# The goldmine business of selling data to frontier labs
**作者**: Vikram Aditya
**日期**: 2026-07-16T07:02:55.000Z
**来源**: [https://x.com/viks_rum/status/2077650169265590727](https://x.com/viks_rum/status/2077650169265590727)
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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 . By December we should land somewhere north of . 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
The category hides 6 different products under one label. Read the invoices and the market splits cleanly.
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, because the revenue mechanics matter more than the revenue. 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. Same invoice, wildly different business underneath. The invoice anyway does 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. 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. It takes all the running they can do to stay in the same place, and on a treadmill the only thing everyone keeps buying is shoes. Data vendors sell the shoes. 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. Look at this market and a funny thing happens: 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
Environments deserve one economics lesson because they are the strangest product in the market. 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. You are selling homework to a student who graduates past it every quarter. 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.
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 and math fell first because correctness is checkable, 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 purchases the cash should never make. 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.
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*导出时间: 2026/7/17 11:26:24*
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## 中文翻译
# 向前沿实验室出售数据这门淘金生意
**作者**： Vikram Aditya
**日期**： 2026-07-16T07:02:55.000Z
**来源**： [https://x.com/viks_rum/status/2077650169265590727](https://x.com/viks_rum/status/2077650169265590727)
---
过去10天里，我聊了3位正在这场游戏中掘金的不同公司的创始人。他们的公司向前沿AI实验室出售训练数据，而他们的谈吐都带着一种脚下的地面正在移动时人们特有的语气。大致是这样的：
> 我们四月份起步。第一个季度我们拿下了3000万美元的订单。我桌上还放着开的采购订单。到十二月我们应该能达到……以上的水平。这些收入都不是经常性的，但都在增长。这个月我们的收入可能达到2000万美元。我们团队不到12个人，也许还有几个实习生。
现在我在这条赛道上听到的每一场对话听起来都是如此。有一阵子我一直在想，这是一枚火箭，为什么没有更多人在谈论它？后来我才恍然大悟，创始人自己其实在问一个更好的问题。他们知道现金是真实的。他们也知道合同不会永远持续。在这种情况下你该怎么办？
## 到底在卖什么
这个品类用一个标签掩盖了6种不同的产品。读一读发票，市场就会清晰地分层。
有些公司卖时间：人类给图像打标签、给聊天机器人的回答打分，这是流水线时代的产品，已经在消亡。有些卖专业判断：医生、律师和物理学家记录下他们的推理过程，每小时100到500美元，因为模型已经穷尽了业余者能教给它们的一切。有些卖世界：模拟的Salesforce实例、假银行、复制的医院，智能体在其中通过数百万次重复来练习一项工作。这里的销售单元是打包成一项任务的专家判断、一个可供行动的世界、一份定义“好”的标准，以及一个给它打分的验证器。有些卖裁决：基准测试、评估、红队，这场比赛的裁判。有些卖身体：传感器设备、触觉手套和戴在真实工人身上的摄像头背带，因为机器人需要观察人手的动作。还有些卖权利：授权档案库，Reddit式的每年价值数千万美元的交易，机构把几十年的文本积累转化为一份年金。
现在看看钱实际上是怎么到账的，因为收入的机制比收入本身更重要。几乎所有一切都是针对交付物的采购订单：一个被接受的数据集、一批通过质检的任务、一个交付的环境。没有任何东西默认续约。你读到的那些惊人的数字是年化后的，通常是最畅销的那个月乘以12，而在一个实验室可以在一个季度内把订单翻倍或砍到零的行业里。而且每个行内人都知道毛收入不等于净收入。市场平台要把账单的60-70%支付给干活专家。也有结构性的例外：从成本较低的地理区域运营交付的公司能保留每美元的70-80%以上，这就是为什么这个市场上一些最赚钱的公司几乎不在估值名单上出现。同样的发票，底下却是截然不同的生意。反正发票并不在乎专业判断是在哪里制造的，至少目前如此。供应商的损益表肯定在乎。
## 无心插柳的巨头
这个市场顶端的几乎没有人一开始就打算做这门生意。
Mercor起步时是一个把自由职业工程师和公司匹配起来的市场平台，用一位AI面试官来做审核。Micro1的起点相同，是一位名叫Zara的AI招聘官。Turing做了好几年远程开发者市场。Handshake做了十年校园招聘网络，在注意到实验室从它自己的会员群体中挖走博士标注员之后转型。它不再出租自己的人脉网络，而是开始出售工作本身，并在大约16个月内从0做到了约10亿美元的年化毛收入。就连Scale最初也是一个Mechanical Turk的API，后来才找到自动驾驶汽车这个方向。
这个模式告诉你产品到底是什么。这些公司获胜不是因为它们懂数据。它们获胜是因为它们已经建造了大规模验证陌生人的机器：谁真的是医生、哪个工程师真能写代码、谁的判断可以在不见面的情况下被信任。当实验室突然需要成千上万经过审核的专家时，这些招聘公司是唯一握有供给的人。数据从来不是产品。经过验证的专业判断才是，而经过验证的专业判断的在位者就是求职平台。
