Uncapped #32 | Kyle Vogt from The Bot Company

[00:00] You think it's like cooking a steak at some point? Yeah, why not? If you think about at the end of the day, you have pick 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 cooking and temperature sensing and what like that. So it's all doable. It's just like, I would not start there. So 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 15 years from now, that's doable. Less than five. Less than five? Yeah. [00:30] I'm really pumped to be here with Kyle Vogt. Kyle, thanks a ton for making time for this. Thanks for having me. So I want to 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? [00:54] used to be the case and what has changed that's like making people so energized right now? Yeah, it is. It's like the most excited I've ever seen people in robotics. And, you know, I guess as an engineer, there's something like romantic about building machines to do the stuff that we don't want to do. And 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,

[01:24] 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 and if things don't line up within a millimeter, the whole thing doesn't work. Except the BattleBots, those were good, actually. Now I'm thinking back. BattleBots were, yeah. You made them with the saws and everything. Ours had a hydraulic axe, which was pretty cool. But calling these robots is a bit of a stretch. They're basically glorified RC cars. Yeah, that's right, with a weapon. Yeah, so with a weapon. What's different now is, for the first time, [01:52] 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, 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 3D 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. [02:22] And even then, the failure rate would be high if you put it in a different room and it doesn't have a map. [02:26] But now it's almost like cheating. You can take all the... [02:29] 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 instantly. You like open the chat GPT. If you open like video, you can like show it anything and it like knows what it is. 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. Like better than us. Like they can look around the room and see stuff better than we can. Yeah. And then on the motion side, you used to have to have a PhD to compute these complex trajectories. You have like 12 joints on a motor or on a robot. How do you get all 12 joints to

[02:59] to like move an arm to a place. And this is a very difficult and computationally intensive problem. And now we just kind of jump over that whole thing. And now if you have a way to teleoperate a robot or to put it in a simulation, you can just learn how to move all those joints to mimic the human operator or to accomplish, you know, some reward function or to maximize it. And so you can skip that whole computational challenge. And so those two things together [03:22] Basically mean that everything we thought we knew about robotics or like what kind of businesses were good businesses or bad businesses, all like that slate has been wiped clean. And so I think you're going to see this Cambrian explosion of different robots for different applications that now suddenly just work. Whereas before they were like really struggled to do the most basic things. 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? [03:52] try to get really, really narrow the successful ones. We're going to focus on this one problem, like factory automation for 3PLs, for putting things in boxes and putting them on a conveyor belt, like very specific. And that's just so you could narrow the problem enough to be good at it. Now, I think you're going to see people broaden the horizons a little bit 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, because it's my view that there's going to be a whole bunch of different shapes and sizes of robots, each optimized for different types of [04:22] 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 going to see some of those, but the vast majority of robots will be more special purpose in nature. 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 going to 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

[04:52] who are at the very front have been working on it for decades are like this is [04:55] Definitely happening now. [04:56] Yeah, if you had like 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. [05:12] and kind of simple. But if you know what you're looking at, you see the signs of life that mean over the next three to five, 10, even less years of development, this will go from an interesting technology in a research organization to like broad [05:27] mainstream appeal. And yeah, those signs of life are happening. Those light bulb moments are happening all over the place right now. 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. [05:44] 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? [05:51] Yeah, you've touched on a bunch of good ones. 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 where things are in the home, they need to interact with and manipulate these objects, like you said, and 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 going to take that and reason about what the next steps I should take as a robot. [06:21] And once you have those next steps you're going to 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,

[06:32] you know, end to end models that basically given a simple task can go execute it. My implicit assumption here is that on some timescale, 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? You know, one of the biggest challenges for something like this is 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 make use of a robot like this? [06:55] And that could be in a home environment, or maybe it's like a manufacturing business that its entire workflow 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 [07:09] will come pretty fast. And I'm pretty confident in that. The part that I think traditionally takes longer is the world has to adapt to basically, now that this new thing exists, 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 or should change to best make use of this new thing? This is where AI software is, where it's obviously much better than what's currently being deployed and used. It takes time to get from the tech is good to now it's implemented everywhere. So you're basically saying it's like the robots will be [07:39] 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? Yeah. And I think the companies building these technologies have a responsibility to help us figure that out. You know, they're closest to technology. And I think they need to think not just about like, what was technology building do? Or, you know, what's the fancy new thing I built in there? But like, you know, three steps removed from that, 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

