Kyle Vogt: Cruise Automation | Lex Fridman Podcast #14

Lex Fridman 0:00 The following is a conversation with Kyle Vogt. He's the president and the CTO of Cruise Automation, leading an effort to solve one of the biggest robotics challenges of our time, vehicle automation. He's a co-founder of two successful companies, Twitch and Cruise, that have each sold for a billion dollars, and he's a great example of the innovative spirit that flourishes in Silicon Valley, and now is facing an interesting and exciting challenge of matching that spirit with the mass production and the safety-centric culture of a major automaker, like General Motors. This conversation is part of the MIT Artificial General Intelligence series and the Artificial Intelligence Podcast. If you enjoy it, please subscribe on YouTube, iTunes, or simply connect with me on Twitter at lexfridman, spelled F-R-I-D. And now, here's my conversation with Kyle Vogt. You grew up in Kansas, right?

Kyle Vogt 0:56 Yeah. And I just saw that picture you had hidden-

Lex Fridman 0:58 Oh, boy.

Kyle Vogt 0:58 ... under there, so I'm a little bit- a little bit worried about that now.

Lex Fridman 1:00 Nervous.

Kyle Vogt 1:00 Yeah.

Lex Fridman 1:00 So in high school, in Kansas City, you joined Shawnee Mission North High School robotics team.

Kyle Vogt 1:07 Yeah.

Lex Fridman 1:07 Now, that wasn't your high school.

Kyle Vogt 1:09 That's right. That was- that was, uh, the only high school in the area that had a f- like, a- a teacher who was willing to sponsor our first robotics team.

Lex Fridman 1:16 I was gonna troll you a little bit, jog your memory-

Kyle Vogt 1:19 Yep.

Lex Fridman 1:19 ... a little bit-

Kyle Vogt 1:19 Yep.

Lex Fridman 1:19 ... about that kid.

Kyle Vogt 1:20 I was trying to look super cool and intense-

Lex Fridman 1:22 You did.

Kyle Vogt 1:23 ... 'cause, you know, this was BattleBots, this is serious business, so we're standing there with a welded steel frame and looking tough.

Lex Fridman 1:30 So go back there. What is it that drew you to robotics?

Kyle Vogt 1:33 Well, I think, I- I've been trying to figure this out for a while, but I've always liked building things with LEGOs, and when I was really, really young, I wanted the- the LEGOs that had motors and other things. And then, you know, LEGO MINDSTORMS came out, and for the first time, you could program LEGO contraptions. And I think, uh, things just sort of snowballed from that. But I remember, um, seeing, you know, the BattleBots TV show on Comedy Central and thinking, "That is the coolest thing in the world. I wanna be a part of that," and, uh, not knowing a whole lot about how to build these 200-pound fighting robots. So I sort of obsessively pored over the, uh, internet forums where all the creators for BattleBots would sort of hang out and talk about, you know, document their build progress and everything. And, uh, I think I read- I must have read like, you know, tens of thousands of forum posts from- from basically everything that was out there on what these people were doing. And eventually, like, sort of triangulated how to- how to put some of these things together, and- and, uh- uh, ended up doing BattleBots, which was, you know, I was like 13 or 14, which was pretty awesome.

Lex Fridman 2:36 I'm not sure if the show's still running, but s- so BattleBots is, uh, so there's not an artificial intelligence component. It's remotely controlled, and you- it's a m- almost like a mechanical engineering challenge of-

Kyle Vogt 2:46 Yeah.

Lex Fridman 2:46 ... building things that can't be broken.

Kyle Vogt 2:49 They're- they're radio controlled. So-

Lex Fridman 2:51 Okay.

Kyle Vogt 2:51 Uh, and I think that they allowed some limited form of autonomy. But, you know, in a two-minute match, you're- you're i- in the way these things ran, you're really doing yourself a disservice by trying to automate it versus just, you know, do the practical thing, which is drive it yourself.

Lex Fridman 3:04 And there's an entertainment aspect, uh, just- just going on YouTube, there's like an- some of them wield an ax, some of them... I mean, there's that fun. So what drew you to that aspect? Was it the mechanical engineering? Was it the dream to create, like, uh, Frankenstein and tell- sentient being, or was it just like the LEGO, you like tinkering with stuff?

Kyle Vogt 3:24 I mean, that- that was just building something... I- I think the- the idea of, you know, this- this radio controlled machine that- that can do various things if it has like a weapon or something was pretty interesting. I agree, it's- it doesn't have the same appeal as, you know, autonomous robots, which I- which I, you know, sort of gravitated towards later on. But it was definitely an engineering challenge, because everything you did in- in that competition was pushing components to their limits. So we would buy, like, these $40 DC motors that came out of a- a winch, like on the front of a pickup truck or something, and we'd power the car with those, and we'd run them at, like, double or triple their rated voltage. So they immediately start overheating, but for that two-minute match, you can get, you know, a significant increase in the power output of those motors before they burn out. And so you're doing the same thing for your battery packs, all the materials in the system. And I think there is something- something, uh- uh, intrinsically interesting about just seeing, like, where things break.

Lex Fridman 4:20 And did you offline see where they break? Did you take it to the testing point? Like, how did you know two minutes, or was it a reckless, "Let's just go with it and- and see?"

Kyle Vogt 4:29 Uh, we- we weren't very good at BattleBots. We lost all of our matches before-

Lex Fridman 4:32 What did yours look like?

Kyle Vogt 4:33 ... the first round. The- the one I built, uh, first, both of them were these wedge-shaped robots, 'cause a wedge, even though it's sort of boring to look at, is extremely effective. You drive towards another robot and the front edge of it gets under them, and then they sort of flip over, kind of like a door stopper. And the first one had a- a pneumatic, polished stainless steel spike on the front that would shoot out about eight inches.

Lex Fridman 4:54 The purpose of which is what?

Kyle Vogt 4:56 Pretty- pretty ineffective actually, but it looked cool. (laughs) And, uh-

Lex Fridman 4:59 Was it to help with the lift so that-

Kyle Vogt 5:01 No, it was- it was just to try to poke holes in the other robot. And then, uh, the second time I did it, which is the following, I think maybe 18 months later, we had, uh, well, a titanium ax with a- with a hardened steel tip on it that was powered by a hydraulic cylinder, which we were, uh, activating with liquid CO₂, which was- had its own set of problems. (laughs)

Lex Fridman 5:23 So, great. So that's kind of on the hardware side. I mean, uh, at- at a certain point, there must have been born a fascination on the software side. So what was the first piece of code you've written?

Kyle Vogt 5:35 Oh, man. So-

Lex Fridman 5:36 If you had to go back there and see, what language was it? What, uh- what was the- was it Emacs, Vim? Was it, uh, a more respectable modern-

Kyle Vogt 5:43 (laughs)

Lex Fridman 5:43 ... IDE? Do you re- do you remember any of this?

Kyle Vogt 5:45 Yeah. Well, I remember, um, I think m- maybe when I was in third or fourth grade-... the school I was at, elementary school, had a bunch of Apple II computers and we'd play games on those. And I remember every once in a while something would, would, uh, would crash or it wouldn't start up correctly and it, it would dump you out to what I later learned was, like, sort of a command prompt.

Lex Fridman 6:05 Mm-hmm.

Kyle Vogt 6:05 And my teacher would come over and type, I actually remember this to this day for some reason, like PR number six, or PR pound six, which is peripheral six, which is the disc drive, which would fire up the disc and load the program.

