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.