[08:09] option curve and make it happen. Are you more in a mindset of like, [08:13] we are Apple and we're going to like, we'll tell you the product kind of, and like, this is how it's going to 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? Well, it was a frustrating answer, but a little bit of both. It's one of those things like strong opinions, weakly 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. Like a product with no opinions is just like, you know, you wouldn't even notice it. So I think you have to [08:43] 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 [08:50] Not stubborn enough, you end up with a 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, I think it's a careful balance. Why did you feel compelled to go for the home? Like you obviously, 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.

[09:20] Yeah. I bring this up because like at this point in my career, I kind of know how I want to spend my time and like what's important to me. And first of all, I want to have a lot of fun and working on home robots that I can use all my friends can use. I couldn't think of anything more interesting that or more fun, especially compared to like robots that are hidden in a factory that no one would ever see. [09:50] 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 our 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 first one of the first streamers to make six figures just playing video games online. And he's like, this completely changed my life. And so like moments like that, when you build some cool technology, but then it [10:20] a needle for someone and they tell you their stories. That's the really motivating thing for me. And you're just not going to get that, you know, if you don't have millions of people using the product. I mean, the idea that you could get like a robot in everyone's home is it's really, 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, it does the right set of things. It seems very believable. And I guess like you probably had some range of considerations

[10:50] possible, cheapest possible thing all the way up to we could try to make a $50,000 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 live along? Because you're not doing humanoid. From day one, uh, [11:14] My concern is that there's always going to be an expectation for what the home robot product can deliver and what reality is, especially in the early days. And that 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 want to do everything possible in our favor to tip the scale towards like value. And so that means like being really aggressive on cost to get the price down and make these affordable. That has the dual benefit of on one hand, you know, making it so that people are delighted by the product. [11:44] because it's not something they spent as much as a new car on. They spent something much, much less. 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, because lots of people can afford them. And at this day and age, data, real world data, is one of the biggest bottlenecks in robotics. And so if you can get lots of robots out there, you're going to have lots of data much sooner, which then creates this feedback loop where the product gets better, [12:07] 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. Yeah. Do you think that the like... [12:19] the humanoid vision, which is obviously extremely sci-fi and cool.

[12:23] 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 to something that like ought to exist? Or is it kind of random? First of all, when I see the videos of human humanoid robots these days, having worked in the field for a long time, it is just so cool. It's so amazing to see what people are able to [12:53] and how dexterous they're getting in terms of the 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 question for me is, if we're talking about putting these robots to work, or people owning them, the question is, at the end of the day, is this the most cost-effective way to deliver the most value I can 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 [13:23] 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. 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. These are not good things for the home. That's true, actually. Like a big, heavy robot falling down your stairs is a huge problem. 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 running into some of the challenges of a humanoid. That said, like there are some things

[13:53] for 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, I buy that argument that there are some uses where we'll want humanoids. But I think it is currently, I think people advertising humanoids are trying to get hype in the space, get more investment in the space, which we need. But I think the actual practical uses of them, it will be a little bit smaller than what is being portrayed currently. 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, [14:23] out by machines like because 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 or like taking care of like you know big sort of like uh patrol areas and things like that so i could see that because 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 [14:46] hurts a kid or an animal or something like that. I mean, maybe in the distant future, we can solve these problems. I think just near term, you're less likely to see them in the home first. Along that curve, though, between now and of course, you know, 50 years out, like obviously these things are going to be, I think it's like cars where it gets safer than people, you know, one day, you know, I assume. But yeah. [15:04] On the way up, what's the regulation going to 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? Right now, you know, it's 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 going to see 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. You could make an argument that this is kind of just a step up from that.

