data business for next 3-4 yrs at least and maybe they should but the buyers here, the frontier labs are chosing to work both sides of the counter. Anthropic reportedly discussed spending over $1B on environments in a year while working with a dozen-plus vendors and making all of them conform to its frameworks, commoditization by procurement. OpenAI has reportedly trademarked an internal data platform aimed at reducing reliance on the very vendors it enriches, and has asked contractors to upload artifacts of real past work, the politest way of saying we would like the source, not the reseller. xAI cut a third of its in-house annotation team to grow specialist tutors instead. Karpathy, bullish on environments as a concept, is publicly bearish on the training technique the whole category monetizes.
This has happened before, inside this same industry. Between 2016 and 2021 a generation of data companies fed on self-driving programs, then the surviving carmakers pulled labeling in-house and the purest suppliers were absorbed or shut. Scale lived because it jumped to the LLM wave in time. Consider Appen. An Australian company, once a $4 billion listed darling supplying human data to big tech, with, at its peak, 80% of revenue from five clients. In January 2024 Google canceled its contract without warning. The stock is down more than 95% from that peak. One customer email, one technique shift, and the incumbent of the entire category became a case study. Pharma went the other way, never took drug trials back in-house, and 40 years later the outsourced trials industry still compounds. Both endings are possible here. Which one you get is decided by one law.
But what is the law? Whatever a machine can verify, machines will eventually learn without you. Whatever still needs a human to say this is good keeps paying humans. Code can be checked so it was the first casualty, and labs now mine their own training tasks from public repositories by the tens of thousands. Taste, ambiguity, regulated judgment, and the physical world fall last, maybe never. There is no unit test for what a senior surgeon sees, and you cannot unit-test a folded shirt. Verification is the scarcity. Sell against it and the clock works for you instead of against you.
What the cash should buy
None of this means the data wave is fake. Money is real, growth is real, and the physics of the treadmill guarantees demand for harder homework for years. It means the wave rewards a very specific shape of company, and punishes the clones, in a niche where a company fully bootstrapped can ship product and an offshore delivery team can undercut any price you quote. When a market’s number one customer is building your replacement while paying your invoices, your product is not the moat. Your position is.
So here is the actual question, the one the founders printing this money ask over dinner. Nobody running a business that generates $100M-500M of PO cash at these margins is going to stop. Nor should they. Take every order. Run the machine flat out. The only mistake available at this stage is treating the windfall as the business instead of as the financing for the business. PO income is a great fuel but what follows is the menu of what it can buy, and an honest read on each option.
Go deeper into data, not wider. The lazy move is horizontal which is more domains, more generalist supply, competing with 4 giants who own the trust. The compounding move is vertical such as pick one domain where verification stays hard, hire the 200 best experts in it as your own, and become the only counterparty labs call for it. One young company owns audio. One owns chip design. One owns advanced mathematics. New rungs will keep appearing as models advance, and labs generating their own data does not end this demand, it moves it up the difficulty curve toward whoever owns the top of a domain. Works when you truly own scarce experts. Fails when your experts are interchangeable with a rival’s spreadsheet.
Go physical, and own the whole loop. The mistake in physical data is thinking the gloves are the business. Hardware capture is the cheap part. The companies that will matter run the collection operation end to end - they employ the workers, build the rigs, hire in-house industry experts who know what a correct weld or suture or lockout procedure looks like, encode how an industry actually operates, and sell the annotated exhaust with exclusivity terms. The emptiest squares on the map that I’ve managed to create in my head are industrial rigs, refineries, factory floors, mines, places where no dataset exists at any price while everyone crowds into retail and finance and health-care related demos. However, this works when you control capture, quality, and rights and fails when you are a middleman for other people’s cameras.
Keep building environments, but sell them up the stack. The $20k website replica tier is already commoditizing into open-source hubs. The durable tier is high-fidelity, expert-graded, exclusive, and it points at 2 buyers, not 1. Labs today. Enterprises tomorrow, and that second buyer changes everything. Satya Nadella has been telling every firm that they pay for intelligence twice, once in money and once in the proprietary judgment that leaks out through every prompt, so they must build their own evals and their own learning environments inside their own walls. Read that as a product spec. The exact skill you built for lab work, turning a messy workflow into a world with rubrics and verifiers, becomes private training gyms behind a customer’s firewall such as their claims process, their trading desk, their hospital, simulated so their agents can learn without their judgment ever leaving the building. It multiplies your buyer count from 5 to 5,000. Works because it rides the same muscle. Fails only if you wait until the lab POs slow before building it.
