Dream Machines: Physical AI, Robotics, Being Human
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.
00:45:39 · Evan Helda, Charles Wong
[00:45:39] S1: Mhmm. [00:45:39] S2: And so that was how we sort of grew that that that share of the pie over time. You know, post, you know, physical AI cabin explosion, now you've got, you know, huge proliferation of, you know, humanoid robotics, you know, robotics and factories, manufacturing, logistics. Right? And now everybody's trying to ship product, you know. Now, know, early stage startups can add... Robotic startups can actually raise enough money where they can, you know, ostensibly build manufacturing capability and do it themselves. Right? And so now the incentives are so different. And so now, you know, it's a it's a good time, I'd say, for dev tools in a space like us. [00:46:18] S1: That's great.
00:46:18 · Evan Helda, Aravind Kandiah
[00:46:18] S1: That's great. [00:46:19] S0: The main thing to look at, like, is really what is the organization trying to optimize for? Right? It's like, are they trying to optimize for reliability and performance, or are they trying to optimize for, like, you know, hey. We wanna try a whole bunch of different architectures to figure out what is the most performant one and what's the right way. So, like, there's, the product companies and then there's, like, the research companies. Right? And we're seeing a lot more product companies come up now who are, like, chasing reliability with a sense of urgency. And they're like, okay. We want to win. Therefore, we need to move as fast as possible. Right? But research is more of that. Okay. There's a whole bunch of questions we need to answer. What's the different types of data types we can try? What's all these different things that we can do? Right? So there's really different... Within robotics, each customer and each type organization has, like, very different incentive structures. [00:47:11] S1: Yeah. It's a bit interesting exercise to go through each of them and map that out by by vertical stage of the business. That's a very useful thing for any salesperson, I think, to to do and think about as they as they go on an approach. So as you approach different companies who have different incentive structures, I'm I'm experiencing this in my day job. There's... There definitely is a not built here tendency. [00:47:35] S0: Yeah.
00:47:35 · Aravind Kandiah, Evan Helda, Charles Wong
[00:47:35] S0: Yeah. [00:47:37] S1: And a lot of them, they they they do hire great talent. They they... They're raising a ton of money. They can build it. A lot of them build it because things didn't exist. But now there's all these tools like you guys coming out to do. What... When you know you're competing against internal tooling that already exists, they've maybe already built, or you hear they've already built something, what are the differentiating moats from like a tooling perspective, infrastructure, UI, UX perspective, whatever it might be that you guys are touting to really help them get off their internal development effort. And and I guess this is another way to think about, like, I'm curious to get into the weeds a bit of the product and the tech and, like, how you guys are differentiating from a lot other companies out there doing this stuff. Like, I'm seeing pale domains doing it in AV. You have applied intuition, I think, does a lot of this stuff. Honestly, I'm seeing like world models, like the world labs are starting to come after this space. And so, yeah, tell me how you think about competing, guess, is another way to ask the question. [00:48:46] S2: Yeah. And Aravind's looking at... You know, just before Aravind gets into the weeds on the features and stuff, I think [00:48:51] S0: Yeah. [00:48:51] S2: We... Some of our biggest customers have internal simulation teams, like, that we routinely talk about and go up against in some in some fashion. Right? [00:49:03] S1: And
00:49:03 · Evan Helda, Charles Wong, Aravind Kandiah
[00:49:03] S1: And [00:49:05] S2: and really... And, you know, this is almost less of a robotic specific conversation or AI specific conversation, just like product specific... Product... More broadly product principles. Right? Mhmm. [00:49:16] S0: And I [00:49:16] S2: think one thing that we really try to hold ourselves to is how do we make their lives easier? Right? Not just the robotics developer, the user, but also, you know, the economic buyer, the procurement guy, the director of autonomous, all the way up the chain. Right? How do we make this a no brainer for them compared to whatever they're about to spend on or whatever whatever they're about to do? Yep. And so which is why, like, when we compare ourselves against some, you know, simulation tooling that's out there, we we... Like, one of our positions is that you don't need to hire, you know, 30 people in your sim team to run this and build this capability for you. What Bifrost, above all, purports is that you just need to provide one AI developer, one user, and you can start generating data within five minutes. Right? And just the ease of onboarding, the ease of use from a non three d, non SIM expert is usually more than enough to get our foot in the door. Right? Once your foot is in the door, then the game begins. Right? Because now you're being attacked from all sides. There's internal politics. There's, like, new new tools coming out, and that's where it gets kind of interesting, I guess. Sure. Aravind?