AI Consolidation, Biotech Opportunities, and World Models with Sarah and Elad

My view is that the market's going to see two types of consolidation. There's going to be product consolidation and there'll be actual buys. And the Codium windsurf acquisition by OpenAI is the first step in that. But if I was a number one or number two in a market and I was a startup, I'd consider merging with the other party. If there were the two main startup players because the real threat will be fighting the incumbents. And so I would kind of get ahead of it and say, okay, let's stop the startup-to-startup war and let's just focus on winning against the three or four incumbents.

People worry too much about integration. What's the culture and what's and what's that? And often it's just merge it, and if it doesn't work, shut down parts of it and move on with life, whatever parts either in the buyer or the seller, it doesn't matter, but just it's who cares pragmatically, like can fix it all sorts of ways. Either the culture meshes or they don't. And if they don't mesh, you don't have to keep everybody, honestly, because everybody's gonna do very well off the acquisition.

The biotech or biopharmaceutical market for founders is very different from the tech market. And the overall market structure is radically different. So basically, if you look at biotech, the last time a $50 billion plus biotech company was started from scratch, excluding Moderna, which was kind of an accident of COVID was in the 80s, I think it was Regeneron. And so it's been almost 40 years since we've had a de novo like tens of billions of dollar company created. And so all these companies are 50, 100 years old.

I understand some temporary physics of the market a little better. Right. It's a race to find the verticals of relevance and then get something to work in a way that users actually want. Maybe you have to go get proprietary data sources that you can retrieve against and get distribution and then ideally have users that can create or derive or extend knowledge from that. The companies you just named. And I don't think you explicitly said up about to put a bridge and open evidence. I think they fit into that shape.

Scaling up model size and training data for big LLMs has given us this really powerful foundation of knowledge and pattern recognition. But everybody talks about agents, like what people want to do from here. The way people think about AI is not just predicting text. They want to go to broader intelligence into taking actions. And practically that could be actions. I feel it's really important to describe what we mean when we say reasoning or actions or something more concretely because I don't know that everybody has a great mental model for these things.

If coding shifted from, hey, let's copy how people write code into let's just solve this problem. How different is a type of code that's written or what sort of traditional approaches are just broken that we can then learn from because you've created a utility function with an unconstrained approach to actually figuring it out. And that happens sometimes in biology. Right. You'll do these molecular evolution experiments where you'll evolve a molecule to do something and sometimes it'll do things in a really weird way that you just completely don't expect.

AI is the market in my career where I've consistently said the more I learned, the less I know. Every other market you kind of learn more, you know more, you keep advancing. And I actually feel like that's shifted in the last couple months where I feel for a subset of areas. Despite the rapid pace of innovation, all the really exciting new models and research findings and everything else, I actually feel like a bunch of markets have consolidated and it's kind of clear now who are the likely winners in two or three big areas.