Lex Fridman 6:15 Yeah.

Kyle Vogt 6:15 And I just remember thinking, "Wow, she's like a hacker." Like, "Teach me-"

Lex Fridman 6:18 Yeah.

Kyle Vogt 6:19 "... these, these codes, these error codes."

Lex Fridman 6:20 Yeah.

Kyle Vogt 6:20 Is, is what I called them at the time. But she had no interest in that, so it wasn't until I think about fifth grade that I had a, a school where you could actually go on these Apple IIs and learn to program. And so it was all in Basic, you know, where every line, you know, the line numbers are all num- or the every line is numbered and you have to, like, leave enough space between the numbers so that if you wanna tweak your code you go back in. If the first line was 10 and the second line is 20, now you have to go back and insert 15, and if you need to add code in front of that, you know, 11 or 12, and you hope you don't run outta line numbers and have to redo the whole thing.

Lex Fridman 6:51 And there's go-to statements?

Kyle Vogt 6:53 Yeah, go-to and ... It's, it's very basic, maybe hence-

Lex Fridman 6:56 Yeah.

Kyle Vogt 6:56 ... the name. But a lot of fun. And that was like, um ... That was, you know, that's when, that's when ... You know, when you first program you see the magic of it. It's like it just, just like this world opens up with, you know, endless possibilities for the things you could build or, or accomplish with that computer.

Lex Fridman 7:10 So you got the bug then? So the even-

Kyle Vogt 7:12 Yeah.

Lex Fridman 7:12 ... starting with Basic and then, what, C++ throughout? Uh, what did you ... Was there computer program and computer science classes in high school?

Kyle Vogt 7:20 Not, not where I went. So I was, um, self-taught. But I did a lot of programming. The thing that, you know, sort of pushed me in the path of eventually working on self-driving cars is actually one of these really long trips driving from my house in Kansas to, uh, uh, to I think Las Vegas where we did the BattleBots competition. And I had just gotten my, I think my learner's permit or early driver's permit, and so I was driving this, you know, 10-hour stretch across western Kansas where it's just you're going straight on a highway and it is mind-numbingly boring. And I remember thinking even then with my sort of mediocre programming background that this is something that a computer can do, right? Let's take a picture of the road, let's find the yellow lane markers and, you know, steer the wheel. And, you know, later I, I'd come to realize this had been done, you know, since, since the, the '80s or the '70s or even earlier, but I still wanted to do it. And sort of immediately after that trip switched from sort of BattleBots, which is more radio-controlled machines, to thinking about building, you know, autonomous vehicles of some scale. Start off with really small electric ones and then, you know, progress to what we're doing now.

Lex Fridman 8:28 So what was your view of artificial intelligence at that point? What, what did you think? So this is, uh, before ... There's been waves in artificial intelligence, right? Uh, the, the current wave with deep learning makes people believe they can solve, in a really rich deep way, the computer vision perception problem. But, like, in the before the deep learning craze, you know, how do you think about ... How would you even go about building a thing that perceives itself in the world, localizes itself in the world, moves around in the world? Like when you were younger, I mean.

Kyle Vogt 9:00 Yeah.

Lex Fridman 9:00 Is there ... What was your thinking about it?

Kyle Vogt 9:02 Well, prior to deep neural networks or convolutional neuro- neural nets, these modern techniques we have, or at least ones that are, uh, in use today, it was all a heuristic space, and so like old school image processing. And I think, uh, extracting, you know, yellow lane markers out of an image of a road is one of the problems that lends itself reasonably well to those heuristic-based methods. You know, like just do a threshold on the color yellow and then try to fit some lines to that using a Hough transform or something and then, uh, go from there.

Lex Fridman 9:32 Traffic light detection and stop sign detection, red, yellow, green. We're good.

Kyle Vogt 9:36 And I, and I think you can, you could ... Uh, I mean, if you wanted to do a full ... I was just trying to make something that would stay in between the, the lanes on a highway. But if you wanted to do the full, the full, you know, set of capabilities needed for a driverless car, I think you could ... And, and we'd done this at Cruise, you know, in, in the very first days. You can start off with a really simple, you know, human-written heuristic just to get the scaffolding in place for your system. Traffic light detection, probably a really simple, you know, color thresholding-

Lex Fridman 10:03 Yeah.

Kyle Vogt 10:04 ... on day one just to get the system up and running-

Lex Fridman 10:06 Yeah.

Kyle Vogt 10:06 ... before you migrate to, you know, a deep learning-based technique or, or something else. And, you know, back in ... When I was doing this, my first one, it was on a Pentium 203- 233 MHz computer.

Lex Fridman 10:17 (laughs)

Kyle Vogt 10:17 And it ... And I, I think I wrote the first version in Basic, which is-

Lex Fridman 10:20 Yeah.

Kyle Vogt 10:20 ... like an interpreted language.

Lex Fridman 10:21 Right.

Kyle Vogt 10:21 It's extremely slow-

Lex Fridman 10:22 Yeah.

Kyle Vogt 10:23 ... uh, 'cause that's the thing I knew at the time. And so there was no, no chance at all of using ... Uh, there was no, no computational power to do, um, any sort of reasonable deep nets like you have today. So I don't know what kids these days are doing. Are kids these days, you know, at age 13 using neural networks in their garage? I mean, that would be awesome.

Lex Fridman 10:40 Yeah, absolutely. G- I get emails all the time from, you know, like 11, 12 year olds saying, "I'm having ... You know, I'm trying to follow this TensorFlow tutorial and I'm having this problem." And the general approach in the deep learning community is of extreme optimism of ... As opposed to you mentioned, like, heuristics. You can pers- ... You can, you can, uh, separate the autonomous driving problem into modules and try to solve it sort of rigorously, or you can just do it end-to-end.

Kyle Vogt 11:08 Mm-hmm.

Lex Fridman 11:09 And most people just kinda love the idea that, you know, us humans do it end-to-end. We just perceive and act. We should be able to use that, uh, do the same kind of thing with neural nets. And that, that kind of thinking, you don't want to criticize that kind of thinking because eventually they will be right. (laughs)

Kyle Vogt 11:24 Yeah.

Lex Fridman 11:25 And so it's exciting, and especially when they're younger to explore that is, is a really exciting approach. But yeah, it's, it's changed, uh, the, the language, the kind of stuff you're tinkering with. It, it's, it's kind of exciting to see when these teenagers grow up. And it's a good thing.

Kyle Vogt 11:40 Yeah, I can only imagine if you, if your starting point is, you know, Python and TensorFlow at age 13, where you end up, you know, after 10 or 15 years of that. That's, that's pretty cool.

Lex Fridman 11:51 Because of GitHub, because ... Is there tools for solving most of the major problems in artificial intelligence are within a few lines of code for most kids.

Kyle Vogt 12:00 Mm-hmm.

Lex Fridman 12:00 And that's incredible to think about. Also on the entrepreneurial side, and, and, and on that point-Was there any thought about entrepreneurship before you came to college? Is, s-sort of doing your, building this into a thing that impacts the world, uh, on a large scale?

Kyle Vogt 12:17 Yeah. I've, I've always wanted to start a company. I think that's, you know, just a cool concept of creating something and exchanging it, um, for value or, or creating value, I guess. So in high school, I was, I, I was trying to build, like, you know, servo motor drivers, little circuit boards-

Lex Fridman 12:33 Mm-hmm.

