The Exchanges

Q So there's a breakthrough, uh, result in CHI-II. Can you give us a sort of lay person's explanation of what the result was and, and the model itself and, and, um, what you think is the most valuable part?

A Sure. CHI-II is our latest series of models, which are state of the art across a number of different tasks, but specifically the one we're most excited about is design. And what we've shown is that we can design a class of molecules known as antibodies, Which are some of the most therapeutically interesting molecules as well. These account for close to 50% of all recent drug approvals, and seven of the top 10 best-selling drugs out there are actually, actually antibodies. And so what we've shown with CHI-II is really the ability to design antibodies against targets that one wants to go after in just a small, what we call a twenty-four-well plate, in just 20 attempts. What this means is that we take a target, run our models, ask the model to design a antibody. We then ship that antibody to the lab. We have about a two week validation cycle in the lab, and two weeks later, we see that roughly close to 20% of these antibodies actually bind their targets in the intended way. So Chaito is a major breakthrough for the field. When we set out on this project, Uh, we were actually only targeting a success rate of one percent. That was the company-wide goal for the entire year. Uh, and the reason we set that goal of one percent is that previous attempts at this problem are maybe successful around .1% or even lower of the time. And that's, those are the computational techniques. If you lo…

AI assessment note: “CHI-II is our latest series of models... what we've shown is that we can design”

Q of dev containers with some of your scientist teammates at the very beginning of the company. And both of you from the beginning, you know, talked a lot about platform investment. And so I actually think that's like a little bit sort of unconventional in terms of such a research oriented team to say, like, we need to make this platform investment. Can you talk a little bit about that?

A Yeah. Uh, so I, I've gone through the experience of going from, you know, zero to 100 on, on engineering, large engineering products before I, I worked on, uh, Stripe Link, which was kind of a multi-year project. And again, Stripe Capital where, uh, engineering teams scaling from zero to 25, 50 people by, by, by the time we were, were, were, were done there. Um, same, same for Link, maybe, maybe more. And. I think you just learn that unless somebody is really taking care to keep the entire system in their head and is an effective technical steward of the architecture, that things just evolve and the sort of the entropy of the software takes over and slows down your rate of progress to zero because nobody can, can get, get work done anymore. And so somebody needs to keep the entire system in, in their heads and the interaction between all those components and make sure that. People who are working on individual subcomponents of your code base actually have to minimize the amount of context they need to load into their heads to understand how to accomplish that task. So these are just the, the principles of really, you know, it's pretty basic. It's just simplicity and modularity, but making sure that's a practice and a kind of cultural, uh, cultural practice, and that everybody's on the same, same page about investing in that, uh, and that You know, people aren't cutting corners.…

AI assessment note: “I think you just learn that unless somebody is really taking care”

Q about the, um, technical approach here, but Jack, you and I met in the context of, you know, you being a beloved engineering and product leader at Stripe, um, coming from the engineering side and looking for like the most interesting problems to work on in AI. Why did you decide to work on this versus like some of the other things we were talking about, like Cogen and such?

A Yes, as you know, Sarah, I spent quite some time thinking about my next steps and what I wanted to do with my, my life after the period I was at, at Stripe. And I give a lot of credit to Josh actually for this, that, you know, we were good friends going back even to the, to college. You know, we were PSET buddies in, in college, uh, at Harvard in, in many of the same classes together. While I was maxing out the CS curriculum, Josh was also doing that somehow for the chemistry and physics and all the other correct scientific curricula as well. Uh, but we, we had landed in a lot of the same classes and, uh, as, uh, as we went our separate ways after college, we really just made a point of keeping in touch every, you know, three, six months. And Josh would always talk to me about, about his research. Once it became clear that, that the research that, that, uh, Josh and us were doing in this space was really no longer just a toy, uh, and, but was, was really going to impact and change the entire industry. That idea became infectious. Right. It sort of become impossible to unsee the future once you have that glimpse. And although you didn't know until very recently that any of this is, it was going to work. And of course there's, there's still a lot left to prove. Once you start to grasp the implications of the fact that over the next few years, we are going to have the ability as a…

AI assessment note: “Once you start to grasp the implications... It's almost hard to work on anything else”

Q maybe Jack, I will start with your amazing engineer. And then you guys also have like a, a very software oriented team working on biological problems. Some of those people come from, you know, long-term research in that space in particular, but for, for yourself, Jack, like, as you said, you're, Your software person, how do you get up to speed on the bio area to go do leading work?

