The Breakthrough For Home Robots with Kyle Vogt, CEO of the Bot Company | Ep. 32

Jack Altman 0:00 Do you think it's like cooking a steak at some point?

Kyle Vogt 0:02 Yeah, why not? If you think about, at the end of the day, you've, you have pick-

Jack Altman 0:05 Mm

Kyle Vogt 0:05 -and-place and simple manipulation, that's what cooking is. They're just like a much higher degree of reliability, and there's other things around food safety and bacteria, and other things that come in cookie, uh, cooking and temperature sensing and what like that. So it's all doable. It's just like, are we not there-

Jack Altman 0:17 Do you think at some point it's like, "Hey, robot, I'm at work right now. There's a steak in the fridge. Please cook it and clean up everything," by the time of, like, fifteen years from now, that's, that's doable?

Kyle Vogt 0:26 Less than five.

Jack Altman 0:27 Less than five?

Kyle Vogt 0:28 Yeah.

Jack Altman 0:29 [upbeat music] All right, I'm really pumped to be here with Kyle Vogt. Kyle, thanks a ton for making time for this.

Kyle Vogt 0:33 Thanks for having me.

Jack Altman 0:34 So I wanna start with talking about, like, why robotics seems to be having such a moment. You know, it's obviously been really important for a long time, but in the last few years, it seems like a lot of really good entrepreneurs, a lot of good investors, have started to pour a bunch of time, money, resources, and effort into this. And I guess I'm curious just to start with sort of laying a foundation of, like, can you put this in some context, and, like, what used to be the case and what has changed that's, like, making people so energized right now?

Kyle Vogt 0:58 Yeah, it is. It's, like, the most excited I've ever seen people in robotics. And, you know, I, I guess as an engineer, there's something, like, romantic about building machines to do the stuff that we don't wanna do, and that's, that's why I've been doing this for so long. First with, you know, a decade on self-driving cars, but for me, even going back to, like, teenage years doing BattleBots and then going to MIT to basically build more robots. But, you know, during that entire spectrum, it's also-- it's always been this niche thing, and frankly, like, robots have never really lived up to their promise. There's always something... They're always overly fragile. Like, in a factory environment, we put them in cages-

Jack Altman 1:30 Yeah

Kyle Vogt 1:30 ... and if things don't line up, like within a millimeter, the whole thing doesn't work.

Jack Altman 1:33 Except the BattleBots. Those were good, actually.

Kyle Vogt 1:35 [chuckles]

Jack Altman 1:35 Now that I'm thinking back-

Kyle Vogt 1:36 BattleBots were, yeah-

Jack Altman 1:37 You made them with, like, the saws and everything?

Kyle Vogt 1:39 Uh, ours had, like, a hydraulic axe, which was, which was pretty cool. But the-- calling these robots is a bit of a stretch. They're basically glorified RC cars.

Jack Altman 1:45 Yeah.

Kyle Vogt 1:45 Right? Um-

Jack Altman 1:46 That's right, with a weapon.

Kyle Vogt 1:47 Yeah, so, so

Kyle Vogt 1:48 [chuckles] with a weapon. What's different now is, you know, for the first time, you have robots that are powered by... Essentially, they have all the brains of an LLM built into this robot, and we're controlling them with neural networks instead of classically engineered algorithms. And so the difference was, before, if you have a robot that's, like, in a room like this, uh, even saying, like, "Go to the whiteboard," is almost, like, an impossibly hard computer science problem. It's like, okay, I have to build an exact three-D map of the world, like, have a detector that can figure out what a whiteboard is, train it on millions of examples of what whiteboards look like, just to be able to do this, and even then, the failure rate would be high if you put it in a different room and it doesn't have a map. But now it's almost like cheating. You can take all the-

Jack Altman 2:29 Yeah

Kyle Vogt 2:29 ... the common sense that's on the internet and inject it into a robot brain, and so if you're like: "Where's the whiteboard?" It knows-

Jack Altman 2:34 Yeah, I mean-

Kyle Vogt 2:34 ... instantly.

Jack Altman 2:34 You, like, open the ChatGPT. Even if you open, like, video, you can, like, show it anything, and it, like, knows what it is.

Kyle Vogt 2:40 Yeah. And so imagine, like, robots before started with zero knowledge of the world and now, like, suddenly have this kind of knowledge of the world.

Jack Altman 2:45 Like, better than us. Like, they can look around the room and see stuff better than we can.

Kyle Vogt 2:48 Yeah, and then on the motion side, uh, you used to have to have a PhD to compute these complex, um, trajectories. You have, like, twelve joints on a motor or on a robot. How do you get all twelve joints to move in tight coordination to, like, move an arm to a place? And this is a very difficult and computationally intensive problem. And now we just kinda jump over that whole thing, and now if you have a way to, to teleoperate a robot or to put it in a simulation, it can just learn how to move all those joints to it-- mimic the human operator or to accomplish, you know, some reward function or to maximize it. Um, and so you can skip that whole computational challenge. And so those two things together basically mean that everything we thought we knew about robotics or, like, what kind of businesses were good at businesses or bad businesses, all, like, that slate has wiped, been wiped clean.

Jack Altman 3:30 Yeah.

Kyle Vogt 3:30 And

Kyle Vogt 3:31 so I think you're gonna see this Cambrian explosion of different robots for different applications that now suddenly just work, whereas before, they would, like, really struggle to do the most basic things.

Jack Altman 3:39 And when you say for different applications, is that-- Are you saying it won't be terribly generalized? Will it be medium generalized? Like, what made you say for different applications or, like, different environments, maybe?

Kyle Vogt 3:49 Yeah. I mean, classically, like, a lot of robot businesses, like, try to re-- get really, really narrow, the successful ones. We're gonna focus on this one problem, like factory au-au-automation for 3PLs who are putting things in box, boxes and putting them on a conveyor belt, like, very specific, and that's just so you can narrow the problem enough to be good at it. Now, I think you're gonna see people broaden their horizons a little bit, um, because it's much, much easier to go from, you know, a piece of dumb hardware to something that's performing a useful task. You know, I say multiple applications, too, 'cause it's my view that there's gonna be a whole bunch of different shapes and sizes of robots, each optimized for different types of work.

Jack Altman 4:21 Yep.

Kyle Vogt 4:22 You know, as opposed to maybe, like, a humanoid robot, which, you know, is very, very expensive, but in theory, could do everything. I think we're probably gonna see some of those, but the vast majority of robots will be more special-purpose in nature.

Jack Altman 4:32 I feel like there was a moment in AI where the researchers who were sort of closest to the work were, like, very sure it was gonna work before the rest of the world sort of knew. Is there an equivalent thing in robotic? Like, have people crossed a similar threshold to, like, whatever that was at, like, the pre-ChatGPT moment in robotics, where, like, some people who are at the very front, have been working on it for decades, are like: "This is definitely happening now?"

Kyle Vogt 4:56 Yeah, if you had, like, uh, secret microphones in, like, robotics labs across the country right now, you'd just be hearing, "Holy shit! Holy shit! Holy shit!" It's, like, constantly happening, and I think finally, like, the light bulb moments are happening, and it all... Like, in the early days of this stuff, it all looks very rudimentary and, uh, kinda simple, but, uh, if you know what you're looking at, you see the signs of life that mean over the next three to five, ten, you know, even less, uh, years of devel-development, this will go from an interesting technology in a research organization to, like, you know, broad mainstream appeal. And, uh, yeah, those, those signs of life are happening. Those light bulb moments are happening all over the place

Kyle Vogt 5:32 right now.

Jack Altman 5:32 So what are the components? Like, there's obviously, you know, we talked about, like, there's, like, vision. There's, like, the ability for the robot to do, like, manipulation the right way, for it to have the right sort of dexterity. There's gotta be something around, like, reliability. I don't know about, like, decision-making, if that's its own sort of... Like, what are the components basically to, like, this up level?

