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.
00:44:02 · Martin Casado, Josh Gruenstein
[00:44:02] S3: Wow. [00:44:03] S0: We are also looking to hire technicians to build robots. [00:44:08] S3: By the way, every layer of the stack. You mean, like, software? [00:44:10] S0: Hardware, materials, electronics. There's nowhere that you can abdicate responsibility. It's robotic. You're gonna engage with it whether you want to or not. The question is whether you understand it. So so we're we're building that team, and I think that's a really unique shape of team, not only because it's so interdisciplinary and collaborative across all those layers of the technical stack Yeah. But because it's... I think it's one of the only places in the universe where you can be on a whiteboard talking about model architecture in the morning, in the afternoon, like, you know, looking at a factory line and seeing how a robot is doing. [00:44:43] S3: And Is this primarily in Massachusetts? [00:44:45] S0: Or So we have a team across The United States. Okay. We have especially folks on the go to market and technicians who are kind of engaging. You know, our customers are everywhere across The United States. That's fair. Alright. Awesome, Dan. Thank you.
00:44:45 · Josh Gruenstein, Erik Torenberg
[00:44:45] S0: Or So we have a team across The United States. Okay. We have especially folks on the go to market and technicians who are kind of engaging. You know, our customers are everywhere across The United States. That's fair. Alright. Awesome, Dan. Thank you. [00:44:58] S2: Thanks for listening to this episode of the a 16 z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or a review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on x at a sixteen z, and subscribe to our Substack at a sixteen z dot Substack dot com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a sixteen z fund. Please note that a sixteen z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a 16z.com forward slash disclosures.