The Information's TITV
00:23:22 · Brittany Greenfield, Anne Wojcicki
[00:23:22] S4: Yeah. [00:23:22] S5: So I'm I'm really excited about the opportunities, and I think that AI is made for biology. Like, it's absolutely made for it because you have so much complexity and so many things happening. But the challenge we have today is we just don't have the data sets. And I don't necessarily have the answers, like, data sets are going to be the right ones to transform? You know, in some ways, like, of the problem that I took on was genetics. Like, how do I understand your genome? Like, I like it because it's like structured data. It's ACG and T. I can collect phenotype data from each person. I can see outcomes. There's in some ways like an ability to actually train models and to understand that really well. Biology, really, really hard. And developing drugs is a humbling experience. So I like... I love people coming in. I love the fact that there is capital that's out there now to put effort into this. But I think, like, I'm I'm a realist in terms of, like,
00:24:29 · Stephanie Palazzolo
[00:24:29] S0: it's hard. Mhmm. Definitely. I mean, I think building AI models is already difficult. And then I think a lot of these labs are gonna realize once they start to run these trials, like what you're saying, that actually clinical trials do take a lot of time. But, well, great. Well, it was so great to have you on, Anne. And again, that was Anne Wojcicki, founder and CEO of 23andMe Research Institute here on TITV. Lori Goler is president of the Chan Zuckerberg Initiative, which is making a big push into AI powered biology. We're talking about its new $1,800,000,000 research collaboration involving Google and the Department of Energy. Thanks so much for joining us, Lori. Thanks for having me. So just to give, you know, our viewers a little bit of a background, there has been this kind of evolution of the Chan Zuckerberg Initiative over the years. I think initially there was this much broader remit that covered things from education to housing. And then, you know, late last year, it seems like you really doubled down on bio research with Biohub. And then, you know, obviously earlier this month, there was this big, you know, dollars 1,800,000,000 research collaboration that was announced. Tell me a little bit more about this initiative and kind of what you're hoping to do with this huge amount of money that is now out Sure.
00:25:54 · Lori Goler
[00:25:54] S6: Yeah, great. So as you mentioned, we're bringing together the power of frontier AI with frontier biology. The mission of our philanthropic endeavor is to help scientists cure and prevent all disease. We're doing that in large part by building AI models that will help to predict various things related to biology, and of course, one of the biggest things that a lot of people talk about is a predictive model of the cell. But the issue is that, the reason it hasn't happened so far, even though there's kind of a lot of excitement about it across the sector and the industry, is that there isn't the same kind of data available for biology as LLMs have had from the Internet. There's this gap where everyone is excited about it, everyone is working on it, but everyone also recognizes that we all really need this data in order to bring it to life and meet the aspiration and achieve the vision of what it can be in the world. In April, we began to think about how can we convene the industry across sectors, because it's really a problem that's much bigger than any one of us can solve alone. It's billions of dollars, and it's really going to take a lot. It really takes all of us to come together. And so, we announced that we would invest $500,000,000 into the initiative, and that helped us catalyze some other players in the industry.
00:27:13 · Stephanie Palazzolo, Lori Goler
[00:27:13] S0: And you talked a bit about the obstacles of just getting data for a field like biology, where obviously you can't just scrape the entire internet the way that, you know, different labs have done to train large language models. What is going to go into this process of finding or developing new data sets? And Just tell me a bit about how you guys plan to [00:27:35] S6: do that. Sure, sure. The history really of data in biology is that it's happened mostly in proprietary silos, because the incentives are such that research institutes have their own data, pharmas have their data, all the players have their own data. It's meant to be kept proprietary in the current incentive structure and the way that it works. The idea is really to bring together the research community with people who are interested in creating the datasets to match potential vendors, whether they're commercial vendors or philanthropic vendors, or to align with the government in terms of the investments they're making to be sure that we're all coordinated on a path to a common scientific agenda. We're really working with all the different players to understand what data they're looking for, what data they have, what new data should be created, novel data sets that need to be created through high throughput means to bring it to everyone. We're looking for all the commonalities across all of that so that we can come up with a core set of priorities, really, that we can help to move forward. It's really a role that we're trying to play.
00:28:39 · Stephanie Palazzolo, Lori Goler
[00:28:39] S0: And for those companies and different groups that are involved, obviously by working together, I'm assuming that they get access to data sets that maybe some of the other organizations had, but were maybe keeping to themselves. But I guess what else is in it for them? You know, do they, I know that maybe you want to at some point open source the datasets, but I believe there's something where does these companies get access for some period of time, right? [00:29:03] S6: Yes. It'll work a little bit differently depending on the funding structure for each of the areas, in the commercial lane of funding. One of the incentives for the companies to participate is that they're going to spend $100,000,000 but they're going to get access to several $100,000,000 worth of data. It really creates some leverage for them in terms of what their return is on the dataset. Basically the business model in that area is that they get a twelve month embargo on the data before it becomes open. But the idea is that all the data becomes open, it becomes available to the public and to the research community and to other commercial and philanthropic players as well over time.
00:29:03 · Lori Goler, Stephanie Palazzolo
[00:29:03] S6: Yes. It'll work a little bit differently depending on the funding structure for each of the areas, in the commercial lane of funding. One of the incentives for the companies to participate is that they're going to spend $100,000,000 but they're going to get access to several $100,000,000 worth of data. It really creates some leverage for them in terms of what their return is on the dataset. Basically the business model in that area is that they get a twelve month embargo on the data before it becomes open. But the idea is that all the data becomes open, it becomes available to the public and to the research community and to other commercial and philanthropic players as well over time. [00:29:40] S0: I think something interesting is, you mentioned earlier, this goal of wanting to, you know, prevent and or cure all diseases. That's obviously super ambitious. And I think it's a sentiment that's been echoed by a lot of leaders in technology and AI, not just Mark and Priscilla, but also folks like Dario from Anthropic, Sam from OpenAI. I guess I'm curious, after a certain point, do you kind of feel like these words have begun to not really resonate as much with people because I think a lot of folks I talk to, at least they tell me that, you know, whenever I hear Sam Altman talking about curing all diseases, that sounds great. But where's the evidence and how do I see the progress that's being made towards that? It feels like the super far off thing that, you know, it just feels like there's no way to get there right now.