The Exchanges

Q starting with building blocks and building up, but modeling cellular behavior is very different from modeling protein folding. The data is very different. The modeling is different. I'm just curious, like, do you think it's all, uh, similar in terms of it's just data and you train stuff, or do you think it's actually, uh, there's some differences in terms of how you actually have to deal with these systems?

A I mean, there are probably some differences. I mean, you can probably talk more to the specifics around this, but like, I mean, I think each layer is going to end up being somewhat qualitatively different, right? I mean, the, the, but you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to cells in a way without understanding the protein modeling. And then if you're trying to understand something like the, you know, the way the immune system works or a bunch of cells interact together, um, then. Um, you know, it's tough to do that without first understanding cells. I mean, you might be able to at like a very high level of abstraction, simulate a system, but if you really want to like understand how it's going to work, you kind of want to build the simulations at each level hierarchically. So that's basically the approach that we're going through, starting with the, um, the building blocks and the, and the protein. But yeah, I mean, I think that there's going to be different types of data that you want to collect for each, um, the modeling techniques, I think we'll see. I mean, that'll all keep on advancing across the board, but I do think that like A big part of the strategy is this view that you need to build it up hierarchically.

AI assessment note: “I think each layer is going to end up being somewhat qualitatively different”

Q uh, asking for a friend, um, uh, you know, you, you guys all believe in, uh, venture-backed companies as a way to have impact on the world. Um, what, was it, like, collecting data on Zebrafish, or the span of the data, or the wet lab work, or just the scale? Like, what makes this a better fit for this big nonprofit, you know, ecosystem effort versus a venture-backed company?

A Um, well, I think we just want to give tools to the whole scientific community. And, I mean, like, so I, I think in order to have the biggest impact, I mean, part of it is just we're, I mean, it's not actually clear that we couldn't run it as a business if we wanted to. I just think that we'll have a bigger impact by getting this in more scientists hands quicker, um, by doing it as open source projects instead. So, um, yeah, I mean, I think that that's, uh, that's, that's kind of the approach. But, um, I don't know. It's an interesting question. I'm not sure that, I mean, obviously you were doing it as a, as a nonprofit, as a for-profit company, um, a bunch of the modeling before. Then you run into certain issues. I mean, you have to raise a large amount of money in order to build the compute clusters. Um, you know, I mean, it's, I think in a lot of ways, the data is actually even more of a constraint. And, um, Because if you look at like the scale of these models compared to language models, they're smaller, but they're smaller because the amount of data is less. In order to get the data, it's not just like there's some factory somewhere that you can pay to produce the data. Like you actually need to invent new novel scientific approaches to be able to do the, you know, for example, the type of cellular engineering we're doing in New York or the types of devices in Chicago, wh…

AI assessment note: “bigger impact by getting this in more scientists hands quicker, um, by doing it as open source”

Q power of open ecosystems in such a large space. Like, I think some of that logic around open source and the breadth or diversity of data collection that you as we're describing, um, it should also apply in the, like, language model world and the multimodal AI world. Like, do you think that's right? Does any of the work you're doing here change how you think about AI and meta?

A I mean, I think it's sort of a similar philosophy overall. And, you know, Priscilla was talking about this, that, you know, a lot of our Our focus is building tools that empower individuals to do things, and that's a sort of a common theme across a lot of the things that I work on is just kind of putting the technology in individuals hands. We don't believe in this like very centralized future where there should be a small number of institutions that, um, that basically are advancing all the stuff. Our vision is not that there's going to be like some central super intelligence that solves all of science. I think Like, people are really important, and I think will be more important in the future, and giving people more tools to be more productive is going to be, like, a critical part of any kind of positive future that both, and that's how progress has always been made historically, right? It's not, um, through centralization. It's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought they were good ideas that already have been done. Um, so I, I think that that's, that's very central to the whole ethos of, um, I mean, to some degree, it's like why you create something like social media, right? To give people a voice. It's, you know, I think a lot of the stuff that we, that I care a…

AI assessment note: “I mean, I think it's sort of a similar philosophy overall.”

