Kyle Vogt: Cruise Automation | Lex Fridman Podcast #14
 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 Lex Friedman, spelled F-R-I-D. And now, here's my conversation with Kyle Vogt. You grew up in Kansas, right? Yeah, and I just saw that picture you had hidden over there, so I'm a little bit worried about that now. So in high school in Kansas City, you joined Shawnee Mission North High School robotics team. Now, that wasn't your high school. That's right. That was the only high school in the area that had a teacher who was willing to sponsor our first robotics team. I was gonna troll you a little bit. Jog you a little bit. I was trying to look super cool and intense, because this was BattleBots. This is serious business. So we're standing there with a welded steel frame and looking tough. So go back there. What is that jury to robotics? Well, I think 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 Legos that had motors and other things. And then Lego Mindstorms came out, and for the first time, you could program Lego contraptions. And I think things just sort of snowballed from that. But I remember seeing 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 not knowing a whole lot about how to build these 200 pound fighting robots. So I sort of obsessively pored over the internet forums where all the creators for BattleBots would sort of hang out and talk about, document their build progress and everything. And I think I read, I must have read like, tens of thousands of forum posts from basically, everything that was out there on what these people were doing. And eventually, sort of triangulated how to put some of these things together. And ended up doing BattleBots, which was like 13 or 14, which was pretty awesome. I'm not sure if the show's still running, but so BattleBots is, there's not an artificial intelligence component. It's remotely controlled. And it's almost like a mechanical engineering challenge of building things that can be broken. They're radio controlled. So, and I think that they allowed some limited form of autonomy, but in a two minute match, and the way these things ran, you're really doing yourself a disservice by trying to automate it versus just, do the practical thing, which is drive it yourself. And there's an entertainment aspect, just going on YouTube, there's like some of them wield an axe, 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 Frankenstein and sentient being, or was it just like the Lego, you like tinkering with stuff? I mean, that was just building something. I think the idea of this radio controlled machine that can do various things, if it has like a weapon or something, was pretty interesting. I agree, it doesn't have the same appeal as autonomous robots, which I sort of gravitated towards later on, but it was definitely an engineering challenge, because everything you did in that competition was pushing components to their limits. So we would buy like these $40 DC motors that came out of 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 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 was something intrinsically interesting about just seeing like where things break. 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 there a reckless, let's just go with it and see? We weren't very good at BattleBots. We lost all of our matches the first round. What did yours look like? The one I built first, both of them were these wedge shaped robots, because the 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, it's kind of like a door stopper. And the first one had a pneumatic, polished stainless steel spike on the front that would shoot out about eight inches. The purpose of which is what? Pretty ineffective actually, but it looks cool. And- Was it to help with the lift? No, it was just to try to poke holes in the other robot. And then the second time I did it, which was the following, I think maybe 18 months later, we had a titanium ax with a hardened steel tip on it that was powered by a hydraulic cylinder, which we were activating with liquid CO2, which had its own set of problems. So great, so that's kind of on the hardware side. I mean, 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? Oh man, so- Go back there, see what language was it, what was it, was it EMAX, VIM? Was it a more respectable modern ID, do you remember any of this? Yeah, well, I remember, I think 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 crash or it wouldn't start up correctly and it would dump you out to, what I later learned was like sort of a command prompt. And my teacher would come over and type, actually remember this to this day for some reason, like PR number six or PR pound six, which is peripheral six, which is the disk drive, which would fire up the disk and load the program. And I just remember thinking, wow, she's like a hacker, like teach me these codes, these error codes, 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 school where you could actually go on these Apple IIs and learn to program. And so it was all in basic, where every line numbers are all, every line is numbered and you have to leave enough space between the numbers so that if you wanna tweak your code, you go back and 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, 11 or 12 and you hope you don't run out of line numbers and have to redo the whole thing. And there's go to statements. Yeah, go to and it's very basic, maybe hence the name, but a lot of fun. And that was like, that was, that's when when you first program, you see the magic of it. It's like this world opens up with endless possibilities for the things