Kyle Vogt 5:51 ... Yeah, you've touched on a, a bunch of good ones. Um, depends on the type of robot, but the ones we're building, like robots that operate in your home, they need to navigate through a home, they need to remember wh- where things are in the home, uh, they need to, uh, interact with and manipulate these objects, like you said, and, um, probably have some way of incorporating your user preferences into all of this. And so you've got a reasoning component. Like, I see a certain thing in a home, plus I know you know the preferences you've told me in the past about how you like things organized or, or how you run things in your home, and then I'm gonna take that and reason about what the next steps I should take as a robot. And then once you have those next steps you're gonna take, you know, drive to the oven, put the towel on it, then go over here. Once you have those discrete steps, then you can move to more one of these, um, you know, end-to-end models that basically, given a simple task, can go execute it.

Jack Altman 6:35 My, uh, implicit assumption here is that on some time scale, you're, like, extremely confident this will all work. But, like, what are you unsure about in the next, let's say, like five to ten years? Or, like, what will drag?

Kyle Vogt 6:45 Mm. You know, one of the, ah, biggest challenges for something like this, it's a brand-new product, is like, how do I use it? Like, how does my life change, and how do I adapt the way I live to best, you know, make use of a robot like this? And that could be, you know, in a home environment, or maybe it's like a manufacturing business that, you know, its entire workflow is, is organized around people standing in work cells, doing a task and handing things on the conveyor belt. Like, how does all this change? And so I think that the technology part will come pretty fast. Ah, and, and I'm pretty confident in that. The part that I think traditionally takes longer is the world has to adapt-

Jack Altman 7:15 Yeah

Kyle Vogt 7:15 ... to basically, now that this new thing exists-

Jack Altman 7:17 Yeah

Kyle Vogt 7:18 - how does everything about how I run my business or how I live in my home, or how do I operate my hotel or whatever it is, need to change-

Jack Altman 7:24 Yeah

Kyle Vogt 7:24 - or it should change-

Jack Altman 7:25 Yeah

Kyle Vogt 7:25 - to best make use of this new thing?

Jack Altman 7:26 It's like this is, like, where, like, AI software is, where, like, it's obviously much better than, like, what's currently being deployed and used.

Kyle Vogt 7:32 Yeah.

Jack Altman 7:32 It, like, takes time to get from, like, the tech is good to now it's, like, implemented everywhere. So you're basically saying it's like the robots will be good enough at some point soon, but then figuring out how to use them in daily life and, like, where does it actually fit into, like, a life workflow, that kind of thing.

Kyle Vogt 7:47 Yeah, and I think the companies building these technologies have a responsibility to help us figure that out.

Jack Altman 7:52 Yeah.

Kyle Vogt 7:52 You know, they're closest to the technology, and I think they need to think not just about, like, what, what is, what is the technology building do-

Jack Altman 7:57 Yeah

Kyle Vogt 7:57 - or, you know, what's the fancy new thing I built in there? But like, h- you know, three steps removed from that, how, how do businesses actually make use of this? And like, you know, what do they need to know about it, and, like, what things do you need to build in so that it's as easy as possible to sort of go on this adoption curve and make it happen?

Jack Altman 8:10 Are you more, ah, in a mindset of like, we are Apple, and we're gonna, like, we'll tell you the product kind of, and like, this is how it's gonna work, and this is what the robot will be, or is it more of like the YC, like, let's just get it into some homes and iterate type-- Like, which mindset do you think you feel closer to?

Kyle Vogt 8:27 Well, this is a frustrating answer, but a little bit of both.

Jack Altman 8:29 Okay.

Kyle Vogt 8:29 It's one of those things, like strong opinions weekly held. So I think you have to have an opinion. You have to have your taste and your preferences built into the design of a product, or it feels bland.

Jack Altman 8:39 Mm-hmm.

Kyle Vogt 8:39 Like, a product with no opinions is just like, you know, you wouldn't even notice it. So I think you have to start off with strong opinions and then be willing to put those in people's hands and then quickly abandon them if it's not, you know, if it doesn't work the way you want to. I think if you're not stubborn enough, you end up with a just product no one is interested in, and if you're too stubborn, then you end up with a flop in the market once it's out there. And so, you know, uh, I think it's, like, a careful balance.

Jack Altman 8:59 Why

Jack Altman 9:00 did you feel compelled to go for the home? Like, you obviously, e- even with this generalized robot sort of idea, there's a lot of things that you could do that aren't just like pack a box in a warehouse type of thing that's sort of, like, more dynamic than that. But, like, you picked home for some reason.

Kyle Vogt 9:14 Yeah, for some reason.

Jack Altman 9:15 Mm.

Kyle Vogt 9:15 Um, so I just turned forty. This is my third, you know, company that I'm working on, uh-

Jack Altman 9:19 Heard of the last two.

Kyle Vogt 9:20 Big one.

Jack Altman 9:20 Yeah.

Kyle Vogt 9:20 Yeah.

Jack Altman 9:21 Yeah.

Kyle Vogt 9:21 But I, I bring this up because, like, at, at this point in my career, I kinda know how I wanna spend my time and, like, what's important to me. And, ah, first of all, I wanna have a lot of fun and working on home robots that I can use, all my friends can use, like, couldn't think of anything more interesting than that, uh, or more fun, especially compared to, like, robots that are hidden in a factory that no one would ever see.

Jack Altman 9:38 Totally.

Kyle Vogt 9:39 I also think that, you know, one of the great promises of working on really cool technology is, you know, you certainly you get some dopamine hits when you're solving a problem, and you make it work. But, like, ten times that or a hundred times more is when you see your hard work go in someone han-- someone's hands, and they use it for the first time, and they come back to you and say, "Oh, this is so cool," or, "My life changed because of this." I remember, you know, one of my favorite stories from, or examples of this, was when we were working on Twitch, and there was this guy who was, like, a carpet cleaner in Minnesota or something, who started streaming on the side and, like, became-- had a really popular channel, and he was making... You know, he was like the fir- one of the first streamers to make six figures just playing video games online. And, uh, he's like: "This completely changed my life." And so, like, moments like that, when you, ah, build some cool technology, but then it actually, like, moves the needle for someone, and they tell you their stories, that's, that's the really motivating thing for me. And you're just not gonna get that, you know, if you don't have millions of people using the product.

Jack Altman 10:27 I mean, the idea that you could get, like, a robot in everyone's home is, um, it's really- [chuckles] it's actually totally believable to me. Like, I could see a future, which I guess this is what you're building towards, if it's the right form factor and price point, and it does the right set of things. It seems very believable, and I guess, like, you probably had some range of considerations-

Kyle Vogt 10:47 Mm

Jack Altman 10:47 ... where you're like, we could make the smallest possible, cheapest possible thing, all the way up to, we could try to make a fifty thousand dollar humanoid, and you picked something at some point along that spectrum, trying to be somewhere there. Did you think about it in sort of like a range of, like, what was technologically possible, what future you thought kind of made the most sense? Like, how'd you pick what sort of like complexity and price point to level along? 'Cause you're not doing humanoid.

Kyle Vogt 11:12 From day one, uh, my concern is that there's always gonna be an expectation for what the home robot product can deliver and what reality is, especially in the early days. And that expat- expectation versus reality kind of goes into value, how much value you perceive you get from this product. There's a scale. There's, like, cost on one hand, value on the other, and we wanna do everything possible in our favor to tip the scale towards, like, value. And so that means, like, being really aggressive on cost, um, to get the price down and make these affordable. That has the dual benefit of, on one hand, um, you know, making it so that people are delighted by the product because it's not something they spent as much as a new car on. They spent something much, much less, um, and they're pleasantly surprised, hopefully. And the other is, if you get the cost low enough, you can sell these to a lot of people 'cause lots of people can afford them. And at this day and age, data, real-world data, is one of the biz- biggest bottlenecks in robotics, and so if you can get lots of robots out there, you're gonna have lots of data much sooner, which then creates this feedback loop where the product gets better and then-... it's worth more to people, and then more people buy it. There are a lot of trades where you can build a cooler robot or add more capabilities, or you can reduce the cost, and we've almost always been in the reduce the cost kind of thing.

