Scaling Europe
00:01:22 · Oliver Vince, Sebastian Johnson
[00:01:22] S1: Raised 140,000,000, which is a great new round of contributions from... I mean, led by S32 over in The US, but contributions from, you know, investors, existing investors, new investors all over the world, across tech, across pharma. One of the... You know, our our longest supporters has been Andre Hoffmann, who's, you know, the vice chairman of Roche to give you the complete other side of of the sort of tech pharma split, if you like. And, yeah, just super proud of all the different people that have come around the table and, you know, excited to build with them, basically. [00:01:56] S0: And I guess a lot has changed in the company since your series b. You raised, I think it was $60,000,000 led by Singular, I think, towards... It it was announced at the end of twenty twenty four. Since then, there's been some really exciting announcements. Can you talk about some of those, maybe, like, some of the models, Eden models, the Gene Atlas you've launched? Can you maybe talk about those two releases and why, I guess, I assume they were so important in unlocking this big series c?
00:02:22 · Oliver Vince
[00:02:22] S1: Yeah. So, I mean, as I said at the start, right, it's it's a relatively simple idea. We're just gonna try and teach AI to understand biology. And then the question is kinda like, what do you do with that? Why? Why do you why do you care? And effectively, we're seeing the same thing happen in these models as happened in the language space, which is the biology is fundamentally a language. You know, you can learn to speak biology if you like. And that basically, the bigger these models get, the more data they have, the smarter they get, and the more capable they get for useful applications. So to run you through some of those, you know, we have been building this database for a long time. We have these partnerships in sort of 30 countries, more than 200 different locations around the world, and we have been scaling this dataset accordingly. On that, and then one of the big releases that might be our first public partnership with NVIDIA was the Eden models. So these are g b d four scale, the largest biological models ever trained, and then we've done a bunch of interesting things with them. So one of the things is, you know, we put it into called science with Anthropic where, you know, you could do things like upload a patient microbiology report to someone who's got an infection. You can put that file into Claude. Claude will work with Eden to design a new antibiotic for that infection and then give you back that antibiotic. And there's some barriers to that to actually implement that in hospitals, of course, but it works very, very successfully in the lab. It works very, very successfully in the first sort of preclinical studies, and so it's sort of demonstrating the point of where these models could go. And then sort of underneath all of that, we've worked... You know, we've partnered with NVIDIA Anthropic, PacBio, and Ultimate Genomics to then build what we call the Trillion Gene Atlas, which is taking our dataset and expanding another 100 x. It's kind of saying, look. We've seen the start of these scaling laws in biology. We believe it's worth continuing, and then there's a sort of a very, very clear sort of technical case for that. But also, you know, at the end of that curve, we believe that there's some sort of profound implications of what these models will be able to do for the benefit of humanity.
00:04:10 · Sebastian Johnson
[00:04:10] S0: It's it's crazy stuff. You've been working with NVIDIA and Anthropic. Both of them have now come in as investors through their sort of investment arms. What do think that says about the partnership so far? Why was it great or or important to have them on board?
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.