you could build or accomplish with that computer. So you got the bug then, so even starting with basic and then what C++ throughout, what did you, was there a computer program and computer science classes in high school? Not where I went, so I was self-taught, but I did a lot of programming. The thing that 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 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 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 steer the wheel. And later I'd come to realize this had been done since 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 autonomous vehicles of some scale, start off with really small electric ones and then progress to what we're doing now. So what was your view of artificial intelligence at that point? What did you think? So this is before, there's been waves in artificial intelligence, right? The current wave with deep learning makes people believe that you 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, localize itself in the world, moves around the world? Like when you were younger, I mean, what was your thinking about it? Well, prior to deep neural networks or convolutional neural analysis, modern techniques we have, or at least ones that are in use today, it was all heuristic space. And so like old school image processing, and I think extracting 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 Huff Transform or something, and then go from there. Traffic light detection and stop sign detection, red, yellow, green. And I think you could, I mean, if you wanted to do a full, I was just trying to make something that would stay in between the lanes on a highway, but if you wanted to do the full set of capabilities needed for a driverless car, I think you could, and we'd done this at Cruise in the very first days, you can start off with a really simple human written heuristic just to get the scaffolding in place for your system. Traffic light detection, probably a really simple color thresholding on day one just to get the system up and running before you migrate to a deep learning based technique or something else. And back when I was doing this, my first one, it was on a Pentium 233 megahertz computer, and I think I wrote the first version in basic, which is like an interpreted language, it's extremely slow, because that's the thing I knew at the time. And so there was no chance at all of using, there was no computational power to do 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 at age 13 using neural networks in their garage? I mean, that would be awesome. I get emails all the time from 11, 12 year olds saying I'm trying to follow this TensorFlow tutorial, and I'm having this problem. And their general approach in the deep learning community is of extreme optimism of, as opposed to, you mentioned heuristics, you can separate the autonomous driving problem into modules and try to solve it rigorously, or you can just do it end to end. And most people just kind of love the idea that, us humans do it end to end, we just perceive and act. We should be able to do the same kind of thing with neural nets. And that kind of thinking, you don't want to criticize that kind of thinking, because eventually they will be right. And so it's exciting. And especially when they're younger, to explore that as a really exciting approach. But yeah, it's changed the language, the kind of stuff you're tinkering with. It's kind of exciting to see when these teenagers grow up. Yeah, I can only imagine if your starting point is Python and TensorFlow at age 13, where you end up after 10 or 15 years of that, that's pretty cool. Because of GitHub, because the state of the art tools for solving most of the major problems in artificial intelligence are within a few lines of code for most kids. And that's incredible to think about, also on the entrepreneurial side. And on that point, was there any thought about entrepreneurship before you came to college, is sort of building this into a thing that impacts the world on a large scale? Yeah, I've always wanted to start a company. I think that's just a cool concept of creating something and exchanging it for value, or creating value, I guess. So in high school, I was trying to build servo motor drivers, little circuit boards, and sell them online, or other things like that. And certainly knew at some point I wanted to do a startup, but it wasn't really, I'd say, until college, until I felt like I had the right combination of the environment, the smart people around you, and some free time. I had a lot of free time at MIT. So you came to MIT as an undergrad, 2004. That's right. And that's when the first DARPA Grand Challenge was happening. Yeah. The timing of that is beautifully poetic. So how did you get yourself involved in that one? Originally, there wasn't a official entry. Yeah, faculty sponsored thing. And so a bunch of undergrads, myself included, started meeting and got together and tried to haggle together some sponsorships. We got a vehicle donated, a bunch of sensors, and tried to put something together. So our team was probably mostly freshmen and sophomores, which was not really a fair fight against maybe the postdoc and faculty-led teams from other schools. But we got something up and running. We had our vehicle drive by wire and very, very basic control and things. But on the day of the qualifying, pre-qualifying round, the one and only steering motor that we had purchased, the thing that we had 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. On the hardware side, so was there a software component? How did your view of autonomous vehicles