Frontier gains outside math and code come from buying data

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 24:59

   of this stable state of where the frontier wins versus open source? I'm a big proponent that a lot of the frontier progress in domains that are not fully verifiable is by buying data that is high quality and giving the models worlds to act in and he'll climb on. And I think there's two things. So you have the math and code thing, and we don't know how big the coding market will be. And then there's kind of everything else, which is still they're targeting this knowledge work, this biology, healthcare, legal.

2. 25:29

   And all of these things are not as verifiable. And I think it is much closer to the story of OpenAI. I bought a lot of robotics data. They're probably going to tell us it's generalization, magic, poof. People love poof generalization. But realistically, it's probably some of A, some of B, but a lot of it comes down to data. When I talk to people at Frontier Labs, there's some people that have amazing jobs.

3. 25:50

   There's some people that contribute by doing a very narrow thing, which is you make a data set and you figure out what the behavior should be for your model in that area. And you figure out how to make the data that helps get that data and get that behavior and you figure out what data you need to buy. And that's going to take, I think we're going to have years of this across different sectors. And that's very capital intensive to buy all of this data.

4. 26:12

   But that's not just like poof, magic, RSI happens. Where I do see in this math and code and some distributed ML engineering, it's very verifiable. You know what your cost is. And the AI models are going to drive that down at a really magical rate. And that's going to just have a lot of surplus benefits. And we can do research faster. I'm very interested in, I think, the efficiency of models is going to drop multiplicatively over years.

5. 26:40

   So we've seen OpenAI have drastic price cuts. And it's like, what does it mean for GPT, Sol, or Astra and these bigger models to reduce in price 2x every eight months for the next three years? You start getting, you start doing this math and it's like, it's 1% of the cost in whatever, like four years. And it's like, are they going to be able to have higher margins due to Jevin's paradox there? Or are their revenues going to decrease because the market fills up?

6. 27:05

   There's just like all these really weird dynamics, but fundamentally, like AI progress is about data and about writing better software to serve your models. And those are very different worlds. Yeah, I mean, at least definitely, I agree with you that observing from the outside, there's a lot of data RL environment companies that are doing very, very well. Selling both to the top two frontier labs, but an increasing kind of longer tail of labs as well.

7. 27:35

   So to your point, I think a lot of the capability gain has really come from just scaling that up. And it's not an easy task. I mean, maybe there's, I'm actually very curious on your opinions between the data collection and environments that are built by hand versus the ones that are, you know, done in sim. There seems to be like different versions of like what could be, you know, what kind of price they command, etc.

8. 28:00

   They all have good things and bad things. Yeah. And I just think there's a lot of talk of like, like we've talked about distillation, but the other side is like the Chinese company buys the RL environment that they use six months later. Exactly, like, oh, anthropic reopening, I bought this. Now there's a premium. The good data will do this. But the data that people are buying is pretty flawed. Like I would say that the method is you buy in mass and then you figure out what works and it's not 100% hit rate.

9. 28:26

   Yeah. And I think, I personally think that this RL environment explosion where there's just so much opportunity to make money is having a lot of people like like cut corners and this can induce things like reward hacking so it's like the models are so smart that if you put them in a world and they're like my job is to solve this task but they didn't give me the like you have a test file and like the test file actually has an empty file instead of the instead of the maze that you have to navigate and they're like the model is like well i should still figure out how to get a high reward anyways and i think a lot of this behavior is downstream of this supply chain. And I think it'll fix it. But it's interesting to see the demand from the labs to eliminate reward hacking and all these researchers spending so much time on it.

10. 29:10

    And then all the data companies buy our data and I think proportionally spend less time on model behavior and more time on world building and revenue. And somehow these two parties need to meet together to actually make higher quality data. Yeah. Well, a lot of the labs are actually