Jack Altman 12:17 Yeah. Do you think that the, like, the humanoid vision, which is obviously extremely sci-fi and cool, like, does it make sense? Obviously, you could build a robot a bunch of ways, and, like, one way you could choose to do it is just, like, shape it like a person, but it's a robot. You know, maybe there's some reason for it, but, like, when you think about, like, the humanoid question, like, does it intuitively make sense as something that, like, ought to exist, or is it kind of random?

Kyle Vogt 12:41 First of all, when, when I see the videos of, of human- humanoid robots these days, having worked in the field for a long time, it is just so cool. [chuckles] It's so amazing to see what people are able to come up with these days and how fluid the mo- movement, uh, looks and, you know, how dexterous they're getting in terms of the, uh, things that they can do. And so I think they're amazing machines, and I think they need to exist in the world. I think the, the, you know, the question for me is, uh, if we're talking about putting these robots to work or, like, people owning them, uh, the question is, like, at the end of the day, is this the most cost-effective way to deliver the most value I can, you know, to that customer or to that person? And I think for humanoids, there are very few uses for which the answer is yes. Most of the time, the answer is, "No, I can build a simpler machine that works in this environment." If it's a factory thing where the floors are all flat and you're just moving things from one place to another, that robot should probably have wheels.

Jack Altman 13:29 Mm-hmm.

Kyle Vogt 13:29 If you're in a home environment and, you know, like, a humanoid presents all these safety issues, like with walking upstairs, if it slips on a banana peel and falls, it becomes a, you know, ballistic missile, basically, going down your stairs.

Jack Altman 13:39 Right.

Kyle Vogt 13:39 These are not good things for the home.

Jack Altman 13:40 That's true, actually. Like, a big, heavy robot falling down your stairs is a huge problem.

Kyle Vogt 13:43 Yeah, so for the home, you probably want to optimize more on, like, low mass, low cost and try to, like, you know, maximize what you can do, but, you know, not, not running into some of the challenges of a humanoid. That said, like, there are some things that'd be really hard for, um, a non-humanoid robot to accomplish. Like, if you're on a construction site and you're climbing up and down ladders and using hand tools designed for humans and all these things-

Jack Altman 14:01 Mm-hmm

Kyle Vogt 14:01 ... I buy that argument that there are some uses where we'll want humanoids, but I think it is-- Currently, I think people, um, advertising humanoids are trying to get hype in the space, get more investment in the space, which we need. Um, but I think the actual practical uses of them, it will be a little bit smaller than what is being portrayed currently.

Jack Altman 14:16 It also could make sense that they don't make the most sense in a home, but they live other places. Like, it would be good, for example, if, like, a lot of, like, defense was carried out by machines, like, 'cause that could, in some world... You know, hopefully, that could, like, save lives, for example. Or, like, you could imagine it sort of like, you know, guarding at, like, you know, a stadium-

Kyle Vogt 14:33 Yeah

Jack Altman 14:33 ... or like, taking care of, like, you know, big sort of like, uh, patrol areas and things like that. So I could see that 'cause it is, like, a very mobile thing. In the home, that example that you just gave, you know, it slips, and it falls on the stairs, and it, you know, hurts a kid or an animal or something like that.

Kyle Vogt 14:48 I mean, maybe, maybe in the distant future, we can solve these problems. I think just near-term-

Jack Altman 14:51 Yeah

Kyle Vogt 14:51 ... you're less likely to see them in the home first.

Jack Altman 14:53 Along that curve, though, between now and, of course, you know, fifty years out, like, obviously, these things are gonna be... I think it's like cars, where it, like, gets safer than people, you know, one day, I, I, you know, I assume. But

Jack Altman 15:04 on the way up, what's the regulation gonna be like for robotics? Like, do you need to be, like, really involved with the government to, like, put these robots in a home, or is part of what you're doing with the design, like, to avoid a lot of that stuff?

Kyle Vogt 15:15 Right now, um, you know, it's very, very different than some of the industries I've worked in, or defense things or automotive things, where they're very, very heavily regulated industries, and for good reason. I think you're gonna see, um, a lot of products in the home, and it depends on your view. On one hand, we have these little robot vacuums going around today, and you could make an argument that this is kind of just a step up from that. But you don't see, for consumer products, a whole lot of targeted regulations for individual products. We have, you know, general product, product liability laws and other things that are generally applicable to everything from chainsaws to blenders or other things that you might have in your home that carry some risk associated with them. Um, but I think there's an immense responsibility on the develop- the developers of these products to try to make them safe and to do everything possible following best practices, um, regardless of whether or not there's regulation. One thing that we may see more of is looking at how the data is used from these products, um, you know, the security of these products. I think that's really important. Obviously, like, the home is one of the most intimate spaces in your life. You know, there needs to be a great degree, degree of trust and responsibility that goes with the companies who are, you know, have these machines that are likely covered with cameras, you know-

Jack Altman 16:11 Right

Kyle Vogt 16:11 ... running around our homes.

Jack Altman 16:12 Yeah.

Kyle Vogt 16:12 Uh, and most of us don't even think about th- today, like, when we buy a robot vacuum, where does it come from? Like, who is the company behind it? Are they trustworthy? Are they gonna do the right thing in my home?

Jack Altman 16:20 Yeah.

Kyle Vogt 16:20 And that's where I'd like to see a lot more scrutiny.

Jack Altman 16:22 So what does that mean you're gonna need to do? 'Cause you're right. It's like, you know, I remember, you know, people got comfortable with it at some point, but like the Alexa problem, where there's, like, a microphone in your home, and, like, now there's, like, a microphone and a camera and, like, whatever else in your home. So, like, what, what does that mean you need as, like, a company to sort of a- be, you know, a trusted brand there? Like, you have to go from day one pretty hard at that, I guess.

Kyle Vogt 16:41 Yeah, yeah. You have to have some principles and, and opinions and be able to talk about it publicly, I think. But, you know, all these products are gonna... Every, every new category of product like this goes through weird snafus in the early days. And, uh, when you mentioned, uh, Alexa, I was thinking to mind when, when those first came out, wasn't there something where there was, like, a TV commercial that came on and said, "Hey, Alexa, something, something," and then, like, across the United States-

Jack Altman 17:00 Oh, my god

Kyle Vogt 17:00 ... thousands of people-

Jack Altman 17:01 Yeah, yeah

Kyle Vogt 17:01 ... bought toilet paper?

Jack Altman 17:02 That's funny. That's crazy.

Kyle Vogt 17:02 You know, and then, uh, recently with the, uh, Meta, Meta glasses, Zuckerberg was on stage, and he said something, and all the people in the audience, their device pinged the server at the same time, and the demo failed. You know, so there's gonna be these weird moments and things that, um, that, that come along in the early days that, um... But anyways, on, on the, uh, on the data side, for us, we have, like, two things we care about. One is transparency. So if there's data being collected in your home, like, what was it? So I wanna be able to know that, that, you know, what that data was and what's going from the robot to anywhere else, and the second is control. You w- you know, if this product is in your home, you own it. You need to have the on/off switch and be able to in- to control, um, what that data is used for.

Jack Altman 17:37 Yeah.

Kyle Vogt 17:37 And I think if you have those two things, and you are principled about those things and hold true to them, uh, and, and basically fulfill your promises, uh, and you give the control to the user, I think that's the best, you know, sort of starting position for something like this, is just establish those principles up front.

Jack Altman 17:51 One last question on robotics, and then we can go to, uh, another topic. AI models behind robotics, how, how distinct is the concept of, like, robotics AI versus, like, other AI?

Kyle Vogt 18:02 So, I mean, there's a lot of, lot of similarities, and I think, uh, in a way, like, LLMs that started off as, uh, a-... you know, like chatbots that exist purely in the text world, and robots, which are like physical machines, very multimodal in natural, in nature. You can see these things kind of converging because the latest, uh, models are multimodal. They can take in audio, images, other things in the same way that your robot is expecting that.

