Alessio Fanelli: uncertainty

Transcript context

…Yeah, and that's maybe where concrete example, I think it certainly is the case that we tell retrospective, to your point about venture, we tell retrospective stories where it's like, well, here was the set of observable facts. This was knowable at the time, and these people made the right call and were able to cross correlate all of these different sources and said, this is the bet we're gonna make. I think that process of idea generation is absolutely automatable. And the question of like, do you ever get somebody who just sets the system running and it's making all of its own decisions like that, and it is truly like doing thematic investing or more of the like what a human analyst would be kind of on the hook for, as opposed to like HFT. But the ability of models to say, here is a fact pattern that is noteworthy, and we should pay more attention here. Because if you think about the matrix of like all possible relationships in the economy, it grows with the square of the number of facts you're evaluating, like polynomial with the number of facts you're evaluating. And so if I want to make bets on AI, I think it's like, what are ways to profit from the rise of AI? It is very straightforward to take a model and say, parse through all of these documents and find second order derivative bets and say, oh, it turns out that energy is like very, very adjacent to investments in AI and may not be priced in the same way that GPUs are. And a derivative of energy, for example, is long duration energy storage. And so you need a bridge between renewables, which have fluctuating demands, and the compute requirements of these data centers. And I think, and I'm telling this story as like, having witnessed Brightwave do this work, you can take a premise and say like, what are second and third order bets that we can make on this topic? And it's going to come back with, here's a set of reasonable theses. And then I think a human's role in that world is to assess like, does this make sense given our fund strategy? Does this, is this coherent with the calls that I've had with the management teams? There's this broad body of knowledge that I think humans sort of are the ultimate like, synthesizers and deciders. And like, maybe I'm wrong. Maybe the world of the future looks like, and the AI that truly does everything, I think it is kind of a singularity vector where it's like really hard to reason about like, what that world looks like. And like, you asked me to speculate, but I'm actually kind of hesitant to do so because it's just the forecast, the hurricane path just diverges far too much to have a real conviction about what that looks like. Awesome, I know we've already taken up a lot of your time, but maybe one thing to touch on before wrapping is open source LLMs. Obviously you were at the forefront of it. We recorded our episode the day that Red Pajama was open source and we were like, oh man, this is mind blowing. This is going to be crazy. And now we're going to have an open source dense transformer model that is 400 billion parameters. I don't know if one year ago you could have told me that that was going to happen. So what do you think matters in open source? What do you think people should work on? What are like things that people should keep in mind to evaluate? Okay, is this model actually going to be good? Or is it just like cheating some benchmarks to look good? It's like, is there anything there? Like, yeah, this is the part of the podcast where people already dropped off if they wanted to. So they want to hear the hot things right now. I mean, I do think that that's another reason to have your own private evaluation corpuses is so that you can objectively and out of sample measure the performance of these models. And again, sometimes that just looks like giving everybody on the team 250 annotations and saying, we're just going to grind through this. And you have to tell, does this meet? The other thing about doing the work yourself is that you get to articulate your loss function precisely. What is the thing that, what do I actually want the system to behave like? Do I prefer this system or this model or this other model? Yeah, and I think the work around overfitting on the test I think is like that 100% is happening. One notable, in contrast to a year ago, say, the incentives, the economic incentives for companies to train their own foundation models, I think are diminishing. So the window in which you are the dominant pre-train, and let's say that you spend five to $40 million for like a, call it kind of a commodity-ish pre-train, not 400 billion would be another sort of-…

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