Pod Insights No Priors Building Hard Tech in Hard Markets: Kyle Vogt on Cruise, Twitch, and The Bot Company About this episode Kyle Vogt joins Sarah and Elad on this week’s episode of No Priors. A serial entrepreneur, Kyle co-founded Twitch, transforming live streaming, and later Cruise, the autonomous vehicle company acquired by GM for $1 billion. Now he’s taking on AI-powered home robotics with The Bot Company. In this episode, Kyle shares his journey building transformative tech companies, the challenges of scaling autonomous systems, and why he believes home robots are the next frontier. They also discuss the parallels between AVs and robotics, overcoming consumer skepticism, US vs. China manufacturing, and the policies needed to foster a competitive robotics industry.
Sign up for new podcasts every week. Email feedback to [email protected]
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @KVogt
Show Notes:
0:00 Introduction
0:29 Founding Cruise
3:12 Tesla vs. Waymo approach
4:44 Scaling autonomous vehicles
10:03 The Bot Company
16:35 Deploying robots in the home
17:56 Parallels between robots and AV markets
20:51 Personifying robots and overcoming consumer skepticism
25:00 Timeline on consumer robots
26:47 Chinese vs. US manufacturing
29:15 Fostering a competitive domestic robotics industry
34:00 Lessons from Cruise & personal philosophies
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:05 Sarah Guo? Hi listeners and welcome back to No Priors. Today, we're joined by Kyle Vogt, a serial entrepreneur who has helped build some of the most influential tech companies. He co-founded Twitch, shaping live streaming. Cruise, the autonomous vehicle company acquired by GM for a billion dollars. And now Kyle has launched the bot company, a startup focused on building consumer robots. Kyle, welcome to No Priors.
0:26 Speaker 01 Awesome, let's get going.
0:27 Speaker 02 Obviously, you've done a variety of different things over time. Everything from co-founding Twitch, you started Cruise, you're now working on a new startup. Can you tell us a little bit more about your Cruise experience? Because I think that whole era was incredibly formative for everything that's happening today in AI. And would just love to get your perspective on why start Cruise when you did, how that all evolved, and then how that's informing what you're doing now.
0:46 Speaker 01 Sure, yeah, we can go back to the beginning. This is 2013, and back then, you know, there wasn't really self-driving car, you know, technology like there was today. There was just Google working on, you know, their self-driving car project. And rumor had it, they had spent like a hundred million dollars and they had the world's best engineers. And so going after something like that was a little bit crazy. And, you know, even after having worked on Twitch, you'd think that'd be enough credibility that as a repeat founder, I could go back and raise money. But it turned out even that was like kind of a crazy enough idea. And Twitch hadn't been acquired yet, that I had a hard time. I had to scrape the bottom of the barrel to raise money. I think I pitched like 120 investors, you know, over the course of probably a couple of years to raise all the money we needed. But our thesis back then was was very simple.
1:28 Speaker 01 We were going to start by finding, you know, instead of going directly after what Google was doing with, you know, self-driving cars, they were trying to make the ultimate self-driving car, I think, as a moonshot. We took an approach of what's the lean startup approach of this? Can you build something that has the minimum quantum of utility that is maybe lower cost or easier to execute so you can get to market more quickly and move from there? And so we started with a retrofit system where we would take, you know, a regular car, put some sensors on it, a computer in the back, and get it to drive. And we got that working pretty quickly. You know, kind of like an early version of Tesla full self-driving.
2:01 Speaker 02 I think that was for just one car model too, right? That was like a BMW or something in the time.
2:05 Speaker 01 That is the challenge with the retrofit business. It's like without the blessing of the carmakers, you have to kind of reverse engineer protocols and figure out how to attach motors to steering wheels. So it wasn't necessarily sustainable, but we were still going to try to figure that out. I'd say that peaked around the time we went to a YC demo day and Sam Altman was in the car, actually, and we rode to turn it on and rode its demo day. And we took that product for about a year and a half and then realized that we had done enough technically that maybe we didn't have to do this lean startup approach. Maybe we had just go straight after the big fish, which would be building robotaxis. And around that time, Uber and Lyft had sort of risen in popularity and now we're becoming these household names and like, you know, talk of going public and all this kind of stuff. And they had this big hole in their unit economics, which is paying the drivers.
2:50 Speaker 01 And so suddenly there was like a strong market pull for self-driving technology, whereas before I had been seen as just kind of a cool sci-fi thing. And so we were able to raise some money from Spark Capital and go straight into that. Within a year of that, I think we were acquired by GM. You know, we had working prototypes driving around San Francisco, obeying traffic lights, changing lanes, you know, going from point A to point B with an iPhone app, you know, back in 2015.