A Well, I think it's two things. First of all, uh, ramping up on any new field is always just a total fight. Uh, you have to, to get to the frontier and to be, have read the right papers and to be knowledgeable about the, the areas that you need, you need to learn. You just have to sort of push your head down and push through. And there are, there are waves of, uh, excitement and misery in that experience, but You can get there fast if you really set your mind to it. And I'd say the second part is that surrounding yourself with just the most incredible team is the best thing that you can do far beyond anything that you can learn by yourself. And we have certainly the most special group of people that I've ever worked with within the company. Our co-founders, Matt McPartland and Jack, uh, who, who are, you know, just rare talents, uh, and Then, you know, the entire team beyond that, some of the former heads of AI at other drug discovery companies, some of the top open source contributors, the team is so, uh, multi-talented. It's, it's small. That's, it's around a dozen people, but, but mighty. And I think as we've seen in other areas of AI, small but mighty teams can go a really, really long, long way these days. And so, you know, the, the, uh, I think there are actually surprisingly few people on our, on our team, even with a computer science degree, Josh himself got a chemistry …

AI assessment note: “read the right papers and... surrounding yourself with just the most incredible team”

Q I want to ask one more question generally about like predictions for biotech. And then I want to talk about the future of Chai as well. What do you think biotech looks like? 25 years from now. I realize that's a ludicrous question to anybody working in AI where you're like, Hey, I didn't know this is going to work at all last year.

A As I mentioned before, there is a lot of doom and gloom in the biotech industry right now due to macro factors, uh, with rates where they are and the long-term investment cycles that are required to make biotech viable. There is just a real pessimism in the industry right now. It's sort of the worst market in, in, in a couple of decades. And I think that it's moments like this, breakthroughs like this, which give us these flashes of light and these These reasons for just immense optimism about the future of this industry, not just in terms of improving timelines and reducing costs, but also in terms of fundamentally enabling those new products. And so if we think ahead over the next 25 years, you know, we've gone from a less than .1% success rate to a close to 20% success rate in a year. Well, who's to say that In another year, that can't be a 50 plus or even a close to 100% success rate. I think if you see our mini protein results, we are, I think, close to 70% on those with picomolar affinities, like really, really tight binders for every single target that we tested. So all five targets we tested worked, and 70% of the designs that we ordered worked. I think that there's no reason that other Class of molecules that those success rates can't be that high as well. And I think once you, you have that, you really enter this, this era where you sort of have a computer aided desig…

AI assessment note: “you really enter this, this era where you sort of have a computer aided design suite”

Q I want to ask strategically like where Chai invests from here. So you talked about other attributes that you want to be able to design in Chai models. But if we just look at this generically as a, like an AI model company, um, where do you think the defensibility is?

A There are two key areas of investment for the company. I, I think firstly, what comes out of these models, these just aren't drugs yet. They're, they're hits, they're antibody hits, but there's a lot more work to be done to actually turn these into Varble molecules that we can put into humans. We have early data, uh, which we, we put in our preprint to suggest that a lot of the properties that one might want from a drug that these, these molecules actually have, but we need to do a lot more further characterization and assays to convince ourselves that we can do that. And then I think there's also the next step, stage beyond that is actually designing entire drug candidates in zero shot right out of the models. And I think a few months ago we might have said this was a pretty futuristic idea and nobody in the, the company was really, really talking much about this, but I think once you see these results and grapple with the implications, the fact that we can get antibody hits in just 20 attempts, there's, there's no reason, uh, that, that we couldn't generate intra drug candidates in that same number of attempts. So I think there's going to be some key investments there in really Yeah. The model right now is a model. It's not really a product. It is a product and it's certainly useful today, but there's a lot better that products can, can get with, with more, more investment in…

AI assessment note: “There are two key areas of investment for the company.”