Kyle Vogt 12:33 ... and sell them online or other, other things like that. And certainly knew at some point I wanted to do a startup, but it wasn't really, uh, I'd say until college, until I felt like I had the, I guess the right combination of the environment, the smart people around you, and, uh, some free time. I had a lot of free time at MIT.

Lex Fridman 12:52 So you came to-

Kyle Vogt 12:53 (laughs)

Lex Fridman 12:53 ... MIT as an undergrad, 2004.

Kyle Vogt 12:55 That's right.

Lex Fridman 12:57 And that's when the first DARPA Grand Challenge was happening.

Kyle Vogt 12:59 Yeah.

Lex Fridman 13:00 So the, the timing of that is, uh, beautifully poetic. So how'd you get yourself involved in that one?

Kyle Vogt 13:05 Originally there wasn't a sort of-

Lex Fridman 13:07 Official entry?

Kyle Vogt 13:07 ... yeah, faculty-sponsored thing, and so a bunch of undergrads, myself included, started meeting and got together and tried to, to, uh, haggle together some sponsorships. We got a vehicle donated, a bunch of sensors, and tried to put something together. And so we had... our team was probably mostly freshmen and sophomores-

Lex Fridman 13:25 (laughs)

Kyle Vogt 13:25 ... you know, which, which was not really a fair, fair fight against maybe the, uh, you know, post-doc and faculty-led teams from other schools. But we, uh, we got something up and running. We had our vehicle drive by wire and, you know, very, very basic control and things. But, uh, on the day of the qualifying, sort of pre-qualifying round, the one and only steering motor that we had purchased, the thing that we had, you know, retrofitted to turn the steering wheel on the truck died, and so our vehicle was just dead in the water, couldn't steer. So we didn't make it very far.

Lex Fridman 13:59 On the hardware side. So-

Kyle Vogt 14:01 Yeah.

Lex Fridman 14:01 ... was there a software component? Was there... like, how did your view of autonomous vehicles in terms of artificial intelligence evolve in this moment? I mean, you know, like you said, from the '80s there's been autonomous vehicles, but really that was the birth of the modern wave, the, the thing that captivated everyone's imagination that we can actually do this. So what, how... were you captivated in that way? Uh, so h- how did your view of autonomous vehicles change at that point?

Kyle Vogt 14:28 Um, I'd say at that point in time, it was, it was, uh, a curiosity as in like, "Is, is this really possible?" And I think that was generally the spirit and the, the purpose of, of that original DARPA Grand Challenge, which was to just get a whole bunch of really brilliant people exploring the space and pushing the limits. And, and I think, like, to this day, that DARPA Challenge with its, you know, million-dollar prize pool was probably one of the most effective, you know, uses of taxpayer money, dollar for dollar, that I've seen. You know, because that, that small sort of initiative that DARPA put, uh, put out, sort of, in, in my view, was the catalyst or, or the tipping point for this, this whole next wave of autonomous vehicle development. So th- that was pretty cool.

Lex Fridman 15:17 So let me jump around a little bit on that point. S- they also did the Urban Challenge-

Kyle Vogt 15:22 Mm-hmm.

Lex Fridman 15:22 ... where it was in the city. But it was very artificial and there was no pedestrians and there was very little human involvement except the, a few professional drivers.

Kyle Vogt 15:30 Yeah.

Lex Fridman 15:31 Do you think there's room... and then there was the Robotics Challenge with humanoid robots.

Kyle Vogt 15:35 Right.

Lex Fridman 15:36 So in your now role as looking at this, you're trying to solve one of the... you know, u- autonomous driving in one of the harder, more difficult places in San Francisco. Is there a role for DARPA to step in to also kind of help out, like challenge with new ideas, specifically, yeah, pedestrians and so on, all these kinds of interesting things?

Kyle Vogt 15:55 Well, I haven't, I haven't thought about it from that perspective. Is there anything DARPA could do today to further accelerate things? And I would say, my instinct is that that's maybe not the highest and best use of their resources and time.

Lex Fridman 16:07 Right.

Kyle Vogt 16:07 Because, like, kickstarting and, and spinning up the flywheel is, I think, what, what they-

Lex Fridman 16:12 Mm-hmm.

Kyle Vogt 16:12 ... did in this case for very, very little money. But today this has become, this has become, like, commercially interesting to very large companies. And the amount of money going into it and the amount of people, like going through your class and learning about these things and developing new skills is just, you know, orders of magnitude more than it was back then. And so there's enough momentum and inertia and energy and investment dollars into this space right now that, uh, I don't, I don't, um... I think they're, I- I think they're, they- they can just say mission accomplished and move on to the next, uh-

Lex Fridman 16:43 Success.

Kyle Vogt 16:43 ... area of technology that, that needs help.

Lex Fridman 16:46 So then stepping back to MIT, you left MIT during your junior year. What was that decision like?

Kyle Vogt 16:53 As I said, I had always wanted to do a company in, uh, or start a company and this opportunity landed in my lap, which was a couple guys from Yale. Uh, we're starting a new company and I Googled them and found that they had started a company previously and sold it actually on eBay for about a quarter million bucks, which was a pretty interesting story. But, so I thought to myself, "These guys are, you know, rockstar entrepreneurs. They've done this before. They must be driving around in Ferraris 'cause they sold their company." And, uh, you know, I, I thought I could learn a lot from them. So I, I teamed up with those guys and, and, you know, went out during... went out to California during IAP, which is MIT's-

Lex Fridman 17:33 Mm-hmm.

Kyle Vogt 17:33 ... uh, month off, uh, on a, on a one-way ticket and basically never went back.

Lex Fridman 17:38 (laughs)

Kyle Vogt 17:38 We were having so much fun. We felt like we were building something and creating something and it was gonna be interesting that, you know, I was just all in and, and, and got completely hooked. And that, that business was Justin.tv, which was originally a reality show about a guy named Justin, which morphed into a live video streaming platform, which then morphed into what is Twitch today. So that was, that was quite a, an unexpected journey.

Lex Fridman 18:05 S- so no regrets?

Kyle Vogt 18:07 No.

Lex Fridman 18:07 Looking back, it was just an obvious, I mean, one-way ticket. I mean, if we just pause on that for a second, there was no...How did you know these were the right guys, this is the right decision? You didn't think? It was just follow the heart kind of thing?

Kyle Vogt 18:22 Well, I didn't know. But, you know, just trying something for a month during IAP seems pretty low risk, right?

Lex Fridman 18:27 Right.

Kyle Vogt 18:28 And then, you know, well, maybe I'll take a semester off. MIT's pretty flexible about that. You can always go back, right? And then after two or three cycles of that, I eventually threw in the towel. But, uh, you know, I think it's, um... I guess in that case, I felt like I could always hit the undo button if I had to.

Lex Fridman 18:44 Right. But nevertheless, from, from, uh, when you look in retrospect, I mean, it's, it seems like a brave decision that, you know, is, is difficult... it would be difficult for a lot of people to make.

Kyle Vogt 18:54 It, it wasn't as popular. I'd say the, the general, you know, flux of people out of MIT at the time was mostly into, you know, finance or consulting jobs in Boston or New York. And very few people were going to California to start companies. But today, I'd say that's, it's probably inverted, which is just a sign of, uh, a sign of the times, I guess.

Lex Fridman 19:15 Yeah. So there's a story about midnight of March 18, 2007, where, uh, where TechCrunch, I guess, announced Justin.tv earlier than it was supposed to, a few hours. The site didn't work. I don't know if any of this is true. You can tell me.

