The Information's TITV
00:34:09 · Nicole Su, Stephanie Palazzolo, Lori Goler
[00:34:09] S3: is one that people have been [00:34:09] S0: very excited about for a long time. And so I think that's really exciting work. I mean, I am curious, like I kind of hinted at earlier, a lot of the AI labs, especially Anthropic, it feels like, and obviously Google DeepMind has been doing work here too. They have been focused a lot on building AI for science, AI for healthcare, AI for bio. I think Anthropic even has its own wet labs, for instance. What about your approach do you think will work better than the approach Anthropic is taking, for instance? [00:34:42] S6: I I hope that we all have success in this area because the more of us that are successful, the better for humanity. But I think one of the things that really makes Biohub different is that we have all the pieces really under one roof. So, for ten years, and Priscilla built frontier biology capabilities. We've added in the last year frontier AI capabilities. We have a meaningful amount of compute. We have, and then we're building all the data sets as we go. We have all of the things we need under one roof, and we're really taking advantage of that. It's really a cohesive system where it's not that we're doing something in one spot, and then we sort of have this other effort over here. It's like we really have an integrated strategy around it.
00:34:42 · Lori Goler, Stephanie Palazzolo
[00:34:42] S6: I I hope that we all have success in this area because the more of us that are successful, the better for humanity. But I think one of the things that really makes Biohub different is that we have all the pieces really under one roof. So, for ten years, and Priscilla built frontier biology capabilities. We've added in the last year frontier AI capabilities. We have a meaningful amount of compute. We have, and then we're building all the data sets as we go. We have all of the things we need under one roof, and we're really taking advantage of that. It's really a cohesive system where it's not that we're doing something in one spot, and then we sort of have this other effort over here. It's like we really have an integrated strategy around it. [00:35:20] S0: And, you know, obviously through this collaboration, you are working with the U. S. Government, Google DeepMind, Isomorphic Labs, Meta as well. Do you see yourself adding other, you know, companies or labs like Anthropic, for instance, moving forward? [00:35:35] S6: Yeah, I hope so. I hope that this will really be an effort. Since the announcement, actually, we've had great interest from a lot of players who've reached out to learn more and to find out what it's really about and to consider kind of joining the initiative. Again, think it's bigger than any one of us. It really is an industry wide cross sector initiative, and I really hope that we'll have lots of different players who are involved in contributing over time.
00:35:35 · Lori Goler, Stephanie Palazzolo
[00:35:35] S6: Yeah, I hope so. I hope that this will really be an effort. Since the announcement, actually, we've had great interest from a lot of players who've reached out to learn more and to find out what it's really about and to consider kind of joining the initiative. Again, think it's bigger than any one of us. It really is an industry wide cross sector initiative, and I really hope that we'll have lots of different players who are involved in contributing over time. [00:35:59] S0: Why does right now feel like the right time to be doing this sort of thing? Right? You know, I've had people tell me that one reason why AI is really good at doing science is because, you know, the average scientist, maybe you're an expert in bio, but maybe you're not an expert in physics or all these other areas. And AI models are really good at connecting these disparate dots across different fields. That's like one reason I've heard. Like, why do you think LLMs, today's frontier AI models, are making it so that now is the right time to go after this?
00:35:59 · Stephanie Palazzolo, Lori Goler
[00:35:59] S0: Why does right now feel like the right time to be doing this sort of thing? Right? You know, I've had people tell me that one reason why AI is really good at doing science is because, you know, the average scientist, maybe you're an expert in bio, but maybe you're not an expert in physics or all these other areas. And AI models are really good at connecting these disparate dots across different fields. That's like one reason I've heard. Like, why do you think LLMs, today's frontier AI models, are making it so that now is the right time to go after this? [00:36:31] S6: Yeah. I mean, it's, you know, all of these efforts have happened in silos in the past. And I think with the new technology, we can really bring it all together. It can synthesize across various different disciplines. And and now is the right time for this because the piece that's missing is the data. You know, we really need more data, and we're at a moment in time where everyone has realized it at the same time. They've And also realized that they can't solve that problem by themselves. And so I think that's what makes it a really great area for collaboration in this very moment. [00:37:01] S0: Super exciting stuff. Well, it was really great having you on, Lori. Thank you [00:37:04] S6: so much.
00:37:04 · Lori Goler, Stephanie Palazzolo, Amanda Peterson Corio
[00:37:04] S6: so much. [00:37:05] S0: We'll have to keep in touch on on how all of your work is going moving forward. But, again, that was Lori Goler, president of the Chan Zuckerberg Initiative here on TITV. Amanda Peterson Corio leads Google's global energy and power strategy, helping to secure the electricity it needs to power its expanding infrastructure. But can the energy great keep up with the AI boom? That's what we'll be talking about today. Amanda, welcome to TITV. [00:37:42] S7: Thank you. Thank you for having [00:37:43] S0: me. So, you know, looking back over the past two years, it feels like, you know, originally just getting the chips themselves was the biggest issue. Now to add on top of that, we have all these other bottlenecks like memory, energy, obviously, which you're very focused on. I mean, how much computing capacity today is being held up by power bottlenecks for Google specifically? It's
00:37:43 · Stephanie Palazzolo, Amanda Peterson Corio
[00:37:43] S0: me. So, you know, looking back over the past two years, it feels like, you know, originally just getting the chips themselves was the biggest issue. Now to add on top of that, we have all these other bottlenecks like memory, energy, obviously, which you're very focused on. I mean, how much computing capacity today is being held up by power bottlenecks for Google specifically? It's [00:38:05] S7: a really interesting question. Think, today most people think about the hold up to AI being either capital or access to chips, as you mentioned. But there is an inherent disconnect between, we are now updating software in seconds. We are now updating our chips in six months to eighteen month cycles. But to build out large power infrastructure takes years. And so, really, when we work in our field and infrastructure, we're not thinking about next year or the year after that. That's already been written. That's all the work that we've done the years prior. We think in terms of '28, '29, '30, or even beyond. And so we're always building that infrastructure three years ahead or four years ahead to be able to meet, future demands or needs. There's plenty of power. It's just being able to thoughtfully design and structure in the right way to be able to get it built in time to meet the needs.