Good to give this talk and just want to say right off the bat this is probably going to be a little different from what you've seen so far at AI conference. I'm here to not deliver an agenda on any company's behalf but just to expose a lot of alpha in data markets. But also just tell you what's really going on behind the scenes in a in a very murky landscape where nobody seems to know how Remarque handshake and a lot of these folks actually produce data. So you know, quick reframe before we start when people hear data markets they picture scale around 2019. They picture these rooms of annotators in like Manila labeling images and that's that's real and it's maybe you know, 10 to 15 billion dollars a year per lab but it's kind of the least interesting part. The models work now what scares and badly priced is the sort of data that takes some from generalist competence into real expertise. We knew this since 2024 when Scale got acquired and we were spamming a lot of GPQA data sets. So look, let me start with this framing. Data is to the white collar revolution what coal and iron basically is to the Victorian age and the information age right now. I have this piece called the TAM is in a vertical it's all of labor. And so the supply chain is basically just doing what every sort of industrializing supply chain does it unbundles two years ago when vertically integrated giant like a Remarque or Surge or Scale AI if you if you guys are unfamiliar they're massive data companies. They had to do all of it but that was because that's the only way that unit economics worked in an immature market today. That's increasingly not the case. Specialists outcompete the giants at a lot of steps sourcing the people, building environments, designing rewards, running evals. The fragmentation I would say, is pretty permanent. It's not transitional. And uh quality increasingly does not scale linearly with quantity, which leads to a sort of cottage industry in data land right now, where you have labs literally mandates uh vendor diversification on the scale of like 20 to 30 different vendors because they inherently distrust their ability to scale quality with quantity. So, to orient you, you know, here's here's the sort of argument as it developed through this year. Um January, we saw a lot of industrialization and unbundling. You'll hear me come back to this mechanism I call the Antikythera mechanisms, which is this sort of bespoke systems that translate messy business context into Evals. Uh increasingly important in labs' hungry quest for real-world seed data to set at seed ends.

Um I also explored this concept called type one versus type two data, uh contrived versus non-contrived for the more researcher types in the audience, and why the GPQA cell playbook that works in 2024 for data acquisition kind of falls apart on long horizon unverifiable work. Um so, you know, going through all these topics, all of which are online, uh I'll also I'll actually just skip to the more interesting part, but I'm bringing this up just here in case any of the particular topics I talk about are are particularly interesting and you want to dive in more. So, model improvement is a function of three inputs. We all sort of see this uh commonly expressed as dodge, compute, data, and talent. I put together this very uh like rudimentary sort of chart online in one of my pieces and a very rudimentary uh equation just to express the fact that, you know, if there's any sort of imbalance in this compute, data, and talent, then you start seeing uh a sort of inefficiency in producing uh what I call like generalized AI model performance, right? But also, uh we we see a sort of inefficiency in in CapEx spend that it that is a sort of result of this equation right now. Um exponentially increasing CapEx spend, but sort of like um AI revenues are are sort of falling far behind. Uh data is sort of the underfunded leg here. It's the one that sort of turns a generalist model into a real expert. It's the one that's uh actually I believe quite lacking in this equation and thus because of the imbalance penalty parameter concepts that I'm expressing, um presents a whole opportunity. But you know, going back to what I said at the start, what is data actually? So, most people picture state-based data, the rows in an ERP, which is like the final output or saved file. That's kind of the 2023 like next token prediction model um and it's mostly personal data wrapped in privacy law. What is actually available nowadays is process-based data, which is the trajectory, the reasoning trade trace, the sequence of decisions. Um so, it it's kind of what gets a professional to get from a blank page to a finished work output and sort of delineates how the work gets done. Um so, on top of that sits a quality axis. This is the vocabulary. I'll use all talk. Um type type one data is a sort of pure capture of real workflows like GitHub commits or session replays with minimal reward shaping by non-experts and type two is contrived data, where you sort of hire experts, you sit them in an arbitrary setting, you have them manufacture examples. Type two, right place to start models when they were reading at a first grade level, I think anybody in the world could teach them as a third grade teacher. But type one is what gets you from 20 to 80% so to say cuz the real the realism is inherited from the work itself and the structural reason why it matters is the data is kind of the most appreciable asset there is. Um a data set is only available in so far as frontier data markets as the frontier moves. So, the only durable supply of it technically is a live business you partner with, not a dead startup's code bases like so many data companies out there are buying today. The dirty secret of the industry though is that everybody sells um type two and bills it as type one.