Kyle Vogt 19:32 (laughs) .

Lex Fridman 19:32 And, uh, you and one, one of the folks at Justin.tv, Emmett Shear, coded through the night. Can you take me through that experience? So wait, let me, let me say a few nice things that, uh, the article I read quoted. Uh, Justin Kan said that you were known for hero coding through problems and being a creative, quote, "creative genius." So, uh, on that night, what, what, what was going through your head? Or maybe put another way, how do you solve these problems? What's your approach to solving these kinds of problems where the line between success and failure seems to be pretty, uh, thin?

Kyle Vogt 20:10 That's a good question. Well, first of all, that's, that's, uh, nice of Justin to say that. I think, you know, I, I would have been maybe 21 years old then and not very experienced at programming. But as with, um, uh, with everything in a startup, you're, you're sort of racing against the clock. And so our plan was the second we had this live streaming camera backpack up and running where Justin could wear it and no matter where he went in a city, it would be streaming live video, and this is even before the iPhones, this is, like, hard to do back then, we would launch. And so we thought we were there and, and the backpack was working and then we sent out all the emails to launch the, launch the company and do the press thing. And then, you know, we weren't quite actually there. And then, um, we thought, "Oh, well, you know, they're not gonna announce it until maybe 10:00 AM the next morning." And it's, I don't know, it's 5:00 PM now. So how many hours do we have left? What is that like? (laughs) You know, 17 hours to go. And, uh, and, and that was, that was gonna be fine.

Lex Fridman 21:10 Was the problem obvious? Did you understand what could possibly... like, how complicated was the system at that point?

Kyle Vogt 21:16 It was, it was pretty messy. So to get a live video feed that looked decent working from anywhere in San Francisco, I put together this system where we had, like, three or four cell phone data modems, and they were... like, we take the video stream and s- you know, sort of spray it across these three or four modems and then try to catch all the packets on the other side-

Lex Fridman 21:37 Yeah.

Kyle Vogt 21:37 ... you know, with unreliable cell phone networks and-

Lex Fridman 21:39 That's pretty low level networking you were doing.

Kyle Vogt 21:41 Yeah. Uh, and, and putting these, like, you know, sort of protocols on top of all that to, to reassemble and reorder the packets and have time buffers and error correction and all that kind of stuff. And, um, the, the night before, it was just staticky. Every once in a while the image would, would go staticky and there would be this horrible, like, screeching audio noise 'cause the audio was also corrupted. And this would happen like every five to 10 minutes or so. And it was a really-

Lex Fridman 22:06 Right. Sure.

Kyle Vogt 22:06 ... you know, off-putting to the viewers.

Lex Fridman 22:08 Yeah. How do you tackle that problem? What was the, uh... you just freaking out behind a computer? There's... were... are there other, other folks working on this problem? Like were you behind a whiteboard? Were you doing, uh-

Kyle Vogt 22:19 Yeah. It's-

Lex Fridman 22:21 ... uh, pair coding?

Kyle Vogt 22:22 It was a little, it was a little lonely. Yeah, it was a little lonely 'cause there was four of us, uh, working on the company and only two people really wrote code. And Emmett wrote the website and the chat system, and I wrote the software for this video streaming device and video server. And so I... you know, it was my sole responsibility to figure that out.

Lex Fridman 22:37 Yeah.

Kyle Vogt 22:37 And I think, I think it's those, you know, setting, setting deadlines, trying to move quickly and everything where, where you're in that moment of intense pressure that sometimes people do their best and most interesting work. And so even though that was a terrible moment, I look back on it fondly 'cause that's like, you know, that's one of those character defining moments, I think.

Lex Fridman 22:54 So in, uh, 2013, October, you founded Cruise Automation.

Kyle Vogt 22:59 Yeah.

Lex Fridman 23:00 So progressing forward, uh, another exceptionally successful company. It was acquired by GM in '16 for $1 billion. But, uh, in October 2013, what was on your mind? What was the plan? How, how does one seriously start to tackle one of the hardest robotics, most important impactful robotics problems of our age?

Kyle Vogt 23:25 After going through Twitch, I, I... Twitch was, was, and is today pretty successful. But the, um, the work was... the, the result was entertainment mostly. Like the, the better the product was, the more we would entertain people and then, you know, make money on the ad revenues and other things. And that was, that was a good thing. It felt, it felt good to entertain people. But I figured like, you know, what is really the point of becoming a really good engineer and developing these skills other than, you know, my own enjoyment?

Lex Fridman 23:54 Right.

Kyle Vogt 23:54 And I realized I wanted something that scratched more of an existential itch, like something that, that truly matters.

Lex Fridman 23:58 Yeah.

Kyle Vogt 23:59 And so I basically made this list of requirements for a new... if I was gonna do another company. And the one thing I knew in the back of my head, that Twitch took, like, eight years to become successful. And so whatever I do, I better be willing to commit, you know, at least 10 years to, to something. And when you think about things from that perspective-You certainly, I think, raised the bar on what you choose to work on. So for me, the three things were it had to be something where the technology itself determines the success of the product, like hard, really juicy technology problems, 'cause that's what motivates me, and then it had to have a direct and positive impact on society in some way. So an example would be like, you know, healthcare, self-driving cars 'cause they save lives, other things where there's a clear connection to somehow improving other people's lives. And the last one is it had to be a big business because for the positive impact to matter, it's got to be a large scale.

Lex Fridman 24:51 Scale, yeah.

Kyle Vogt 24:52 And I was thinking about that for a while and I made like a... I tried writing a Gmail clone and looked at some other ideas, and then it just sort of light bulb went off, like self-driving cars. Like that was the most fun I had ever had in college working on that.

Lex Fridman 25:03 Mm-hmm.

Kyle Vogt 25:04 And like, well, what's the state of the technology? It's been 10 years. Maybe, maybe times have changed and maybe now is the time to make this work. And I poked around and looked at the only other thing out there really at the time was the Google self-driving car project.

Lex Fridman 25:16 Mm-hmm.

Kyle Vogt 25:16 And I thought surely there's a way to, you know, have an entrepreneur mindset and sort of solve the minimum viable product here, and so I, I just took the plunge right then and there and said this, this is something I know I can commit 10 years to. It's the-

Lex Fridman 25:28 Whoosh.

Kyle Vogt 25:28 ... probably the greatest applied AI problem of our generation.

Lex Fridman 25:31 That's right.

Kyle Vogt 25:32 And if it works, it's gonna be both a huge business and therefore like probably the, the most positive impact I can possibly have on the world. So after that light bulb went off, I, I went all in on Cruise immediately and got to work.

Lex Fridman 25:45 Did you have an idea how to solve this problem? Which aspect of the problem to solve, you know, s- s- slow, like we, we just had Oliver from Voyage here.

Kyle Vogt 25:53 Yeah.

Lex Fridman 25:53 Slow-moving retirement communities, uh, urban driving, highway driving. Did you have like a... Did you have a vision of the city of the future where, you know, uh, the transportation is largely automated, that kind of thing? Or was it sort of, uh, more fuzzy and gray area than that?

Kyle Vogt 26:12 My analysis of the situation is that Google's putting a lot... had been putting a lot of money into that project. They had a lot more resources. And so... And they still hadn't cracked the fully driverless car, you know, this is 20, 2013, I guess. So I thought, what, what can I do to sort of go from zero to, you know, significant scale so I can actually solve the real problem, which is the driverless cars? And I thought, he- here's the strategy. We'll start by doing a really simple problem or solving a really simple problem that, uh, creates value for people. So e- eventually ended up deciding on automating highway driving.

