00:04:24 · Oliver Vince
[00:04:24] S1: So I think that the, you know, the the work that we do has a lot of different technical elements to it. Right? There's, you know, to split it into three categories, you've got the data collection arm, which requires, you know, running expeditions around the world, building partnerships, and, you know, we've partnerships in Africa, Australia, like, all seven continents, but, you know, some of, like, the most exciting, you know, environmental places all around the world. And so that's sort of one arm. Then there's the tech arm, which is basically, right, how do you scale datasets? How do you scale models trained on these datasets to sort of, you know... Currently, a g b four scale going beyond that now. And then there's a whole sort of, like, pharma set, which is, like, how do you actually take that and make medicine? Yeah. You know? How do you how do you get that to the point where it could actually be in a in a patient in a hospital down the line? And I guess all of those different pieces have very, very different expertise, and effectively work with NVIDIA and Anthropic on that sort of middle piece. And not to, you know, not to do ourselves a disservice, but to, like, help us to to leverage what they have learned in that space and apply it to our field rather than trying to relearn the same thing, you know, ourself, you like.
00:05:31 · Sebastian Johnson
[00:05:31] S0: And it is... Since that you've been very... It's been very thoughtful around who you're getting involved. Right? Because you mentioned, you know, Andre Hoffmann, you've got NVIDIA, Anthropic. Like, there's a stack of people from across biotech, pharma, AI. It feels like you're trying to build in, like, this, like, Avengers scale, almost like cap table for this company. Have you been very intentional about that?
00:05:57 · Oliver Vince
[00:05:57] S1: You... You're trying to get people who are both a combination of what I call realistic and optimistic. Right? Like, you need to be... You're you're trying to do something that is very serious, very dangerous, you know, in terms of, like, you're trying to treat people with very serious diseases who are, you know, in a very tough situation. This is not... You're not training chatbots that, you know, if they make a spelling mistake, it kind of is okay. You're training things to have, you know, the potential to have a really meaningful impact on people's lives, and so you need to be realistic, sensible about what that is. You can't just be as flippant as some of the other companies have been in sort of the AI space. But at the same time, you need to be optimistic that the way we treat cancer, the way we treat autoimmune diseases isn't going to be the same for the future. And the way we look at it, just as a little bit about that, right, take something like cancer, it's a very, very difficult disease to treat, but the reasons are relatively simple, right? It's individuals, so each person generally has a new type of cancer. So even if you have the same broader cancer, it's normally individual to you. It's very, very complex. There's never normally one thing that's causing it, and it's also a moving target, right? So you've not got very long to treat it. And so the way that we approach the problem from an optimistic sense is that, you know, the only way to treat that will ever be... You know, for for lots and lots of people, of course, is with an algorithm that can come up with a solution to that sort of very, very complex set of challenges and develop a solution. But the realistic part of you also needs to be there, which is that this is very, very difficult, very, very, you know, hard to do. And we need people around us who have spent their lives and their careers actually doing it to make sure we have the trust and the credibility to allow ourselves to actually get to that point. Because I think in, you know, in the in the deep domains, we've been working on this for a long time. We will be working on this for a long time. It's a very different, you know, to to just scaling software if you like.
00:07:47 · Sebastian Johnson
[00:07:47] S0: Oh, yeah. Absolutely. This is, yeah, an order of magnitude more complicated and more regulated and a whole other host of issues I imagine. But when you talk about specific diseases, spoke about cancer, autoimmune problems, do you think there are certain health issues, whether it's bacterial infections or cancers or degenerative brain diseases, where this type of technology that you're developing can have the impact first?
00:08:14 · Oliver Vince
[00:08:14] S1: I... So the... What we are at a very simple level trying to do is learn to read and write DNA. And so it will be applications where DNA has a primary driving cause and that the solution can be found by designing DNA. So examples of that include genetic medicines. So this is something that, you know, Anthropic has talked about going after, which is their sort of rare disease or neglected disease piece. But what that basically means is a disease where a piece of the DNA has gone wrong or maybe not gone wrong, but it's it's sort of changed in a way that is adversarial or causes, you know, some form of illness in the patient. And, effectively, you know how to fix it, but it... You have to do a very specific fix for that person, and that's sort of normally, at this point, expensive to do. And if you can get an algorithm to figure out how to fix that piece of broken DNA, then you have a solution. In cancer, it's sort of similar, slightly different, slightly more challenging. But in cancer, it's still a disease of the DNA very fundamentally. You know, your d... Your DNA has gone wrong, and your body has not recognized that mistake, and so your body has not recognized to shut that down. And what you're trying to do is teach the body to recognize that. And so when we say curing cancers, it's it's less about we're gonna develop another chemotherapy, and it's gonna hit it even harder. It's saying everybody gets cancer all the time because every day, you know, your your cells mutate and your cells divide in ways that are not great, your your immune system shuts it down most of the time. And so in a scenario where you get the disease like that, what you need to do is teach the immune system to say, hey. Look. Look. This is bad, and go after it. And then you really can just, like, restore your body effectively back to its healthy state, right, which is what you're trying to do. So that's all... Not all. A very simplistic way you can think of it is all just recoding the DNA. And then, you know, you've got things like the antibiotic model we've already produced, which is where the pathogen is in DNA and then the the the antibiotic is also in DNA, so you can sort of figure out how to get it to work on that... On those sorts of problems. There's a whole host of things you can do.
00:10:04 · Oliver Vince, Sebastian Johnson
It doesn't mean that DNA is the only thing you ever need to do or it's the only thing you'll ever want to do, But if you can figure that out, there's a long way to run. [00:10:12] S0: And can you talk about where, I guess, where you're at in this process? So I know that a large part of this announcement and this this money is gonna take you, I guess, towards more clinical development, But you've been gathering this data, you've been training the models. The models I understand have also been used to some extent within cloud science. So where are you? You know, where do you at? Where where are you at today? Where have you seen people taking and running with some of these models or designing things? And where do... What what do you need to do to get to clinical development?