[15:34] targeted regulations for individual products. We have general 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. But I think there's an immense responsibility on the developers of these products to try to make them safe and to do everything possible following best practices, 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, the security of these products. I think that's really important. Obviously, the home is one of [16:04] there needs to be a great degree of trust and responsibility that goes with companies who are, you know, have these machines that are likely covered with cameras, you know, running around our homes. And most of us don't even think about 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 going to do the right thing in my home? And that's where I'd like to see a lot more scrutiny. So what does that mean you're going to need to do? Because 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, like now there's like a [16:34] What does that mean you need as like a company to sort of be, you know, a trusted brand there? Like you have to go from day one pretty hard at that, I guess. Yeah, yeah. You have to have some principles and opinions and be able to talk about it publicly, I think. But, you know, all these products are going to every every new category of product like this goes through weird snafus in the early days. When you mentioned Alexa, I was thinking to mind when when those first came out, wasn't there something where there's like a TV commercial that came on and said, hey, Alexa, something, something. And then like across the United States, like thousands of people bought toilet paper, you know.

[17:04] 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. So there's going to be these weird moments and things that come along in the early days that... [17:16] But anyways, on the data side, for us, we have two things we care about. One is transparency. So if there's data being collected in your home, like what was it? I want to be able to know what that data was and what's going from the robot to anywhere else. And the second is control. If this product is in your home, you own it. You need to have the on-off switch and be able to control what that data is used for. And I think if you have those two things and you are principled about those things and hold true to them and basically fulfill your promises, [17:46] I think that's the best starting position for something like this is just establish those principles up front. One last question on robotics, and we can go to another topic. AI models behind robotics. How distinct is the concept of robotics AI versus other AI? So, I mean, there's a lot of similarities. And I think, in a way, like LLMs that started off as robots. [18:08] like chatbots that exist purely in the text world, and robots, which are like physical machines, very multimodal in nature, you can see these things kind of converging. Because the latest models are multimodal, they can take in audio, images, other things in the same way that your robot is expecting that. And so over time, I think they're converging a little bit. And in fact, a lot of the training approaches, pre-training, post-training, those concepts exist in the robotics world. However, there's still a lot of things that are unique to robotics that you would never do if

[18:38] mixing in real-world data, different ways of collecting it, different ways of using simulation, and figuring out how to tie that to all the intelligence that's embedded in a modern LLM. And then the data is super important here, obviously. Data is important today. I think this is a now problem. If you look at LLMs, I think the reason that you can see so many different companies, like 20 different companies, all building foundational models and get within a stone's throw of each other in terms of performance, small teams, large [19:08] which is the internet and everything that can be downloaded from it. And, you know, that data kind of determines the quality of the model that you can get. And there's certainly some alpha 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, you know, or generate robot data from other things like [19:38] videos and trying to 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. 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? I don't know. I think they'll probably be both. In fact, I've probably talked to it. [19:58] 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, especially as this data void exists. But when that starts to be filled and we start to see useful robots in the world, I do think the majority of data collection will come from robots and less from people getting data. I would also think that for your product, for example, any data set that is not your products in the wild is going to be approximating the data. And the perfect data set I would imagine would be

[20:28] out in homes giving you data. If your technology... [20:32] is sufficiently advanced that you can do transfer learning from other forms of data, other robots, YouTube videos, whatever it is, any source of data, and you can use that to train your robot, that's like an advantage because you don't, you know, then that total size of that data set may be much larger than just the data set 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 [20:57] deploy a model on. Totally. Yeah. And we'll see if that changes over time. Yeah, it makes sense. So we alluded to this before, but you obviously started Twitch, you started Cruise, you're doing it again. First of all, 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 you've had two really successful companies, which I want to talk about, particularly Cruise, because I think it's related. [21:27] like what's driving you now? [21:29] to do this again. I mean, I had, you know, a very, very short-lived existential crisis after Cruise. I was like, oh my gosh, I'm done with this company. 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. Which is kind of like, yes. Are those the same thing, I think? I don't know. Just kidding. We work super hard. And then after thinking about that for a while, I realized like the thing that, you know, outside of spending time with my family and friends, the thing that brings me the most joy