Enter enterprise workflows with open eyes. Deploying agents inside companies is forward deployment work, mapping how invoices really move, discovering the SOP is fiction, sitting with the team until exceptions stop (I recently wrote a full piece on this). It is a real destination, and some data companies will build real businesses there. But know the physics before committing the cash to this. Data revenue arrives as 25M POs signed in weeks, enterprise revenue arrives as $500k-$2M pilots signed in quarters, and roughly 95% of enterprise AI pilots today show no measurable return. The move works as a separately run unit with separate expectations and its own leadership. It fails as a side project staffed by whoever the data business can spare, because the muscle is different and needs patience, embedding, and glue code instead of throughput.
Buy compute only if compute feeds your product. More than one founder in this market is asking whether the cash should become GPUs and a hosted RL platform. The honest answer is renting out raw compute is a commodity squeezed between hyperscalers and neoclouds, and a treasury full of depreciating silicon is not a moat. The version that works is narrower, hosting the training loops that run inside your own environments, where utilization is yours to guarantee and the customer is buying the world plus the gym plus the compute as one product. Prime Intellect already runs this play in the open. It gave away a hub of 2,500+ community environments and sells the compute and hosted training that run on top. The environments are the storefront. The GPUs are the checkout. That is a venture bet, not a cash-parking decision. If I was a founder doing this, I would make the decision deliberately or not at all.
Acquire the next rung instead of building it late. The most instructive capital allocation in this market so far is that one giant used its PO windfall to buy 2 environment startups within 5 months, purchasing its way onto the new rung while competitors were still hiring for it. In about 18 months, the model will likely position companies with real environment engineers who will love to get acqui-hired. Speed is the only advantage here. You’re sitting on piles of cash - so a war chest plus a clear map of which rung comes next beats organic speed in a market that re-deals every 18 months.
Sell to governments. There is a new customer class arriving. Governments buying sovereign AI programs will need national data pipelines, native-language corpora, local evals, and physical data from their own factories and fields, for the same reasons they buy their own grids.
And convert what you can into revenue that renews. POs are like weather. Some of it can be turned into climate such as evaluation subscriptions instead of one-time benchmark sales, environment maintenance contracts instead of one-time builds, data refresh retainers, certification programs that bill annually. None of it will look as spectacular as a $50M PO and that’s the tricky part to hedge yourself with less shiny pieces. Because, all of it survives the quarter when the PO doesn’t arrive.
And I’ve failed as a founder to know this - there are two mistakes to avoid. Entry into the giants’ generalist lane, where the trust premium cannot be replicated from zero. And a moderate raise at an immoderate multiple, which buys obligations priced like software on economics that are anything but, while closing the two exits that actually exist here like staying private and rich, or becoming infrastructure someone must own.
Trust is the asset that compounds
Every option on that menu above runs through the same gate. Enterprises will not hand you their claims process, labs will not hand you frontier training priorities, and governments will not hand you national corpora unless trust has been built deliberately, and trust in this market is not a vibe, it is a stack of verifiable commitments.
The companies that get this build it like a product. First comes convincing the company you’re taking the data from that you are not taking anything sensitive out and that they are not making any mistake as far as the law goes that will put them in trouble. Security and residency certifications before the customer asks are the norm. Public benchmarks are another form of trust machine here. On the other end, the labs buying this data also want provenance rails such as camera-verified sessions, credential attestation, proof that a specific human did the thinking, because the supply chain’s dirty secret is annotators pasting model output back as human work.
It helps to have neutrality covenants for example no lab on the cap table, no single buyer above a set share of revenue, learned the hard way by everyone who watched a rival’s customers flee the day a lab bought half of it - though for the Scale AI team maybe it was a brilliant outcome. Expert certification programs help if you can build a brand, so that “rated by your network” starts to mean something an industry recognizes. Every one of these is an asset that compounds while task formats die or change. When the format changes, and it will, roughly every 2 years, the trust is what transfers to the next product.
The 50th company
Scale and Mercor got there first and got there huge, so what should the 50th company do?