Jack Altman 18:23 Mm.

Kyle Vogt 18:24 And so over time, I think they're converging a little bit. A- and in fact, a lot of the training approaches, pre-training, post-training, those concepts exist in the robotics world. Um, however, there's still a lot of things that are unique to robots, robotics, that you would never do if you're working purely on an LLM, and that's a lot, um, basically like mixing in real-world data, different ways of collecting it, different ways of using simulation, um, and figuring out how to tie that to all the intelligence that's embedded in, like, a modern LLM.

Jack Altman 18:46 And then the data's, like, super important here, obviously.

Kyle Vogt 18:49 Data's important, uh, today. I think this is, like, a now problem. You know, if you look at LLMs, I think the reason that you can see, uh, so many different companies, like, like, twenty different companies all building foundational models and get, you know, within an, a stone's throw of each other in terms of performance-

Jack Altman 19:04 Mm.

Kyle Vogt 19:04 -small teams, large teams, whatever it is, is because essentially, they're all starting from the same dataset, which is the internet and everything that can be downloaded from it. And, you know, that data kinda determines the quality of the, the model that you can get, and there's certainly some alpha o- on top of that from individual teams. But in the robotics world, there's no corpus of data like the internet that exists. There aren't-- There isn't an entire internet of point clouds or camera images of, you know, robots manipulating objects. And so right now, we're in this early days, where you've got to either bootstrap that data yourself, you've got to pay people to collect it for you, or you've got to try to interpret, um, you know, or generate robot data from other things, like watching YouTube videos and trying to-

Jack Altman 19:39 Mm

Kyle Vogt 19:39 ... infer from hand motions how a robot should do the same thing. And so we're just kind of in the early, early days of that for robotics.

Jack Altman 19:45 Do you think there should be, like, a scale AI for robotics data, or will it be that a company like yours just generates its own data and gets smarter as a result of that?

Kyle Vogt 19:54 I don't know. I think they'll probably be both. In fact, I've, I've probably talked to at least a dozen companies who want to be the scale AI for robotics, and I think that there's going to be plenty of customers for that in the near term, um, especially as, you know, as this data void exists. But when that starts to be filled and we start to see useful robots in the world-

Jack Altman 20:12 Right

Kyle Vogt 20:12 ... I do think the majority of data collection will come from robots and less from people giving data to robots.

Jack Altman 20:17 Well, I, I would also think that for, like, for your product, for example, any dataset that is not your products in the wild is going to be approximating the data. And the perfect dataset, I would imagine, would be if you had armies of robots out in homes giving you data.

Kyle Vogt 20:30 If your technology is sufficiently advanced that you can, um, do transfer learning from other forms of data, other robots, YouTube videos, whatever it is, any source of data, and you can use that, uh, to train your robot, that's, like, an advantage because you don't-- You know, then that total size of that dataset may be much larger than just the dataset that would be collected on your specific robots. However, where we are today, it's much easier to get robots to do amazing things if the data collected came from the exact robot that you're trying to deploy a model on.

Jack Altman 20:58 Totally, yeah.

Kyle Vogt 20:59 And we'll see if that, that changes over time.

Jack Altman 21:01 Yeah, it makes sense. So, um, we alluded to this before, but you obviously started Twitch, you started Cruise. You're doing it again. First of all, why, uh, why are you so motivated to keep doing these hard companies? And so, like, many people after this much success wouldn't go back to the beginning, and, um, you've had two really successful companies, which I want to talk about, particularly Cruise, because I think it's related. But I guess to get started, like, what's driving you now to do this again?

Kyle Vogt 21:30 I mean, uh, I had, you know, a, a very, very short-lived existential crisis after Cruise. I was like, oh, my gosh, I'm, I'm done with this company. This, this is, like, you know, practically my identity for a full decade. What next? I spent some time thinking about that. There's, you know, you could retire, you could become a venture capitalist, you know-

Jack Altman 21:45 Which is kind of like... Yeah, it's-

Kyle Vogt 21:46 Oh, is it the same thing, I think?

Jack Altman 21:47 No, no, that half right.

Kyle Vogt 21:48 I'm just kidding. Yeah. [chuckles]

Jack Altman 21:49 No, we work super hard.

Kyle Vogt 21:50 A- and then after, after thinking about that for a while, I realized, like, the thing that, you know, o- outside of spending time with m- my family and friends, the thing that brings me the most joy is solving really hard peop- or w- really hard problems with really smart people. And so, like, that is retirement for me. That's, like, the, the most fun, you know, satisfying thing that I could possibly think of to do, and it also ends up being you can do more of that and do it at a larger scale if you work with, like, a big team of people, and you do it in the form of a company as opposed to a hobby or, or something that you're doing it on your own. And so to me, I, I think there's no better thing, and maybe at some point I'll run out of energy to, to go hard like I am right now, uh, but for now, like, this is, this is great. We have a brilliant team. We're going on this, you know, building this exciting new product in a big market, uh, and that is energizing to me.

Jack Altman 22:32 I want to talk about a couple of the things that you've said a- about how you want to build it this time. One that stuck out to me was that you never want to be more than a hundred people.

Kyle Vogt 22:39 Yeah.

Jack Altman 22:40 And first of all, I actually didn't know, is that, like, literal or is that directional?

Kyle Vogt 22:44 Uh, to be, to, to be seen.

Jack Altman 22:45 Okay.

Kyle Vogt 22:46 Yeah, I think right now we're, we're taking it very seriously. So if, if that is actually your belief, we can talk about why-

Jack Altman 22:50 Yeah

Kyle Vogt 22:51 ... but if that's actually your belief, then you make very different hiring decisions. It's like, well, you know, if I think about the future company having a hundred people in it, I can allocate this many people to this type of role. That means every person in ev- every seat has to be the best in the world at this for the company to be successful. And so you end up, you know, passing on a lot of people that are great people, really talented, but they're not, they're not at that specific level we want for that particular role. And I think, you know, if you're successful in doing that, you end up with this... There needs to be a name for it, but like in the early days of a startup, when everyone is, like, on the same page, like maybe j- just the founders, they're all in it one hundred and ten percent. They're all usually, like, brilliant, working together, they're l- almost mind-melded. Um, and, and then you have, like, insane productivity for some period of time until you, until you get bogged down by the organization growing and adding more functions and, you know, teams of people and management layers, all this kind of stuff.

Jack Altman 23:39 The incentives get disconnected.

Kyle Vogt 23:40 All-

Jack Altman 23:40 You have communication issues.

Kyle Vogt 23:41 Corporate politics.

Jack Altman 23:41 Yeah, yeah, all that.

Kyle Vogt 23:42 And so you get this drift away from this, like, pure, like, force of energy-

Jack Altman 23:46 Mm-hmm

Kyle Vogt 23:47 ... that is in the beginning stage of a company. And so the reason for trying to have a cap on the size of the company is to keep it so that we're always in that-... pure high-output zone, uh, and you can't get that if you have, like, too much of a range of, of people in the company. I, I really think of it more like a pro sports team. Like, you're not gonna have, you know, the Lakers. I don't think you're gonna have, like, Lebron- Lebron James and a bunch of high school kids on the team. It's like they're all players that are the best in the world, so that, you know, when they work together as a team, they can outperform a team that is, like, a mix of, of talents and levels.

Jack Altman 24:12 I also think, like, if you have, like, the Lebrons with the high school players, like, the Lebrons are like: "What are we doing here?"

Kyle Vogt 24:17 Yeah, exactly.

Jack Altman 24:18 And then they lose productivity.

Kyle Vogt 24:19 They don't wanna stay. They, they wanna, they wanna go play with the best people in the, in the world against the best people in the world.

Jack Altman 24:23 Yeah.

Kyle Vogt 24:23 And, uh, and that's how you get better and grow. And, you know, people who are, people who are the, the best in the world at what they do typically got there 'cause they had this growth mindset. They constantly wanna get better. And, you know, what better way to do that than to surround yourself with people of different skill sets that are all the best in the world at what they do, and sort of absorb from that?