[21:58] is solving really hard problems with really smart people. And so that is retirement for me. That's the most fun, 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 a big team of people and you do it in the form of a company as opposed to a hobby or something that you're doing it on your own. And so to me, I think there's no better thing. And maybe at some point I'll run out of energy to go hard like I am right now. But for now, this is great. We have a brilliant team. [22:28] this exciting new product in a big market. And that is energizing to me. I want to talk about a couple of the things that you've said about how you want to build this time. One that stuck out to me was that you never want to be more than 100 people. Yeah. And first of all, I actually didn't know. Is that like literal or is that directional? To be seen. Yeah. I think right now we're taking it very seriously. So if that is actually your belief and talk about why, but if that's actually your belief, then you make very different hiring decisions. It's like, well, you know, if I think [22:58] people to this type of role, that means every person in every seat has to be the best in the world at this for the company to be successful. And so you end up [23:05] passing on a lot of people that are great people, really talented, but they're not at that specific level we want for that particular role. And I think 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 just the founders, they're all in it 110%. They're all usually like brilliant working together. They're almost mind melded. And then you have like insane productivity for some period of time until you get bogged down by the organization

[23:35] functions and, you know, teams of people and management layers, all this kind of stuff. Disconnected. You have communication issues. Yeah. And so you get this drift away from this, like, [23:44] pure, like force of energy 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 [23:53] pure high output zone. And you can't get that if you have like too much of a range of people in the company. I really think of it more like a pro sports team. Like you're not going to have, you know, the Lakers. I think you're going to have like 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 talents. Like if you have like the LeBrons with the high school players, like the LeBrons are like, what are we doing here? Yeah, exactly. They want to go play with the best people in the world [24:23] And that's how you get better and grow. And people who are the best in the world at what they do typically got there because they had this growth mindset that constantly want to get better. And 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. I think 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 10 non-engineering roles. [24:53] they probably can't do it alone. You've got like, you're going to have all these like physical parts, you're going to 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 outsourced? 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 deal place? Yeah, it's a good question. That's, that's part of why I said to be seen, like this is a great mental model now and it may not.

[25:23] 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. 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 and good for these companies. So it'd be a question for us. Do we partner or outsource things? I think, 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 [25:53] And for things like a lot of operations or facilities or 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, they have lots of funding, they have lots of teams. It's almost like they take on these responsibilities because they can, not necessarily because they should. I feel like one of the most important things, which I think you've obviously shipped in self-driving in a way that very few have, but I think in a lot of these sort of [26:19] more sci-fi areas, it's very easy to not [26:22] be in like shipping mindset. And like, I think you did this really well at Cruise, obviously, like opening, I was doing this like well before chat GPT. 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 got to be the mindset rather than just like, hang in a warehouse building the perfect robot forever. I think for that, it's it's starting the thing you want to build and then working back to what is the main what is the constraint? What are the constraints or bottlenecks that we need

[26:52] than, you know, what that one bottleneck or constraint would dictate. And for self-driving, that's a combination of safety, trust, and public acceptance. And so, you know, those are different work streams where 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 it, you know, Cruise, for example, see the metrics were the single thing we talked about every week, week over week, over week, making progress towards those. And I think [27:22] designer metrics around kind of sets the tone for the company. And it's got to be aligned with that, you know, whatever the constraints are. What do you think you can do in a home first? Like, what do you think would be the first activity that can really be done well in a home? And then like, what are the things that you think are close to maybe follow in the next 12 to 24 months or something? Yeah, I mean, there are hierarchies, I think, of tasks for a home robot. And if you look at, I think, two, like a classic two by two grid, I guess one is maybe the [27:52] 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 the, the, [28:01] 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. [28:09] So I have two kids, 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 running around picking up toys. 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.