3:12 Speaker 02 That's pretty amazing. How do you think about the different approaches that people are taking today? So there's Tesla on one side with a very specific approach, kind of moving everything to things that are a bit more camera-centric, but training on sort of a richer set of sensors and approaches. There's sort of the Waymo approach, which is much heavier on the hardware side in terms of what's actually on the vehicle. Both seem to be doing very interesting things. One is robotaxis, one is kind of still building mainly cars. How do you think about the different approaches, both from a business model perspective but also from a technology perspective?
3:43 Speaker 01 To be fair, Elon nailed it from a business model perspective. He's been making billions of dollars of profit while developing self-driving cars, whereas everyone else has been burning billions of dollars to try to get to basically the same point. I think in the end, when you start with custom vehicles with lots of sensors, really expensive, and you make it work in a constrained environment. And Tesla starts with unconstrained environment, low-cost sensors, but it doesn't quite work without a driver. They're all trying to get to the same spot in the end, which is low-cost, works everywhere, commodity sensors. Different path to get there. I think Elon won that hands down.
4:16 Sarah Guo? What do you think of the criticism that you can't get there? You can't get to full self-driving from mostly self-driving?
4:22 Speaker 01 That statement is almost certainly wrong, given a long enough time span. Again, going back to what Elon's approach was, he doesn't have to finish by a certain date or run out of money. He's making money along the way. And so I think the only risk is that customers get fed up and rage quit his program, but they're getting something they like along the way, Tesla full self-driving. So I think that's the right approach. But in 2013, for sure, and 2015, even 2018, it really wasn't viable to have a full driverless car that just used cameras and low-cost sensors. It just wasn't. The technology was not there. I think now, if you take a fresh look at where we are today with large language models and generative models and other things, that sort of class of technology, applied to the classical challenges of perception
5:05 Speaker 01 for autonomous driving, even motion planning for autonomous driving, completely changed the game in terms of the magnitude of compute that you need, the expense of that. And I think now, from cameras, as long as you have sufficient redundancy and low-light sensitivity and some robustness there, you can extract from a single camera image, not even stereo. You can get beautiful depth data, really accurate, and those models are getting better every day. And so if you're making a bet on the right technical approach in 2025, it does not involve a bunch of expensive LiDARs or exotic sensors. It involves the most commodity, sort of high-voltage, readily available sensors you can get, and probably just several more of them than you would find on a typical driver-assistance system. So I think that's the path from here on out.
5:46 Speaker 02 Is there anything else you think is lacking from a technology perspective, either in terms of hardware or just scaling models? Or as you know better than anyone, everybody started moving towards end-to-end deep learning over the last, you know... Year or two, and that's really made a big difference. But is it just scaling that up or is something else lacking?
6:02 Speaker 01 End approach will be an end-to-end type model. I mean, it's sort of hard to put it in a bucket of end-to-end or smaller models because there's such a spectrum in between. And everything I've seen is a mix and match of various technologies. If I look at the limiting factors, at least on the hardware side, at least previously, it's been hard to get high-performance compute in an automotive, you know, high-temperature range, safety-critical environment. And so, you know, Cruise made custom chips. I'm sure Waymo makes custom chips. And sort of piecing together things from the supply chain to solve that is a little challenging. And so there is room for more high-performance compute automotive silicon. And I've seen some things happening in that space. That's one. And then I'd say the other piece, you know, most robotaxi deployments I've seen rely on some form of remote assistance.
6:46 Speaker 01 And so there's a question of like, how you get reliable connectivity to a vehicle from anywhere. Using multiple cell networks, like what Cruise has done and Waymo, I'm sure others, works, provided you have cellphone coverage. I think the missing piece, maybe Starlink or something similar, where you can have always-on connectivity between Starlink and maybe cellphone and some other fallback. I think that really opens up the opportunity and the number of places you can deploy robotaxis. Whereas before, it's sort of an open question, what you do if you're driving down Highway 1 in California and there's no cell coverage there. Like, should you still have an AV on that road? Given, you know, if there's an issue or, you know, a customer needs some help, you like literally can't get in touch with them.
7:25 Sarah Guo? In retrospect, is there, was there like a right ear to start a self-driving car company? You were so ahead of the ball on this.
7:32 Speaker 01 It takes a long time to sort of spin up automotive pipeline and everything. So probably like, you know, circa 2020 or so would be the right time to get started. And then, you know, around now, I think you'd be having like a combination of hardware and software that's mature enough. If you have a nimble enough engineering team that's able to adopt new technologies when they pop up and quickly pull them into the pipeline, you're actually well-positioned, even if you started a while ago and your tech stack was based on some older technologies. If you have all the infrastructure in place for validation and testing and training models and deploying on public roads with test drivers, I think you can go a lot faster, even if you have to like sort of rip out and change some of your tech stack to adapt with the times.
