00:04:03 · Josh Gruenstein, Raghu Raghuram
[00:04:03] S0: It came from kind of this thought experiment, where we had this insight that, hey, there are 8,000,000,000 people on Earth who all have this baseline physical intelligence that we want our robots to have. And so imagine you had infinitely powerful software that could connect any human being on Earth to any robot and kind of embody that robot with intelligence. That's kind of like this deus ex machina of suddenly you have unlimited, infinite robot intelligence. Let's say you take that for granted, now what? Right? The problem isn't actually solved. Right? You still need to actually manufacture robots at scale. Right? Robots are this cottage industry today in The United States. Robots are rounding error even in the most automated industries, like manufacturing logistics. So that doesn't exist. Certainly none of the software exists, and none of the customers exist because, again, the robots can't do any of the jobs that customers want robots to do. So robotics is not, I think, any one layer of the stack. It's not just about intelligence. It's not just about hardware. It's not just about the customer. It's about kind of figuring out how to put all these three pieces together, and it's the combination of all three that actually lets you build, in my view, a successful platform at any one of those layers. [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.
00:05:10 · 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?