00:38:58 · Adam Miller, Josh Meier, Sachin
[00:38:58] S2: Do you think philosophically there might be skeptics about curing everything almost on demand? Like, for example, with ChatGPT, you've still got a large percentage of the population that are just plainly anti AI. They think it might erode our cognitive abilities. They think it's going to hurt labor markets, etc. Do you think there'll be these anti health skeptics eventually in the future? [00:39:21] S0: I think it's a bit different in our space as well. We're working on generating a molecule. There's a very clear objective function of what you want. I want a molecule that does X, Y, Z. We can measure that in the lab, and it's all about finding a molecule that does that. So if we can do it with AI, or we can do it with yeast, or, you know, it almost doesn't really matter to the end user. People often ask us, like, who our competitors are and actually tell them yeast, right? Like that is what we're going up against. People have been doing something in this field for a long time. It actually works like, you know, it's been tuned, to work somewhat well, and that's the bar that we need to overcome. And now we're past that, so it's all about like, now the competitor is Chai two, or we're making Chai three, right? And with Chai four, it'll be Chai three, but it's a very unique space to work in where the progress that we can create here can translate into value so rapidly. [00:40:08] S1: I'm really curious on how you spend your day to day now. Like, are you actually in the weeds training the models or are you doing more on the business side?
00:40:08 · Sachin, Josh Meier, Adam Miller
[00:40:08] S1: I'm really curious on how you spend your day to day now. Like, are you actually in the weeds training the models or are you doing more on the business side? [00:40:16] S0: I mean, it changes every day, right? It could be that we need to go in, get the next foundation model going, and there's some blocker around that. It could be pouring over lab data, figuring out like, what does this mean about the models? How do we make them better? It could be getting a deployment up and running with a customer. Those are always some of my favorite moments as well, and actually seeing this translate into real medicines. I've been working on this research for most of my career, and to finally be at a point where it's not just, you know, core results on benchmarks, but actually we're making molecules that are being pushed forward in real pipelines and real drug companies, is pretty incredible. [00:40:52] S2: Awesome. Well, I mean, this whole mission is, incredibly exciting and I think like, you know, we very rarely get to meet companies, that we think that could actually change the world, and maybe this is changing the world in terms of the biggest problem that we have, in terms of sickness and illness. So it's been a really exciting day. [00:41:09] S1: Yeah, extremely inspired and everyone we've met here has also been so genuine and thoughtful. It's a very different energy than some of the other more application layer companies we go into or some of the other ones we explore. So, yeah, it feels special and thanks for having us here.
00:41:09 · Sachin, Josh Meier, Adam Miller
[00:41:09] S1: Yeah, extremely inspired and everyone we've met here has also been so genuine and thoughtful. It's a very different energy than some of the other more application layer companies we go into or some of the other ones we explore. So, yeah, it feels special and thanks for having us here. [00:41:23] S0: Yeah. Thanks for coming by. [00:41:24] S2: Appreciate Thank it. Cool.