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.
00:42:35 · Karol Hausman
[00:42:35] S0: I think we're still reasoning through it. I don't think we have all the answers yet. I think the important piece about how how to think about physical intelligence is we are not very dogmatic. It's not that we sit down and think very, very hard and then come up with the solution and this is our bet. I think the the better way to think about it is that we are really truth seeking, and we run experiments, and we don't know the solution. And and we know that we don't know the solution, so we want to follow the scientific method and really try to find the truth and really try to find what works and what doesn't. Because I think there is a true answer out there. We just need to be very humble in finding it. So that's, I think, how we arrive at the current set of answers. We run a lot of experiments. We try many different ideas, and we see which ones stick, and then we we double down on them. Now in terms of... Based on what we've seen, based on the evidence we've we've seen, how we think it's going to work or how I think it's going to work. These models will need to be able to absorb very diverse datasets, where it's less about picking the right data or picking the right way of collecting data, more about building the engine that allows you to absorb all kinds of data, whether it's videos of people, whether it's teleoperation data from robots, data from handheld devices, video data, really, or simulation data, really anything. And the more data they can absorb, the better the better the model will be. And I think we're kind of at this stage of of robot learning where we kind of try to throw anything we can at these models, build them in a way that they can absorb as much of it as possible, and get them to the threshold of being able... Being deployable, where you can actually deploy them in the in the world and have have robots out there collecting data for real doing economically valuable tasks. And I think once you're at that stage, once you're at that threshold of they can actually work and do valuable valuable tasks, That's when you enter this next stage of now deploying robots at scale, and deploying it in multiple verticals in diverse environments and actually delivering value. And I think that second stage is actually going to be the stage where we get most data from.