00:15:48 · Laura Connell
[00:15:48] S1: Yeah, absolutely. I think one of the things that's clear is that the barrier to entry into building at the application area is basically none these days. And the biggest bottleneck to broad based technological adoption specifically to really realize the promise of generative AI has been cost and latency depending on the job to be done. And so, and as well as the criticality of the job being done from a regulatory perspective. And of course, I'm thinking about highly regulated industries like healthcare, which Corti is obviously addressing, but also meeting kind of sovereign deployment requirements. And so there we're talking about, we really need to reason about what's the nature of the infrastructure that needs to be built to support the deployment at scale in a way that is cost effective, of course, but it also meets the requisite criteria from a risk and sovereignty perspective. And so this is not just a European story. This is proof that, of course in Europe, you know, we have a pretty high bar in terms of GDPR and the like, but this is a problem that touches on IP leakage and sovereignty globally. So there is a kind of broad based topic around how we build infrastructure that really addresses the needs of different types of deployments and different data requirements, etcetera. And I think there, given the hurdle to building in Europe is higher and not to put lipstick on a pig, so to speak, but that does force founders to build with a level of scrutiny that they can then pass forward out globally. Now where I was wrong or the things are still to be played out is it's still unclear whether or not the solution necessarily needs to be kind of locally owned. And so I think really the emphatic point here is we're not looking for European founders to build just to address the European problem. This has to be a kind of global perspective and a global based ambition, trying to solve some of the issues I mentioned.
00:17:46 · Sebastian Johnson
[00:17:46] S0: And that feels like a common theme among some of the companies that you've backed, like the The Exploration Company, Corti Physics, these are companies that are building, it feels like a sort of European way and quite a different type of company than some of those coming out of America but fundamentally global markets, globally huge markets are there any other things that connect the type of founders that you backed or the companies that you backed that sort of pulls you in and I think those three are relatively deep tech in different ways But is there something that gets you particularly excited about certain companies?
00:18:19 · Laura Connell, Sebastian Johnson
[00:18:19] S1: Yeah, I think the one is just that from a founder perspective, just the ambition, the audacity to go after absolutely enormous markets. That's the first thing. Right. And I think that that's always what we look for at any stage. And by the way, this could be a specific industry, it could be a specific problem within it, just all of those companies have in common, just this incredibly audacious goal going after potentially an absolutely enormous and in some ways unsizable market. So that's the first thing. I think the second thing, given the nature of the technologies that they're building, you to assess for the quality of the team from a technical perspective. And the way I often put it is these are founders that can move at least as quickly as frontier teams building in tier one hyperscalers or perhaps startups that are further along theoretically from a funding perspective. So really solving for technical talent density. And so, one of the things that we'll habitually do is beyond the founders, of course, like look at the quality of the rest of the team when you know that R and D is the key asset to really create compounding value. And so that's the second thing. And then I think the third thing is a team that has a sense of incredible urgency. And I think that to build fast now, it was always the case that you needed to move quickly, but now the speed at which markets are evolving often [00:19:41] S0: have
00:19:41 · Sebastian Johnson, Laura Connell
[00:19:41] S0: have [00:19:41] S1: to pivot and iteratively in a much shorter amount of time than was ever the case, which is also why you're seeing much larger rounds to boss a balance sheet, right? To kind of buy yourself the call option to do that iteratively. So those would be three of the kind of key things. And fourth, I mean, this would be more generally about any kind of frontier investment is that you really have to believe that there's something compelling, interesting to be done from an IP perspective where you're really talking about unique IP. [00:20:09] S0: When we talk about talent, this kind of change in the market towards much more technical academic talent, you spent a couple of years being Gavin, I think an assistant researcher in brain things, brain, I don't know quite what it was, but has that given you some sort of unique advantage or insight to then assessing highly technical academic talent, do you think?
00:20:09 · Sebastian Johnson, Laura Connell
[00:20:09] S0: When we talk about talent, this kind of change in the market towards much more technical academic talent, you spent a couple of years being Gavin, I think an assistant researcher in brain things, brain, I don't know quite what it was, but has that given you some sort of unique advantage or insight to then assessing highly technical academic talent, do you think? [00:20:33] S1: Well, I think what it gives you is a enormous sense of humility. The things that I spend a lot of time on now over fifteen years ago now, the half life of that knowledge is just rapidly diminishing. And so, I think that's an appreciation for how quickly things are moving. My research was in cognitive, the intersection of cognitive neurosciences and AI. So I was in a team called the brain sciences team at Imperial. And in some ways, really got out answering some of the questions that I thought were quite interesting in my first degree, which were in philosophy, kind of theory, the theory of knowledge and intelligence and cognition. So, you know, epistemology and the like. And a lot of those questions actually, a lot of the topics that were part and parcel of both of those degrees really underpin a lot of a discussion of course now around AGI and how to reason about reason and a mechanistic view of human intelligence and computation. So I think at least perhaps it doesn't give you a particular edge, but at least it gives you an understanding of the type of questions you should be asking when you're looking at things that touch on AGI and AI generally.
00:20:33 · Laura Connell, Sebastian Johnson
[00:20:33] S1: Well, I think what it gives you is a enormous sense of humility. The things that I spend a lot of time on now over fifteen years ago now, the half life of that knowledge is just rapidly diminishing. And so, I think that's an appreciation for how quickly things are moving. My research was in cognitive, the intersection of cognitive neurosciences and AI. So I was in a team called the brain sciences team at Imperial. And in some ways, really got out answering some of the questions that I thought were quite interesting in my first degree, which were in philosophy, kind of theory, the theory of knowledge and intelligence and cognition. So, you know, epistemology and the like. And a lot of those questions actually, a lot of the topics that were part and parcel of both of those degrees really underpin a lot of a discussion of course now around AGI and how to reason about reason and a mechanistic view of human intelligence and computation. So I think at least perhaps it doesn't give you a particular edge, but at least it gives you an understanding of the type of questions you should be asking when you're looking at things that touch on AGI and AI generally. [00:21:44] S0: At this moment in time, we're sort of living in the age of the academic or the researcher, we're seeing science being pushed forward quite drastically and there's a real opportunity to take science, probably to say like solve science, right, with sort of like a super intelligence, Does that make you miss research at all, academia at all?