8:09 Speaker 02 One thing I've heard, two opposing viewpoints on, is the autonomous vehicle market in China. And one point of view is, well, it's not that real and it's mainly teleop and it's a little bit more sizzled than steak. And then the other opposing view is, well, actually, they've advanced dramatically, really rapidly. There's fewer safety constraints. So you can do more, try more, et cetera. And the models and approaches there are at least a parody with the leading contenders in the Western world. Which of those two views do you subscribe to? Or how do you think that market will evolve?
8:40 Speaker 01 From what I've seen so far, and I don't have, you know, a lot of inside information just from what I've seen, videos online and other thing. It does still seem like there's a lot of teleoperation. Like, I think even, even someone like Tesla may start off with like a one to one ratio of remote operators to vehicles. Cruise and Waymo probably start off pretty close to that. And then I think over time move to a smaller ratio. And so I think in the interim to like, get the deployment numbers up to get more experience to accelerate data collection, people are brute forcing it, which means there's probably a lot of remote operation. I actually think that's fine because it doesn't take much, once you get to like, you know, 50% remote assistance, or even 25%, at 25% remote assistance, you've already reduced the labor costs, you know, 75%. And so you're almost already at the
9:22 Speaker 01 diminishing returns point. And it sounds on one hand, kind of crazy to say like, oh, if there's a, you know, 1,000 AVs out there, then maybe 250 people monitoring them. But that's actually not crazy from a cost, from a unit economic standpoint. It actually makes a ton of sense. And a ratio of one to four is like trivial, I think, with today's technology. Over time, you could get to 20 to one or 50 to one, but you know, you're just talking single digit points of margin at that point. The real benefit is just going, you know, anywhere less than one full human in a car makes the economics of this really good. And I think, you know, puts you on a pathway towards, you know, better safety for the vehicles because they're primarily driven by a robot that has great reflexes and is going to avoid situations, but then also lower cost to consumers over time.
10:04 Speaker 02 You did amazing work on Cruise. And then you decided to start another company, which I always think is a really brave endeavor because anybody who's been through multiple startups knows how painful and terrible it is. Could you tell us a little bit more about the impetus behind the Bock company and what you're doing there?
10:18 Speaker 01 Yeah, well, we talked about this a little bit when I was making that decision, what to do next. And I did some soul searching and determined that I I'm just a builder. I like building things and I'm sitting on the sidelines or helping other entrepreneurs or doing something else. I think would be fun, but not quite scratching that same itch. I'm 39. I feel like I got at least one more startup in the tank. So a question became of like, what to do? And I look back on my career. This is my, I guess, depending on how you count, like third major startup. And the first one was Twitch and Justin TV and that was straight out of college. And that was just doing anything, doing a startup and trying to make it work was the priority. And that ended up being video games and entertainment. The second time around for Cruz, after doing entertainment, I decided I want to focus on impact.
11:00 Speaker 01 So like, what's something where we can use technology to meaningfully improve people's lives? Self-driving cars, they save lives, they give you tons of time back. That was like squarely in the impact category. Third time around, I definitely care about impact, but also fun. So it's like working with people I like on problems I like. Really challenging technical problems and building amazing products. And so we're building home robots. And the impact side of that is one of those things that's hidden in plain sight. There's only 24 hours in a day. And if you're sleeping for eight hours, working for eight to 10, there's precious few hours left that are actually your time. And people spend a surprising amount of that remaining time doing like essentially unskilled labor.
11:42 Speaker 01 Acting like robots every day, like making the bed, doing the dishes, folding the laundry, like picking up toys after your kids. These are not things that make us human. These are actually things that detract from our humanity. And they're the perfect criteria for that reason to be automated by machines. And I think, you know, when you describe that to people like, oh, you don't have to do all those things anymore. There's a machine that could do this for you. It clicks instantly. People are like, that is so obvious. To the point where I think, you know, in five years, maybe 10 years, it will seem as insane to have like a house without multiple home robots. As it would be to have a house without a sink or like a laundry machine or like a toilet. Like, these are going to be like critical things that, you know, if you can afford them and we want to make them really affordable.
12:22 Speaker 01 Are just going to seem like extremely common sense to like, why wouldn't I want to have the time in my home be my time? Not, you know, consumed by these chores.
12:30 Speaker 02 I thought that analogy was really interesting because we never really think about plumbing as a technology and it is. And to your point, up until reasonably recently, you know, most of human history, we had no running water. You'd like walk down a hill with a bucket and you'd bring it into the house. And I actually think that's a fascinating analogy because nobody really talks about some of these things that actually our technology is technology and the degree to which we now just take it all for granted.
12:52 Speaker 01 Yeah, so plumbing and electricity were sort of turn of the century things. And then I'd say in the 1950s and 60s, there was a resurgence, but around like home appliances. There's some great advertisements from the 50s and 60s, if you look back, it's like this is 1950s time, but it's like, you know, the housewife standing in the kitchen and there's the microwave and the dishwasher and all these like new appliances that like make it so they have more time and it can do more and be more productive. The last time we had like a surge of excitement and progress like in our own homes, you know, in like 50, 70 years. So I think it's time to revisit that. And going back to the robotics side, like actually pulling this off, it's basically been the dream for people working on robots since the dawn of robotics, to build a robot that can like go to the fridge and get you a beer or something like that.
