00:37:41 · Josh Gruenstein, Raghu Raghuram
[00:37:41] S0: No, it's strategic. Right? These businesses, I think, are faced with a really hard choice, which is they know the age of their workforce. They know that demand is ramping up due to all these secular trends, and the cost basis of fulfilling that work is going up. And they're like, how does our business pencil out? Because, you know, these are not software businesses that have these, you know, huge gross margins that you can you can play with and can compress a little bit, and it's like, it's fine. It's like, no. The business either works or it doesn't. And so, I think our customers are ultimately looking for a long term partner that can do the robots. Right? If one of our customers is manufacturing deodorant, they're doing the deodorant. Right? And they're gonna be operational experts in deodorants, or operational experts in crayons, or whatever the thing they manufacture is, and they need a partner who's the expert on robots. [00:38:34] S1: I had a question on the nature of factories, I mean, you can ship a robot to anybody, anytime, not today because of your shortages, but at some point in the future, right? And if your robots become more and more general purpose over time, what does it mean for where factories are located and how people organize around these factories, and you do these one town factories, one one factory town, whatever the expression is. Yeah. A lot of that fundamentally changes now, right?
00:39:05 · Josh Gruenstein, Martin Casado
[00:39:05] S0: Yeah. I think a lot changes, right? There's a lot of geopolitics in this. I think one big thing is this is going to be a great equalizer between The United States and other Yep. Excuse me. Other countries, like China, invested in the previous regime of industrial automation. And in some ways, made them very successful, and in other ways, I think, in hindsight, that might end up being an enormous mistake. Because in The United States, we just discussed, we're doing this greenfield builds of automating our industrial capacity using generally capable robots. In China, they're gonna have to tear down. We're not going to have to do that in The United States in the same way. I think another thing that's going to shift, which is going to be really interesting, is we're just going to have way more factories in the same way that we have way more businesses, because the cloud and all of the software tooling let people build new businesses. Anybody will be able to start a business in the physical world. Anybody will be able to scale a business in the physical world if we kind of have this infinite flexible labor substrate. [00:40:02] S3: You brought up China. We actually published a report on the supply chain in robotics, and it seemed to be that China was so central to it. So can you talk a little bit about your dependency on it, and then how that evolves over time, and how much of it there is there risk in China to the supply chain?
00:40:02 · Martin Casado, Josh Gruenstein
[00:40:02] S3: You brought up China. We actually published a report on the supply chain in robotics, and it seemed to be that China was so central to it. So can you talk a little bit about your dependency on it, and then how that evolves over time, and how much of it there is there risk in China to the supply chain? [00:40:22] S0: Yeah. I think ultimately, no matter what anybody says, today in 2026, we have a highly interconnected, interdependent global supply chain, and this is one of many examples of this. And I think both US companies and Chinese companies are thinking very hard about what does the long term strategy of this look like, and it's Chinese companies investing in The United States, it's United States companies investing in China, and ultimately everybody trying to build their industrial capacity. [00:40:49] S3: Yeah. I've I've noticed that, you know, you've gotten, as far as I can tell, you've verticalized more. Is that right? Like, you're taking more in house? And do you continue... Or do you plan to continue to do so?
00:40:49 · Martin Casado, Josh Gruenstein, Raghu Raghuram
[00:40:49] S3: Yeah. I've I've noticed that, you know, you've gotten, as far as I can tell, you've verticalized more. Is that right? Like, you're taking more in house? And do you continue... Or do you plan to continue to do so? [00:41:01] S0: Yeah. Well, there's this tough thing in supply chain where... And again, because robotics is such a cottage industry, is you might start in a world where subcomponents are coming from OEMs, and then you grow your volume to the point of they're not OEMs anymore. They're they're glorified contract manufacturers. Right? Once you're doing a material percentage of their volume, and at that point, just the relationship and the dynamics change. Right? It's like, that the right command for that thing? Do I wanna be using a command, or do I wanna be manufacturing it in house? [00:41:27] S3: Yeah. I mean, Tutor must be one of the largest deployers of robots in The United States. You know, as a result, you must be, like, one of the largest customers for some of these OEMs. You know, as a result, I would imagine over time, like, you just continue to, like, drive, you know, these components yourself. [00:41:43] S0: I think that's right. Does that [00:41:44] S3: mean taking it in house or just telling them what to do? I mean, it just... Like, are they still suppliers? [00:41:48] S1: Some combination, I guess.
00:41:48 · Raghu Raghuram, Josh Gruenstein
[00:41:48] S1: Some combination, I guess. [00:41:49] S0: Some combination. I think, ultimately, for Tutor to be successful, we will need a mature robotic supply chain in The United States. Yeah. And we're in this position where, because we are so much of the demand for that supply chain, I think it's both our obligation and our opportunity to invest very, very, very heavily to building this capacity in The United States. That's why we build robots in Watertown, Massachusetts. It's why we expect to continue to verticalize and build more and more of our robot componentry in The United States. [00:42:18] S1: Again, going back to when we first started talking, I don't remember, was probably the second meeting when we actually started about the research, right? Most of the meeting because we had such a great business. But that's a big strength of yours, right? And talk about how you approached it, and there's all sorts of theories about which robotic learning models will win and so on and so forth. How are you approaching the learning bet Yeah. In research?
00:42:44 · Josh Gruenstein, Martin Casado
[00:42:44] S0: I think we're thinking about how this played out in LLMs, and what's similar and what's different. And I think one thing that clearly has been very, very important in LLMs is there are lots of heterogeneous sources of data, but ultimately there's a top of the data period, which is real world reinforcement learning traces doing the thing you want to be able to do. And so, we built our research program in such a way where we are thinking about how do we get the most out of robot experience out in the real world. And so, we built this 100 robot fleet called Data Factory One in Boston. This is, to our knowledge, the largest sort of semi humanoid data collection farm in The United States. And we're able to use this as kind of this research test bed of how do we do real world reinforcement learning at scale with robots. Know, reinforcement learning is a technique family where it doesn't really work at small batch sizes. And so if you have a very large fleet out in the real world, you can start to play with these methods, which I think people haven't been able to play with either in academia or in industry. [00:43:47] S3: So I I I presume, like, building a team for this is quite different, right, than, like, you know, labs. So maybe talk about kind of, like, the construction of the team, and maybe if there's anybody you're looking to to hire? [00:43:56] S0: We're looking to hire everybody. Everybody. So we're looking to hire researchers and engineers, and that's across every single layer of the stack. [00:44:02] S3: Wow.