[28:22] push a button, turn it on, and then when you're gone for the day, all the toys are magically put away by the time you get home. It's like a mind-blowing experience. And let's say it screws up and two out of the 100 toys are still on the floor when you get home. That's okay. Still better. Yeah. So that, you think about... [28:37] nines of reliability for engineering. Like maybe one nine is fine for that particular task. There are other things like putting a wine glass in a dishwasher where the technical complexity is a little higher and the-- [28:50] What's hard about that, by the way? Is it like the grabbing? Yeah. So if you think about picking up objects, this microphone, which is going to make a noise when I squish it, 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 going to shatter this microphone into a million pieces. For a wine glass, the margin is very thin. And so from a dexterity standpoint, it's a little more fragile. [29:20] 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. It is. And the evolution to how we get there is interesting too, because my one-year-old daughter, her hands are like open closed. There's nothing in between. She grabs objects. The wine glass is shattered. And at some point along the way, we developed much more nuanced skills and abilities. But so wine glass is another one. The other thing is challenging is if you're putting a wine glass on a rack and, you know, it's a thin stem or something

[29:50] 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 going to be your customer anymore. That's right. And so that's like several nines of reliability. And so I think that this this sort of spectrum of technical difficulty and basically forgivability is going to dictate the types of things you see home robots do first. [30:20] 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 like for laundry if you put the red sock in with the whites you know have pink laundry that's like game over right 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 and i think it'll happen pretty quick but you know i think you think it's like cooking a steak at some point yeah why not if you think about at the end of the day you've you have pick in place and simple manipulation that's what [30:50] 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 cooking and temperature sensing and what like that. So it's all doable. It's just like, I would not start there. 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 15 years from now, that's doable. Less than five. Less than five? Yeah. Yeah. [31:12] This stuff is going fast. Again, if you look at the robots you can buy today in the world, like the nice robot vacuums, you may not think that. [31:20] 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 kind of know where things are going, you absolutely believe that. 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? 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,

[31:49] too simplistic or not enough 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 [31:59] the more [32:00] mechanical complexity or capability that you add to a hand, the more sensing ability. In theory, it would require less, you know, sort of rocket science to figure out how to do a task with that hand. The trade, 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. 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 want to do at the lowest cost while also being able to [32:30] straightforward manner. But I think in The Limit, you know, there's a lot of, you know, we think by analogy a lot and we have two hands and two arms. And so, [32:37] like a lot of the robots you see today have two hands and two arms, but it is really interesting thought experiment. Like what is the ultimate hand arm thing look like? And I think it was Rodney Brooks who said this the other day, but I actually do kind of think maybe it ends up being some crazy octopus tentacle looking thing in the future. That's like very adaptable and can reach into small spaces. Interesting. Well, I think that, you know, the human hand was ended up where we are due to probably some impossible to unravel sequence of evolutional pressures. Well, it's like you start down some path and then you do your best, you know, evolution does its [33:07] given some somewhat random starting point, I suppose, right? Yeah. So if you could go back like a million years and hit the reset button on human evolution, like 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 challenge will be like, can we figure out what that is? I have a couple of stupid questions about the robot at home. One is how strong could it be? Like, is it, is a hundred pound robot, like it must be ridiculously stronger than a person,

[33:37] certainly make it... [33:39] maybe stronger in some dimensions. There are some things that are [33:43] sort of soft biological muscles are pretty good at. Are stronger than like... [33:46] like a physical robot pound for pound kind of thing? Yeah, 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 of generations will be. So that's kind of interesting. 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. Yeah, I've been going down the rabbit hole on this a little bit, thinking about like, you know, as again, our focus on affordability and cost, like is a electromagnetic gear motor where you've got a... [34:15] magnets and copper winding and a bunch of gears in a 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 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... [34:39] power density, 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. Isn't like hydraulic is pretty strong, like hydraulic pressure is pretty strong? Hydraulics can be extremely powerful, but they have other trades, typically noisy. The valves and things are pretty expensive, can be harder to control and get high fidelity motion. And so in terms of power density may be good, but there are other trades and the reasons you don't see this on a lot of robots. That makes sense.