13:35 Speaker 01 You know, you sit on the couch, it's like, it's like the dream for nerds working on robots. And that's like, obviously a tiny subset of what you'd want a household robot to do. But that's really hard. And like, why is it so hard to just have a robot like open fridge, get a drink and bring it to you? And the reality is it's similar to self-driving cars in some regard where it's a very unstructured environment, like every home is different. Everybody has, organizes their home in a different way. The layout is different, the objects in the home are different, how they live in their home is different. And so having a robot that that sort of lives in this unstructured environment, it's like the polar opposite of a factory assembly line where everything is rigid and repetitive and precise. In a home, it's like sloppy and changes every day. And so using classical approaches, where you have computer vision and you're trying
14:19 Speaker 01 to like reconstruct 3D objects or fit to a map, would make this like a really, really challenging and computationally intensive problem. Moving to more modern techniques like end-to-end learning or imitation learning, even reinforcement learning. Now, if you can teleoperate a robot and demonstrate how to do something or collect data from humans in some way or from internet videos, you can kind of imbue a robot with a sense of common sense, an ability to make sense of these unstructured environments. And on top of that, you can talk to the robot in natural language, using your voice, rather than typing into an app or on a keyboard. So I think, you know, you asked when the time is to start a robotics or a self-driving company, maybe that was 2020. I think for home robots, it feels a little bit early. So like now is definitely the time, in my view.
15:04 Speaker 02 How much of what you did for the people have learned at places like Cruise or Waymo or others is also useful in the context of home robots? In other words, what sorts of things overlap? And then what things are just completely different or new? Because people would often talk about driving environments as similarly chaotic and messy. And you know, the canonical example is always like the kid chasing the ball across the road suddenly or things like that. Is it even more difficult in the home? Is it less? Is it more structured? I'm still a bit curious about the analogies that could be pulled there. Funny.
15:37 Speaker 01 To start with, the big difference between the two is that for a driverless robot taxi, you basically have no product until it achieves superhuman safety performance. Whatever you establish that is like just slightly better than humans or 10 times better. I think most driverless cars that are on the road today fall somewhere in that category. And to get there, it means there's no MVP. There's no launching with something that's partially useful. It's like you have to reach that human safety performance. And on public roads, it's hard to constrain the environment to the point where you make the problem much easier. You can operate at night or in sparsely populated areas. But the reality is, just like you said, at any moment, anywhere, a kid could dart out in front of that vehicle. And so you need like a high number of nines of reliability to have any sort of product.
16:19 Speaker 01 I think in the home and most consumer applications and even most industrial applications, safety is still critically important. But the bar that you need to reach or the functionality that you need to reach or the constraints you could put on the system enable you to launch a product much more quickly. And so I think that's one big difference.
16:34 Sarah Guo? On this topic, how do you imagine deployment to work? You're obviously like, hey, Tesla had the right path here. Is there a Tesla analogy where you make billions along the way? It's not obvious. Like, are there constraints you can put around it where you have telop, right? Or just a couple tasks or a more constrained environment in the messiness of a home?
16:54 Speaker 01 There's a number of ways to attack that. Approaches I've seen are like you sell a really high priced robot today, like a humanoid or something resembling a human that's like fully teleopt. And you just tell someone this is going to cost, I don't know, like something crazy, like $50,000 and $1,000 a month. But it's the first robot you can buy that will like do stuff in your house. I think that's one approach to try to make money along the way. I think your market size is pretty small doing that, but that's a viable approach. And the other side would be to sell robots that don't fulfill like the promise of a household robot that does all your chores, but do like little bits of useful things. I just saw at CES this year, they have like little iRobot Roomba type things that have a tiny little hand that could come out and like pick up a sock that's in the way. And so these are like incremental approaches to sell products and get data and hopefully like
17:38 Speaker 01 learn about what it would take or train models or try things to be able to work up that ladder, I guess, to the holy grail, which would be like a robot that takes the place of your butler and your housekeeper and just about anything else that you would ever want. If you could have an infinite staff of people or robots doing things in your home.
17:55 Speaker 02 I guess we'll run the analogy to self-driving. If you look at what happened from a market structure perspective there, there originally dozens of startups that raised collectively billions of dollars. And one could argue that the end winners, the things that actually somehow worked in the market were two incumbents, Tesla and Waymo, Cruise slash GM, and then maybe to a secondary extent, applied to tuition, which is building more general software for cars and things like that. And then most of that market kind of didn't end up with the outcomes one would have hoped for. Do you think there's going to be a similar sort of shakeout here in robotics? And do you think there will be incumbent bias? Do you think there's a lot of room for startups? How do you think about how that market will evolve?
