Dream Machines: Physical AI, Robotics, Being Human
00:39:23 · Evan Helda, Aravind Kandiah, Charles Wong
[00:39:23] S1: Got it. [00:39:23] S0: Right. So they... They'll come to us and they're like, oh, no syntax data, then our our price point is a little high. So they'd be like, oh, it's... You know, we don't need this now. We could just, like... We can use real data. And this is typically when they have they have a good enough large amount of data, but they haven't faced the problem of actually trying to deploy the robot. Right? So they haven't faced the visceral, it's failing in production, pains yet. Right? They can deploy in a one constrained environment, and it's, like, working reasonably well. Mhmm. [00:39:56] S2: The irony is that, you know, synthetic data is incredibly useful, useful, you know, all the way at the beginning of the AI development process. When you don't have enough data, you need to bootstrap, you need to get something out for your first demo, your first POC. But the problem is that, you know, from a customer's POV or user's POV, the... Unless they've put in the cycles they've actually tried to deploy something, they realize it doesn't work, they need a better data approach, that that feeling of like, synthetic data is the answer here, doesn't doesn't sit as viscerally with them. Right? Mhmm. Yeah. You know, the best customers that we've worked with actually have tried to build simulation in house. Right? [00:40:33] S1: They've I know the pain.
00:40:33 · Evan Helda, Charles Wong, Aravind Kandiah
[00:40:33] S1: They've I know the pain. [00:40:34] S2: They've experimented with the tools. They've tried to roll it out, and they realized that, oh, this this is not something my team can just whip up overnight. I think this is something either... Like, whole company has to focus on. Right? And then they come to us and they realize then that the price point is very is very... Makes a lot of sense. [00:40:53] S0: Yeah. There's this weird graph, that you have is, like, essentially, there's like a life cycle of these, like, physical AI, robotics teams. And the very, very start when they have almost, like, little to no data, synthetic data, they love it. They're like, oh my god. It's great. Bootstrap. And then there's, like, a... It moves slightly further along, and they have, like, significant amount of data, like real world data. Then they're like, oh, I have enough data. I don't need synthetic data. And then they accumulate more and more, and then they try to deploy the system. And it's like, oh my god. Okay. I need synthetic data. So it's like, at the very start and at the end, it's like, okay. I need synthetic. But in the middle, you think you don't need data because, ah, I have enough. I'm getting, like, reasonable performance. So let's keep going. [00:41:34] S1: I feel like you guys need to create some kind of meme. Yes. Some kind meme. [00:41:39] S2: The meme is real. So... Yeah. [00:41:41] S1: It's like a synthetic data epiphany meme. [00:41:43] S0: Exactly. And like, you know, if you just look at the world's top, like, systems that have deployed autonomy. Right?
00:41:43 · Aravind Kandiah, Evan Helda
[00:41:43] S0: Exactly. And like, you know, if you just look at the world's top, like, systems that have deployed autonomy. Right? [00:41:51] S1: Mhmm. [00:41:51] S0: It is Tesla. It is Waymo. They're actually out driving the cars. All of them have huge internal simulation teams. Yep. Right? Because there's no way you can do testing at scale at the speed of software. Right? You wanna roll out a model update, you need to test a whole bunch of stuff, but you can't expect to just, like, test it on the road itself. Right? [00:42:16] S1: I might have to go [00:42:17] S0: quick to take a hard lesson. [00:42:18] S1: After this. I'm writing a piece right now about the future of simulation and and, like, robot ops in general. I'll probably have to put it in there. And I'll I'll I'll give you guys credit for it. Don't worry. Okay. So let's turn to the strengths where you're just like a hot knife through butter and you're winning super easily. What what what vertical markets is that occurring in and for what use cases and what kind of autonomous systems today? [00:42:44] S0: Yeah. Yeah. Charles, can you take this?
00:42:44 · Aravind Kandiah, Charles Wong, Evan Helda
[00:42:44] S0: Yeah. Yeah. Charles, can you take this? [00:42:46] S2: Yeah. Sure. I I think, you know, we can kind of think of this in in two phases. Right? Pre and post physical AI Cambrian explosion. Right? I think, you know, pre... And pre 2025 even. Right? The... When, you know, physical AI was just a burgeoning term. It was just like, you know, that we hadn't really come into this world of humanoid robotics and, like, actual proliferation of VLAs and things like that. You know, in the world of robotics and autonomy, traditional warehouse robotics, robotic arms were definitely not interesting as a market. Right? It was too fragmented, too many different tasks, like, no consolidation, not enough to, you know, make a market. Right? [00:43:24] S1: Mhmm. [00:43:24] S2: And then you look at the broader autonomy space. You think about everything from drones, satellites, cars, boats, planes, and urban... Of course, the classic... The big boy in the room, urban autonomy. Right? The cruisers, way modes of the world. Right? [00:43:38] S1: Uh-huh.
00:43:38 · Evan Helda, Charles Wong
[00:43:38] S1: Uh-huh. [00:43:38] S2: Interesting. When we when we first... Because I came from the world of self driving. Right? We thought, you know, urban autonomy was gonna be our first customer. Right? That we would go hard and this would be the the space we grew. But I think when we we did our user interviews and we very quickly realized the incentive structures of the first gen of full stack self driving companies was wrong. They... Because when you think about those early companies, their prerogative was to build as much tech as possible, build as much IP as possible, have it in house, and then eventually get bought over by an OEM, and then that's how they vehicles. Right? No one at the time was thinking about actually manufacturing and shipping product. Right? And, you know, since you're an r and d play and not a product and revenue play, the way you procure technology is different. Right? We... Bifrost has a critical but ultimately point solution for a problem that you're probably facing. Right? And they're like, oh, simulation, you know, sounds really important and like a useful bit of IP. I think we'll we'll we'll build it in house. We don't
00:44:42 · Evan Helda, Charles Wong
[00:44:42] S1: want to All of them did. Right? Like a Gobbi, Torc. Yeah. They all kind of applied intuition. Well, they're a little bit different actually, but they're also proud of their simulation stack. It's part of their product, but it's also so orthogonal to what their real endgame is too. So it's interesting why they're so proud of it. It's like, great. You spent so much energy and time resources doing a thing that you... Isn't really the IP that matters. Totally. Especially over time. Yeah. [00:45:11] S2: Yeah. And so we had to... And, you from a business POV, we had to shift our our focus into other areas, like maritime, like aerial, and we realized that here, people were actually shipping product. Right? They were trying to ship drones. They're trying to ship autonomous vessels, and now there was urgency. Now there was a timeline. Right? And they didn't have, you know you know, the talent... Good talent is hard to find. They're not gonna spend that talent, you know, reinventing the wheel. They're gonna buy the best data stack, the best solutions stack they can find. Right? [00:45:39] S1: Mhmm.