So, now let me talk about the central axis of how you can think about the sort of verification and deciding which application layer domains are are like maturing first and why. Uh you know, there's a reason why cloud science came out so far um you know, now in the future after models got good before a lot of other application layer advances. So, Jason Wei is a researcher online whose blog is great. You should read him. He has this law. It's called Verifier's Law. The ease of training a model to do a task is proportional to how verifiable the task is. So, I break verifiability to three axes here. And once you have them, a huge amount of this market stops being mysterious. First, asymmetry of verification. How hard is it to decompose a task into checkable steps? Veracity of verification. How much consensus is there about what correct even means? And then thirdly, proliferation of verification. How often does the real world sort of hand you fresh examples of verified work? If you think about like coding as the first mature AI app layer market, that's really no accident because we were blessed to have something called GitHub from web 2.0 which solved all three of these at once. Unit tests, they sort of give you objective decomposable correctness. The community agrees on what working code means generally. And there are effectively infinite public examples with these commit messages as basically free reasoning traces. So, they score high, high, and high on these three axes. Now, look where the money is trying to go now. Biology, security, taste, finance, health care, law. These sit pretty low on veracity, pretty low on verification, and the verification examples are sort of like locked all in these enterprise workflows. No web 2.0 system ever captured really or very sparingly few. Uh that's why, you know, probably maybe you've been approached by Mercari to buy your Slack logs or your Jira logs as of late if you work at an AI company. And that's the whole game, right? It's it's predictive. It's not just descriptive. Classify any professions like tasks on these three axes, and you can sort of tell which markets mature most. So, it no surprise that after code, we went to search, and after search, we went to finance, and after finance, we went to healthcare and law. And after healthcare and a law, well, I'd say cyber, biological, uh and scientific discovery. And maybe even taste, which is probably the most unverifiable out of all these.

So, a verification is a sort of bottleneck. Um let me talk about why the industry sells a lot of snake oil here, and why most benchmarks you see are are sort of quietly fake. Um the dominant type two recipe is like let's hire domain experts. Um let's have them chat use chat to generate plausible tasks. Let's have them solve those tasks, and let's cherry-pick the ones where the model diverged, and let's package them as a hard North Star benchmark. And then, this is the perverse part, let's sell the data to hill climb that same benchmark. It's basically um and maybe some of you guys here in SF have heard this a little too much. It's Goodhart's law with a profit motive. Basically, the moment your measure like becomes a target, and then the target is set by people who aren't true domain experts, it stops sort of measuring anything real. And so, the whole market, uh I like to call it sits in a sort of fog of war. Labs, vendors, and enterprises, they're all kind of guessing which of the data actually improves a model. Nobody can see this clearly. There's a structural tell. Contrived benchmarks, they only ever test a single isolated in-distribution question. They can't test whether a model sort of sustains like correct reasoning across a long dependent episode because these tests were all sort of meant to be solved in isolation. Uh and may I spent a lot of time in this. Um increasingly, uh in a lot of Anthropic blog posts, but also like a lot of other researchers have noted that cross-harness differencing and cross-infrastructure differencing is the primary cause for a lot of benchmark divergences in performance. A lot of you are probably very familiar with like the frontier squeezes of the world and the deep squeezes of the world benchmarks. Um there are issues that all of these benchmarks have uh related to that specifically. In particular, just high false positive and false negative rates that are not immediately obvious when you look at the above head stats on the benchmark. So, a single benchmark number under a single scaffold is like basically one sample from a distribution who's basically with nobody measured. And that's why there's so much what I call benchmark psychosis today. It's like a it's a pretty noisy sample. So, um you know, really briefly on an example I took I I always have like I basically have an internal version of Val's AI and a lot of private benchmarks. Um so I I took three finance tasks from three real-world vendors. Um like an ARR waterfall reconciliation and LBO valuation memo and a sort of like long short pair trade for hedge fund uh trading. So, it's a long horizon non-verifiable finance tasks, relatively robust like deterministic verifiers paired with LLM as a judge uh applied correctly. So, um when you actually run these, I think you'll you'll notice very clearly Opus 4.8 is worse than 4.7 on a lot of these rubrics. Uh you'll you'll start noticing things if you actually do rubric analysis that the 4.7 to 4.8 sub over-engineered self-reflection. Um you'll you'll notice that uh GPT 5.5 and Opus 4.08 score within three points on the same task. They fell in like exactly opposite directions, whereas GPT nails the arithmetic um but Opus nails the the methodology but loses arithmetic, which is to say uh it's it's clear if you actually do very good agnostic benchmarking, um you you can tell where the post-training directions for a lot of these teams went. And subsequently, the it informs, I think, like a lot of the data that that comes for this. Um so, look, you know, going back to the start, a single benchmark number is a sample from a distribution nobody measured, and it's basically like taking a Swiss Army knife and using the screwdriver bit to cut cheese and like concluding the knife is broken. Um so um you know, the the receipts from the last slide, this this goes into an RL environment report that is sent to labs. Um but no single leaderboard number ever shows you this part of um you know, data, which some of the most sophisticated RL environment companies, some of which are actually here at this conference, um would would show you this. So uh how do we read sort of which domain is next instead of chasing hype, and you can actually use this as a proxy data markets as an upstream indicator of what next application layer products that labs will come out with. In in January, uh Anthropic was spending a lot on cybersecurity data from new vendors. And in March and April, they were spending a lot on biological data um from from associated vendors, and what do you know happened like 2 to 3 months after? Well, Metis and Cybor and Claude bio / life sciences today.