Lex Fridman 26:51 Mm-hmm.

Kyle Vogt 26:51 Which is relatively more straightforward as long as there's a backup driver there. And, uh, I'll... You know, the go-to-market will be we'll retrofit people's cars and just sell these products directly. And the idea was we'll take all the revenue and profits from that and use it to do the... To sort of reinvest that in research for doing fully-

Lex Fridman 27:11 Fully auto.

Kyle Vogt 27:11 ... driverless cars. And that was the plan. The only thing that really changed along the way between then and now is we never really launched the first product. We had enough interest from investors and enough of a signal that this was something that we should be working on that, um, after about a year of working on the highway autopilot, we had it working, you know, on, at a prototype stage, but we just completely abandoned that, uh, and said we're going to go all in on driverless cars. Now is the time. Um, can't think of anything that's more exciting and if it works, more impactful. So we're just gonna go for it.

Lex Fridman 27:41 The, the idea of retrofit is kind of interesting.

Kyle Vogt 27:43 Yeah.

Lex Fridman 27:43 Uh, of being able to... It's, it's how you achieve scale. It's a really interesting idea. Is it something that's still in the, in the back of your mind as a possibility?

Kyle Vogt 27:52 Not at all. I've come full circle on that one.

Lex Fridman 27:54 Uh-huh.

Kyle Vogt 27:55 Af- trying to build a retrofit product, and I'll touch on some of the complexities of that, and then also having been inside an OEM and seeing how things work and how a vehicle is developed and validated, when it comes to something that has safety critical implications like controlling the steering and y- uh, other control inputs on your car, um, it's pretty hard to get there with, with a retrofitter. Or if you did, even if you did, it, it creates a whole bunch of new complications around liability or how did you truly validate that or, you know, if something in the base vehicle fails and causes your system to fail, whose fault is it? Or if the car's anti-lock brake systems or other things kick in or the software has been... It's different in one version of the car you retrofit versus another and you don't know because the manufacturer has updated it behind the scenes. There's basically an infinite list of long tail issues that can get you. And if you're dealing with a safety critical product, that's not really acceptable.

Lex Fridman 28:49 That's a really convincing summary of why it's really challenging ............................ Yeah.

Kyle Vogt 28:53 But I didn't know all that at the time.

Lex Fridman 28:54 Right.

Kyle Vogt 28:54 So we tried it anyway. Uh-

Lex Fridman 28:55 But as a pitch also at the time-

Kyle Vogt 28:57 Yeah.

Lex Fridman 28:57 ... it's a really strong one.

Kyle Vogt 28:58 Yeah.

Lex Fridman 28:58 'Cause that's how you achieve scale and that's how you beat the current... The, the leader at the time of Google or the only one in the market.

Kyle Vogt 29:04 The other big problem we ran into, which is perhaps the biggest problem from a business model perspective, is, uh, we had kind of assumed that we'd... We, we started with an, uh, Audi S4 as the vehicle we retrofitted with this highway driving capability. And we had kind of assumed that if we just knock out like three make and models of vehicle, that'll cover like 80% of the San Francisco market.

Lex Fridman 29:25 Right.

Kyle Vogt 29:25 Doesn't everyone there drive, I don't know, a BMW or a Honda Civic or one of these three cars? And then we surveyed our users and we found out that it's all over the place. We would... To get even a decent number of units sold, we'd have to support like, you know, 20 or 50 different models. And each one is a little butterfly that takes time and effort to maintain, you know, that retrofit integration and custom hardware and all this. So it was a, it was a tough business.

Lex Fridman 29:49 So GM manufactures and sells over nine million cars a year. And what you with Cruise are trying to do some of the most cutting edge innovation in terms of applying AI. And so how, how do those... You've t- you've talked about it a little bit before, but it's also just fascinating to me. We work with a lot of automakers.

Kyle Vogt 30:09 Mm-hmm.

Lex Fridman 30:09 Uh, you know, the difference between the gap between Detroit and Silicon Valley, let's say. Just to be sort of poetic about it, I guess. Uh, wh- how do you close that gap? How do you take GM into the future where a large part of the fleet will be autonomous perhaps?

Kyle Vogt 30:24 I wanna start by acknowledging that, that GM is made up of, you know, tens of thousands of really brilliant, motivated people who wanna be a part of the future. And, uh, so it's, it's pretty fun to, to work with them. The attitude inside a car company like that is, you know, uh, embracing this, this transformation and change rather than fearing it. And I think that's a testament to the leadership at GM, and that's flown all the way through to, to everyone you talk to, even the people in the assembly plants working on these cars.

Lex Fridman 30:50 Right.

Kyle Vogt 30:51 So, that's really great. So that, starting from that position makes it a lot easier. So then when the, the people in San Francisco at Cruise interact with the people at GM, at least we have this common set of values, which is that we really want this stuff to work 'cause we think it's important and we think it's the future. That's not to say, you know, those two cultures don't clash. They absolutely do. There's different, different sort of value systems. Like, in a car company, the thing that gets you promoted and, and sort of the reward system is following the processes, delivering the, the, the program on time and on budget. So, any sort of risk-taking is, uh, discouraged in many ways because if a program is late or if you shut down the plant for a day, it's, you know, you can count the millions of dollars that, that burn by pretty quickly. Whereas I think in a, uh, most Silicon Valley companies and, and in, in, in Cruise and the methodology we were employing, especially around the time of the acquisition, the reward structure is about trying to solve these complex problems in any way, shape, or form, or coming up with crazy ideas that, you know, 90% of them won't work. And, uh, and so, so meshing that culture of sort of continuous improvement and experimentation with one where everything needs to be, you know, rigorously defined upfront so that you never slip a, a deadline or, or miss a budget was a pretty big challenge. And that, we're, we're over three years in now, uh, after the acquisition, and I'd say, like, you know, the investment we made in figuring out how to work together successfully and who should do what and, uh, how, how we bridge the gaps between these very different systems and way of doing engineering work, uh, is now one of our greatest assets 'cause I think we have this really powerful thing. But for a while, it was both, both GM and Cruise were, were very steep on a learning curve.

Lex Fridman 32:37 Yes, I'm sure it was very stressful. It's really important work 'cause that's, that's how to revolutionize the transportation, uh, really to revolutionize any system. You know, you look at the healthcare system or you look at the legal system. I have people, like lawyers come up to me all the time, like, everything they're working on can easily be automated. But then that's not-

Kyle Vogt 32:56 That's not a good feeling, yeah.

Lex Fridman 32:57 Well, it's not a good feeling, but also there's no way to automate because the, the, the entire infrastructure is really, uh, you know, based, is, is older and it moves very slowly. And so, so how do you close the gap between I haven't ... Uh, how can I replace ... Of course, lawyers don't want to be replaced with an app, but you could replace-

Kyle Vogt 33:16 Mm-hmm.

Lex Fridman 33:16 ... a lot of aspect when most of the data is still on paper. And so the same thing with, with automotive. I mean, it's fundamentally software. So it's, it's basically hiring software engineers. It's thinking in a software world. I mean, I'm pretty sure nobody in Silicon Valley has ever hit a deadline. (laughs) So and then, uh, on, on GM's-

Kyle Vogt 33:36 (laughs) That's, that's probably true, yeah.

Lex Fridman 33:37 (laughs) On GM's side, it's probably the opposite.