Jack Altman 24:38 I feel like so much of what gets hard is as you start getting into, like, scaling operations, and you get into, like, that side of things, it just gets so hard to keep it really small. You know, like, even you think about, like, let's say you only had, like, ten non-engineering roles.

Kyle Vogt 24:51 Mm-hmm.

Jack Altman 24:51 It's like, well, some- someone's gotta run finance. They probably can't do it alone. You've got, like... You, you're gonna have all these, like, physical parts. You're gonna have to have buildings for things. Like, so how do you think you'll actually try to keep a limit on that? Like, will you partner? Do you go sort of, like, work with people outsource, or do you actually think that, like, you know, maybe with, like, new AI tooling, you could just go way further with people and it's just sort of like a demand joe place?

Kyle Vogt 25:14 Yeah, it's a good question. That's, that's part of why I said to be seen.

Jack Altman 25:16 Yeah.

Kyle Vogt 25:16 Like, this is a great mental model now, and it may not, uh-

Jack Altman 25:19 No, I think it's a great push

Kyle Vogt 25:20 ... may not scale, but-

Jack Altman 25:20 And by the way, I think most of the... One of the healthiest changes I feel like I've seen from five years ago is the shift from thinking that, like, big teams are cool to thinking big teams are lame.

Kyle Vogt 25:29 Yeah, I mean, things seem to ebb and flow, right? Like, I'm taking the extreme position here, but I do think if that has the effect of, you know, causing a small shift in that direction, that's probably net good for the industry, uh, and good for these companies. So it'd be a question for us: Do we partner or outsource things? I think it... You know, keeping the team small also forces you to focus on, like, what are our core competencies, the things that we need to do uniquely because we think we can actually do them better than any other company that we could potentially work with. And, you know, for things like a lot of operations or facilities or, you know, buildings, these are things where maybe we have no reason to think we would be the best in the world at this, so we should partner. And a lot of companies, like, they have lots of funding, they have lots of teams, like, it's almost like they just take on these responsibilities because they can-

Jack Altman 26:08 Yeah

Kyle Vogt 26:08 ... not necessarily because they should.

Jack Altman 26:10 I feel like one of the most important things, which I think you've obviously shipped in self-driving in a way that, like, you know, very few have, but I think in a lot of these, um, sort of more sci-fi areas, it's very easy to not be in, like, shipping mindset. And, like, I think you did this really well at Cruise. Obviously, like, OpenAI was doing this, like, well before ChatGPT.

Kyle Vogt 26:30 Uh-huh.

Jack Altman 26:30 And so you basically probably are in a mindset, I assume, of figuring out, like, how quickly can we ship and, like, iterate, and, like, that's gotta be the mindset rather than just, like, hang in a warehouse building the perfect robot forever.

Kyle Vogt 26:42 I think for that, it's, it's starting with the thing you wanna build, and then working back to: what is the ma- what is the constraint? What are, what are the constraints or bottlenecks that we need to be- that we need to make our number one priority? Because it cannot go faster than, you know, what that one bottleneck or constraint would dictate. And for self-driving, that's a combination of, uh, safety, trust, and public acceptance. And so, you know, those are different work streams. We're basically like, unless those are all green, you don't have a product. It doesn't matter how good the technology is. And there are similar things, you know, for a home robot or really any business. And so, like, you know, mapping out what those are and basically making that the company's top priority, like at, you know, Cruise, for example.

Jack Altman 27:15 Yeah.

Kyle Vogt 27:16 Safety metrics were the single thing we talked about every week, week over week over week.

Jack Altman 27:19 Mm-hmm.

Kyle Vogt 27:19 Making progress towards those. And I think for any company, like, what you talk about, what you ma- design your metrics around, kind of sets the tone for the company, and it's gotta be aligned with that, you know, whatever the constraints are.

Jack Altman 27:27 What

Jack Altman 27:28 do you think you can do in a home first, rely... Like, what do you think will be the first activity that can, like, really be done well in a home? And then, like, what are the things that you think are close, but maybe follow in the, you know, next twelve to twenty-four months or something?

Kyle Vogt 27:38 Yeah, I mean, there are hierarchies, I think, of, of tasks for a home robot. Um, and if you look at, uh, I think two, like, a classic two-by-two grid, I guess one is maybe the, um, technical complexity of the task, like, how hard is it to get a robot to do this successfully? Uh, and then the second is, like, what is the success rate that is acceptable to a customer of a product like this? And I'll give you an example. If you are, you know, in, in the, the easy side of things from the technical capability and also the very forgiving side of things in terms of success rate, is probably, like, picking up your kids' toys. So I have, you know, two kids, um, a one-year-old and a seven-year-old, and they're, between the two of them, are constantly making messes, and toys are all over the house. Kind of they're running around, picking up toys.

Jack Altman 28:18 Same.

Kyle Vogt 28:18 And so if you have a product that you can buy, you can go to the store, buy this thing, put it in your house, push a button, turn it on, and then when, when you're gone for the day, all the toys are magically put away-

Jack Altman 28:26 Yeah

Kyle Vogt 28:26 ... by the time you get home, it's, like-

Jack Altman 28:28 That'd be great

Kyle Vogt 28:28 ... a mind-blowing experience. And let's say it, it screws up and, like, two out of the hundred toys are still on the floor when you get home.

Jack Altman 28:33 That's okay.

Kyle Vogt 28:34 Doesn't skip a beat.

Jack Altman 28:34 Still better, yeah.

Kyle Vogt 28:35 Yeah. So that, so that, like, you think about nines of reliability for, for engineering. [chuckles] Like, maybe one nine is fine for that particular task. There are other things, like putting a wine glass in a dishwasher-

Jack Altman 28:46 Mm

Kyle Vogt 28:47 ... where the technical complexity is a little higher, and the, the, um-

Jack Altman 28:50 What's hard about that, by the way? Is it, like, the grabbing or is it like-

Kyle Vogt 28:53 Yeah, so, so if you think about picking up objects, this microphone, which is gonna make a noise when I squish it-... is, uh, is, is compliant. And so if I'm off a little bit on where I grip it or, like, how much I squeeze it, I'm not gonna shatter this microphone into a million pieces.

Jack Altman 29:06 Yeah.

Kyle Vogt 29:06 For a wine glass, the, the margin is, is very thin, and so from a dexterity standpoint, it's a little more fragile.

Jack Altman 29:11 Actually, sometimes I think about that's, like, a good example of a thing where I'm like, "It's amazing that people can do certain things, like squeeze a wine glass the right amount, or, like, hit, you know, a ball, you know, with a racket or a golf club with the right angle or something like that, or, like, catch something that's flying while you're moving." Like, it's actually pretty crazy what you can do, like, mechanically.

Kyle Vogt 29:30 It is, and the, and the evolution to how we get there is interesting, too, because my one-year-old daughter, her, her hands are, like, open, closed. There's nothing in between-

Jack Altman 29:36 Yeah

Kyle Vogt 29:36 ... she grabs objects.

Jack Altman 29:37 The wine glass is shattered. [chuckles]

Kyle Vogt 29:38 And at some, yeah, at some point along the way, we, we developed much more nuanced skills and, and abilities. But so wine glass is another one. The other thing that's challenging is if you're putting a wine glass on a rack and, and, uh, you know, it's a thin stem or something-

Jack Altman 29:47 Mm-hmm

Kyle Vogt 29:47 ... and you nick, uh, you, you, like, bump into something, you might break the stem off, right? And so not only is it more difficult from a technical standpoint, but if you shatter a wine glass in someone's dishwasher, they're probably not gonna be your customer anymore.

Jack Altman 29:58 That's right.

Kyle Vogt 29:59 And so that's like-

Jack Altman 30:00 Not a dishwasher customer

Kyle Vogt 30:00 ... you maybe need several nines of l- reliability. And so I think that this, this sort of spectrum of technical difficulty and, uh, basically forgivability is going to dictate the types of things you see do- home robots do first. And, um, and I think we'll work our way up towards, you know, I think the holy grail of a home robot, which is, like, dishes, laundry, end-to-end, maybe cooking. These things, all of them have, like, all of these little, uh... It's like a minefield. You do one thing wrong, and you ruin the whole process.

