00:13:24 · Josh Gruenstein, Martin Casado
[00:13:24] S0: I think this is one of the big questions, and I think really it ultimately comes down to what is the data and where are getting it from? And the magic of the internet, right, is if you are a company and you provide at the application layer tools for software engineers to accelerate their software development, you are naturally in flow of all of this really, really, really economically valuable work. Right? If you can make money as a business, then you're probably also making data as a business. Right? Those economic incentives are aligned. And similarly for robots, if you're like, okay, we're just gonna strap cameras on everybody's heads and send them throughout Indonesia to collect data doing x, y, and z, you you have no guarantee. Right? You you can guess, but there's no, you know, invisible hand on the market that's guiding you to ensure that that data is actually economically valuable. [00:14:12] S3: Right. [00:14:13] S0: But if it's actually this, like, kind of emission byproduct, if you have robots in real customer sites that customers are paying for, for those robots to do that task, you know for sure that that data is economically valuable. [00:14:27] S3: Doesn't it kind of depend on also the dimensionality of the solution set though? It could be the case that the economically valuable stuff is so high dimensional that you could solve 90% of it, but the last 10% you'll need a human being to do for a while just because getting that data set is just gonna require too many tutors over a period of time.
00:14:27 · Martin Casado, Josh Gruenstein, Raghu Raghuram
[00:14:27] S3: Doesn't it kind of depend on also the dimensionality of the solution set though? It could be the case that the economically valuable stuff is so high dimensional that you could solve 90% of it, but the last 10% you'll need a human being to do for a while just because getting that data set is just gonna require too many tutors over a period of time. [00:14:47] S0: And that might be fine. Right? You might be willing to accept that on the economics. That also might be a place where other forms of data matter. I think we're not purists. Again, we're willing to take any trade off. Right? We love using simulation to kind of widen these data distributions. We love using internet scale data to widen these distributions. So we have a lot of tools in our toolbox other than that real world robot data. [00:15:09] S1: And talk a little bit more about the simulations and these other tools and the impact models like Astra are having on that. Yeah. That seems to be another cartridge industry of excitement for the robotics [00:15:20] S0: Yeah. [00:15:20] S1: World, right? How are you using that?
00:15:20 · Raghu Raghuram, Josh Gruenstein
[00:15:20] S1: World, right? How are you using that? [00:15:24] S0: It's a sea change, right? I think we lived in this world for a while where everybody knew that all of this general knowledge about the world was baked into these models, but they couldn't really express it. Right? They didn't have that last mile of spatial reasoning to connect the dots from the stuff that, like, you know, a GPT model knows to something that an action diffusion transformer head can know. And that's changed. Right? The labs are now post training their models on three d spatial data like CAD and like three d modeling, which, by the way, they're doing because there's this direct economic incentive because there's a real market for it. So they can immediately monetize it, and it turns out that's the exact spatial reasoning that our models need. So I think we've been catapulted many years ahead in the future because we're starting to be able to use all of that general intelligence that lives in these big frontier models for robots. [00:16:20] S1: And because it's a systems problem, the net effect for you is accelerator Yeah. As opposed to slowing you down or saying somebody's gonna come and eat your lunch.
00:16:20 · Raghu Raghuram, Josh Gruenstein, Martin Casado
[00:16:20] S1: And because it's a systems problem, the net effect for you is accelerator Yeah. As opposed to slowing you down or saying somebody's gonna come and eat your lunch. [00:16:29] S0: Yeah. No. We... And we've seen a couple of these happen. Right? We started this business in 2021. We were doing this initially with convolutional neural networks. Right? So like, we've gone through every sea change, and the thing that is always valuable is the flow of data. Yeah. Right? And the flow of data is always the thing that uniquely you can do. And it's like, yeah, those like, the a million architectural tricks, do them if it helps you accelerate the flywheel. Right? If it helps you deploy more robots, collect more data, manufacture at a bigger scale, do more tasks, they're worth doing. But but ultimately, we think the thing that has kind of the long standing generational value in a business is the flywheel and loops themselves. [00:17:11] S3: Can can you talk a little bit about what you mean when you say you build robots? Yeah. I think on one end of the continuum, it's like Yes. You buy a unit tree. Yeah. Know? And you like prompt it. And the other end, you know, like whatever, you're like machining parts. Like, where are you on this? [00:17:24] S0: Yeah. So, for context, for everybody, in 2025, The United States deployed 38,000 industrial robot worksups, which is tiny compared to [00:17:35] S1: roughly Like, you're not counting the Fanucs and the old robots.
00:17:35 · Raghu Raghuram, Josh Gruenstein, Martin Casado
[00:17:35] S1: roughly Like, you're not counting the Fanucs and the old robots. [00:17:37] S0: The Fanuc, right. But that's Nobody deployed any unitaries in The United States into any... They would have shown up in those numbers, but they were probably zero. And so, the supply chain that exists for robots is both too small, and as you called out, wrong. Right? Like, the robots that Fanuc and ABB are building, they're mostly not building in United States, but but even when they are building them in United States, they're building these objects that are designed for systems integrators, that are designed for fast repetitive motions. They are not cut from the set of trade offs that you would want to co design with frontier models. And so we're kind of starting from scratch on the hardware side. So Tutor, in order to deploy robots at scale, has actually been forced to design and manufacture our own hardware. For us today, like, we're not building actuators. Right? So we're not building cameras. There's a whole layer of stuff that we're sourcing from OEMs where they're manufacturing at a much larger scale. But the actual robot itself of, you know, the thing that drives around and has the arm on it and can do all the tasks, we actually are building at scale in Watertown, Massachusetts. [00:18:43] S3: Can you talk about the morphology of the robot? Is that what... Is that called morphology? Anatomy? [00:18:47] S0: You you can use whatever robot What [00:18:49] S3: do they look like? [00:18:50] S1: What do [00:18:51] S0: they look like? We make two. So one robot we make
00:18:51 · Josh Gruenstein, Raghu Raghuram
[00:18:51] S0: they look like? We make two. So one robot we make [00:18:55] S1: Look at his website, Martin. Yeah. Go ahead. Yeah. Send it.