## 实验室为什么不断付钱
实验室签署九位数采购订单的原因，是一场它们无法退出的战争。没有任何实验室还能保持持久的领先。没有人能保持一整个赛季的王冠，开源模型落后前沿仅数月，每一个价格档位都在不断坍塌。它们需要拼尽全力才能停留在原地，而在跑步机上，所有人唯一持续购买的东西就是跑鞋。数据供应商卖的正是跑鞋。它们的收入不需要押中赢家。这是对“没有人赢”征收的一种税。
Alex Karp这个月一直在指责硅谷过度吹嘘AI，告诉公众不要相信自己撒谎的眼睛。看看这个市场，有趣的事情发生了：采购订单站在他这一边。如果模型已经接近完成，实验室就不会为人类判断支付这么多钱。这个行业的每一张发票都是一份关于模型仍然做不到什么的供词。
但同一条跑步机也在不断处决它自己的供应商。2023年的产品是众包工人给回答打分。一旦模型超越了评分者，评分就变成了噪音，2024年属于有资质的专家。然后推理模型学会了对照可验证的答案给自己打分，2025年资金流向了环境和评分标准。每一代模型都会“毕业”于训练它的数据。前沿之下的梯级不断消失。前沿不断付钱。
周末我和一位前沿实验室的朋友聊天，问他直接合作的数据供应商有多少家。七家，他说。这七家的任务都是生产同一类型的数据集。不用说，一年后，其中一些将眼看着那份采购订单消失。这一则轶事就概括了整个市场：巨大的需求、被刻意复制的供应，以及一个掌控时钟的买家。
## 每份合同内部的时钟
环境（environments）值得上一堂经济学课，因为它们是这个市场上最奇怪的产品。Epoch AI 的研究人员采访了供应商并公布了价目表——一个用于智能体训练的简单网站复刻版约 2 万美元，据报道一家实验室一次性买了数百个，就像驾校买锥筒一样。一个由专家撰写任务的高保真企业工具克隆版则要 数千美元。单个任务定价在 200 到 2000 美元之间，而排他性会使一切乘以 4-5 倍，因为一个你的竞争对手也在用来训练的任务，对如何击败他们毫无启发。
但转折在于：一旦模型对某个任务的通过率达到约 70%，这个任务就会被弃用。这个产品因成功而贬值。你是在把作业卖给一个每季度都会超越它的学生。这保证了源源不断的重复订单，这就是为什么收入曲线看起来是垂直的；但这也保证了没有任何东西能自动转化为年金。一切都必须不断被更难地重建，永远如此。
我感觉这个领域的创始人们对未来至少 3-4 年的数据业务持乐观态度，也许他们确实应该如此，但这里的买家——前沿实验室——正选择同时坐在这场交易的两侧。据报道，Anthropic 曾讨论在一年内为环境花费超过 10 亿美元，同时与十几家供应商合作，并让所有供应商遵循它的框架——通过采购实现商品化。据报道，OpenAI 已为一个旨在减少对其正在养肥的供应商依赖的内部数据平台注册了商标，并要求承包商上传真实过往工作的成果——这是在委婉地说：我们要源头，不要转售商。xAI 裁减了三分之一的内部标注团队，转而扩充专家导师。Karpathy 看好环境这一概念，却公开看衰整个类别赖以变现的训练技术。
这种事以前发生过，就在同一个行业里。2016 年至 2021 年间，一代数据公司靠自动驾驶项目为生，随后幸存的汽车制造商将标注业务收回内部，最纯粹pure-play的供应商要么被收购，要么倒闭。Scale 活了下来，因为它及时跳上了 LLM 浪潮。想想 Appen。这家澳大利亚公司曾是身价 40 亿美元的上市宠儿，为大科技公司提供人类数据，巅峰时期 80% 的收入来自五个客户。2024 年 1 月，谷歌毫无预警地取消了合同。该股较峰值下跌超过 95%。一封客户邮件，一次技术转变，整个类别的龙头就成了一个案例研究。制药业则走了另一条路，从未将药物试验收回内部，四十年后，外包试验行业仍在复利增长。两种结局在这里都有可能。你会得到哪一种，由一条定律决定。
但这条定律是什么？凡是机器能够验证的东西，机器终将不需要你就能学会。凡是仍需要人类来判断“这很好”的东西，就会持续为人类付钱。代码和数学最先沦陷，因为正确性是可以检验的，而实验室如今从公开代码库中以数万计地挖掘自己的训练任务。品味、模糊性、受监管的判断以及物理世界则最后沦陷，也许永远不会。资深外科医生所看到的东西没有单元测试，你也无法对一件叠好的衬衫进行单元测试。验证才是稀缺性。围绕它去销售，时钟就会为你运转，而不是与你为敌。