Jack Altman 30:27 Yeah.

Kyle Vogt 30:27 Like, for laundry, if you put the red sock in with the whites, you now have pink laundry. That's, like, game over, right?

Jack Altman 30:32 Yeah.

Kyle Vogt 30:32 And it's, you know, so there's things like that. For cooking, it's the same thing. You put too much salt or pepper in there, and the dish is ruined. You know, so these are things that I think we'll get to-

Jack Altman 30:40 Oh, cooking

Kyle Vogt 30:40 ... and I think it'll happen pretty quick.

Jack Altman 30:42 Wow.

Kyle Vogt 30:42 But, you know, I think-

Jack Altman 30:43 Do you think it's like cooking a steak at some point?

Kyle Vogt 30:45 Yeah, why not? If you think about, at the end of the day, you've, you have pick and place and simple manipulation, that's what cooking is.

Jack Altman 30:50 Yeah.

Kyle Vogt 30:51 They're just, like, a much higher degree of reliability, and there's other things around food safety and bacteria and other things that come in cookie, uh, cooking and temperature sensing and what like that. So it's all doable. It's just like, I would not start there.

Jack Altman 31:00 Do you think at some point it's like, "Hey, robot, I'm at work right now. There's a steak in the fridge. Please cook it and clean up everything"? By the time, like, fifteen years from now, that's, that's doable?

Kyle Vogt 31:10 Less than five.

Jack Altman 31:11 Less than five?

Kyle Vogt 31:12 Yeah. This stuff is going fast. Again, if you saw-- if you look at the robots you can buy today in the world, like the nice robot vacuums, um, you may not think that. If you see what's happening behind closed doors at the best robotics companies in the world, you might think that. And if you're the leadership of these companies, the technical leadership, and you kinda know where things are going, you absolutely believe that.

Jack Altman 31:30 The hand seems really... As we're talking about this, I was sort of, like, stupidly, like-- I was like, "Actually, a hand's pretty good." Like, your fingers are, like, pliable. You have, like, a lot of degrees of freedom. You have, like, multiple grip points. Is the hand the optimal thing?

Kyle Vogt 31:42 The hand is really important to get right because it is the robot's interface to every object that it interacts with. If you make it too simplistic or not enough, um, sensing capabilities or whatever, then you have to have a much, much smarter brain to figure out how to use this primitive tool to accomplish a complicated task. And so the more mechanical complexity or capability that you add to a hand, the more sensing ability, in theory, it would re- require less, you know, sort of rocket science-

Jack Altman 32:08 Mm-hmm

Kyle Vogt 32:08 ... to figure out how to do a task, um, with that hand. The trade-off, of course, is the more technology, the more degrees of freedom or motors that you pack into a hand, the more complicated it becomes, which impacts durability and also cost.

Jack Altman 32:19 Yeah.

Kyle Vogt 32:19 And so there's push and pull there to find that sweet spot, where you can basically come up with the simplest hand possible to do the tasks you wanna do, uh, at the lowest cost, while, while also being able to accomplish everything in a fairly straightforward manner. But I think in the limit, you know, there's a lot of, uh... You know, we think by analogy a lot, and it, and we, we have two hands and two arms, and so like, a lot of the robots you see today have two hands and two arms, but it is a really interesting thought experiment. Like, what does the ultimate hand, arm-

Jack Altman 32:44 Yeah

Kyle Vogt 32:45 ... thing look like? And I think it was Rodney Brooks who said this the other day, but I actually do kinda think maybe it ends up being some crazy octopus, tentacle-looking thing in the future [chuckles] that's, like, very adaptable-

Jack Altman 32:53 Why?

Kyle Vogt 32:53 ... and can reach into small spaces.

Jack Altman 32:55 Interesting.

Kyle Vogt 32:55 Well, I think the, you know, the, the human hand was, ended up where we are due to probably some impa- impossible to unravel sequence of evolutional pressures, right?

Jack Altman 33:03 Well, it's like you start down some path, and then you do your best, you know, evolution does its best, given some somewhat random starting point, I suppose, right?

Kyle Vogt 33:10 Yeah, so if you could go back, like, a million years and hit the reset button on human evolution-

Jack Altman 33:14 You might start as a different direction

Kyle Vogt 33:15 ... maybe something, a new fork would emerge, and it would be more tentacle-like or who knows what? But I am skeptical that the way that human hands and arms evolved is the ultimate, and so the, the challenge will be, like, can we figure out what that is?

Jack Altman 33:26 I have a couple stupid questions about the robot at home. One is, how strong could it be? Like, is it, is a hundred-pound robot like dr- it must be ridiculously stronger than a person, right?

Kyle Vogt 33:35 I would think, uh, for a hundred-pound robot, you could certainly make it, uh, maybe stronger in some dimensions. There are some things that, like, our sort of soft biological muscles are pretty good at.

Jack Altman 33:45 Are stronger than, like, like, a physical robot, pound-for-pound kind of thing?

Kyle Vogt 33:49 Yeah, it's, it's really hard to say. I think so. I think probably the state-of-the-art Boston Dynamics robot seems like it's on par, if not, you know, more capable than a human, and if not now, I'm sure the next couple generations will be. So that's kind of interesting when you think about it.

Jack Altman 34:00 That's surprising that a soft muscle is stronger than like... I don't know why I would think a robot could be dramatically stronger.

Kyle Vogt 34:06 Yeah, I've been going down the rabbit hole on this a little bit, thinking about, like, you know, as, as, again, our focus on affordability and cost, like, is a electromagnetic gear motor, where you've got, uh, magnets and copper winding and a bunch of gears and housing, is that the most cost-effective, durable way to generate motion for a robot? And the answer in the short term is probably yes, but I think there are some interesting things happening, where we're trying to mimic either some of the chemical processes or electrostatic a- actuators or other things that are similar in how they work to, like, a human muscle. And the benefit there is you can get a higher cycle count, more silent operation, and potentially more power density-

Jack Altman 34:40 Yeah

Kyle Vogt 34:40 ... like how much strength can you get into a physical volume, than what we have today in gear motors and then potentially much, much beyond what humans have in our muscles.

Jack Altman 34:47 Isn't like, hy- hydraulic is pretty strong? Like, a hydr- like, hydraulic pressure is pretty strong.

Kyle Vogt 34:51 Yeah, hydraulics can be extremely powerful, but they have other traits, typically noisy. The valves and things are pretty expensive, can be harder to control and get, you know-

Jack Altman 34:57 Yeah

Kyle Vogt 34:57 ... high-fidelity motion. And so in terms of power, density may be good, but, you know, there are other trades, and the reason you don't see this on a lot of robots.

Jack Altman 35:04 That makes sense....

Jack Altman 35:05 Another question I had that's sort of like probably off-spec, but while we're talking, is this gonna be something that p- would have, like, home security applications as well? Or does that then take you into weird territory that's just not worth going to?

Kyle Vogt 35:18 Yeah, I think so. I mean, one of the challenges with a, with a home robot is it's kind of general purpose, and so, like, you know, what are people gonna use this thing for? And I think it's, it's hard if you just have a laundry list of fifty different items that the thing can do, and security is one of them. But I do think a lot of people will be out and about, and kn- with their home robot at home, be like, "Oh, I wonder if I forgot to turn off the gas on the stove," and send-

Jack Altman 35:38 Yeah

Kyle Vogt 35:38 ... the robot over there to just, you know, tell you or even take, take it on for yourself. In the same way, um, you could be like, "Hey, robot," like, you know, "if you see any person in my home or any doors open, like, let me know."

Jack Altman 35:47 Yeah, if you see me getting burglarized, like, do something.

Kyle Vogt 35:50 But I, I don't know if-

Jack Altman 35:51 That's risky, right?

