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?
00:11:17 · Steve Xie, Jonathan Stephens
So so so back to your point, you know, NVIDIA is building simulation infrastructure as we are too. Right? And all of us need to work together in order to make simulation successful. So then I will say this is the part of the sim... The the fixes over, which is extremely important. The second thing is actually the SimReady Assets. And I think to a lot of people's misconception, especially if the people is coming from gaming industry, coming from self driving industry, they may not put that much attention of SimReady Assets because they can feel... It's just typical technical artist workflow. Right? And I I I I even get people asking me, hey. I can buy this asset, $9.9 on websites. Right? And I was like, no. It's totally different. Right? The $9.9 is just a virtually realistic asset, but what we are looking for is a physically accurate asset. For example, a fridge. Right? You need to have, you know, the magnetic force of this fridge. You need to have all the hinges, the joints. Right, in order to get it, accurate, to connect with the physics solver, in order to generate the most accurate forces. Right? Mhmm. So the SimReady Asset is extremely important because you need to calibrate all those physical parameters, the forces. But, also, you need to, you need to optimize it to make it highly efficient to run on GPU physics hours. So that's the SimReady Asset, which, you know, Lightwheel specializes in. Then there is another thing. [00:12:59] S1: Is that where we started, by the way? Is that where is that where Lightwheel started with the assets?
00:13:04 · Steve Xie
[00:13:04] S0: That that... That's that's, exactly where we started because, you know, we solve a problem that no one else wanted to solve. They they they feel that they are looking for other people to solve, but they are not solving themselves. But the more I look into it at the time, the more I found out it is truly intriguing. It is truly difficult. It is truly difficult. The more I work on it, the the more I look into it, the more I found out it is extremely difficult to build. This high quality SimReady Assets at scale. Because, basically, from my part, I love the problems, which is, I would say, like, we we we... Which is both much needed, but also extremely challenging. I... So so I love to solve those high impact problems. So that's how we jump into why and also how we jump into SimReady Assets. Yeah. But I would say that, you know, that's just just, like, a very superficial level of this physical AI simulation problem. Right? The more we work on SimReady Asset, the more we found out... Actually, there's also another core problem need to be solved in order to build the high quality physical accurate simulation, which is the physical measurement. Right? So why is that? So a lot of people may think, hey. You are building simulation, so everything you are doing is in computer. Right? So I'm I'm just looking at your computer screen so I can find out all of your product, but that's actually not it. We are operating probably one of the world's largest, robotics arm factory. Right? With with hundreds of arms of doing automated data collection, manipulation with all the real objects. For example, the fridges, you know, the microwave, the oven, the dishwasher, the table, you know, all the different type of tools, things like that, in a factory. Right? It's a it's a real factory. Why do we need that? The major is factory, we're calling that. Right? Oh, yeah. We we are are calling it the physical environment factory. We are calling it the physical environment factory. I gotta get that right out. Yeah. Oh, sorry to cut you off. No worries. No worries. So actually, we are showcasing a minimum factory in GTC next week, right, which I'm super excited of. But basically, the, the core concept is the more we work on SimReady Asset, the more we found out, hey. We need to scale the data collection of the real world, especially the real assets.