[35:04] Another question I had that's sort of like probably off spec, but while we're talking, is this going to be something that would have like home security applications as well? Or does that then take you into weird territory that's just not worth going to? Yeah, I think so. I mean, one of the challenges with a home robot is this kind of general purpose. And so like, you know, what are people going to use this thing for? And I think it's hard if you just have a laundry list of 50 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 with their home robot at home, [35:34] I wonder if I forgot to turn off the gas on the stove and send the robot over there to just... [35:39] you know, tell you or even take it on for yourself. In the same way, 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. Yeah. If you see me getting burglarized, like do something. But I don't know if you will 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. Totally. Alerting probably is good. Taking actions, probably not. I hadn't thought about that side of that. You know, that's not really in our... That's good. That makes sense. I'm just thinking because like, you know, [36:09] There's this brilliant, capable robot in the house. My guess is you're going to have a lot of people want it to start doing a ridiculous number of things for them. Yeah. The arc of time. And then you'll have to choose from that set, like what goes in. [36:21] Yeah, I think so. But for sure, on the security side, I would hope, though, [36:25] rather than having like physical deterrence and like, you know, having your home robot turn into a security guard with a baton or something. It's more so that it just becomes unattractive to rob homes or do, you know, break in 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. There's very little incentive to break into cars. It's not worth the risk. Well, I mean, even like a security system just makes a loud sound and calls the police. I think that's pretty effective. I think it's extremely effective. Yeah.

[36:55] are pretty old and you know they're deeply embedded and but yeah 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 and then the sirens blaring and all that stuff 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 well i mean it's touched on something interesting 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 that immediately what comes to mind is like the thing that's most annoying to you today [37:25] to do in your home. And I think that's good. We want to help with the annoying stuff. - What comes out almost like laundry probably? - Yeah, laundry dishes, picking up after my kids, wiping surfaces, cleaning, these are the things you would expect. 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. [37:43] 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, a little chocolate on the pillow, all these like little flourishes. Like, I don't know. 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. Yeah. And so I love the idea that if you can afford a really affordable home robot, we're going to give you a lifestyle that, you know, would otherwise be inaccessible to you.

[38:13] 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? Yeah. And maybe don't need all these things. But the point is like, you know, your time is more valuable than that. It's very scarce. Like humanity's time, I think, is really important. But for a robot that's got 24 hours to sit around in your home and like try to make your life better, what could we come up with for it to do? What could it come up with to do for you? That's [38:43] on what you've learned from the way self-driving cars played out and how it might [38:48] 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 [39:01] could you know be impactful in the robotics land well it's hard to say that you know very different approaches to getting to market but it does seem like they're both trying to converge at the same thing which is you know self-driving cars everywhere i think one thing that uh [39:15] 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 make this self-driving product. Waymo, by comparison, has taken [39:36] for almost a couple of decades at this point, maybe not quite that long, and probably tens of billions of dollars of investment. And the revenue relative to that has been fairly meager 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. And I think it's no coincidence that the only companies who succeeded in that approach or are on track to succeed in that approach are owned by Amazon, Google, or

[40:06] you know, like a major 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 benefactor. And instead, we can find clever ways to get to market that... Which I guess is why you need to get to market and be selling something along the way to fund all of this. Well, I think if your development cycle means [40:36] You'll have to get acquired. It means you're entirely dependent on either being acquired or the capital markets being pointed in the right direction. And historically, things tend to cycle back and forth. In a five to 10 year timeline, you're getting awfully close to almost guaranteeing that you straddle a down cycle as well as an up one. And that can be a killer for these companies. We don't need to talk about. [40:57] sort of cruise and GM too much, but I am curious about sort of, you know, I saw you share on [41:03] cheeky pint about like not wanting to sell. And I'm just curious, like 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. I think, you know, and I said this before, but my conclusion is like, if you are selling a company, [41:20] It should be because the reason you started the company or the thesis that you had in mind or the thing you wanted to build, something has changed. And maybe you're no longer interested in it, your life circumstances have changed, whatever. But I think it is a fantasy to believe that you can sell your company, have your cake and eat it too. Sell your company in order to further the mission. And further the mission. I think in theory this can happen sometimes, but it is so, so rare. It is rare.