Kyle Vogt 35:51 ... you would think of it as a security robot so much as, like, this is just one of the many responsibilities of my home robot, is to keep tabs on my home.

Jack Altman 35:58 Totally. Alerting probably is good. Taking action is probably not.

Kyle Vogt 36:02 I hadn't thought about that side of that. I, you know, that's not really in our [chuckles] -

Jack Altman 36:06 That's fine. That makes sense.

Kyle Vogt 36:07 Yeah.

Jack Altman 36:07 I'm just thinking because, like, you know, in my head, I'm like, okay, if there's this brilliant, capable robot in the house, my guess is you're gonna have a lot of people want it to start doing a ridiculous number of things for them-

Kyle Vogt 36:18 Yeah

Jack Altman 36:18 ... over the arc of time, and then you'll have to choose from that set, like, what goes in.

Kyle Vogt 36:21 Yeah, I, I, I think so. And, uh, but for sure, I mean, on the security side, I would hope, though, rather than having, like, physical deterrence and like, you know, having your, um, home robot turn into a-

Jack Altman 36:31 Yeah

Kyle Vogt 36:31 ... security guard with a baton or something-

Jack Altman 36:33 Uh-huh

Kyle Vogt 36:33 ... it's more so that it just becomes unattractive to rob homes or do, you know, break and, and enter into a home. Maybe in the same way that, you know, a world full of cars, where everyone has, like, that Tesla Sentry Mode.

Jack Altman 36:44 Yeah, yeah.

Kyle Vogt 36:44 There's very little incentive to break into cars. It's not worth the risk.

Jack Altman 36:48 Well, I mean, even, like, a security system just makes a loud sound and calls the police. You know, I wonder-

Kyle Vogt 36:52 That's pretty effective, isn't it?

Jack Altman 36:53 I think it's extremely effective.

Kyle Vogt 36:54 Yeah.

Jack Altman 36:54 Yeah, and I think those systems are pretty old-

Kyle Vogt 36:56 Yeah

Jack Altman 36:57 ... and, you know, they're deeply embedded and, but yeah.

Kyle Vogt 36:59 It's like you may figure out how to disable the alarm and sneak into the house, but if there's a robot, you know, rolling around-

Jack Altman 37:05 And then the sirens blaring-

Kyle Vogt 37:06 Yeah

Jack Altman 37:06 ... and all that stuff.

Jack Altman 37:07 I just think it'll be interesting where if this gets in there, my guess is people will start to... I could see a future where people expect a ridiculous amount from these things.

Kyle Vogt 37:15 Mm. Well, I mean, it touches on something interesting I've, I've thought about is, like, when you ask people, um, or we ask people: What would you do with a home robot? You know, the, the-- immediately what comes to mind is, like, the thing that's most annoying to you today to do in your home, and I think that's good. We want to help with the annoying stuff and-

Jack Altman 37:28 What comes up most, like laundry, probably?

Kyle Vogt 37:30 Yeah, laundry, dishes, picking up after my kids, um, you know, wiping surfaces, cleaning. Like, these are the things you would expect.

Jack Altman 37:35 Yeah.

Kyle Vogt 37:35 And so we're gonna chip away at those things for sure. But what I also like to think about is the things that we don't do because we value our time more than that. Um, you know, the example is if you've ever gone to, like, a really nice hotel, you know, the slippers are laid out for you, there's a glass of water on the nightstand-

Jack Altman 37:49 Yeah

Kyle Vogt 37:49 ... a little chocolate on the pillow. All these, like, little flourishes.

Jack Altman 37:52 That'd be nice.

Kyle Vogt 37:52 Like, I don't know. I, I, I think that, you know, robots should not only automate the things that we don't want to do but also, like, elevate our standard of living to some degree.

Jack Altman 37:59 Yeah.

Kyle Vogt 37:59 And so I love the idea that if you can afford a really affordable home robot, we're gonna give you a lifestyle that, you know, would otherwise be inaccessible to you.

Jack Altman 38:06 Totally. I mean, that's actually a really interesting point, that, like, a lot of the types of things you're talking about don't require any new inputs. It's just about taking care of your home in a certain way that's, like, beyond what you would normally need. But it's like you've got a bunch of towels that are, like, sitting in the laundry room that are clean, but can you, like, put those by, you know, the shower and, like, roll them up nicely?

Kyle Vogt 38:25 Yeah. And maybe you don't need all these things, but the point is, like, you know, your, your time is more valuable now. It's very scarce. Like, humanity's time, I think, is really important. But for a robot that's got twenty-four hours to sit around in your home and, like-

Jack Altman 38:35 Yeah

Kyle Vogt 38:35 ... try to make your life better, what could we come up with for it to do? What, what could it come up with to do for you? That's, that's an interesting question.

Jack Altman 38:40 Wow,

Jack Altman 38:41 I'm curious about reflecting on what you've learned from the way self-driving cars played out and how it might matter here. Maybe one interesting sort of case study is, you know, the Tesla versus Waymo approaches. Do you think in any way that how that played out or any learnings there that poured over to, like, what could, you know, be impactful in the robotics land?

Kyle Vogt 39:05 Well, it's hard to say. They're, you know, very different approaches to getting to market, but it does seem like they're both trying to converge at the same thing-

Jack Altman 39:11 Yeah

Kyle Vogt 39:11 ... which is, you know, self-driving cars everywhere. I think one thing that, uh, was really brilliant about Tesla's approach, they found a way to sell the product essentially before it was fully complete, if we're looking purely at the self-driving side, and generate billions of dollars of cash flow, which they could use to bolster their core business but also continue to invest in R&D to, to make this self-driving product. Waymo, by comparison, uh, you know, has taken, uh, what, almost a couple of decades at this point, um, maybe not quite that long, and probably tens of billions of dollars of investment, and the revenue relative to that has been fairly meagre-

Jack Altman 39:45 Yeah

Kyle Vogt 39:45 ... right, compared to that total investment over time. Which basically means that the only companies in the world who can do this are the ones with that kind of capital on their balance sheet to basically fund this crazy amount of R&D year over year.

Jack Altman 39:56 Yeah.

Kyle Vogt 39:57 And I think it's no coincidence that the only companies who've succeeded in that approach or, or are on track to succeed in that approach are owned by, you know, Amazon, Google, or, um, you know, like a major car, car company. And that was even a struggle for a company like General Motors. And so in the home robot space, I hope we don't repeat that. I hope it doesn't become the case that the only companies that make it are ones that are basically kept alive through billions or tens of billions of dollars from, you know, a corporate, uh-

Jack Altman 40:21 Yeah

Kyle Vogt 40:22 ... benefactor. And instead, we can find clever ways to get to market that, uh-

Jack Altman 40:25 Which I guess is why you need to get to market and be selling something along the way to fund all of this.

Kyle Vogt 40:31 Well, I think if, if, if your development cycle means you don't get to meaningful revenue for five to ten years after the company has started-

Jack Altman 40:36 You have to get acquired

Kyle Vogt 40:37 ... it means you're entirely dependent on either being acquired or the capital markets, you know, point-- being pointed in the right direction.

Jack Altman 40:43 Yeah.

Kyle Vogt 40:43 And, you know, historically, things tend to cycle back and forth. In a five-to-ten-year timeline, you're getting awfully close to almost guaranteeing that you straddle, like, a down cycle as well as an up one, and that can be a killer for these companies.

Jack Altman 40:55 I... We don't need to talk about sort of Cruise and GM too much, but, um, I am curious about sort of, you know, I saw you share on Cheeky Pint about, like, not wanting to sell, and I'm just curious, like,

Jack Altman 41:08 your mindset about the sense of autonomy and how you think about selling a company since you've been through it, you know, a couple times and everything.

Kyle Vogt 41:14 I think, uh, you know, and, and I said this before, but my conclusion is, like, if you, if you are selling a company-... um, it should be because the reason you started the company or the, the thesis that you had in mind, or the thing you wanted to build, something has changed, and maybe, like, you're no longer interested in it, your life circumstances have changed, whatever. But I think it is a, a fantasy to believe that you can sell your company, like, have your cake and eat it too. Like, sell your company-

Jack Altman 41:36 Sell your company in order to further the mission.