in terms of artificial intelligence evolve in this moment? I mean, like you said, from the 80s has been autonomous vehicles. But really, that was the birth of the modern wave, the thing that captivated everyone's imagination that we can actually do this. So were you captivated in that way? So how did your view of autonomous vehicles change at that point? I'd say at that point in time, it was a curiosity, as in like, is this really possible? And I think that was generally the spirit and the purpose of that original DARPA Grand Challenge, which was to just get a whole bunch of really brilliant people exploring this space and pushing the limits. And I think to this day, that DARPA challenge with its million dollar prize pool was probably one of the most effective uses of taxpayer money dollar for dollar that I've seen. Because that small sort of initiative that DARPA put out, in my view, was the catalyst or the tipping point for this whole next wave of autonomous vehicle development. So that was pretty cool. Let me jump around a little bit on that point. They also did the Urban Challenge, where it was in the city. But it was very artificial, and there's no pedestrians. And there's very little human involvement except a few professional drivers. Do you think there's room? And then there was the Robotics Challenge with humanoid robots. In your role as looking at this, you're trying to solve one of the autonomous driving, one of the harder, more difficult places in San Francisco. Is there a role for DARPA to step in to also help out, challenge with new ideas, specifically pedestrians and so on, all these kinds of interesting things? Well, 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 in time. Because kick-starting and spinning up the flywheel is, I think, what they did in this case for very little money. But today, this has become commercially interesting to very large companies. And the amount of money going into it, and the amount of people going through your class and learning about these things and developing these skills is just 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 I think they can just say mission accomplished and move on to the next area of technology that needs help. So then stepping back to MIT, you left MIT Junior Year. What was that decision like? As I said, I always wanted to do a company or start a company. And this opportunity landed in my lap, which was a couple guys from Yale were 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 rock star entrepreneurs. They've done this before. They must be driving around in Ferraris, because they sold their company. And I thought I could learn a lot from them. So I teamed up with those guys and went out to California during IAP, which is MIT's month off, one-way ticket, and basically never went back. We were having so much fun. We felt like we were building something and creating something. And it was going to be interesting that I was just all in and got completely hooked. And that business was Justin TV, which is 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 quite an unexpected journey. So no regrets? No. Looking back, it was just an obvious one-way ticket. 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? Well, I didn't know. But just trying something for a month during IAP seems pretty low risk. And then, well, maybe I'll take a semester off. MIT's pretty flexible about that. You can always go back. And then after two or three cycles of that, I eventually threw in the towel. But I think it's, I guess in that case, I felt like I could always hit the undo button if I had to. Right. But nevertheless, from when you look in retrospect, I mean, it seems like a brave decision. It would be difficult for a lot of people to make. It wasn't as popular. I'd say the general flux of people out of MIT at the time was mostly into 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 probably inverted, which is just a sign of the times, I guess. So there's a story about midnight of March 18, 2007, 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. And you and one of the folks at Justin TV, Emma Shear, coded through the night. Can you take me through that experience? So let me say a few nice things that the article I read quoted. Justin Khan said that you were known for bureau coding through problems and being a creative genius. So on that night, 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 thin? That's a good question. Well, first of all, that's nice of Justin to say that. I think I would have been maybe 21 years old then and not very experienced at programming. But as with everything in a startup, 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 the city, it would be streaming live video. And this is even before the iPhones. This is hard to do back then. We would launch. And so we thought we were there. And the backpack was working. And then we sent out all the emails to launch the company and do the press thing. And then we weren't quite actually there. And then we thought, oh, well, they're not going to announce it until maybe 10 AM the next morning. And it's, I don't know, it's 5 PM now. So how many hours do we have left? What is that? Like 17 hours to go. And that was going to be fine. Was the problem obvious? Did you understand what could possibly be? How complicated was the system at that point? 