18:35 Speaker 01 I think for sure, both in AI generally, like just pure software companies. And then also, I think the next wave is starting on robotics. There will be like that bubble effect. And this is just like kind of how the Silicon Valley ecosystem and venture capital market works. There's either a couple of big rounds that get everyone excited and then other investors start throwing money into the same space because they see the markups happening quickly. And that following effect kind of floods the market. And when there's a lot of investors talking about funding these companies, more people kind of drop out of their PhD programs or quit their job to start a company. And I think the majority of those companies, I would say, are like low quality in that they're a founder that's like half into it, or a founding team that's like half into it and half hedging, going back to work or whatever. Maybe they're hoping it's a get rich quick
19:16 Speaker 01 thing. Or they're just the wrong founder market fit or founder product fit isn't there, even though they're smart technically. I saw this in the self-driving wave, like people who are really brilliant academically, but have the wrong mentality, not a product-centric mentality, they will leave their academic program because they want to commercialize their research, which to me is like a huge red flag because that means you are You're saying I'm not going to be flexible on how I solve the problem. I'm going to force my solution into the, you know, square peg into the round hole. And I think that can be very problematic for a startup when you're constantly wrong. I need to adapt to whatever you see. So most of those companies will be low quality. And as a result, we'll say that the bubble popped and like, you know, there's a huge wipeout in the industry inevitably, whether it's robotics or AI. But I think really what it was is there was a handful of good companies that were started during that time and before.
20:01 Speaker 01 And those companies did just fine. I don't think they'll be affected by the collapse. It's just all the noise and sort of the follow on and, um, you know, sort of the hype and mania that follows that sort of gives the impression that, you know, these things are collapsing or not viable, when in reality, I think there are lots, you know, a handful of companies doing really good work. I don't know if they're necessarily limited to the incumbents, but that is possible, especially in hardware. It's, it's really hard. But, you know, there were companies like Aurora and Zoox that one of them went public and one of them is, uh, is, uh, acquired by Amazon. And so has the resources to keep going. Cruise fell into that category. So these were not incumbents. These were companies that were started from scratch during, during the beginning of that self-driving car cycle and are enduring and I hopefully do well.
20:42 Speaker 02 Thanks for that overview and explanation of what happened in the industry. I mean, you had a great point in terms of Zoox and others also being part of the things that, um, either had an exit or worked in different ways over time or continue to be built against. One other thing that a lot of people do in the context of robotics, and you can see this maybe even being accentuated more in the context of a home robot, is they ascribe personality or they kind of project personhood onto these machines. Is that something that you think is worth leaning into? Is it something that's worth avoiding? Like the anthropo- anthropomization? I can never say that word. The humanization of these devices? Like, how do you think about that as somebody who's actually building things that will be in the home with consumers and, you know, may get interpreted in different ways by the customer?
21:23 Speaker 01 You know, to start with analogy, and the self-driving car industry was interesting because we named our cars, every car had a name and people would personify it when it came up and, you know, A car in itself doesn't look like a creature. It looks like a car, something that you drive. But once it starts moving on its own, your brain plays some tricks on you and starts treating it like it's an entity or a creature of some kind. And so you can try to pretend that that doesn't exist. And then you have this weird cognitive dissonance where you're saying it's a machine, but it seems like it has its own consciousness or life force in some way. Or you can lean into it and acknowledge that and find a way to integrate it in the right way. The challenge, I think, with anthropomorphism, I think I said that word right? You're just showing off now. Yes.
22:07 Sarah Guo? That was the hard part of the bot company.
22:10 Speaker 01 Yeah. Yeah, but too much anthropomorphism, see, I screwed up there, can imply like a set of human-like behaviors that don't exist in that product. And I think Rodney Brooks wrote an essay about this, basically saying with robots in particular, the appearance of them sets the expectation for what that product will do. And so if you make something that looks exactly like a human, and in fact, the more human-like you make it, the higher the expectations I think the average person will have for that machine. They'll say, "It looks like me, it walks like me, it has a face and talks like me. So it must be capable of doing all the things that I can do." And today, 2025, I think it would be a leap of faith for any company to sell a humanoid
22:52 Speaker 01 robot or something like that and imply that it can do all of these things. Because we're still, I think, many years out from that, at least from meeting those expectations. And so I think that there's a lot of thought that can go into the design of a robot, the form of a robot and other things, to try to match the expectations that you have for a robot when you see it to what it can actually do. Or even go the other direction, instead of over-promising by showing a humanoid, maybe you can do something in the other direction and surprise people with how much it can do. And that's kind of how I think about it personally. I like to surprise and delight customers rather than set them up for disappointment.
23:24 Sarah Guo? Maybe on this front of just consumer acceptance and expectations, are there lessons that transfer from self-driving to home robots?