## 这些现金应该买什么
这一切并不意味着数据浪潮是虚假的。钱是真实的，增长是真实的，跑步机般的物理规律保证了未来数年对更难作业的需求。它意味着这波浪潮奖励一种非常特定形态的公司，而惩罚那些克隆者——在这个利基市场里，一家完全自力更生的公司就能交付产品，而一个离岸交付团队就能报出低于你任何报价的价格。当一个市场的头号客户一边支付你的账单一边建造你的替代品时，你的产品不是护城河。你的位置才是。
所以这才是真正的问题，是那些正在印这些钱的创始人们在晚餐桌上问的问题。任何一家以这样的利润率产生 1 亿至 5 亿美元采购订单现金流的公司都不会停下来。他们也不应该停。接下每一笔订单。让机器全速运转。在这个阶段唯一可能犯的错误，是把这笔横财当作业务本身，而不是当作业务的融资。采购订单收入是极好的燃料，但接下来是它能买到的东西的菜单，以及对每个选项的诚实评估。
在数据上做得更深，而不是更宽。偷懒的做法是横向扩张——更多领域、更多通才型供应，与掌握信任的四巨头竞争。能带来复利的做法是纵向深耕——选择一个验证依然困难的领域，把其中 200 名最优秀的专家招为自有，成为实验室在这一领域唯一会打电话的对象。一家年轻公司垄断了音频。一家垄断了芯片设计。一家垄断了高等数学。随着模型进步，新的阶梯会不断出现，而实验室自产数据并不会终结这种需求，只会把需求沿难度曲线向上推向拥有某个领域顶端的人。当你真正拥有稀缺专家时，这条路行得通。当你的专家可以被竞争对手的一张表格替换时，它就会失败。
走向实体化，掌控整个闭环。物理数据领域的错误在于认为手套就是生意本身。硬件采集是便宜的部分。真正重要的公司会端到端地运营数据采集业务——它们雇佣工人、搭建采集设备、聘请懂行的内部行业专家（知道合格的焊缝、缝合或上锁挂牌程序是什么样），把一个行业实际的运作方式编码下来，然后以独家条款出售这些标注后的“副产品”。在我脑海中构建的地图上，最空白的区域是工业钻井平台、炼油厂、工厂车间、矿井——那些出任何价钱都买不到数据集的地方，而所有人却都挤在零售、金融和医疗相关的演示里。不过，这条路在你掌控采集、质量和权利时才成立，而当你只是别人摄像头的中间商时就会失败。
继续构建环境，但要把它们卖向价值链上游。2万美元的网站复刻档位已经在向开源中心商品化。持久耐用的档位是高保真、专家评级、独家授权，并且指向2个买家，而不是1个。今天是实验室，明天是企业，而这第二个买家会改变一切。萨提亚·纳德拉一直在告诉每家公司：他们为智能付了两次钱，一次是金钱，一次是通过每个提示词泄露出去的专有判断力，所以他们必须在自己的围墙内构建自己的评估体系和自己的学习环境。把这当作产品规格书来读。你为实验室工作打造的 exactly 那项技能——把混乱的工作流转化为带评分标准和验证器的世界——会变成客户防火墙后面的私有训练健身房，比如他们的理赔流程、他们的交易台、他们的医院，通过模拟让他们的智能体学习，而他们的判断力永远不会流出大楼。这会把你的买家数量从5个乘到5,000个。它之所以有效，是因为它复用了同一块肌肉。只有在实验室订单放缓之前你不着手建设，它才会失败。
带着清醒的认知进入企业工作流。在企业内部部署智能体是前沿部署工作，要摸清发票实际如何流转，发现SOP不过是虚构，和团队坐在一起直到例外情况消失（我最近就此写了一篇完整文章）。这是一个真实的目的地，一些数据公司会在那里建立起真正的业务。但在投入现金之前要先了解这里的物理规律。数据收入是几周内签下的2500万美元订单，而企业收入是一个季度又一个季度才签下的50万至200万美元试点项目，而且如今大约95%的企业AI试点项目没有可衡量的回报。这一步棋应该作为一个独立运营、有独立预期和独立领导层的部门来执行。如果作为由数据业务腾不出手的闲散人员凑成的副业项目，它就会失败，因为所需的肌肉不同，需要的是耐心、嵌入和胶水代码，而不是吞吐量。
只有当算力能喂养你的产品时才购买算力。这个市场上不止一位创始人在问，这笔现金是否应该变成GPU和托管的RL平台。诚实的答案是：出租原始算力是一件被夹在超大规模云厂商和新兴云厂商之间的商品，而一库房不断贬值的硅片不是护城河。行得通的版本更窄：托管运行在你自己环境内部的训练循环，利用率由你自己保证，客户购买的是世界+健身房+算力合为一体的产品。Prime Intellect已经在公开地打这个打法。它免费开放了一个拥有2,500多个社区环境的中心，然后出售其上运行的算力和托管训练。环境是店面，GPU是收银台。这是一次风险投资押注，而不是现金停放决策。如果我是做这个的创始人，我会要么深思熟虑地做出这个决定，要么干脆不做。