Kyle Vogt 33:39 Yeah.

Lex Fridman 33:39 Uh, so that's, that culture gap is, is really fascinating. And so you're optimistic about the future of that?

Kyle Vogt 33:45 Yeah, I mean, from what I've seen, uh, it's impressive. And I think, like, especially in Silicon Valley, it's easy to write off building cars because, you know, people have been doing that for over 100 years now in this country, and so it seems like that's a solved problem. But that doesn't mean it's an easy problem. And, uh, I think it would be easy to sort of overlook that and think that, you know, we're Silicon Valley engineers, we can solve any problem. You know, building a car, it's been done, therefore it's, you know, it's, it's, it's not a, it's not a real engineering challenge. But after having seen just the sheer scale and magnitude in industrialization that occurs inside of an automotive assembly plant, that is a lot of work that I am very glad that we don't have to reinvent, um, to make self-driving cars work. And so to have, you know, partners who have done that for 100 years and have these great processes and this huge infrastructure and supply base that we can tap into is just remarkable because the scope and surface area of, of the problem of deploying fleets of self-driving cars is so large that we're constantly looking for ways to do less so we can focus on the things that really matter more. And if we had to figure out how to build and assemble and, you know-

Lex Fridman 34:57 Tests and s- yeah.

Kyle Vogt 34:58 Yeah, build the cars themselves, I mean, we, we work closely with GM on that, but if we had to develop all that capability in-house as well, you know, that, that would just make, make the problem really intractable, I think.

Lex Fridman 35:09 Mm-hmm. So yeah, just like your first entry at the MIT DARPA Challenge when there was, what, the motor that failed?

Kyle Vogt 35:17 Mm-hmm.

Lex Fridman 35:17 If somebody that knows what they're doing with a motor did it, they could just focus on the soft-

Kyle Vogt 35:20 It would've been nice if we could focus on the software and not the-

Lex Fridman 35:22 Yeah. (laughs)

Kyle Vogt 35:22 ... not the hardware platform. Yeah.

Lex Fridman 35:24 Right. So, uh, fr- from your perspective now, you know, there are so many ways that autonomous vehicles can impact society in the next year, five years, 10 years. What do you think is the biggest opportunity to make money in autonomous driving, uh, so- sort of make it a financially viable thing in the near term? What do you think will be the biggest, um, impact there?

Kyle Vogt 35:49 Well, the things that, that drive the economics for fleets of self-driving cars are, there, there's sort of a, a handful of, of variables. One is, you know, the cost to build the vehicle itself, so the material cost. How many ... you know, what's the cost of all your sensors plus the cost of the vehicle and everyth- all the other components on it? Another one is the lifetime of the vehicle. It's very different if your vehicle drives 100,000 miles and then it falls apart versus, you know, two million.

Lex Fridman 36:14 Right.

Kyle Vogt 36:16 And then, you know, if you have a, a fleet, it's kind of like an airplane where, or, or a airline where once, um, you produce the vehicle, you want it to be in operation-

Lex Fridman 36:27 Right.

Kyle Vogt 36:27 ... as many hours a day as possible producing revenue. And then, uh, you know, the other piece of that is-... how are you generating revenue? And I think that's kinda what you're asking. And I think the obvious things today are, you know, the ridesharing business, because that's pretty clear that there's demand for that. Uh, there's existing markets you can tap into and, um-

Lex Fridman 36:46 Large urban areas, that kinda thing.

Kyle Vogt 36:48 Yeah, yeah. And- and- and I think that there are some real benefits to having cars without drivers compared to sort of the status quo for people who use rideshare services today. You know, your privacy, consistency, hopefully significantly improved safety, all these benefits versus the current product. But it's a- it's a- it's a crowded market. And then, uh, other opportunities which we've seen a lot of activity in the last, really in the last six to 12 months is, uh, you know, delivery. Whether that's parcels and packages, uh, food or- or groceries, um, those are all sort of, I think, opportunities that are- that are pretty ripe for these, you know, once you have this core technology, which is the fleet of autonomous vehicles. There's all sorts of different business opportunities you can build on top of that, but I think the important thing, of- of course, is that there's zero monetization opportunity until you actually have that fleet of very capable driverless cars that are- that are as good or better than humans. And that's sort of where the entire industry is sort of in this holding pattern right now.

Lex Fridman 37:46 Yeah, they're trying to achieve that baseline. So- but you said sort of reliab- not reliability, uh, consistency. It's kinda interesting, I think I heard you say somewhere, uh, not sure if that's what you meant, but, you know, I can imagine a situation where you would get in a- in an autonomous vehicle and, uh, you know, when you get into an Uber or Lyft, you don't get to choose the driver in a sense that you don't get to choose the personality of the driving. Do you think there's a- there's room to define the personality of the car the way it drives you in terms of aggressiveness, for example? In terms of sort of pushing the bo- the- one of the biggest challenges in autonomous driving is the- is the trade-off between sort of safety and assertiveness.

Kyle Vogt 38:27 Mm-hmm.

Lex Fridman 38:28 And is- do you think there's any room for the human to take a role in that decision? To accept some of the liability, I guess?

Kyle Vogt 38:38 I mean, wi- w- uh, I- I wouldn't as- no, I'd say within reasonable bounds as in we're not gonna... uh, I think it'd be highly unlikely we'd expose any knob that would let you, you know, significantly increase some sort of safety risk. I think that's- that's just not something we'd be willing to do. But I think driving style or, like, you know, are you gonna relax the comfort constraints slightly or things like that? All of those things make sense and are plausible. I see all those as, you know, nice optimizations once, again, we get the core problem solved and these fleets out there. But the other thing we've sort of observed is that you have this intuition that if you sort of slam your foot on the gas right after the light turns green and aggressively accelerate, you're gonna get there faster, but the actual impact of doing that is pretty small. You feel like you're getting there faster, but- so the- so the same would be true for AVS even if they don't slam their, you know, the pedal to the floor when the light turns green. They're gonna get you there within, you know, if it's a 15-minute trip, within 30 seconds of what you would've done otherwise if you were going really aggressively. So I think there's this sort of self-deception that- that, uh, my aggressive driving style is getting me there faster.

Lex Fridman 39:44 Well, so that's, you know, s- some of the things I study, some of the things I'm fascinated by the psychology of that. And I- I don't think it matters that it doesn't get you there faster. It's- it's the emotional release. Driving is- is a place, being inside our car, somebody said it's like the real world version of being a troll.

Kyle Vogt 40:02 (laughs)

Lex Fridman 40:02 Uh, so you have this protection, this mental protection, you're able to sorta yell at the world, like release your anger, whatever is bo- so there's an element of that that I think autonomous vehicles would also have to s- you know, ha- giving an outlet to people, but it doesn't have to be through s- through- through driving or honking or so on.

Kyle Vogt 40:19 Yeah.

Lex Fridman 40:19 There might be other outlets. But I think to just sort of even just put that aside, the baseline is really, you know, that's the focus, that's the thing you need to solve, and then the fun human things can be solved after. But- so from the baseline of just solving autonomous driving and you're working in San Francisco, one of the more difficult cities to operate in, wha- wha- what is- what is the, in your view, currently the hardest aspect of autonomous driving? I- is- is n- negotiating with pedestrians? Is it, uh, edge cases of perception? Is it planning? Is- is- is there a mechanical engineering? Is it data, fleet stuff? I- wha- wha- what are your thoughts on the challen- the more challenging aspects there?