23:33 Speaker 01 One thing I saw in self-driving, which I guess you could say it's intuitive, but it was still very striking, was that most people on the whole were very skeptical of self-driving cars. Like, it was like 75, 80% of people were like, "I'm never going to trust one of those things." That dropped to like 20 or 30% after one ride.
23:51 Sarah Guo? Amazing.
23:52 Speaker 01 And so it's one of those things where it's like you just simply do not believe it. The more transformative and the more science fiction a technology feels, the higher the skepticism will be for that kind of thing. And so I think anytime you're doing something new, whether it's self-driving or a home robot, which let's be honest, that sounds like sci-fi. I'd love to have that, but like, you know, can this be real is the question. And I think there will be a barrier. There will always be skepticism and people say this is impossible or it's never going to scale or whatever it is. Maybe the introduction with any new technology. What I would say, looking back is the most powerful thing to overcome that was just people using the product, like people using the product and telling other people about the product and saying, no, no, no, I wrote in this thing or I tried this thing and it's real, you've got to try it. And so I think leaning into too much sort of classical marketing and trying to like,
24:37 Speaker 01 tell people like what this thing will do or how, what its specs are and all that is very different than like hearing from someone you trust that I have this thing in my home, one of your most intimate spaces, and it's working and it's like, and I love it. And so that's, that's kind of how I think about it for something like this where it's just hard to go straight at people who are skeptical and just don't believe that a sci-fi thing can exist and try to convince them through any other medium other than just trying it themselves.
24:59 Sarah Guo? What's the timeline for that? Like you mentioned in passing, like a pretty important claim that said, like maybe five, 10, 20 years until everyone who can afford them expects robots in the house like they expect home appliances. Like what changes that timeline between the five to 20 year span or whatever it ends up being?
25:17 Speaker 01 Well, I'm at my office, so it's basically when I get off this podcast and go back to work.
25:21 Sarah Guo? Okay, so we should let you go is what you're saying.
25:22 Speaker 01 No, no, not at all. No, it's, I think to me, and I felt this way in 2013 when I, when I started Cruise, it seems like the basic building blocks are there. I can point to all the challenges for a low cost home robot and low cost is important to me because I want a lot of people to have this. I can point to all the technical challenges that at least today that I think we're going to face. And I can point to a technology we've either built, you know, the last year or some research that came out or like a product or like a chip that's coming in the pipeline, whatever it is. I can see, you know, from where I sit today, the path to put these things together and assemble a great product and a great product experience. And so I think it comes down to like execution, how, how quickly and how well those things are put together. And then the big question, which you always face in something like this is what are the unknown unknowns?
26:07 Speaker 01 Like what can I sit here today and just simply not see because we haven't put enough robots in homes and haven't tried this out? And I think there could be like something, some new discovery that happens. Maybe it turns out that. You know, people are not happy with a home robot unless it does X or whenever a robot does this other thing, it, you know, it makes people never want to use it again. And so we're kind of early in our own journey to figure out what those things are. And as far as I've seen, there's no one else really doing this. And so it's a big, the cloud of uncertainty is, there's a lot of it in front of us. And that's, that's why I can't be more specific on the timeline. You know, it could be like, it could be like one year or it could be like 20 years. And so either the best way to figure that out is just charge forward and try to discover it as quickly as possible.
26:45 Sarah Guo? All of those are really exciting as timelines. We talked about Chinese, AV. How do you think about manufacturing and supply chain, given competition with China and like Chinese robotics companies?
26:55 Speaker 01 Yeah, I've spent a lot of time thinking about that. There is this sentiment in robotics, or I'd say in the hardware company space that I would say the sentiment that I've heard is almost like you shouldn't even try.
27:06 Sarah Guo? Yeah.
27:06 Speaker 01 Because if you're just making a widget, you'll make it using US engineering, which is, you know, a hundred plus thousand dollars a year for an engineer. You're going to go to US-based machine shops and US-based tooling, all this kind of stuff. And it's going to be slower. You're not going to iterate as faster. You're going to pay more for it and the quality may or may not be as good. So don't even try. And I think like it is possible to be a global company today and have a manufacturing footprint in another country, like use contract manufacturers, tap into existing supply bases. It takes more work and you have to be willing to like travel and go like, you know, pound the ground and make these connections and get access to these things, but it's not impossible. I think like probably, you know, for a US-based company, you're going to have a hard time competing on pure engineering services. And so if that's all you've got, if you're just like doing mechanical engineering and
27:51 Speaker 01 cranking out products, you're going to have a hard time on the margin side and have to build a brand instead in order to create that margin. I think when you get into more complex machines, and I saw this with, you know, with self-driving, I think even though they may be doing a lot of teleoperation, other things there, I do think that China is still years behind, behind the best US companies for self-driving. And it's been my experience that anytime you have a sufficiently complicated technical problem on the software side where you can't just copy it by like, you know, measuring something and then recreating it in CAD. Or it has to do with taste, like the product experience is more than just like a light switch where you flip it on and off. Then it becomes a little bit harder to quickly copy that and commoditize it. You know, even if you're a fast follower, if you are aggressive enough as a company and can maintain a lead and keep innovating and keep building new products, I think, I
28:36 Speaker 01 think there's room to be a hardware company in the US, you know, provided that you take steps to ensure that if your cost is higher than a potential Chinese competitor or somewhere in Asia, it's not that much higher to the point where you can win on the merits of your product and brand and other things. But in a place like, you know, for home robots and other things, this is, this is like the Wild West. There's no, there's no established stuff to copy. Um, you know, we've got to build a lot of stuff ourselves and I think it'll be interesting to see how this plays out too, especially if these end up being like connected devices and they're constantly getting new software updates with better models on them or whatever it is. Uh, I can see a bunch of angles in which, you know, these companies are very durable, whether it's us or another one.
