Physical AI that Moves the World — Qasar Younis & Peter Ludwig, Applied Intuition

“The what's different about us is we're deploying intelligence onto a lot of things that don't have screens. Most of the value we provide is putting intelligence that is in safety critical environments. So that the those two words are really important because learned systems can make mistakes if you're asking for something like tell me about these podcast hosts, but you can't do that when we run driverless trucks in Japan right now. We can't have errors.”

“In the physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about is actually deploying them in the hardware to give you. And so then there's just a reality is of safety critical systems. So those end up being the your limiting factors rather than, let's say, a limiting factor for a foundation model company which is going to be just capital.”

“Physical machines today are more akin to the state of the phone market before Android and iOS existed. Part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones. At the time they had 50 different operating systems. It was virtually impossible for Google to make their app run on all 50 devices equally well. The state of the physical industry right now is a little bit like that.”

“In more traditional development, right, you you oftentimes would have, more black and white answers to questions. But what's changed now is with these models, everything is statistics. Right? Like, you no longer have a black and white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can can I get in the system, and how can I how can I prove that to be true?”

“To do reinforcement learning on an end to end model, you now need to actually simulate all the sensor data. This becomes we we call our our work in this neural simulation, but it's think of it like a hybrid of Gaussian splatting and diffusion methods where you really care about performance. Performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile in the end.”

“This is truly compounding technology. A lot of the work that we do just compounds it. We don't throw it away. It gets better. The operating system work gets better. The dev tooling gets better. The models get better. And so we're really gonna get a I I think you see it in Waymo as an example. If you can put a little constraint on commercials that has a small ability for you to more likely see the other end of that walk.”

“The interviews that we give now, I think, are way harder than they've ever been, but but we also allow, right, selective use of AI tools to solve the problems. And I think in that, you you start to see more of a bimodal distribution of engineers. Right? You you start to see, like, wow. There's there's this subset of of people that they they really get it. They're all in, and they've clearly invested the hours needed to learn these tools.”