00:00:13 · Sebastian Johnson, Raffi Kamber
[00:00:13] S0: It is my absolute pleasure. You've been doing some really cool stuff, and you've had a... What looks from the outside actually had a phenomenal year. The first thing I wanna talk about is Basecamp. Crazy company. I interviewed the founder, what, like, two weeks ago, think the round was announced last week. Tell me about the company and why you invested in it. [00:00:31] S1: Yeah. Sure. I mean, it's a phenomenal company, and I think you you already had a chat with some of the founders in the past. Listen. I think if you take a step back, when it comes to biology and the evolution of biology, It doesn't make sense that in the long term, keep iterating on finding medicines the way they are today. You've seen the impact of AI on anything that we use on a daily basis. Biology is much more complex, so it's going to take a bit more time, but we're going to get there as well. And Basecamp is in these thesis that if you can understand the language of biology, if you can scale it because of the level of complexity it implies, you can get to the same type of experience you're having on ChatGPT or Claude today, meaning inputting a request for inventing a medicine, inputting data from the patient, and you're gonna get there because the the model is gonna respond to it. So it's quite a fascinating space. Everything's moving at a very fast pace. And Basecamp is quite unique in that in that spectrum.
00:00:31 · Raffi Kamber, Sebastian Johnson
[00:00:31] S1: Yeah. Sure. I mean, it's a phenomenal company, and I think you you already had a chat with some of the founders in the past. Listen. I think if you take a step back, when it comes to biology and the evolution of biology, It doesn't make sense that in the long term, keep iterating on finding medicines the way they are today. You've seen the impact of AI on anything that we use on a daily basis. Biology is much more complex, so it's going to take a bit more time, but we're going to get there as well. And Basecamp is in these thesis that if you can understand the language of biology, if you can scale it because of the level of complexity it implies, you can get to the same type of experience you're having on ChatGPT or Claude today, meaning inputting a request for inventing a medicine, inputting data from the patient, and you're gonna get there because the the model is gonna respond to it. So it's quite a fascinating space. Everything's moving at a very fast pace. And Basecamp is quite unique in that in that spectrum. [00:01:38] S0: And you you... I think you first got involved with the series b, I think, at the end of twenty twenty four. Yep. It feels like they've had this remarkable journey since then. What's it been like, I guess, being close to the company during this transformational time?
00:01:52 · Raffi Kamber
[00:01:52] S1: It's it's it's a very interesting company actually, because I think everybody agrees on the direction the market is going. And anyone from the pharma companies to the labs understands that there's a need for digitizing the process for finding medicine. But there's a variety of ways that are being attempted in the market. And the guys at Basecamp have quite a unique view on that. Their thesis is that you need to have, to understand the language, which is the data coming from genomics, for being able to build the best models possible. And the more data you absorb, the more language you understand, the better medicine you can get as an output. That's that's the fundamental thesis. So once you understand the way they are approaching this market, there's a few challenges behind it. One is being able to find the data, which is what they've done in 30 plus countries by now, building the the knowledge graph of life, basically. Then it's about building the models, and then it's about being able to deliver the first medicines. And what we've observed in the last two years we've been working together is that data is cracked, models are getting to maturity. In in the next two, three, four years, we'll see how fast they can get there. We're gonna have the first indications that you can generate valuable stuff. And that's what's exciting to observe in such a company because it's it's really game changing for the industry.
00:03:17 · Sebastian Johnson, Raffi Kamber
[00:03:17] S0: And there's been a few investments from Singular in this sort of science AI space. And that... So you got a few more in that portfolio. The one thing that I thought was quite interesting is that none of them are about using LLMs within science. The point that you make around, like, almost a proprietary data, it feels like that's true, I think, of all of those sort of scientific companies that you've invested in. Is that part of a broader thesis? [00:03:42] S1: I think the broader thesis first is that Europe has something unique when it comes to building companies at the intersection of AI and science. That's the core thesis. And then within the core thesis, there's also a few options we've taken. But let's take a step back. If you look at the talent density when it comes to scientific backgrounds in Europe, it's extremely distributed across Europe as well. You have amazing talent that are working on quite fundamental topics. What was lacking until five, ten years ago maybe was the transition from research to making them companies. But the new generation of researchers also are... Have this like entrepreneurial DNA. So you see a wave of companies coming out of Europe that are both relatively unique and very exciting, and that have the founders that are able to make them companies. So we are very excited about this opportunity. And to come back to your question, there's, especially in biology, the need for having fundamental data because it's not available. It's one of the foundations for being able to nurture the LLMs and make them, deliver.
00:03:42 · Raffi Kamber, Sebastian Johnson
[00:03:42] S1: I think the broader thesis first is that Europe has something unique when it comes to building companies at the intersection of AI and science. That's the core thesis. And then within the core thesis, there's also a few options we've taken. But let's take a step back. If you look at the talent density when it comes to scientific backgrounds in Europe, it's extremely distributed across Europe as well. You have amazing talent that are working on quite fundamental topics. What was lacking until five, ten years ago maybe was the transition from research to making them companies. But the new generation of researchers also are... Have this like entrepreneurial DNA. So you see a wave of companies coming out of Europe that are both relatively unique and very exciting, and that have the founders that are able to make them companies. So we are very excited about this opportunity. And to come back to your question, there's, especially in biology, the need for having fundamental data because it's not available. It's one of the foundations for being able to nurture the LLMs and make them, deliver. [00:04:57] S0: On your point around this opportunity that Europe has, you've been investing for a long time before this wave of sort of academic or research founders has kind of cropped up in the last five or ten years. How has your role changed as an investor to sort of accommodate this new generation of founders who are academic? Does it change the way that you are assessing founders, sourcing founders, supporting them?
00:04:57 · Sebastian Johnson, Raffi Kamber
[00:04:57] S0: On your point around this opportunity that Europe has, you've been investing for a long time before this wave of sort of academic or research founders has kind of cropped up in the last five or ten years. How has your role changed as an investor to sort of accommodate this new generation of founders who are academic? Does it change the way that you are assessing founders, sourcing founders, supporting them? [00:05:21] S1: Yeah. That's a great question. I think there's a slight difference of DNA between people coming from research, and and others. I think that their their role has slightly evolved because especially when you come from tech investments, you wanna help them understand how their scientific research can can evolve into becoming a company within the tech industry. And they have a pretty great understanding on what the science is about. They know the limitations of it. They know the iterations of it. But I think that the the partnership we built is to help them understand what's happening in the in the broader industry. Who are their allies for continuing to build this? Who are the long term partners for this, what are the steps for building an asset that's gonna be valuable. And I think that's there. There's a slight difference, from before. Obviously, these guys are are some of the smartest people we've ever met because they're working on extremely advanced tech and science. They don't need us to help them think about what they already know, but we can help them understand the foundations of building a company.