29:15 Sarah Guo? You've already been through the wringer once on the regulatory front with AV. If you could wave a magic wand, we just call Trump right now, what do you think is the right policy approach to make sure we have a competitive domestic robotics industry, if that's relevant?
29:28 Speaker 01 First of all, I mean, I think there should be tons of, uh, regulation on AV. It reminds me, what's happening in the AV space and sort of, you know, companies like Cruise got wiped out, companies like Waymo are growing or expanding, I think, much more slowly than they should, based on the merits and the safety of their technology. Uh, and it reminds me of like when the first airlines were formed in the US and there was no FAA, there was no regulation. If you had basically any kind of plane crash, you would get sued out of existence and you just wiped out. And many of the first airlines are no longer in existence because of this. And so the FAA was created because, you know, the government decided that actually we should have airlines and if they keep going out of business, no one's going to start an airline anymore. And so create the FAA, monitor airplane manufacturers and airlines.
30:11 Speaker 01 Make sure they meet safety criteria and in exchange, give them reasonable protections and limits on liabilities so they can actually operate in a society like the US. That hasn't happened for self-driving cars. And so I think the only, today, the only companies that stand a chance are the ones where they can afford to take on that liability because they're a giant tech company that makes money in other places. Otherwise, it's very bleak. So I would recommend that for ATMs and for home robots. I think a similar thing is true. There are no regulations right now on cybersecurity, for example. You can have a Chinese manufactured robot in your home with cameras and a microphone running and sending that data who knows where. And in fact, many of us do, and that's not regulated, that's not inspected by any government agency, and I think that's a major concern.
30:55 Speaker 01 On the safety side, I would love to see that, too. There's lots of best practices from the industrial robot industry. They're not a great fit for home robots. There's lots of good best practices for other consumer products, but particularly home robots is a bit of a vacuum. And I think that generally, regulation is a very, very good thing for companies operating in environments like this, that are especially ones that are unpredictable. It encourages discussion of best practices, it encourages oversight. All of these things lead to better outcomes for, I think, both the companies and for consumers. So in terms of regulation, even though I think Trump administration is anti-regulation, it's like a necessary enabler to get these industries going, as odd as that sounds.
31:33 Speaker 02 Seems like there's some circumstances where your point and clear regulatory framework helps a lot. For the crypto community during the Biden administration, a lot of what they wanted from the SEC was just guidance in terms of what to do, and then everybody's going to go do it, and it was that ambiguity that really hurt. The argument I've heard around drones in particular is due to the FAA, the US is now behind on the drone side, and China was really able to get a leg up. If you look at everything from drone shows to just what DJI drones can do, it's sort of very low cost, and they're being used for military and other applications in Ukraine and other places. And so there are the arguments, well, the FAA overregulated drones in airspace, and that kind of prevented the US market from evolving down a proper route. So I'm a little bit curious how you think about that balance between...
32:17 Speaker 02 You know, too much and too little regulation and how that may apply to the robotics world, given what's happened in drones.
32:23 Speaker 01 Yeah, it's a good point. I mean, I agree. I would love to see more transportation innovation, generally, but aviation, like electric takeoff and landing airplanes, there were tons of startups in that space, and they've all kind of like fizzled out, it seems like, or bumped into these like FAA certification challenges. And as far as I understand, there's still no pathway, even today, to make a pilotless plane of any kind. There's like baby steps that we're taking, but I would love to see these sort of two-track approaches where you have a very mature track for existing industries and technologies, like passenger airlines today, and then an innovation track where basically you're almost encouraged. I mean, there's grants or other programs to like spur innovation in this category, and then maybe a phased release
33:04 Speaker 01 process to go from like a working prototype to being regulated, but being able to operate at scale. Like, I think it would be okay for us to sit down and say, here's what we expect to see at each level of maturity, and then provided you demonstrate to us that you meet that level of maturity, we'll progressively open up the regulations or the areas that you can operate. And this is like a standard thing. This is like what we did in self-driving cars, but also like even new airplanes, like the boom supersonic jet that just made history. They started off with like a low and slow flight, and then gradually, as they saw the data check out, ratcheted up until they hit supersonic. And so I think regulations are there to prevent irresponsible people from going zero to supersonic. But there are plenty of responsible people willing to take this stepped approach, provided that there is a
33:46 Speaker 01 pathway to do so. And so I think that's the right balance of having regulation versus, overregulation versus none at all, is having these like phased approaches.
