00:06:56 · Alex Higginbottom, Sebastian Johnson
[00:06:56] S1: Yeah. So this is, you know, a really, really hard problem. And this model we're releasing next week is really the first step. And just like other, you know, Frontier Labs, we'll be releasing new iterations of the model every month, every couple of months, and the... It'll just get better and better, a broader and broader capability. You know, I mentioned the speed is the first value proposition. The second one increasingly will be training directly on experimental data, and there's this idea of closing the sim to real gap. And then you won't just have something which is a million times faster, but also more accurate to reality. And that ties into a bigger vision that we have of the company, which is, you know, maybe one day, ten years, maybe less, we won't need to build five practice Starships. We can do a million in a world model and then build the final one straight away. That's really the kind of North Star that maybe we'll never hit, but that's really what we were going for. Yeah. And then in terms of... Sorry. What... And then and then what what... Was there other points to your question? [00:08:02] S0: It was... Yeah. It was what stage of the product development or, like Right. Are there users out there using some of the things that you've built already?
00:08:02 · Sebastian Johnson, Alex Higginbottom
[00:08:02] S0: It was... Yeah. It was what stage of the product development or, like Right. Are there users out there using some of the things that you've built already? [00:08:09] S1: Yeah. Great question. Yeah. Of course. So, yeah, this first year of the company, you know, we... We've been hiring, like, the a team, you know, building our product, our initial product, which was huge effort. You know, some of the best researchers in the world, massive conviction, extremely long hours to build this. But still, yeah, we've been fortunate to work with some of the leading fusion companies. We can't share exactly who that is right now, but we will be sharing that very shortly. And already, they are getting value from it. They are using it in their day to day workflows. And, also, we'll be announcing shortly various academic partnerships that we have with leading institutions as well, and they've they've played a a strong role in shaping, you know, how... Where... What is the most useful for for advancing the state of the art. [00:09:00] S0: Amazing. And and let's talk about the round itself. Right? So you raised $10,000,000. You're still pretty, like, relatively early on in the company's journey or history. Backed is backing you, Lunar, Seraphim, MMC. What gave this roster of investors the conviction to invest?
00:09:00 · Sebastian Johnson, Alex Higginbottom
[00:09:00] S0: Amazing. And and let's talk about the round itself. Right? So you raised $10,000,000. You're still pretty, like, relatively early on in the company's journey or history. Backed is backing you, Lunar, Seraphim, MMC. What gave this roster of investors the conviction to invest? [00:09:17] S1: I think we really are tackling a a generational problem is the first thing. If we can achieve our mission, the world will change. And we've seen throughout history, you know, Moore's Law, complexity is increasing, and you can't... The tools of today or of yesterday will not keep up the rate of innovation in fusion and semiconductors in aerospace in in other fields, going into the twenty first century as these systems get more complex. So we need something new yesterday, and we can do that. So I think that is truly a mission which is important, but also obviously economically significant. We also, I think, have been able to pull together a team that is genuinely world leading. So our chief scientist, Lou, he invented many of the biggest breakthroughs in AI for physics in the last five years. Wow. Our core team of researchers from, you know, Meta, NVIDIA, Anthropic. So I think... And and then and then the early proof points that we've been able to demonstrate technically with our model and with our partners, I think, yeah, brought brought everyone together.
00:09:17 · Alex Higginbottom, Sebastian Johnson
[00:09:17] S1: I think we really are tackling a a generational problem is the first thing. If we can achieve our mission, the world will change. And we've seen throughout history, you know, Moore's Law, complexity is increasing, and you can't... The tools of today or of yesterday will not keep up the rate of innovation in fusion and semiconductors in aerospace in in other fields, going into the twenty first century as these systems get more complex. So we need something new yesterday, and we can do that. So I think that is truly a mission which is important, but also obviously economically significant. We also, I think, have been able to pull together a team that is genuinely world leading. So our chief scientist, Lou, he invented many of the biggest breakthroughs in AI for physics in the last five years. Wow. Our core team of researchers from, you know, Meta, NVIDIA, Anthropic. So I think... And and then and then the early proof points that we've been able to demonstrate technically with our model and with our partners, I think, yeah, brought brought everyone together. [00:10:28] S0: Amazing. And and you've got this money. You've obviously built the team. How are you gonna be deploying that capsule? Where are gonna be spending your time on to kind of get you from where you are today to where you wanna be in six, twelve months?
00:10:28 · Sebastian Johnson, Alex Higginbottom
[00:10:28] S0: Amazing. And and you've got this money. You've obviously built the team. How are you gonna be deploying that capsule? Where are gonna be spending your time on to kind of get you from where you are today to where you wanna be in six, twelve months? [00:10:41] S1: Well, a lot of it is getting poured straight into compute. We quite far have into the into the cloud providers. So... And that's both for generating data. So we own a lot of the data stack ourself, and we can cover a huge amount of trapped space with with, yeah, a lot of capital poured into data and training models. We we have very large models, you know, on the scale of multiple billions of parameters. So that requires a lot of compute. And then also, you know, building out our team both in London and in San Francisco near our customers. [00:11:18] S0: You know, it's it's such a... An essential part of any sort of, like, startup spending is now on compute. What does that mean about your own fundraising journey, especially when you are now building, you know, you sort of, like, frontier AI, big models that are gonna require, as you say, lots of compute. How do you plan for fundraising in the future? You know, are there things that you need to unlock? Are there ways of predicting how much more compute you're gonna need in six or twelve or eighteen months? And how are you planning for that for the company?
00:11:18 · Sebastian Johnson, Alex Higginbottom
[00:11:18] S0: You know, it's it's such a... An essential part of any sort of, like, startup spending is now on compute. What does that mean about your own fundraising journey, especially when you are now building, you know, you sort of, like, frontier AI, big models that are gonna require, as you say, lots of compute. How do you plan for fundraising in the future? You know, are there things that you need to unlock? Are there ways of predicting how much more compute you're gonna need in six or twelve or eighteen months? And how are you planning for that for the company? [00:11:46] S1: Yeah. That's a really great question. So we spent quite a lot of time this summer actually trying to figure out the scaling laws for both on data and on, the model side. And we've demonstrated now scaling laws that we have quite a lot of trust in that we've been able to track and and kind of blind test. And we... And that is how we're doing a lot of our planning going forward. So, you know, much much like what the LLMs do, although maybe still an order of magnitude discrepancy there. [00:12:17] S0: And how have you found the transition? You know, this is, I I guess, like, your first job in many respects, right, like, coming out of studying or coming out of being an academic or researcher. How have you found that transition from, you know, being in a in a lab or in a university environment to now running a company?