The Information's TITV
00:17:33 · Jessica Lessin, Anne Wojcicki
[00:17:33] S1: I think... Look, I think that one of [00:17:35] S5: the most important things is people like my sister or, you know, parents of kids with rare disease, like, are always asking the question, why? And I think it's really hard when you don't know why. And I think that one of my hopes here is that 23andMe will be able to help people get that answer. And, you know, understanding if you have a genetic risk factor or if there are environmental components, like, what is it that you... You know, that potentially caused something? And I think that we have a we have a real opportunity of helping people understand that why. And I think the advantage of knowing that is then you can prevent for the next generation. And I think for all of us, like I look today, the people who can understand if they have a BRCA variant, and then you can have increased screening and prevent breast cancer, people who have, you know, familiar hypocholesterolemia and have just really high cholesterol, like those people can take a medication to actually reduce it. Like, it's a bit of, like, a... You know, like, an acceptance. Like, you can't necessarily just change that with environment. You have to take a a therapy for that. So my hope here is that, you know, people will be able to... We're really going to be able to answer that question as to why, and I think that we'll really be able to help people prevent.
00:18:52 · Stephanie Palazzolo, Nicole Su
[00:18:52] S0: Mhmm. And obviously, you know, 23andMe's mission here is to be able to take... As you're describing, be able to take people's genetic data and be able to flag to them, you know, things that they might have to get early checks for, for instance. [00:19:06] S3: Yeah. [00:19:07] S0: A lot of people, think, are also just taking their health data into their own hands and feeding it into chatbots and seeing what comes out. Yeah. I'm curious what you think of that because I think, yeah. I think there are strong arguments to both, you know, the pro and the con side.
00:19:07 · Stephanie Palazzolo, Anne Wojcicki
[00:19:07] S0: A lot of people, think, are also just taking their health data into their own hands and feeding it into chatbots and seeing what comes out. Yeah. I'm curious what you think of that because I think, yeah. I think there are strong arguments to both, you know, the pro and the con side. [00:19:20] S5: And I see... I think I think there's a couple of things. Like, one, it's always interesting when people experiment. But it's not... I think that's not mainstream. You know? Like, the reality is, like, most people want to have it curated for them. Secondly, I think one of the issues is about data quality. And I think, again, this administration, others also all recognize this. There's a lot of publications that come out that are in very small populations. They're small numbers of people. Like, doesn't... It's not really a gold star kind of study. So understanding what is real is important. And so part of what we will do is we'll put a curation, and we... This is what we do do now is, like, we have the curation layer on top of it. So we're curating the information so that when I give it back to you, that I know it's actually all things that are well validated, that are meaningful for you. Mhmm. And I think that the, you know, the chattypety, like all of the LM, like interpretations are remarkably good. But there's aspects where I'd say, okay, I am getting results that I would not say are necessarily scientifically valid that I would want to follow. Mhmm. [00:20:29] S0: So kind of... Yeah. Kind of being able to filter out maybe what is accurate
00:20:29 · Stephanie Palazzolo, Anne Wojcicki, Nicole Su
[00:20:29] S0: So kind of... Yeah. Kind of being able to filter out maybe what is accurate [00:20:33] S5: about layers to like what what scientific papers... What... What's actually really well valid in the literature. And then I think the other thing is like, okay, looking at your ancestry and your population, there's all kinds of research around, you know, some things have population specifics. So something might just be impacting an African American population more than a Latino population than a European population. Mhmm. So understanding that background as well. [00:20:56] S0: Totally. There's broader context as you're saying that, you know, might not always think to tell a chatbot about your parents' cancer history and Right. Your racial background, all these sorts of things. [00:21:08] S5: But it is good. I mean, [00:21:10] S3: I think it's... Like, I [00:21:12] S5: think there's a lot of interesting opportunities there. Like, I do believe there's a world where you're gonna pull in your medical records, your blood, your genetic data. And I do think what's mainstream more... Like, people are going to want to have an app and experience. And part of what 23andMe has spent a lot of time doing is, like, how do you return this information in a way that makes people understand? How do you make it so that you can compare everything together? How do you also, like, tie in your ancestry? So that's a lot of the work that we are doing. But I do appreciate, like, I think it's going to be the ability for you to have interpretation very broadly is exciting for people.
00:21:12 · Anne Wojcicki, Stephanie Palazzolo
[00:21:12] S5: think there's a lot of interesting opportunities there. Like, I do believe there's a world where you're gonna pull in your medical records, your blood, your genetic data. And I do think what's mainstream more... Like, people are going to want to have an app and experience. And part of what 23andMe has spent a lot of time doing is, like, how do you return this information in a way that makes people understand? How do you make it so that you can compare everything together? How do you also, like, tie in your ancestry? So that's a lot of the work that we are doing. But I do appreciate, like, I think it's going to be the ability for you to have interpretation very broadly is exciting for people. [00:21:49] S0: And when you think about, you know, who's going to be first to some of these major medical discoveries, do you think obviously, you know, maybe a bit biased, but do you think it will be a company like Anthropic, for instance, that's coming from it at this very this broad approach, you know, obviously building these very generalized language models and then applying them to Bio, for instance? Or do you think it'll be companies that have a much more, you know, specialized approach like 23andMe where, you know, you have obviously spent years and years working with genetic data? Do you have a sense of like kind of which approach will win out or be first I think...
00:22:24 · Anne Wojcicki, Brittany Greenfield
[00:22:24] S5: Look, I think I, I love that everyone is getting into biology. Like I, I joked in some ways, I was like, it's like the new vineyard, know, like, people make a lot of money, they come, they're like, I'm starting a biotech. Like, this is great. And I really, really appreciate, like, all these people are coming in, and they're taking different approaches, and they are making a difference. The challenge with biology is that it's spectacularly complicated. And clinical trials clinical trials take time. And ultimately, it's very different than, like, me testing a marketing campaign. Like, I can run 100 tests, you know, a second, like, in in... Like, get marketing campaigns. In biology, you have to wait to see, like, okay, I developed this molecule. I can develop a target. I can develop a molecule. I put in humans, and I have to wait to see. Do they have a heart attack? Do they not have a heart attack? So the recursive learning is just much slower. [00:23:22] S4: Yeah.