Start with what Mercor’s rise actually teaches, because everyone copies the wrong part. The visible part is speed. Scale took about 4 years to reach its first . The next cohort took 2. Mercor took under 20 months, Micro1 and AfterQuery closer to a year, and one environments startup went from $1M to $63M in 6 months. Founders read this as the market getting kinder. It is the opposite. Each rung is steeper and shorter, and the same acceleration that pulls a newcomer to $100M in a year pulls the rung out from under them just as fast. Speed is a property of the wave, not the boat - think about this and you will have second thoughts about riding in that boat because this game is not for everyone.
The part worth copying is quieter. Mercor built its verification engine before the demand existed, for a different business entirely, so when the wave arrived it onboarded trustworthy experts faster than anyone. It never needed to embed engineers inside customers or run services teams, the marketplace stayed the machine, and when the next rung appeared it bought its way there instead of building from behind. And the bootstrapped leader in this market teaches the inverse lesson with the same moral by staying profitable and never selling equity, it kept the option everyone else sold, the option to say no, to any customer, any deal structure, any quarter. In a market where your customers are your future competitors, optionality is not a luxury. It is what your margins are buying.
So the 50th company enters where the ladder is still being built such as one hard domain owned completely, rubrics and verifiers and environments sold instead of hours, benchmarks published from day one, the second buyer class built before it is needed, capital story decided on day one, bootstrap and keep the option or raise big and buy rungs, never the middle. And if you are not founding one but deciding whether to join one, ask the same questions from inside that which of the 6 products does this company actually sell, whose trust does it hold, what clock is its current format on, where is the PO cash going, and who is the second customer after the labs. A company with good answers to those is worth joining because because on rocket-ships often teaches you things on a compressed timeline.
5 years out
What’s the point of writing all this if I’m not incredibly right or horribly wrong about some of these things. So here is my 5-yr view.
The gross market grows for years. The demand mechanism does not pause while the lab race stays unresolved, and there is one scheduled stress test on the calendar - the first lab IPOs (which is very close today as of July 2026), when data spend becomes a line item public analysts question every quarter. My hunch is the composition rotates violently underneath the growth. Hours die first, and are mostly dead already. Generic environments commoditize into open hubs. Value concentrates in frontier judgment, verification and provenance, referees, physical capture, and private gyms for enterprises, and if I had to rank those, verification and enterprise gyms first because both get stronger as the labs get stronger, physical second because it is the only segment where supply rather than demand is the bottleneck. Of the 100+ companies selling into labs today (I created a list and gave up midway when I realized that the attempt was futile because it gets outdated the next minute), I expect fewer than 10 still independent and at scale in 2031. Most would cease operations with some rich founders. The rest get absorbed, by the giants buying rungs, or by the labs themselves, quietly, for the people.
The winners are legible if you watch what they are already doing. The bootstrapped quality leader becomes the standard setter, the name whose acceptance is itself a certification. The acquisitive giant becomes an exchange where expert work is priced, verified and sold, whoever the buyer is, and if labs are ever displaced as customers, employers stand next in line. The environment builders that survive wake up as the enterprise-simulation industry. The referees, if they stay unowned, end the decade looking like rating agencies, written into procurement rules and maybe into law. And somewhere in the physical world, a company collecting sensor-fused industrial data is compounding toward being the Scale of the embodied era, 5 years earlier on that curve than everyone crowding the digital one.
One founder in this market has argued that human data becomes a trillion dollar a year affair, and he gets the deepest thing right, models learn from humans at every stage, forever. What the trillion misses is that it prices human time, not the intermediary. The intermediary’s cut is decided by whether it owns something scarcer than a spreadsheet of contractors. The good news for everyone building here is that the scarce things are now known, and every one of them is buildable with exactly the cash this market is throwing off - owned expert networks, provenance rails, referee franchises, closed loops in the physical world.
Every gold rush ends one of two ways, the gold runs out or the miners industrialize. This one ends a third way. The gold learns to mine itself. When it does, the suppliers left standing will be the ones who sold the mine the one thing it can never dig up, the answer to the question every model asks and none can settle: what does good look like? Hold that answer in one narrow domain and you have a company. Hold it credibly enough, for long enough, and you stop being a vendor in someone else’s race. You become part of how the race is scored.
