00:05:10 · Raghu Raghuram, Martin Casado
[00:05:10] S1: Yeah. In total, we talk about robotics being the ultimate systems company Yeah. Yeah. As opposed to being one one of the other. [00:05:17] S3: But you know what's funny about this though, is the question I wanted to ask you is, I mean, you've got a pretty deep technical background. Right? I mean, MIT robotics. Yeah. Is that fair? But whenever I talk to you, I feel like I'm talking to an operations guy that talks about the... That talk about unit economics and operations and supply chain. I mean [00:05:33] S1: And even a go to market guy. [00:05:34] S3: Yeah. Go to market guy. So it's kind of... You've done this, the transformation is complete. And so maybe this is kind of an open ended question, but like I've always wondered, is the harder problem here actually that stuff?
00:05:45 · Josh Gruenstein
[00:05:45] S0: No. But I think you have to do all of it, is kind of a thing. And when I was just a roboticist, right, and only working on the technology side of this problem, I think my strength as a roboticist was that it was very full stack. And I was willing to think about the manufacturing, the hardware. I was willing to think about FPGAs and ASICs. If you view robotics in the same way that I do, that it's this ultimate systems problem, the only way you can make forward progress is by figuring out all of the clever trade offs across as many layers of the stack as you can. Yeah. And if you throw out one of the layers of the stack, and you're like, well, we're not gonna touch that, you've eliminated 90% of your trade off space to make progress. Interesting. And then, a business, it became really clear to me that, like, oh, okay, robotics is not actually full stack, like, model, hardware, software. It's full stack like model, hardware, software, supply chain, customers, product. Right? And similarly Who's his model? You ask the question, why hasn't robotics worked? Right? There's been a lot of robot startups. I would argue no robot startup has been like really massively economically successful. Why is that? And I think it's because one, you don't have this core primitive, which is intelligence, and that lets you kind of bring the unit economics of the system to a certain level. But two, it's that it's the super sort of disaggregated market. And you have infinite players, they're all trying to work with each other. And the reality of what we need in robots is to be able to make trade offs across the full stack.
00:07:09 · Martin Casado, Josh Gruenstein
[00:07:09] S3: So this is an observation that we have had, and it's interesting to articulate it this way, which is most robot companies start horizontal and they all go vertical. Every one of them, literally start like we have a model or whatever, and then they start buying three d printers and do you think that that is kind of because the market is so immature like you had to do in the early days of cars or like you think it's actually inherent in this domain? Like even in twenty years you will have verticalize just because the economy of movement or something requires that. [00:07:42] S0: No, I think it's a maturity in the market, and you can see similar things in like the early computing market. And so to us the question then becomes, okay, how do you build kind of the general platforms that let people build businesses that aren't robotics businesses? Right? We have all these incredible software businesses today, and the people who run them don't have to think about chip architectures. Right? How do we make that possible in the physical world with robotics, and how do go about building that platform? And I agree with you. People have tried to solve that problem by thinking about very narrow layers of the stack. And in our view, the reality is you need to build a full stack platform. And the only way to know that full stack platform really works is to actually have successful business units operating on top of it.
00:07:42 · Josh Gruenstein, Martin Casado
[00:07:42] S0: No, I think it's a maturity in the market, and you can see similar things in like the early computing market. And so to us the question then becomes, okay, how do you build kind of the general platforms that let people build businesses that aren't robotics businesses? Right? We have all these incredible software businesses today, and the people who run them don't have to think about chip architectures. Right? How do we make that possible in the physical world with robotics, and how do go about building that platform? And I agree with you. People have tried to solve that problem by thinking about very narrow layers of the stack. And in our view, the reality is you need to build a full stack platform. And the only way to know that full stack platform really works is to actually have successful business units operating on top of it. [00:08:19] S3: I mean, are you open to, like, it being the case that actually the physical world is different than the digital world and the reason we horizontalize compute is because you have clean abstractions that are generally applied and that's just not the case for, like, navigating physical space? [00:08:36] S0: Think the beauty of something like a Humoid is it's the same... It's that clean abstraction. Right? You think about what is a CPU? And it's this general computing machine. And it's not perfectly optimized for every use cases, right? But it's general enough that you can run most computing
00:08:36 · Josh Gruenstein, Martin Casado, Raghu Raghuram
[00:08:36] S0: Think the beauty of something like a Humoid is it's the same... It's that clean abstraction. Right? You think about what is a CPU? And it's this general computing machine. And it's not perfectly optimized for every use cases, right? But it's general enough that you can run most computing [00:08:50] S3: workloads Oh, so that's your horizontal component within the human. Exactly. For example, would you use the same brain or sub components to make a human as you would to make a drone or to make some other form factor? [00:09:03] S0: No. But you can make that same analogy in compute. Right? We have CPUs. We have GPUs. We have basics. Right? So I think you track the maturity of the market, and you start in this world of specialization, custom machines, because that's the only thing the market can support. You move towards general purpose, and in robotics, this is gonna look like humanoids and similar form factors. And then eventually, you kind of break past to that re specialization as the market is big enough. [00:09:25] S1: Why is this thing called Tutor Yeah. Intelligence?
00:09:29 · Josh Gruenstein
[00:09:29] S0: Tutor was founded kind of with this thesis that there is a whole Internet of human intelligence that is networked, That is posting on social media, that's writing emails, and that network of human intelligence for LLMs and other digital world models, by natural emission, has created this beautiful dataset that we can use to train general purpose models. The issue is, our work in the physical world is not similarly networked. Right? Like, I pick up my coffee cup and nobody knows. Right? Like, nobody recorded all of the pressures of my fingertips and where they were in the poses, and so we don't have this asset. And the core idea behind Tutor is what if instead of trying to find kind of this algorithm where from no data, we could build urban intelligence? And for us, we're like, yeah, that seems like a problem that's rigged against us. Right? That's not how this worked in computer vision. It's not how this worked in NLP. What if we could build this network of robots out in the real world? We could connect them with human tele operators, which we call tutors, that teach the robots how to do new tasks. And in the same way the Internet, kind of by a mission, created this dataset to train these frontier models, we can do the same with robots. And we can build this internet of robot training data. And there are all these beautiful emergent properties where suddenly it's not this massive capital project. It is to go build this network, but also you're creating incredible economic value as you're doing it. Right? You're making everybody's goods cheaper as you deploy these robots and collect this data. And so it's a very positive sum, I think, this very unique way. And so when we thought about, okay, what are we gonna name this business? You know, it's not strictly artificial intelligence, there's some artificial intelligence, but there's ultimately like this Tutor Intelligence that we're building that was connecting and controlling these robots.