00:07:53 · Adam Miller, Josh Meier
[00:07:53] S2: Do you think a lot of those doubters, those doubts will change once Chai three comes out, and some of the sort of philosophical reasons behind why some people say AI isn't gonna be able to, you know, tackle this problem within biology? Do you think those doubts will change? [00:08:07] S0: I think when people see the data, then it does, yeah. I think first it's seeing it in a paper, for example, like the work that we put out, then it happens with our customers actually using our product, making molecules, seeing in their own hands that the thing works. So I think, you know, these are scientists at the end of the day, and they are convinced by data, and again, they were right before, like the data wasn't there, and they were right to ask these questions. These people are responsible for some pretty big campaigns. It takes a long time to discover a drug, it's very expensive, you can't afford to just go for the latest hype, you really need to go with something that's trusted and something that's working. At the same time, also have to innovate, and in this industry, in the pharma industry, you can't sell your drug forever, you'll lose exclusivity after a couple of years, like your drug goes off patent, and you always need to invest in the next thing. So that's the bright side of this too, people are always willing to try new things, and once they see it's working, then they'll start adopting it at scale.
00:08:07 · Josh Meier, Sachin
[00:08:07] S0: I think when people see the data, then it does, yeah. I think first it's seeing it in a paper, for example, like the work that we put out, then it happens with our customers actually using our product, making molecules, seeing in their own hands that the thing works. So I think, you know, these are scientists at the end of the day, and they are convinced by data, and again, they were right before, like the data wasn't there, and they were right to ask these questions. These people are responsible for some pretty big campaigns. It takes a long time to discover a drug, it's very expensive, you can't afford to just go for the latest hype, you really need to go with something that's trusted and something that's working. At the same time, also have to innovate, and in this industry, in the pharma industry, you can't sell your drug forever, you'll lose exclusivity after a couple of years, like your drug goes off patent, and you always need to invest in the next thing. So that's the bright side of this too, people are always willing to try new things, and once they see it's working, then they'll start adopting it at scale. [00:08:58] S1: In the past, what's been, like, the rate limiter to Chai's progress? Is it the amount of compute that you can throw at the problem, or has there been other things? And, you know, maybe the context of the question is is, looking around, I was so shocked that there's only, about 30 people on this floor right now. I thought there'd be hundreds of hundreds for the amount of kind of progress you guys have made.
00:09:17 · Josh Meier, Adam Miller, Sachin
[00:09:17] S0: Yeah. So I I think, almost everything is a bottleneck in the company right now, which actually means, like, nothing is a bottleneck. It's pretty much like any lever we push right now, we're getting better results, whether that's compute or data or even customer interactions, right? So it's a very exciting time where we're able to push all of these things. So actually, to answer your question, it's, at the moment, it's not like there's any one of those things. Over the you know, past couple of months, it's it's ebbed and flowed always, right? It's like, okay, this week we need to get more compute because we discovered something new for the models, or now, like, we really need to go and double down things on the product, make it more reliable or ship some new features that our customers really want, Or maybe it's we need to go in the lab, we need to generate a lot of data. Maybe it's we need to go stress test this really hard drug design problem that no one in the industry has been able to crack, and we need set up a benchmark to see whether our models can do it. So it, you know, changes week by week, but I think that's what's so exciting when you have so many of these bottlenecks at the same time. You're going to learn something new every week, and that just creates a ton of excitement within [00:10:14] S2: the company. [00:10:14] S1: Josh, we're really curious about what diseases Chai aims to kind of target first.
00:10:14 · Sachin, Josh Meier, Adam Miller
[00:10:14] S1: Josh, we're really curious about what diseases Chai aims to kind of target first. [00:10:20] S0: One of the interesting things with these models are that they don't seem to really care about the disease that they're going after. They seem to kind of work across the board. So we actually don't think of it as like, this is the one specific disease that we wanna go after first. We actually wanna parallel track many of these, and I think that gives us the highest chance of of seeing success across the board. [00:10:37] S2: So are you saying that there isn't going to be any more difficulty in solving certain diseases than others? [00:10:43] S0: I'm just saying that one is, yeah, from the model's perspective, it doesn't think that one is necessarily harder than another. There might be some biology reasons, it might be hard to find the patients, but if we think about it on the molecular level, the targets that the model is looking at, it doesn't have a preference. It's not like cancer is easier than immunology or something like that. [00:10:59] S1: Okay. Are you going to stack rank what you prioritize according to what's most impactful in society, or has that been something that's up to pharma to prioritise? [00:11:08] S0: Well, I think that will naturally happen via our pharma partnerships, so people will approach the problems that have the deepest unmet need or the biggest patient populations, so I think that will happen naturally. [00:11:17] S2: Yep, and what other ones that you think pharma will then sort of pick and prioritise?
00:11:17 · Adam Miller, Josh Meier, Sachin
[00:11:17] S2: Yep, and what other ones that you think pharma will then sort of pick and prioritise? [00:11:23] S0: I probably can't talk about all of those details. [00:11:25] S2: Yeah, okay. [00:11:27] S1: Sitting opposite you, you kind of ooze this kind of confidence, and it seems like everything's just going up into the right for Chai. If we were to kind of like look inside your internal monologue, what would surprise us with maybe some of the doubts you have or some of the things that don't feel like they're going up into the right?
00:11:44 · Josh Meier
[00:11:44] S0: That's a good question. Again, I sometimes wake up, it's like I have to pinch myself, are these results real? Actually, with Chai-2 was a very big thing because no one had shown results like this. People have been working on it for a very long time. People even told us when when we were getting started, oh, the antibody AI field is crowded. Like, you know, so many people have tried these models. It's like there's literally 100x improvement that we're able to build within a year. And, until we had all the data back from the lab, we didn't fully believe it. There were all these, you know, last experiments we needed to do to make sure that the results were real, and we published all that, of course. So I think it's always you see these big results and it's like, what's the catch, right? Did we do something wrong here? You know, and usually it ends up being being right, but that's why before we put anything out at Chai, we like to be really rigorous about this, ask all the right questions, you know, dot all our i's, cross all our t's. So, you know, there's always concerns. We're pretty skeptical and rigorous group here. It's one of the reasons why I love working with this team. You know, we're we're do... We're working in one of the most hyped up spaces, AI, right? But at the same time, everyone here is very realistic, asks all the hard questions. And and that means, you know, we're always thinking about these things, but we're very open about, you know, discussing those doubts per se, and then we can go do the work in order to, you know, in order to massage them.