Q You have, um, uh, this, like, I think, uh, incredibly ambitious mission at Biohub, and yet, you know, um, the AI scientists that work here could also go work in commercial enterprises. How do you think about the talent and, like, how to bring people to Biohub?

A Um, I mean, where do you want to start? You know, um, yeah, I mean, it's, it's a very, um, hot market for AI researchers, but I think that part of the, part of what that means is that, um, there's a lot of, uh, demand and you, like, they're very in demand and can work on the things that they want to work on. Um, and I think this gets back to this point again about frontier AI and frontier biology, right? So if, um, so yeah, I mean, I think, like, the AI researchers who work here, Could go work on, on language models or things at any of the, the main labs. Um, but those labs don't have the frontier biology part attached to it. So I think that there's like also a just very large mission component of this, which is like, there's an ability to do this unique work here that you just can't really do at the other places. Um, if, so if you're, if that's what your focus is, then this, um, then, you know, I, I don't actually think that there's any other organization in the world that's doing both the frontier biology and the frontier AI.

AI assessment note: “there's an ability to do this unique work here that you just can't really do”

Q Very last question for you. Snapshot of it's mid-twenty-twenty-six. What's the biggest update in your own thinking about Biohub or the domain from the last year?

A Well, from the last year, I mean, you joined in the last year. I mean, I think the, the biggest thing that, that we basically rotated, and, and I think in the last year, we basically kind of formalized that Biohub is the main focus of our philanthropy. So I think this is like, I've been a very big shift. Um, but Alex and the team coming in, I think has been interesting, not only because it's, it's a world-class group, right? I mean, you guys have worked together for a while, I think also, I mean, you talked about how stuff is changing so much in the field. I think one thing that's underrated is like, this is like a extremely talented group of people who also are like, know each other and work well together and like are stable and good. And like, I think that that also is underestimated in terms of the compounding benefit of like people being able to like work well in a stable environment over time. Um, so I think that that's a really important piece. Um, but Part of what we wanted to do was prior to Alex leading the effort, the previous leaders of the Biohub were basically primarily biologists who were interested in technology, right? And now I think we, this is the point where we really flipped that, right? Where, I mean, obviously you have a background in biology as well, but like you are primarily an AI researcher who has a background in, in, in AI and, in, in biology. I thi…

AI assessment note: “we basically kind of formalized that Biohub is the main focus of our philanthropy.”

Q approach is you have these big models that you build that can then apply anywhere. I know that you mentioned earlier that you were going to try and cure prevent all diseases, um, within a hundred years, and you mentioned, hey, it could actually be sooner now given all the advances in AI. Do you have some thought of when we think we'll be closer to that goal or something?

A I mean, I'm optimistic it'll be sooner. I mean, I think that the thing that's complicated is that It's a dynamic system, right? So if you fix something, there will obviously be future things that you need to work on. So I don't think that the current set of things that we're aware of are going to be the only things that need to get worked out. But I don't know. I think that the progress with AI is, is really, um, is, is obviously, you know, very exciting on this. Um, the other thing that, that I'd say, just adding to, to what you were saying, um, a second ago is we really look at More kind of systems than, um, than specific diseases. So for example, one area that seems really important to understand is inflammation. We talked about this a bunch. This is a big focus of the Chicago bio hub. There's a lot of data on that. And that's very, it's, um, it seems quite clear that it's connected to a bunch of different diseases, but we don't, rather than studying the specific diseases, we think that by trying to understand inflammation more broadly, that will Make it so that other companies that can then use these tools can work on specific therapies. Um, another example is when I think that the, um, the immune system, I think is a very good, um, case to study for some of the work that we're doing in cellular engineering. And when we're kind of ladder up from proteins to cells to like wh…

AI assessment note: “I mean, I'm optimistic it'll be sooner... we really look at more kind of systems”