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 three or four cell phone data modems. And they were like, we take the video stream and sort of sprayed across these three or four modems and then try to catch all the packets on the other side with unreliable cell phone networks. It's pretty low level networking. Yeah. And putting these protocols on top of all that to reassemble and reorder the packets and have time buffers and error correction and all that kind of stuff. And the night before, it was just staticky. Every once in a while, the image would go staticky. And there would be this horrible screeching audio noise because the audio was also corrupted. And this would happen every 5 to 10 minutes or so. And it was really off putting to the viewers. How do you tackle that problem? You're just freaking out behind a computer. Are there other folks working on this problem? Were you behind a whiteboard? Were you doing hair coating? Yeah, it's a little lonely because there's four of us 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 it was my sole responsibility to figure that out. And I think it's those setting deadlines, trying to move quickly and everything 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 because that's one of those character defining moments, I think. So in 2013, October, you founded Cruise Automation. So progressing forward, another exceptionally successful company was acquired by GM in 2016 for $1 billion. But in October 2013, what was on your mind? What was the plan? How does one seriously start to tackle one of the hardest robotics, most important impact for robotics problems of our age? After going through Twitch, Twitch was, and is today, pretty successful. But the result was entertainment, mostly. The better the product was, the more we would entertain people and then make money on them, add revenues and other things. And that was a good thing. It felt good to entertain people. But I figured, what is really the point of becoming a really good engineer and developing these skills other than my own enjoyment? And I realized I wanted something that scratched more of an existential itch, like something that truly matters. And so I basically made this list of requirements for a new, if I was going to do another company. And the one thing I knew in the back of my head that Twitch took eight years to become successful. And so whatever I do, I'd better be willing to commit at least 10 years to something. And when you think about things from that perspective, you certainly, I think, raise 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, because 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 health care, self-driving cars, because 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 at large scale. And I was thinking about that for a while. And 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. That was the most fun I had ever had in college working on that. And well, what's the state of the technology? It's been 10 years. 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. And I thought, surely there's a way to have an entrepreneur mindset and sort of solve the minimum viable product here. And so I just took the plunge right then and there and said, this is something I know I can commit 10 years to. It's probably the greatest applied AI problem of our generation. And if it works, it's going to be both a huge business and therefore probably the most positive impact I can possibly have on the world. So after that light bulb went off, I went all in on cruise immediately. And got to work. Did you have an idea how to solve this problem? Which aspect of the problem to solve? Like we just had Oliver from Voyage here, slow moving retirement communities, urban driving, highway driving. Did you have a vision of the city of the future where the transportation is largely automated, that kind of thing? Or was it sort of more fuzzy and gray area than that? My analysis of the situation is that Google had been putting a lot of money into that project. They had a lot more resources. And they still hadn't cracked the fully driverless car. This is 2013, I guess. So I thought, what can I do to sort of go from zero to significant scale so I can actually solve the real problem, which is the driverless cars? And I thought, here's the strategy. We'll start by doing a really simple problem or solving a really simple problem that creates value for people. So eventually ended up deciding on automating highway driving, which is relatively more straightforward as long as there's a backup driver there. And the go-to-market will be able to 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 sort of reinvest that in research for doing fully 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 after about a year of working on the highway autopilot, we had it working at a prototype stage. But we just completely abandoned that and said, we're going to go all in on driverless cars. Now is the time. Can't think of anything that's more exciting. And if it works, more impactful. So we're just going to go for it. The idea of retrofit is kind of interesting. Being able to, it's how you achieve scale. It's a really interesting idea. Is it something that's still in the back of your mind as a possibility? Not at all. I've come full circle on that one. After 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 other control inputs on your car, it's pretty hard to get there with a retrofit. Or if you did, even if you did, it creates a whole bunch of new complications around liability or how did you truly validate that. Or 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. That's a really convincing summary of why it's really challenging. But I didn't know all that at the time, so we tried it anyway. But as a pitch also, at the time, it's a really strong one. That's how you achieve scale and that's how you beat