Kyle Vogt 41:01 That's a g- that's a good question. I think before (laughs) we- before we go to that though, I just wanna- I- I like what you said about the, uh, psychology aspect of this-

Lex Fridman 41:07 Sure.

Kyle Vogt 41:07 ... 'cause I think one observation I've made is, I think I read somewhere that, uh, uh, I think it's maybe Americans on average spend, you know, over an hour a day on social media.

Lex Fridman 41:16 Oh.

Kyle Vogt 41:16 Uh, like staring at Facebook. And so that's just, you know, 60 minutes of your life you're not getting back, and it's probably not super productive. And so that's 3,600 seconds, right? And, uh, that's- that's time- you know, that's a lot of time you're giving up. And if you compare that to people being on the road, if another vehicle, whether it's a human driver or autonomous vehicle, delays them by even three seconds-

Lex Fridman 41:39 Right.

Kyle Vogt 41:39 ... they're laying in on the horn.

Lex Fridman 41:41 Yeah.

Kyle Vogt 41:41 You know, even though that's- that's, you know, 1/1000 of the time they waste looking at Facebook every day. So there's- there's definitely some, you know, psychology aspects of this I think that are pretty interesting. Road rage in general, and then the question, of course, is if everyone is in self-driving cars, do they even notice these three-second delays anymore 'cause they're doing other things or reading or, uh, working or just talking to each other? So it'll be interesting to see where that goes.

Lex Fridman 42:03 In a certain aspect, people- people need to be distracted by something entertaining, something useful inside the car so they don't pay attention to the external world. And then- and the- and then they can take whatever psychology and- and bring it back to Twitter.

Kyle Vogt 42:16 Yeah. (laughs)

Lex Fridman 42:16 And then focus on that as opposed to sort of interacting, uh, e- eh, sort of putting the emotion out there into the world. So it's a- it's an interesting problem, but baseline autonomy.

Kyle Vogt 42:27 Yeah, I guess you could say self-driving cars, you know, at scale will lower the collective blood pressure of society probably by a couple points, uh-

Lex Fridman 42:33 Yeah.

Kyle Vogt 42:34 ... without all that road rage and stress. So that- that's a good- good externality.

Lex Fridman 42:38 Mm-hmm.

Kyle Vogt 42:38 So back to your, your question about the, um, technology and the, the, the, I guess, the biggest problems, and I have a hard time answering that question because, you know, we've been at this, like specifically focusing on driverless cars and all the technology needed to enable that, for a little over four and a half years now.

Lex Fridman 42:55 Right.

Kyle Vogt 42:55 And even a year or two in, I felt like we had completed the functionality needed to get someone from point A to point B. As in, if we need to do a left turn maneuver or if we need to drive around a, you know, a double parked vehicle into oncoming traffic or navigate through construction zones, the, the scaffolding and the building blocks were- was there pretty early on. And so the challenge is not any one scenario or situation for which, you know, we fail at 100% of those. It's more, you know, we're benchmarking against a pretty good or pretty high standard, which is human driving. All things considered, humans are excellent at handling-

Lex Fridman 43:34 Yep.

Kyle Vogt 43:34 ... edge cases and, and unexpected scenarios, whereas computers are the opposite. And so beating that, that, uh, baseline set by humans is the challenge. And so what we've been doing for quite some time now is basically, it's this continuous improvement process where we find sort of the, the most, um, you know, uncomfortable or, or the things that, that, um, could lead to a, a, a safety issue, other things, all these events. And then we sort of categorize them and, uh, rework parts of our system to, to make incremental improvements and do that over and over and over again. And we just see sort of the overall performance of the system, you know, actually increasing at a pretty steady clip. But there's no one thing. There's actually like thousands of little things and just-

Lex Fridman 44:17 Mm-hmm.

Kyle Vogt 44:18 ... like polishing functionality and making sure that it handles, you know, every ver- every version and possible permutation of a situation by either applying more deep learning systems or just by, uh, you know, adding more test coverage or new scenarios that, that we develop against and just grinding on that. It's- we're sort of in the, the unsexy phase of development right now, which is doing the real engineering work that it takes to go from prototype to production.

Lex Fridman 44:44 You're basically scaling the, the grinding, so he- s- sort of taking seriously that the process of, uh, all those edge cases, both with human experts and machine learning methods to, to cover, to cover all those situations.

Kyle Vogt 44:59 Yeah, and the exciting thing for me is I don't think that grinding ever stops-

Lex Fridman 45:02 Right.

Kyle Vogt 45:03 ... because there's a moment in time where you, you f- you cross that threshold of, of human performance and become superhuman. But there's no reason, there's no first principles reason that AV capability will tap out anywhere near humans. Like there's no reason it couldn't be 20 times better, whether that's, you know, just better driving or safer driving or more comfortable driving, or even 1,000 times better given enough time. And we intend to basically chase that, you know, f- forever, to build the best possible product.

Lex Fridman 45:32 Better and better and better and always new edge cases come up and new experiences so... And you, you wanna a- automate that process as much as possible.

Kyle Vogt 45:40 Mm-hmm.

Lex Fridman 45:42 So what do you think in general in society, w- when do you think we may have hundreds of thousands of fully autonomous vehicles driving around? So first of all, predictions, nobody knows the future. You're a part of, uh, the leading people trying to define that future, but even then, you still don't know. But if you l- think about a hu- hundreds of thousands of vehicles, so a significant fraction of vehicles in major cities are autonomous, do you think... Are, are you with Rodney Brooks, who is 2050 and beyond, or are you more with Elon Musk, who is we should've had that two years ago?

Kyle Vogt 46:20 Well, I mean, I- I would've loved-

Lex Fridman 46:22 I, I don't mean to use those people.

Kyle Vogt 46:22 ... to have it two years ago, but-

Lex Fridman 46:24 (laughs)

Kyle Vogt 46:25 ... um, (laughs) we're not there yet. So I guess the, the way I would think about that is let's, let's, uh, flip that question around. So what would prevent you to reach hundreds of thousands of vehicles? And-

Lex Fridman 46:36 That's a good, that's a good, uh, rephrasing it.

Kyle Vogt 46:38 Yeah, so the... I'd say the... It seems the consensus among the, the people developing self-driving cars today is to sort of start with some form of an easier environment, whether it means, you know, lacking inclement weather or, you know, mostly sunny or whatever it is. And then add, add capability for more complex situations over time. And so if you're only able to deploy in areas that, that meet sort of your, your criteria or the, the current domain- you know, operating domain of, of the software you developed, uh, that may put a cap on how many cities you could deploy in. But then as those restrictions start to fall away, like maybe you add, you know, capability to drive really well and, and safely in heavy rain or snow, you know, that, that probably opens up the market by two, two or threefold in terms of the cities you can expand into and so on. And so the real question is, you know, I, I know today, if we wanted to, we could produce that, that many autonomous vehicles-

Lex Fridman 47:39 Mm-hmm.

Kyle Vogt 47:40 ... but we wouldn't be able to make use of all of them yet 'cause we would sort of saturate the demand, um, in the cities in which we would want to operate initially. So if I were to guess like what the timeline is for those things falling away and reaching hundreds of thousands of vehicles-

Lex Fridman 47:55 Maybe a range is better?

Kyle Vogt 47:56 ... I would, I would say less than five years.

Lex Fridman 47:57 Less than five years?

Kyle Vogt 47:58 Yeah.

