00:01:15 · Steve Xie, Jonathan Stephens
[00:01:15] S0: Yeah. Thank you so much, Jonathan. You have been hosting this really amazing podcast, and, yeah, it's my great pleasure to be here. [00:01:22] S1: Yeah. So the, you know, the goal for us on this podcast, in general for The Thinking Machine is about getting just industry voices, people who have gone through the different experiences of building startups in, in physical AI, people who are doing research, all sorts of different aspects. [00:01:40] S0: Well, I'm not I'm trying to [00:01:41] S1: get different voices. And I think what's great about having you on this is we'll intro you here in a minute, but is that, you know, you're building a company in physical AI, and you have experience of, of working at not not in just startup, you've done it with OEMs and large, large companies. So you have this really broad experience, but I'm excited to have you on. You're not just a researcher, you're not just a company builder. So I just want to start with that then was your past you in the past you worked at, was it Cruise, NVIDIA? What were you doing at those companies that kind of led you towards starting Lightwheel?
00:02:19 · Steve Xie
[00:02:19] S0: Yeah. So I was at Cruise back in, 2018. So at that time, I was leading, the simulation department at Cruise. So, basically, Cruise is a alpha robotastic company, and the simulation is, extremely important for the company, to help them to, do the testing in simulation and also to generate the synthetic data, to help to train the, so so at that time, the conservation, the prediction algorithms. But when I was there, you know, it was quite early, and simulation was mainly like a toy. You know? It is great at showcasing to investors, but it wasn't that useful internally. So, you know, we face a lot of challenges and also in internal doubt. Right? And at the time, basically, I was mainly focused on two things there. The first one is being able to measure where we are with simulation, like how realistic we are with simulation, because I feel that's the most important thing. If we don't even know where we are, then how can we actually tell other people that they can trust us? Right? So that's the first thing I focus on. The second thing is when they knew where we were, then how can we improve it? We mainly improve it through, you know, AI, through generative AI at the time. And after, you know, we were able to do those two things there, you know, we were able to successfully showcase to internal teams that the synthetic data is truly useful, to help to improve the algorithm performance, to help to benchmark the algorithm performance, the two, key goals for simulation at the time. So that's basically my experience at Cruise because, you know, I was doing pretty good job at Cruise, and it's pretty known in this industry in Silicon Valley. Then NVIDIA, one one wanted to talk to me because at that time, they were looking for a simulation leader for their autonomous driving department too. Right? So that's how I got into NVIDIA. But I would say for NVIDIA, it is very different than Cruise. Right? Because Cruise, it is one alpha company. It's vertically integrating everything itself. For NVIDIA, it's probably one of the best provider in the world, right, building a platform for everyone. So, basically, I get to understand about the angle as a provider for OEMs, for for other large large OEMs of how we should be building simulation.
00:05:00 · Steve Xie, Jonathan Stephens
And, also, at the time, you know, I was pretty lucky that I got to talk to Ralph, who who was the VP of Omniverse at the time. And he showcased me of the the vision of Omniverse, which is, you know, the simulation platform of everything physical from, you know, robotics to self driving to industrial simulation. And and that's where I I... So I start to see that the simulation is not just a tool or a time machine for self driving, but it could be a platform, a next generation platform, a next generation infrastructure for this whole physical AI industry. So that got me... And there's some more simulation and more passionately about building my own company of simulation. [00:05:49] S1: So so what did you see then at NVIDIA? You know, you're working there, you're, you're, you're building or you're working on the simulation, you're building that platform for them. Then why would you want to leave? You know, so a question I always get, why would someone who is in leadership position at a large, you know, one of these big companies that are making things happen. Why... What what did you see in that market that said, you know what? I need to I need to do my own thing.
00:06:16 · Steve Xie
[00:06:16] S0: Totally. So, I mean, NVIDIA still is, in my opinion, the best place to work. It's really, really amazing place. The culture is amazing. The people there is amazing. They are really working on the hardcore technology stuff. You know? It's a really, really great place to work. But for me, I also have my own goal. You know? I always dreamed to be an entrepreneur. When I was a young kid, probably five years, I I I started dreaming, aiming that myself to be an entrepreneur someday. Right? It is just my family culture that I wanted to be an entrepreneur. And the more excited I was at NVIDIA, the more great work that I did at NVIDIA, the more that I felt I was becoming more and more ready to start my own thing. The other thing there is I I I really see that in order for this big... Bigger vision that NVIDIA is pulling off, you know, the three computers, especially, you know, the simulation computer for physical AI, NVIDIA also needs partners. Right? And I could be a external partner, probably making much more contribution than as an internal employee. Right? So from those considerations, you know, I decided to chase my dream to find my own company. You know, there's there's a DNA of an entrepreneur. You you can't you can't pull them
00:07:42 · Jonathan Stephens
[00:07:42] S1: out of it. I I'm not an entrepreneur myself. I mean, everyone is a little bit in their chair if they want to be, But I get that I used to work at a big company as well. And I didn't like how fast they moved. NVIDIA does move pretty fast on things, but just I wanted to work in cutting edge and what's more cutting edge in startups cause they're on the bleeding edge and it's got to work or it doesn't, right? So you know that DNA you can't get away from that makes sense. Like why would you want to leave home? You know, that's part of you. I admire that. So then you, you founded Lightwheel and what one thing is Lightwheel builds simulation infrastructure. Right? That's that's what we do. That's what you do. I guess I get the question then a lot of then isn't NVIDIA's Omniverse simulation infrastructure? What's the difference between what... You know, how's that different?
00:08:31 · Steve Xie
[00:08:31] S0: Yeah. Totally. So I I would say, yeah, both of us are building the infrastructure. So I really think that's, you know, simulation is such a big a big problem to solve, but also a big opportunity. And, everyone should be building the simulation infrastructure. And by simulation infrastructure, I mean, you know, the physics solver. So so I mean the SimReady Assets. I also mean the framework, and also the applications to generate successful simulation. Right? And by successful simulation, I I I I don't mean, the typical graphics, pretty much like the gaming simulation rather than the physics. Right? Because, so first, we need to understand about the mission of, simulation for physical AI. The mission is not to, light people to enjoy another game, right, to to, to to enjoy the game in virtual world, but to help accelerate, deploy robotics in the physical world. So physical realism, physical accuracy, physical quality is a key of simulation for physical AI. Right? And that's actually where, you know, we are putting the most of the work. And so I would say that, you know, for Lightwheel, you know, we are focusing on... In order to provide a cycle simulation, which provides the most high quality physics. Right? And in order to provide the... By simulation for physics, then we need a few things there. First, we will need to have a really, really awesome physics server. A really, really awesome physics server. Right? The physics server needs to be real... Like, not only realistic, but also highly efficient to run on GPUs because we need to run them on parallel, right, at a very, very large scale. And, also, the software needs to be connected with a solar ecosystem. Right? It couldn't be a a a... Like like, one independent solver, for example, maybe a rigid body solver, and that's it. No. It it it needs to be connected to every other solver together as a ecosystem. Right? So that's how we collaborate with Newton of NVIDIA. Right? Because Newton is really a great... The next generation GPU accelerated physics solar ecosystem. Right? And by doing that, we not only build SimReady Assets on top of it, we not only calibrate the server, but also we are building the server ourselves on top of a new thing. Right? Together building this open ecosystem. So I think the openness of the ecosystem is really, really important. Right?