the leader at the time of Google or the only one in the market. The other big problem we ran into, which is perhaps the biggest problem from a business model perspective, is we had assumed that we started with an Audi S4 as the vehicle we retrofitted with this highway driving capability. And we had assumed that if we just knock out three make and models of vehicle, that'll cover 80% of the San Francisco market. Doesn't everyone there drive 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. To get even a decent number of units sold, we'd have to support 20 or 50 different models. And each one is a little butterfly that takes time and effort to maintain that retrofit integration and custom hardware and all this. So it was a tough business. So GM manufactures and sells over 9 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 do those, you've talked about it a little bit before, but it's also just fascinating to me. We work a lot of automakers. The difference between the gap between Detroit and Silicon Valley, let's say, just to be sort of poetic about it, I guess, how do you close that gap? How do you take GM into the future where a large part of the fleet would be autonomous perhaps? I want to start by acknowledging that GM is made up of tens of thousands of really brilliant, motivated people who want to be a part of the future. And so it's pretty fun to work with them, the attitude inside a car company like that is embracing 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 everyone you talk to, even the people in the assembly plants working on these cars. So that's really great. So starting from that position makes it a lot easier. So then when 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 because we think it's important and we think it's the future. That's not to say those two cultures don't clash. They absolutely do. There's different sort of value systems. Like in a car company, the thing that gets you promoted and sort of the reward system is following the processes, delivering the program on time and on budget. So any sort of risk taking is discouraged in many ways because if a program is late or if you shut down the plant for a day, you can count the millions of dollars that burn by pretty quickly. Whereas I think most Silicon Valley companies and 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 90% of them won't work. And so meshing that culture of sort of continuous improvement and experimentation with one where everything needs to be rigorously defined upfront so that you never slip a deadline or miss a budget was a pretty big challenge. And we're over three years in now after the acquisition. And I'd say the investment we made in figuring out how to work together successfully and who should do what and how we bridge the gaps between these very different systems and way of doing engineering work is now one of our greatest assets because I think we have this really powerful thing. But for a while, both GM and Cruise were very steep on the learning curve. Yes, I'm sure it was very stressful. It's really important work because that's how to revolutionize the transportation. Really, to revolutionize any system, you look at the health care system or you look at the legal system. I have people like Laura come up to me all the time. Everything they're working on can easily be automated. But then that's not a good feeling. Yeah. That's not a good feeling, but also there is no way to automate because the entire infrastructure is really based, is older, and it moves very slowly. And so how do you close the gap between I have an app? How can I replace? Of course, lawyers don't want to be replaced with an app, but you could replace a lot of aspect when most of the data is still on paper. And so the same thing with automotive. I mean, it's fundamentally software. 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. And then on GM? That's probably true, yeah. And GM side is probably the opposite. So that culture gap is really fascinating. So you're optimistic about the future of that? Yeah, I mean, from what I've seen, it's impressive. And I think, especially in Silicon Valley, it's easy to write off building cars because 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 I think it would be easy to sort of overlook that and think that we're Silicon Valley engineers, we can solve any problem. Building a car, it's been done. Therefore, it's not a real engineering challenge. But after having seen just the sheer scale and magnitude and 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 to make self-driving cars work. And so to have 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 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 a symbol and build the cars themselves, I mean, we work closely with GM on that. But if we had to develop all that capability in-house as well, that would just make the problem really intractable, I think. So yeah, just like your first entry at the MIT DARPA challenge when there was the motor that failed, if somebody that knows what they're doing with the motor did it, they could just focus on the software. Not the hardware platform. Yeah. Right. So from your perspective now, 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, sort of make it a financially viable thing in the near term? What do you think would be the biggest impact there? Well, the things that drive the economics for fleets of self-driving cars, there's sort of a handful of variables. One is the cost to build the vehicle itself. So the material cost, what's the cost of all your sensors plus the cost of the vehicle and 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 2 million. And then if you have a fleet, it's kind of like an airplane or an airline where once you produce the vehicle, you want it to be in operation as many hours a day as possible producing revenue. And then the other piece of that is, how are you generating revenue? I think that's kind of what you're asking. And I think the obvious things today are the ride sharing business because that's pretty clear that there's demand for that. There's existing markets you can tap into. Large urban areas, that kind of thing. Yeah, yeah. 