Lex Fridman 47:59 And, of course, you're working hard to make that happen. So you started two companies that were eventually acquired for, each for a billion dollars. So you're a pretty good person to ask, what does it take to build a successful startup?

Kyle Vogt 48:14 Hmm. I think, uh, there's, there's sort of survivor bias here a little bit, but I can try to find some common threads for the, the things that worked for me, which is, you know, I... In, in both of these companies, I was really passionate about the core technology. I actually like, you know, lay awake at night thinking about these problems and, and how to solve them. And I think that's helpful because when you start a business, there are, like to this day, there are, there are-... these crazy ups and downs. Like one day you think the business is just on, you're, you're just on top of the world and unstoppable, and the next day you think, "Okay, this is all gonna end." You know, it's, it's just, it's just going south and it's gonna be over tomorrow. Um, and, uh, and so I think, like, having a, a true passion that you can fall back on and knowing that you would be doing it even if y- you weren't getting paid for it helps you weather those, those tough times. So that's one thing. I think the other one is really good people. So I've always, uh, been surrounded by really good co-founders that are logical thinkers, um, are always pushing their limits, and have very high levels of integrity. So that's Dan Kohn in my current company and actually his brother and a couple other guys for Justin.tv and Twitch. And then I think the last thing is just, uh, I guess persistence or perseverance. Like, and, and that, that can apply to sticking to sort of a, the, or, or having conviction around the original premise of your idea, and, and sticking around to do all the, you know, the unsexy work to actually make it come to fruition-

Lex Fridman 49:38 Yeah.

Kyle Vogt 49:38 ... including dealing with, you know, whatever it is that you, that you're not passionate about, whether that's finance or, or HR or, or operations or those things. As long as you are grinding away and working towards, you know, that north star for your business, whatever it is, and you don't give up, and you're making progress every day, i- it seems like eventually you'll end up in a good place. And the only things that can slow you down are, you know, running out of money, or I suppose your competitors destroying you. But I think most of the time, it's, it's people, uh, giving up or, or somehow destroying-

Lex Fridman 50:07 Right.

Kyle Vogt 50:07 ... things themselves rather than being beaten by their competition or running out of money.

Lex Fridman 50:11 Yeah, if you never quit, eventually you'll arrive. S- so, uh-

Kyle Vogt 50:15 That was a much more concise version of what I was trying to say.

Lex Fridman 50:17 (laughs)

Kyle Vogt 50:17 That was good.

Lex Fridman 50:18 So you went the Y Combinator route twice.

Kyle Vogt 50:21 Yeah.

Lex Fridman 50:21 Uh, w- what do you think, uh, in a quick question, do you think is the best way to raise funds in the early days? Or not, not just funds, but just community, develop your idea and so on. Can you do it, uh, solo or maybe with a co-founder with... Like, self-funded? Do you think Y Combinator's good? Is it good to do VC route? Is there no right answer or is there, from the Y Combinator experience, something that you could take away that that was the right path to take?

Kyle Vogt 50:48 There's no one-size-fits-all answer, but if your ambition, I think, is to, you, you know, see how big you can make something or, or, or rapidly expand and capture a market or solve a problem or whatever it is, then, then, you know, going the venture back route is probably a good approach, so that, so that capital doesn't become your primary constraint. Y Combinator, I love because it puts you in this, uh, sort of competitive environment while you're... where you're surrounded by, you know, the top maybe 1% of other really highly motivated, you know, peers who are in a same, same place.

Lex Fridman 51:21 Mm-hmm.

Kyle Vogt 51:22 And that, uh, that environment, I think, just breeds, breeds success, right? If you're surrounded by really brilliant, hardworking people, you're gonna feel, you know, sort of compelled or inspired to, to try to emulate them and, or beat them. And, uh, so even though I had done it once before and I felt like, you know, I'm pretty self-motivated, I thought, like, "Look, this is gonna be a hard problem. I can use all the help I can get." So if surrounding myself with other entrepreneurs is gonna make me work a little bit harder or, or push a little harder, then it's worth it. And so that's why, why I did it, you know, for example, the second time.

Lex Fridman 51:54 Let's, uh, let's go philosophical, existential. If you could go back and do something differently in your life, starting i- in high school and MIT, leaving MIT, you could've gone the PhD route, doing the startup, going to see about a startup in California, any th- or maybe some aspects of fundraising, is there something you regret, something you th- not necessarily regret, but if you could go back, you could do differently?

Kyle Vogt 52:22 I think I've made a lot of mistakes. Like, you know, pretty much everything you can screw up, I think I've screwed up at least once. But I, you know, I don't regret those things. Uh, I think it's, it's hard to, it's hard to look back on things, even if they didn't go well, and call it a regret, because hopefully, you know, it took away some new knowledge or learning from that. So... I would say there, there was a period... Yeah, uh, the closest I can, I can come to is this. There was a period, um, in, in Justin.tv, I think after seven years, where, you know, the, the company was going one direction, which was towards Twitch, uh, and video gaming. I'm not a video gamer.

Lex Fridman 52:58 Mm-hmm.

Kyle Vogt 52:58 I don't really even use Twitch at all. And I was still, uh, working on the core technology there, but my, my heart was no longer in it, because the business that we were creating was not something that I was personally passionate about.

Lex Fridman 53:10 It didn't meet your bar of existential impact.

Kyle Vogt 53:12 Yeah. And I'd say I, I, I, I probably spent an extra year or two working on that, and, uh, a- and I'd say, like, I would've just tried to do something different sooner. 'Cause those, those were two years where I felt like, um... You know, from this philosophical or existential thing, I, I just, I just felt like something was missing. And so I, I would've, I would've... If I could look back now and tell myself, it's like, I would've said exactly that. Like, "You're not getting any meaning out of your work personally right now. You should, you should find a way to change that." And that's, that's part of the pitch I use to basically everyone who joins Cruise today. It's like, "Hey, you've got that now by coming here."

Lex Fridman 53:48 Yeah. Well, maybe you needed the two years of that existential dread to develop the feeling that ultimately was the fire that created Cruise. So you never know. You can't regret-

Kyle Vogt 53:56 Yeah, good theory. Yeah.

Lex Fridman 53:57 So last question, what does 2019 hold for Cruise?

Kyle Vogt 54:02 After this, I guess we're gonna go and, and, and I'll talk to your class.

Lex Fridman 54:04 Yeah.

Kyle Vogt 54:04 But one of the big things is going from prototype to production, uh, for autonomous cars, and what does that mean, what does that look like? And 2019 for us is, is the year that we try to cross over that threshold and reach, you know, superhuman level of performance to some degree with the software, and, uh, have all the other of the thousands of little building blocks in place to, to launch, um, you know, our, our first, uh, commercial product. So that's, that's what's in score for us, or in, in store for us. And we've got a lot of work to do. We've got a lot of brilliant people working on it. So it's, it's all up to us now.

Lex Fridman 54:37 Yeah, from Charlie Miller and Chris Wales, like the people I've crossed paths with-

Kyle Vogt 54:42 Oh, great. Yeah.

Lex Fridman 54:43 ... uh, in a few... It, it sounds like you have an amazing team. So, um, like I said, it's one of the most, I think, one of the most important problems in artificial intelligence of this century. It'll be one of the most defining. So it's super exciting that you're working on it. And, uh, the best of luck in 2019. I'm really excited to see what Cruise comes up with.

Kyle Vogt 55:01 Thank you. Thanks for having me today.

Lex Fridman 55:02 Thanks, Kal.