33:54 Sarah Guo? What's the number one thing that you do differently, given the cruise journey, besides like, I guess, have more fun, don't sell it to GM?
34:02 Speaker 01 Yeah, I learned a lot. You know, big companies that have their core business in another domain, doing an acquisition that's not in that domain, selling cars to people who buy pickup trucks and SUVs in the Midwest versus robotaxis in urban environments, these are not compatible things. And when push comes to shove, they're going to pick that one over the other. And we got completely decimated by GM's lack of priority and then completely abandoning cruise. So I guess, lessons learned is plenty. First is, I'm never going to sell another company again, ever. And so like, maybe it will IPO or something like that, but there will never be an acquisition in my life again. The reality is, like, if I'm working on something today, I'm working on it because I think it's important and I care about it. And the day you sell the company is the day that you have to let that go. And so, you know, if I'm, by definition, if this is something I care about, I'm not going to let it go.
34:47 Speaker 01 The second thing is like team size. I, along with many other people, I think, who started companies around the same time, fell victim to the Silicon Valley dogma of traditional engineering management for Silicon Valley companies. And so that's like, hire the VPs first, and then they hire the directors, and then they hire the senior managers who hire the managers. And there can't be a ratio of more than eight to one for fan outs. And you do performance review cycles and all these things that creates bureaucracy and structure, creates like communication gaps between the people actually doing the work and the people making the decisions. And so the solution, of course, is just to keep companies very small, like have fewer employees, make every seat count, get the absolute best person in every role, in every seat, and just never grow the company to be so large that it sort of like crumbles
35:30 Speaker 01 under all the structure and bureaucracy and politics that seep in. These are natural things that happen to large groups of people when they are organized together in companies. Traditionally, that would mean you have to limit the scope of what you want to do. If you want to keep your company small, you have to have small ambitions. Like it needs, you need a large company to do large things. I think now that is not true. I think with coding assistants that continue to get better, things like deep research, like I've found that nearly every job function can be partially automated. And I think that trend is going to continue. And as someone who spends like a lot of time programming, I felt my ability to take on things where I would have had to hire a team of people or like a specialist in iOS development or a specialist in like, you know, low-level Rust programming for motor drivers. If you have a couple of good engineers, they can adapt and do all those things
36:14 Speaker 01 when, when sitting next to an LLM and some good coding tools. So I think it actually is viable to do what I want to do, which is like lesson learned. I'm going to keep the company small to build like a company that has grand ambitions, but a very tiny team. And that's, that's what we're going to try to do.
36:27 Speaker 02 It's pretty amazing. I think you've had a really amazing career arc overall, right? You've taken on three high-risk, complex companies back to back with little downtime in between. You've run seven marathons on seven continents in three and a half days. Where does your drive and stamina come from? Is there a supplement we should all be taking? Is there like some Brian Johnson style, like? We should inject ourselves with young blood. Like, what's the, what's the deal?
36:52 Speaker 01 I haven't tried that. If you do that, let me know how it goes. I have a problem, which is that once I get an idea in my head, it just, it just like burns a hole in my brain. I cannot, I cannot sleep. I cannot do anything. I can't focus until I like, you know, see this idea through. And this, for whatever reason, it doesn't happen. It's not like, you know, 20 ideas a day. It's like, I'll get this idea that, hey, the stars aligned. This thing should happen. Now is the time. I need to go for it. And then once I'm on that track, I just cannot let go until it's done. And so I think I just latch onto these problems and have this like sense of delayed gratification where I want to work on it for a long time and get the results. But that's also really satisfying to me, the notion of like putting in a ton of effort, building something really complicated and hard, sort of taking on these really difficult challenges and making a little bit of progress each day.
37:36 Speaker 01 That is motivating to me. And so, you know, you could say starting these companies in higher risk and things is difficult and it's hard. And it is, but I enjoy every day. And so I can't imagine doing anything else.
37:48 Speaker 02 That's awesome. I think that's what makes Silicon Valley so great. So, thank you so much for joining us today.
37:52 Speaker 01 Thanks for having me.
37:55 Sarah Guo? Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.
Previous How Harvey AI is Changing the Legal Industry with Winston Weinberg Next Virtual Cell Models, Tahoe-100 and Data for AI-in-Bio with Vevo Therapeutics and the Arc Institute Page updated October 12, 2026 · transcript and summary are machine-generated