收购下一级台阶，而不是晚些时候再去建造。迄今为止这个市场上最具启发性的资本配置是：一家巨头用订单暴利在5个月内收购了2家环境初创公司，在竞争对手还在为此招兵买马时，就用买的方式登上了新台阶。大约18个月内，这个模式很可能会催生一批拥有真正环境工程师的公司，他们会乐于被收购式挖角。速度在这里是唯一的优势。你手里攥着成堆的现金——在一个每18个月重新洗牌的市场里，一个战争基金加上一张清晰的“下一级台阶在哪”的地图，胜过有机增长的速度。
卖给政府。一个新客户群体正在到来。购买主权AI计划的政府将需要国家级数据管道、母语语料库、本地评估，以及来自他们自己工厂和田野的物理数据，理由和他们购买自己的电网一样。
并且尽可能把收入转化为可持续续费的收入。订单就像天气。其中一部分可以转化为气候——比如评估订阅而不是一次性基准测试销售，环境维护合同而不是一次性构建，数据更新年费，按年计费的认证项目。这些都不会像一笔5000万美元的订单那样耀眼，而这正是需要用这些不那么闪亮的碎片来对冲自己的微妙之处。因为，当订单没有到来的那个季度，所有这些都还在。
作为一个创始人，我曾在这一点上失败过——有两笔钱是永远不该花的。一是进入巨头的通用赛道，那里的信任溢价无法从零复制。二是以不合理的倍数进行适度融资，它买来的是按软件定价的义务，而底层经济却完全不是软件，同时还关闭了这里实际存在的两条退出路径——私有化并保持富有，或者成为别人必须拥有的基础设施。
## 信任是复利的资产
上面清单里的每一个选项都要通过同一道门。企业不会把他们的理赔流程交给你，实验室不会把前沿训练优先级交给你，政府也不会把国家语料库交给你，除非信任被刻意地建立起来——而在这个市场上，信任不是一种氛围，它是一叠可验证的承诺。
懂得这一点并将其当作产品来打造的公司是这样做的。首先要说服你从中获取数据的公司，让他们相信你没有带走任何敏感内容，而且他们不会在法律上犯下会给他们带来麻烦的错误。在客户提出要求之前就提供安全认证和数据驻留认证是行业常态。公开的基准测试是这里建立信任机器的另一种形式。另一方面，购买这些数据的实验室也需要来源可追溯的保障机制，例如经摄像头验证的工作会话、资质证明、证明某个具体人类确实进行了思考的证据——因为这个供应链肮脏的秘密是标注员把模型输出粘贴回来冒充人类工作。
拥有中立性约束会很有帮助，例如没有任何实验室出现在股权结构表中、没有任何单一买家占营收超过设定比例——每一个目睹过竞争对手的客户在某家实验室买下其一半股份当天就集体出逃的人，都是惨痛教训换来的（不过对 Scale AI 团队来说，也许那是个绝妙的结局）。专家认证项目如果你能建立起品牌，也会很有帮助，这样“由你的网络评级”就会开始具有行业认可的意义。这每一项都是能复利增值的资产，而任务格式则会消亡或改变。当格式改变时——它一定会改变，大约每 2 年一次——传承到下一个产品上的是信任。
## 第五十家公司
Scale 和 Mercor 最先抵达那里，而且规模巨大，那么第五十家公司该怎么办？
从 Mercor 的崛起真正教会我们的东西说起，因为每个人都在抄袭错误的部分。看得见的部分是速度。Scale 用了大约 4 年时间达到第一个1 亿美元 ARR。下一批公司用了 2 年。Mercor 用了不到 20 个月，Micro1 和 AfterQuery 接近一年，还有一家环境类初创公司在 6 个月内从 100 万美元做到了 6300 万美元。创始人把这解读为市场变得更友好了。事实恰恰相反。每一级阶梯都更陡峭也更短促，同样这种能在一年内把新公司拉到 1 亿美元的加速力，也会同样快地把阶梯从他们脚下抽走。速度是波浪的属性，而不是船的属性——想想这一点，你就会对登上那条船产生顾虑，因为这个游戏并不适合所有人。