Kyle Vogt 41:37 And further the mission. I think in theory, this can happen sometimes, but it is so, so rare.

Jack Altman 41:41 It is rare.

Kyle Vogt 41:41 And I think more likely than not, you'd be disappointed with that outcome. And therefore, like, for me, I, uh, I can't imagine being in a situation where I would trade, you know, the opportunity to build this amazing thing and control it and make sure it, you know, happens in the way that I want it to, uh, for, for some kind of partnership or liquidity. Uh, so that just doesn't make sense to me. Maybe not for everyone, and maybe that's just because I'm so excited about this thing and bringing this new idea of a home robot into the world, that, like, it just wouldn't even cross my mind, the thought of, like-

Jack Altman 42:06 You're-

Kyle Vogt 42:06 ... handing over the reins to someone else, yeah.

Jack Altman 42:07 Yeah, you're sort of at a point now where, like, this type of company is such a forever project, and, like, you're now able to start a company that's like, you know, it's not-- it is not some little thing, like if this works. It's just such an important thing. Which also, I guess, that probably also drives you to want to sort of hold on to it indefinitely.

Kyle Vogt 42:24 Yeah, perhaps, but I think I, I, I, I'm not selfish about it. Like I-- if I, you know-

Jack Altman 42:28 Yeah.

Kyle Vogt 42:28 I, I feel like I have an obligation to stay true to, you know, our, our investors, the employees, and the mission. So, you know, even though, uh, I certainly want to hold on to this, I'm not treating it like a pet project. I actually do want to, like, fulfill this broader vision that, that we all share.

Jack Altman 42:40 Yeah.

Jack Altman 42:41 Maybe as a final, uh, thing to touch on, you did this crazy, like, marathon around the world experience. What was that? And, like, why'd you do something that seemed so, you know, hard?

Kyle Vogt 42:51 Deep in the, in the middle of Cruise, I was... I think I was frankly, like, kind of frustrated that, like, we were putting in all this energy, and sometimes there would just be periods where the metrics wouldn't always go up and to the right. We'd take a regression and then go back and forth, and so, you know, the result wasn't always proportionate to the energy going in. And, uh, for running, for me at least, that was not the case. You put in the time-

Jack Altman 43:09 You run, you get better

Kyle Vogt 43:10 ... you get better.

Jack Altman 43:10 Yeah.

Kyle Vogt 43:10 Yeah. And so that's very deterministic and satisfying, and so I needed something to balance that, I feel like, in my life. Um, and as I do, I went down a rabbit hole reading about, like, extreme marathons that you can do. I was, like, sort of an amateur marathon runner and came across the World Marathon Challenge. It's this thing you can sign up for. They take you to each continent, one continent per day, and you run a marathon on each one, and then, like, the next day, you fly to the next continent. And I thought, "That is insane." And then in fine print on the website, it's like the world record is, like, five days and ten hours by this, this one guy, and then that-

Jack Altman 43:38 That's crazy

Kyle Vogt 43:39 ... got the wheels turning. It's like, well, I wonder what the theoret- engineer brain clicks on. I wonder what the fastest theoretical time you could do is if you, if you optimized the... it's like the traveling salesman problem.

Jack Altman 43:49 What part of the continent?

Kyle Vogt 43:50 Like, yeah, where you land, optimize for customs, in and out, and logistics, and, like, really dial it up to 11.

Jack Altman 43:55 Mm.

Kyle Vogt 43:55 And that turned into an 18-month obsession, got stuck in my head, and I ended up writing some software to find the shortest route between the seven continents.

Jack Altman 44:03 That's crazy.

Kyle Vogt 44:03 Spending a-

Jack Altman 44:04 My problem is that I couldn't-

Kyle Vogt 44:05 Yeah

Jack Altman 44:05 ... run a half marathon. That's where I would struggle.

Kyle Vogt 44:08 The, the sort of stubbornness and attachment to this idea meant that part of this was I had to train my body physically to be able to do this, and that's a whole-

Jack Altman 44:13 'Cause, like, you're running a marathon, and then you're not resting afterwards.

Kyle Vogt 44:17 Yeah, so the, the, the cycle, for example, is, you know, you start in Cape Town, so that you fly to Antarctica. You have to start there 'cause the weather is so unpredictable.

Jack Altman 44:24 Oh, yeah, the Antarctica one, that's tough.

Kyle Vogt 44:25 You need like a, a, at least a, call it a six-hour window of decent weather, and you're, like, looking at the weather forecast. When it clears, then you fly in-

Jack Altman 44:32 Wow

Kyle Vogt 44:32 ... and you land, you run the marathon, and you get out, 'cause that can throw off the timing.

Jack Altman 44:35 Where in Antarctica do you do this?

Kyle Vogt 44:36 On, like, the most temperate outer part of Antarctica. So it's on the continent, but we're not talking, like, South Pole.

Jack Altman 44:41 Yeah, I mean, I-

Kyle Vogt 44:42 So it's cold, but it's not, it's not like-

Jack Altman 44:43 But it's not snowy?

Kyle Vogt 44:45 I mean, it's icy, and you can't see. It's, like, barren, all ice, and you land the plane on ice, you run on ice.

Jack Altman 44:49 You're running on ice?

Kyle Vogt 44:50 On ice.

Jack Altman 44:50 Yeah.

Kyle Vogt 44:50 It's like, kind of like crunchy ice. Like, uh, we-- there was a, there was, like, a ski slope groomer that did a course down there, uh, it's like a six-mile loop or something, and so it was like running on, uh... It was like trail running.

Jack Altman 45:01 Got it.

Kyle Vogt 45:01 Still pretty crazy.

Jack Altman 45:02 That's crazy.

Kyle Vogt 45:03 Any- anyways, yeah, so organizing the logistics for the training, training my body and everything, doing it in the end, ended up doing this in about three and a half days, which blew the world record aside. And then after 18 months doing this and finishing it, you know, I gotta say, like, the, it was, it was-- have you ever, like, finished the last item on your to-do list, and it's just like a dopamine hit, and you're like-

Jack Altman 45:21 It's great

Kyle Vogt 45:21 ... "Oh, that feels so good"?

Jack Altman 45:21 Yeah.

Kyle Vogt 45:22 That's what it was. It was like relief. It's, like, check the box. Now I can-- my tormented brain, which wouldn't let this go for 18 months, can finally relax.

Jack Altman 45:28 Where do you go? Cape Town, Antarctica, South America?

Kyle Vogt 45:31 Yeah, southern tip of South America, and then to Panama City-

Jack Altman 45:33 Right

Kyle Vogt 45:33 ... and then I think to Madrid, and then, uh, Oman.

Jack Altman 45:36 And you're just like, by the last one, are you just crazy fried, or were you in shape to just not be, you know, so beaten down by it?

Kyle Vogt 45:43 I was, I was pretty fried. Pretty fried.

Jack Altman 45:45 Yeah.

Kyle Vogt 45:45 Uh, but it's like, uh, you know, the training I did peaked at, at doing three marathons within a 24-hour period in three different cities. That was like the, the stress test for this, if you will. And my coach was like, you know, I was like, "Only three marathons, that's not seven. Like, is that actually the right amount of training?" And he said, "I promise you, when you're doing the really th- real thing, and you have this whole crew of people with you-

Jack Altman 46:06 Adrenaline

Kyle Vogt 46:06 ... and everything is on the line, and the adrenaline going, you're-- if you can do three in 24 hours, you can do the rest," and he was right.

Jack Altman 46:11 That's wild. That's, like, the hardest physical task imaginable.

Kyle Vogt 46:14 It, uh, you know, mental toughness is important for start-ups, and I feel like it, it really helped me [chuckles] quite a bit in that domain.

Jack Altman 46:19 Yeah, totally. All right, Kyle, this was really fun. Thanks for making time for it.

Kyle Vogt 46:21 Thank you. [upbeat music]