00:35:52 · Karol Hausman
[00:35:52] S0: But then... And he should tell the story rather than me, but I believe what was what was happening is that he was in... He's been investing in that point for a few years. And as long as he's been investing, he always thought that he doesn't wanna be investing forever. He wants to find something else that he can fully devote himself to and he wants to build things. And the one area he was always fascinated by was robotics. And he was seeing over the past few years that there was a lot of innovations happening there. There was some kind of moment. Robotics was having its moment. And particularly, he was impressed. He was seeing over and over papers coming from Chelsea Finn and Sergey Levine's lab, as well as our papers from Google. So he he talked to a lot of a lot of his friends that if ever Chelsea, Sergey, or Karol or anybody from those teams are thinking of starting a company, please connect me to them. I would at least want to invest or at least talk to them. So that's when we got introduced by by a friend, and we pitched to Lachy as a way to to to have him invest. And at the end of that pitch, it became clear that he would like to do much more than just invest. This is the opportunity he's been looking for for all the years he's been investing, and he really wanted to to to dive in. So then the next step was us trying to get to know each other as quickly as possible, spend as much time together as as we could. And and then as we were doing that, it also became clear that this is the person that that I've been looking for that could help us on on the on the commercial side, on fundraising side, on operation side, kind of one
00:37:32 · Mario Gabriele
[00:37:32] S1: of the the missing links that we really needed. Were there certain philosophical alignments that you needed to to find in someone who you hadn't worked with before? What were the sort of, you know, even beyond maybe the tactical pieces, the the traits that you you saw in him that made you feel like, yes, this is someone I can can bring into this very trusted group where, you know, we've already built all this history together?
00:37:57 · Karol Hausman
[00:37:57] S0: There were there were a few. I think maybe the the biggest one was that he immediately got it. At that point, were a lot of investors or other people that I talked to, other business people that I talked to, and it was very hard to get that idea across, that idea that you have to do research right, you need to build the technology first and you can be distracted by short term revenue. And if we do do this right, this is gonna completely change the world and it's going to be most valuable business of all time. But you need to have the patience to let let us do it the right way rather than short circuit that path and and kinda cap the ceiling. And he got it, I think, within like the first minute. So that was very reassuring. And then on top of that, what what I really liked about our conversations was that in in many of those previous conversations with with other people, I always felt that, like, I'm the one pushing the ambition of this project of how far it could go if... Set it up right. And I think this was the first time where I had somebody else set up even higher ambitions for us. That was really cool to see because it it felt like we're all pushing in the same direction. He will make us even better and more ambitious. So I was really glad to find someone who will make sure that we are not gonna... That we're not gonna short circuit this. He's fully aligned in doing this in the biggest way possible. He makes us more ambitious, and he's the person in the areas that we really need more expertise. And he'll make sure that those areas are fully aligned with how we want to develop this company. So it was it was basically a no brainer at that point.
00:39:41 · Mario Gabriele
[00:39:41] S1: You had this insight that this was the right moment to to go and solve this problem. But at least from the outside, one could imagine many different form factors that might take. Like, another version of this company might be saying, hey. We're gonna try and build the the best humanoid robot ourselves, or, you know, we're gonna take this approach to getting the data we need to to run these models very effectively versus another approach. How did you sort of land on the version of physical intelligence as it is today?
00:40:13 · Karol Hausman
[00:40:13] S0: I think from the get go, we had an idea of... We had the thesis for the company. And that thesis was around all the research that we've done up until this point. That similarly to what happened with language, it's not gonna be specialist models that's really going to solve this problem. It's going to be generalist models that work across all kinds of different tasks, all kinds of different environments, and all kinds of different robots. And it was similar to lag... To what we've seen in language where, you know, you would think that the best translator would be would be just specialized for translation or the best coder would be just specialized or just trained on code. But it turned out that the way to be the best at all of those fields is to train one generalist and, you know, generalist that that takes in poetry data and the coding data and translation data, and then it turns out to be much better than all of those specialists at those specialist tasks. And we started seeing something similar in robot learning, in robotics, that if only we could collect enough data, if that data was very, very diverse, and if we do it the right way, we should be able to build the best generalists. If we solve that, that is really gonna allow us to have this world of many diverse form factors and robots that can be very intelligent. So I think the the kind of the two thoughts that we had from the beginning is that one is intelligence has always been the bottleneck for robotics. But rather than trying to start a robotics company that focuses on a specific robot, how can we tackle this problem head on and just focus on the intelligence? And then the second thought being that the way the way to solve intelligence is to take the lessons that we learn from vision and language and other fields and really take the foundation model approach, which includes things like cross embodiment learning, large diversity of data, a lot of real world data, and do the research necessary to figure out how how to build these models.
00:42:10 · Mario Gabriele
[00:42:10] S1: So you sort of land on this idea of, you know, the AI brain for these different robot types, put it sort of simply. How how did you start to think about the right way to gather the data? Because that's clearly such a big piece of it, and, obviously, there are different pieces... Different players that take different approaches there. So I imagine you must have had to reason through that in a million different ways to to land on the one you have.