值得抄袭的部分则安静得多。Mercor 在需求出现之前就打造好了它的验证引擎，最初是为了一个完全不同的业务，所以当浪潮到来时，它比任何人都更快地吸纳了可信的专家。它从不需要在客户内部派驻工程师或运营服务团队，市场平台始终是那台机器，而当下一级阶梯出现时，它是花钱买上去的，而不是从后面追赶着去建造。而这个市场里白手起家的领跑者则以同样的道理教给了我们反向的教训：通过保持盈利、永不出售股权，它保住了其他所有人都卖掉的那个选项——说“不”的权利——对任何客户、任何交易结构、任何季度都可以说“不”。在一个你的客户就是你未来竞争对手的市场里，选择权不是奢侈品。它正是你的利润率所买到的东西。
所以第五十家公司应该进入阶梯仍在建造之中的地方，例如完全拥有某一个艰难的领域，出售的是评分标准、验证器和环境而不是工时，从第一天起就发布基准测试，在需要之前就建好第二类买家，第一天就定好资本故事——要么自力更生保住选择权，要么大额融资去买阶梯，绝不走中间路线。而如果你不是要创办一家这样的公司，而是在决定是否要加入一家，那就从内部问同样的问题：这家公司实际销售的是这 6 种产品中的哪一种，它掌握着谁的信任，它当前格式的时钟还剩多久，PO 的现金流向何处，以及实验室之后谁是第二个客户。一家对这些问题有好的答案的公司值得加入，因为在火箭飞船上往往能在被压缩的时间线里教会你很多东西。
## 五年之后
如果我对其中一些事情不是极其正确或错得离谱，那写这一切又有什么意义呢。所以，以下是我对 5 年后的看法。
总市场会增长多年。只要实验室竞赛悬而未决，需求机制就不会停摆，而且日程表上还有一场按计划进行的压力测试——第一批实验室 IPO（截至 2026 年 7 月已近在眼前），届时数据支出将成为公开市场分析师每个季度都要质询的报表科目。我的直觉是，在增长的表象之下，构成成分会发生剧烈轮动。工时最先消亡，而且基本上已经死了。通用环境将商品化，沦为开放的集散地。价值将集中在前沿判断、验证与溯源、裁判、物理采集以及企业私有训练场上，如果让我给这些排序，验证和企业训练场排第一，因为它们都随实验室变强而变强，物理采集排第二，因为它是唯一一个瓶颈在供给侧而非需求侧的细分领域。在如今向实验室销售的 100 多家公司中（我做过一个名单，做到一半就放弃了，因为我意识到这种努力是徒劳的，它下一分钟就过时了），我预计到 2031 年能保持独立并具备规模的将不到 10 家。大多数将停止运营，留下一些富起来的创始人。其余的将被吞并——被购买阶梯的巨头，或者被实验室自己，悄无声息地，为了那些人才而吞并。
赢家是谁其实并不难看清，只要观察他们现在正在做什么。那个自力更生的质量领跑者将成为标准制定者，成为一个名字本身就是认证的品牌。那个善做收购的巨头将成为一个交易所，专家工作在其中被定价、验证和出售，无论买家是谁，而如果实验室有朝一日不再作为客户，雇主将排在队伍的下一位。存活下来的环境建造者一觉醒来会发现自己成了企业仿真行业。裁判们，如果能保持独立不被收购，将以此十年收尾时的样子酷似评级机构——被写进采购规则，也许还会写进法律。而在物理世界的某个地方，一家正在收集传感器融合工业数据的公司正朝着成为具身智能时代之 Scale 的方向复利式成长，在那个曲线上，它比拥挤在数字世界里的所有人都早了 5 年。
这个市场的一位创始人曾论证，人类数据将成为一个每年万亿美元规模的生意，而他说对了一个最深刻的道理——模型在每一个阶段、永远都在向人类学习。这个“万亿”漏掉的是，它定价的是人类时间，而不是中间商。中间商的抽成取决于它是否拥有某种比一份承包商电子表格更稀缺的东西。对于在这里创业的所有人来说，好消息是稀缺之物如今已经明朗，而且每一项都可以用这个市场正在产生的现金来打造——自有专家网络、数据溯源通道、裁判员特许经营权，以及物理世界中的闭环。
每一场淘金热都以两种方式之一结束：金子被挖完，或者矿工实现工业化。而这一场将以第三种方式结束：金子学会自己采矿。到那时，能留下来的供应商，将是那些把矿永远挖不出来的东西卖给矿的人——也就是对每个模型都在问却无一能回答的问题的答案：什么才是“好”？在某个狭窄领域内掌握这个答案，你就拥有一家公司。如果足够可信地、足够长久地掌握它，你就不再是别人竞赛中的供应商。你会成为这场竞赛如何计分的一部分。
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*导出时间: 2026/7/17 11:26:24*