[41:50] thing and control it and make sure it happens in the way that I want it to for some kind of partnership or liquidity. 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 handing over the reins to someone else. Yeah, you're 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, [42:20] also drives you to want to sort of hold on to it indefinitely. Yeah, perhaps. But I think I'm not selfish about it. Like if I, you know, I feel like I have an obligation to stay true to, you know, our investors, the employees in the mission. So, you know, even though 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 we all share. Yeah. Maybe as a final thing to touch on, you did this crazy like marathon around the world experience. What was that? And like, why did you do something that seemed so, [42:50] Hard. Deep in the middle of Cruise, I think I was frankly kind of frustrated that 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... [43:02] you know, the result wasn't always proportionate to the energy going in. And for running, for me, at least, that was not the case. You put in the time, you get better. Yeah. And so that's very deterministic and satisfying. And so I needed something to balance that, I feel like, in my life. 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.

[43:32] And then in fine print on the website, it's like, the world record is like five days and 10 hours by this one guy. And then I got the wheels turning. It's like, well, I wonder what the theoretic engineer brain clicks on. I wonder what the fastest theoretical time [43:45] you could do is if you if you optimize the it's like the traveling salesman part of the continent like yeah where you land optimize for customs in and out and logistics and like really dial it up to 11. 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 that's crazy spending my problem is that i couldn't run a half marathon that's where i would struggle this sort of stubbornness and attachment to this idea meant that part of this was i had to train my body physically to be [44:15] and then you're not resting afterwards. Yeah, so the cycle, for example, is you start in Cape Town so that you fly to Antarctica. You have to start there because the weather is so unbeatable. Oh yeah, the Antarctica one, that's tough. You need at least a six-hour window of decent weather and you're looking at the weather forecast. When it clears, then you fly in and you land, you run the marathon, you get out because that can throw off the... Where in Antarctica do you do this? On the most temperate outer part of Antarctica. So it's on the continent, but we're not talking South Pole. Yeah. So it's cold, but it's not like... But it's not snowy? [44:45] It's icy and you can't say it's like barren, all ice. You land the plane on ice, you run on ice. You're running on ice. On ice. It's kind of like crunchy ice. Like there's a, there's like a ski slope groomer that did a course down there. And it's like a six mile loop or something. And so it was like running on, it was like trail running. Got it.

[45:01] It's still pretty crazy. That's crazy. 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 got to say like it was, it was, you ever like finished the last item on your to-do list and it's just like a dopamine hit and you're like, oh, that feels so great. Yeah. That's what it was. It was like relief. It was like, check the box. Now I can, my tormented brain, which wouldn't let this go for 18 months, can finally relax. Where do you go? Cape Town, Antarctica, South America? [45:31] South America and then to Panama City. And then I think to Madrid and then Oman. And you're just like, by the last one, are you just crazy fried? Or were you in shape to just not... [45:41] be [45:42] you know, so beaten down. I was pretty fried. Pretty fried. Yeah. But it's like... [45:47] The training I did peaked at doing three marathons within a 24-hour period in three different cities. That was like the stress test for this, if you will. And my coach was like, only three marathons, that's not seven. Is that actually the right amount of training? And he said, I promise you, when you're doing the real thing and you have this whole crew of people with you and everything is on the line and the adrenaline going, if you can do three in 24 hours, you can do the rest. And he was right.

[46:17] to me quite a bit in that domain. Yeah, totally. All right, Kyle, this was really fun. Thanks for making time for it. Thank you.