Thomas Ahle: belief

Transcript context

…yeah. Yeah. So, but but it's interesting to make you know, to to discuss, is that effectively the same? Like, if we actually had some hypothetical real time adaptive divergent Claude, you know, would it be much better than the Claude that we already have? And, it's also related to the to the work that you guys do, because, the process of intelligence, in my view, is the creation of these coarse grainings, skills. And that's kind of like what you guys are doing with ASICs. So you're building this customized hardware for making certain types of computation go really, really quickly. And it's almost like that's the result of intelligence. So you say, I'm gonna take a very, very complicated thing, and I'm going to whittle it down and represent it in the best way I can, and then I'm gonna bake it into a non adaptable hardware substrate. Is that fair? I think that is 1 that is what we're working on right now is this this idea of of trying to to build hardware that best fits like the models we have right now and just like really make a super efficient inference. You could say like the in the past at least the issue has been you stop yourself from innovating on the software side, but if you lock down your hardware too much, right, like the NVIDIA chips has been pretty good for innovation, like they're pretty flexible. Of course, they also have guided the way we do AI in a lot of ways like towards matrix multiplications and so on. Still people have been able to innovate. But on the other hand, I think also hardware is getting easier and easier to make with these kind of AI for EDA tools. So it's sort of shortening the like maybe now people are thinking about like making their cooler kernels, right? It's like it's not so unreasonable to think that soon we'll just be doing our AI for making like instead of CUDA kernels, we'll just like make some custom circuits for every single thing we want. So if you come up with a if you want your new algorithm to run really fast, you just design like a specialized circuit for that. And the AI helps you like optimize it and check that it's correct. Of course you still have the fab, but people are we're getting better at like batching things for the fab. I mean I also think having the really flexible hardware is gonna be very interesting, like the stuff where it's learning on chip and ideally like maybe the more adaptability that it has and the less we need to bake in. It's I mean, don't know. It's just more cool, I think. Yeah. But it isn't that a wonderful example of this recursive self improvement? Because as you say, like, we're we're we're building the AI and then the AI is helping us build better kernels, better software, better hardware, which then in turn makes the AI better and then, you know, you get this this kind of loop. IONS is a recursive self improvement in a way. Right? Exactly. Exactly. But but but we should bring this to life. And so there's this concept called auto form formalization. And and and you guys on your website, you say basically that you have done something similar to AlphaProof in respect of building chips. And roughly speaking, AlphaProof is when, you know, they it was they won silver at the IMO in 2024. And what they did back then was they used a language model to generate, a bunch of lean code. And then obviously was a little bit messy, so some of the formalization was by hand and they did multiple renditions and so on. And then they would do verification with lean. And you're kind of doing something like that for chips. Yeah. Yeah. So it's interesting because…

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