Shared tools can tackle science NIH grants leave unfunded

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 3:30

   Do you want to answer that one? Yeah, well, I mean, we're not going to cure all diseases, to be clear. I mean, the strategy is to help scientists and the scientific community cure all diseases. So the strategy is really one of accelerating the pace of basic science. And the theory that we had was if you look at the history of science, most major breakthroughs are basically preceded by the invention of a new tool to observe phenomena in a new way.

2. 3:57

   So think about things like the microscope. being able to observe bacteria or other fields, the telescope or, you know, but it's just to use an engineering example, without those kind of tools, it's kind of like you're coding without being able to step through the code and debug things, right? That's like the old days. Yeah, yeah, yeah. So our whole approach on this is basically, let's help build tools that will accelerate the pace of the whole field. And I think that there's a niche that I think fits that Because if you look at how funding works in science, the vast majority of funding comes from the government and NIH grants.

3. 4:35

   It's parceled out into these relatively small grants that allow individual investigators to investigate usually pretty near-term things. And the development of these kind of new types of tools, whether it's imaging or building now a lot of AI things like virtual cell models, are longer term, oftentimes more expensive to develop. So think about, like, on the order of maybe $100 million to a billion dollars over a 10- to 15-year period, and then you try to unlock those tools and give them to the scientific community to accelerate the pace.

4. 5:06

   So that's kind of the theory. Right, and it seems like there's also something that is you don't really get credit for the tools in a lot of ways. I mean, we have companies that use your tools and they're very happy about it, but I didn't even know that that was the case. That's why it's philanthropy. Yeah, well, it is, but most people do philanthropy to get credit, too. I mean, that's kind of a part of it.

5. 5:28

   So, I guess, did you think about that, or were you just like, no, like, this is going to work, and if it works, that's all we need? We're super focused on, like, actually making every scientist better. And beyond science, like, startups, startup founders, because the point is we can't do this alone. And when we first set out the goal to cure and prevent disease by the end of the century, people like, honestly, most scientists couldn't look at us with a straight face.

6. 5:56

   You're crazy. Yes. And it was true because if you just decided to spend the money funding the next best grant for every single lab in the country, like, there's no pathway to that being true. But if you forced people to really think about this and like, okay, okay, what is the most credible pathway to doing this? And what are the barriers to that credible pathway? Then we sort of got somewhere, right?

7. 6:21

   They were like, well, like, there's no shared tools or we're not working on big projects and building the right data sets. And we're like, okay, well, then we can start doing something about that. And so that's where the idea of building shared tools, because no one right now in the science... Well, that's so interesting. So basically, you're like, we're going to cure all disease. And they're like, that can't be done.

8. 6:41

   Why can't it be done? Well, because we don't have the tools. Okay. That's a pretty cool sequence. Yeah. I mean, there's also this funny thing where the biology folks, I think, looked at it as if it were crazy ambitious. And then the AI folks are like, well, that's kind of boring. That's just automatically going to happen. That's okay. There's something in between there that needs to be bridged. And if you can like kind of use the kind of modern AI tools in order to build the types of tools that biologists need. So that's a big part of how we think about our work is... AI's got to be the most overestimated and underestimated technology ever, like simultaneously. I mean, yeah, we'll probably like the internet early on. But we kind of think about ourselves and the work that we're doing at the Biohub as frontier biology paired with frontier

9. 7:27

   AI, right? So there are labs that do frontier AI that basically, you know, are building the most advanced models. And then there are lots of biological research organizations that effectively do very leading-edge research to build, to either discover new data sets or looking to certain challenges. But so far, there hasn't been anyone who's tried to do both of those at once. And when you look at, I mean, even something like AlphaFold, which is amazing, right?

10. 7:56

    It was built off of this data set that was a public... dataset that had been produced decades ago, right? And what I think you have the opportunity to do if you do both of those together is produce specific datasets for the purpose of training AI models to build virtual cells that can do specific things. So I think that that's like a pretty interesting zone to be in. And of all the things that we've worked on, you know, actually when we started CZI, we kind of actually focused on a number of areas. And what we found is just that the science research has had by far the biggest returns. We've just doubled down on it over and over and over until now we're at the point that we're 10 years in and Biohub is really the main focus of our philanthropy at this point. But yeah, I mean, that's kind of, that's basically