The Information's TITV
00:08:16 · Anne Wojcicki
[00:08:16] S5: The new chapter is very exciting. And it's very exciting largely because it allows us to lean into the original reasons why we started the company, which was the mission. And the mission is really twofold. One, we believe every single person can benefit from genetic testing. And there's all kinds of reasons why it's not offered in the medical system and all kinds of issues around prevention. But fundamentally, every single person can benefit from knowing what their genetic data says about them. Secondly, most people in this country are never given the opportunity to participate in research. So we're all... We all have family members who suffer. We all have that experience of going to the doctor and not getting the right answer. We all understand that there's a huge need for research. And so 23andMe gives every single one of our customers the opportunity to participate. Over 90% of our customers opt in, which means that of our 14,000,000 people, we have a huge number of people who have opted in to participate in research. So over 11,000,000 people. It's an extraordinary data set. It's an extraordinary community of people who are all trying to make a difference in the world. And so you look at things like we were able to discover a, very rare mutation that shows up in a gene called EGFR that makes people very high risk for lung cancer. 23andMe has about seven hundred people who have that genetic variant. We now are going to have the opportunity to reach out to them and say, you should be going ahead and getting low dose chest CTs. We can potentially save your life. So that kind of work is extremely meaningful for us. So as a nonprofit, I'm leaning in heavily to to these two sides, continuing to, like, fight the fight that, like, every single person should have affordable access to genetic testing. And secondly, the research that we have that we can do that is so powerful, it's no longer just going to be 23andMe that is making those discoveries, but we're going to open it up to the entire academic community. So everyone who wants to make a difference in the world should have the ability to actually leverage the information to make discoveries. And I think about people like my sister who had lung cancer. She was very clear. She's like, it shouldn't just be Stanford that's using my data.
00:10:34 · Anne Wojcicki, Stephanie Palazzolo
Like, I want everyone who's studying lung cancer to move it forward. And you see, like, when every single person has access to resources, we can make a huge difference. And ultimately, the goal for us is like, I want to have a significant impact on prevention and treatments for diseases. And I think the way that we can do that best is as a nonprofit. And [00:10:57] S0: talk to me about, like, how much has it helped having some of these, you know, large language models, frontier AI models? How much have those helped specifically in analyzing this genetic data versus, you know, older forms of AI that you could have used in the past?
00:10:57 · Stephanie Palazzolo, Anne Wojcicki
[00:10:57] S0: talk to me about, like, how much has it helped having some of these, you know, large language models, frontier AI models? How much have those helped specifically in analyzing this genetic data versus, you know, older forms of AI that you could have used in the past? [00:11:12] S5: What's interesting about AI is, you know, those are language models. And so specifically, what we have is we have a DNA language model. And so AI, particularly in those earliest versions, like, did not really understand DNA. And so part of what we've been working onand we had a paper that came out last year at NeurIPS about, like, how can you actually use AI and these models to actually look at all of the data and really be able to improve risk prediction? So there's two things that we see as a huge opportunity by working with partners or by training models ourselves. And that is going to be one, can you just really interpret the genome? And so you hear about all kinds of families that are wondering, like, I have a child who's sick and no one can figure out why. Or people... Like my sister will say, like, did I have a genetic risk factor for lung cancer? Like, how do we actually read your whole genome and how can we understand it? I think that leveraging these large language models by training data on a large number of data sets, like, I think we're going to be able to really be able to understand why some people have disease and how do you predict what you're really at risk for.
00:11:12 · Anne Wojcicki, Stephanie Palazzolo
[00:11:12] S5: What's interesting about AI is, you know, those are language models. And so specifically, what we have is we have a DNA language model. And so AI, particularly in those earliest versions, like, did not really understand DNA. And so part of what we've been working onand we had a paper that came out last year at NeurIPS about, like, how can you actually use AI and these models to actually look at all of the data and really be able to improve risk prediction? So there's two things that we see as a huge opportunity by working with partners or by training models ourselves. And that is going to be one, can you just really interpret the genome? And so you hear about all kinds of families that are wondering, like, I have a child who's sick and no one can figure out why. Or people... Like my sister will say, like, did I have a genetic risk factor for lung cancer? Like, how do we actually read your whole genome and how can we understand it? I think that leveraging these large language models by training data on a large number of data sets, like, I think we're going to be able to really be able to understand why some people have disease and how do you predict what you're really at risk for. [00:12:22] S0: And talk to me about the process of training this sort of DNA language model. I imagine a big part of it is using a different, you know, different training data sets than the ones that are probably used for...
00:12:22 · Stephanie Palazzolo, Anne Wojcicki
[00:12:22] S0: And talk to me about the process of training this sort of DNA language model. I imagine a big part of it is using a different, you know, different training data sets than the ones that are probably used for... [00:12:33] S5: Yeah. We're not...... Flawed, but I mean, we're not... We're actively... So we did a bunch of work previously. And again, pre Chapter 11, we are doing a bunch of work, and that's where we published this paper on NeurIPS. We are actively looking now at what is that right path forward for us. So we're not doing anything today, but we're really focusing on, like, there's a huge amount of opportunities. We... We're working in small bits internally, but we think a lot about, like, who's the right partner for us? Where do we wanna be putting our efforts? Like, what are the first priorities? So one thing that we're doing a lot of work on now is, you know, actually getting whole genome data. So 23andMe works on SNP data, meaning, like, we look at a 100,000,000 variants in humans. As you start to get long read data and as you start to get the whole genome, you're gonna be able to make all kinds of new discoveries. So it's about get... Putting that right data set together. We'll identify the right partners, and then we'll start to move forward. [00:13:30] S0: Mhmm. Who... Like, what are some examples of, you know, partners that you would consider? Like, are these other model developers? Like, obviously, I know, like, Anthropic, for instance, has gotten very into AI for bio. Who might be some of the partners that you would think of
00:13:30 · Stephanie Palazzolo, Brittany Greenfield, Anne Wojcicki
[00:13:30] S0: Mhmm. Who... Like, what are some examples of, you know, partners that you would consider? Like, are these other model developers? Like, obviously, I know, like, Anthropic, for instance, has gotten very into AI for bio. Who might be some of the partners that you would think of [00:13:46] S4: to work [00:13:47] S0: with on this sort of thing? [00:13:48] S5: Interestingly, there hasn't... Like, when we went to NeurIPS, we were the only genetics paper there. And so that was last year. So it's been really interesting to see, you know, Anthropic, OpenAI, Google, like, everyone is coming. Like, everyone looks at biology and health care now, and it's super interesting to people. So there's a lot of really interesting players out there, you know, a lot of expertise in little pockets of some of these companies. I don't know. We'll have to see sort of like who makes the most sense. And I think for us as a nonprofit, what's always top priority for us is that mission. Like, are we doing... How can we fulfill the mission that our customers have entrusted us with? Like, I wanna really understand risk prediction. I wanna be able to give that information back to individuals. I wanna be able to predict disease because then you can actually do something about it and try to prevent.