Uh So if you want to use this checklist I use, classify the professional's tasks into three axes, apply to long horizon bar, the the ones real labs use, um enforced step length heterogeneous tool calls that aren't interchangeable, state transitions that genuinely constrain future actions, mandatory failure recovery. Uh you'll notice a pretty mixed bag of of how long horizon is even defined from the vendor perspective in terms of specs. Um and then three, you want to look at the raw data for five signals. So, sequential decisions against like one entity, an inferable action expert action per step, outcomes recorded by independent parties. Um but moreover, just economically available high-wage work. So, uh so subsequently, one one counterexample to keep you honest, like robotics, the modality is not settled. Um ego versus teleop versus UMI, but moreover, I I find just generally a huge degree of unsophistication within robotics data vendors today. A huge degree of unsophistication. Yeah. Uh I don't know. Some of you guys are probably robotics researchers in a crowd. I don't know how many times like people have come up to you and they're like, "Oh, here's like 100,000 hours of like iPhone video from my friends in India. Do you want to buy this for ego data?"

[laughter] Um so uh in a in in that sort of domain your vendors uh choices entangled with an unsolved research question. At the end of the day, you have to realize like all environment companies if they actually succeed, they're more so research accelerators. It's a boutique industry. If it's venture scalable, it's because the infrastructure they're building agnostically in house helps an enterprise application layer use case rather than assuming that data markets today will stay as they will forever. So, don't die in a modality hill, basically. So, so one last exhibit just for my work. This is the map under everything I just described. Look, this share of the white-collar work is sort of on the vertical axis. This task horizon is on the horizontal. Short horizon is a is very addressable right now, but you think about this long tail on the right side, this deep dependent long horizon work. That's where the real economic value and data build out sort of both lives. And this threshold line that kind of kind of moves rightward every time somebody builds a real-world data pipeline. Um now I want to talk a bit more about the model layer. Um it changes who needs to build what. So, historical fact, no pioneer of an infrastructure technology has actually held more than 10% of the market in the long run. Um I'm not saying that this applies to Anthropic, but you know, I'm just those who forget history are condemned to repeat it. Railroads built company towns. They charged tyrannical rents. They got nationalized as soon as the automobile moved in. AWS and Google that consolidated the infrastructure layer and still never captured the application layer. And right now, OpenAI and Anthropic are carving out these fiefdoms, but like all these pressures from anti-distillation, export appeals, enterprise exclusivity. They're basically the equivalent of real-world brands in their road. Right? Like the automobile in some cases has already arrived like GLM 5.2 surpassing GPT on a lot of real-world rubrics. Um is is is pretty hard proof that a lot of app layer companies can decouple themselves from the model layer. And because models differ on efficiency and modality, they're not fungible like electricity. So, it's not it's not exactly leading to nationalization, but it's definitely not heading to durable lock-in either. And the whole question on what to build next hinges on whether a general enterprise can decouple models from foundation model labs. Um But luckily, you know, we for a lot of data companies, this is actually where they're headed. I came here to give a talk on data markets. I'm here to tell you that that successful data companies nowadays are all pivoting to enterprise. I maybe shouldn't say this in a public setting, but I'm recording a handshake. People don't notice an incredibly large amount of their revenues are enterprise now. Enterprise in ways you wouldn't expect a data business to do. Once enterprises stop renting out labs intelligence and starts owning their own, you need an entire abstraction layer that doesn't exist yet. There's like five jobs serve and route small models targeted by costly latency and performance profiles. Um And uh I to manage your RL data sets instead of like um across base model migration. So, when you swap to a new open source base, you rerun post training automatically instead of starting over. Three, the Antikythera mechanisms that I talked about before. Um and in in the interest of time, happy to talk more about like emerging infrastructure needs afterwards if you want to. So, let me bring it all together. I think data companies all realize that they have to be neo labs. Data businesses do not stay data businesses because the durable value sort of accrues to the services and app layer of actual work. So, two takeaways, you know, if you're a researcher, stop outsourcing your definition of realism to the same vendors you buy your eval's and tasks from. Um, that's kind of just letting the task test writer grade the task. And if you're a builder, your moat's not the data, it's the sort of pipeline into real-world work. Plus the infra to keep retraining on it as the models improve underneath you. I'll close on this. Uh, I'm building um, I'm working on something new. This is the first public announcement of it. I'm building a ticket through mechanisms. I'm working with a lot of real-world companies to monetize their data assets, but moreover, help implement RL as a service with a lot of the enterprises in the world, mitigating a lot of the pitfalls of a lot of the companies I mentioned. If you guys want to talk about it afterwards, I'm on Twitter. I always write a lot on Twitter and Substack and um, I'm around afterwards, too. Thank you. >> [applause] [music]