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 ride share services today. You get privacy, consistency, hopefully significantly improve safety, all these benefits versus the current product. But it's a crowded market. And then other opportunities, which you've seen a lot of activity in the last six or 12 months, is delivery, whether that's parcels and packages, food, or groceries. Those are all sort of, I think, opportunities that are pretty ripe for these. 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 course, is that there's zero monetization opportunity until you actually have that fleet of very capable driverless cars 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. Yeah, they're trying to achieve that baseline. But you said sort of not reliability, consistency. It's kind of interesting. I think I heard you say somewhere. I'm not sure if that's what you meant. But I can imagine a situation where you would get an autonomous vehicle. And 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 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 bound? One of the biggest challenges in autonomous driving is the trade-off between sort of safety and assertiveness. And do you think there's any room for the human to take a role in that decision? Sort of accept some of the liability, I guess. I wouldn't say. No, I'd say within reasonable bounds, as in we're not going to, I think it'd be highly unlikely we'd expose any knob that would let you significantly increase safety risk. I think that's just not something we'd be willing to do. But I think driving style or are you going to relax the comfort constraints slightly or things like that? All of those things make sense and are plausible. I see all those as nice optimizations. Once again, we get the core problem solved in 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 going to get there faster. But the actual impact of doing that is pretty small. You feel like you're getting there faster. So the same would be true for AVs. Even if they don't slam the pedal to the floor when the light turns green, they're going to get you there within, if it's a 15 minute trip, within 30 seconds of what you would have done otherwise if you were going really aggressively. So I think there's this sort of self-deception that my aggressive driving style is getting me there faster. Well, so that's some of the things I've studied, some of the things I'm fascinated by the psychology of that. I don't think it matters that it doesn't get you there faster. It's the emotional release. Driving is a place. Being inside of a car, somebody said it's like the real world version of being a troll. So you have this protection, this mental protection. You're able to yell at the world, release your anger, whatever. So there's an element of that that I think autonomous vehicles would also have to give an outlet to people. But it doesn't have to be through driving or honking or so on. There might be other outlets. But I think to even just put that aside, the baseline is really, that's the focus. That's the thing you need to solve. And then the fun human things can be solved after. So from the baseline of just solving autonomous driving, you're working in San Francisco, one of the more difficult cities to operate in. What is, in your view, currently the hardest aspect of autonomous driving? Is it negotiating with pedestrians? Is it edge cases of perception? Is it planning? Is there a mechanical engineering? Is it data, fleet stuff? What are your thoughts on the more challenging aspects there? That's a good question. I think before we go to that, though, I like what you said about the psychology aspect of this. Because I think one observation I've made is, I think I read somewhere that I think it's maybe Americans on average spend over an hour a day on social media, like staring at Facebook. And so that's just 60 minutes of your life you're not getting back. And it's probably not super productive. And so that's 3,600 seconds. And 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, they're laying in on the horn, even though that's 1 1,000th of the time they waste looking at Facebook every day. So there's definitely some 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? Because they're doing other things, or reading, or working, or just talking to each other. So it'll be interesting to see where that goes. In a certain aspect, 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 they can take whatever psychology and bring it back to Twitter and then focus on that, as opposed to sort of interacting, sort of putting the emotion out there into the world. So it's an interesting problem, but baseline autonomy. I guess you could say self-driving cars at scale will lower the collective blood pressure of society probably by a couple of points, without all that road rage and stress. So that's a good externality. So back to your question about the technology and I guess the biggest problems. And I have a hard time answering that question, because we've been at this specifically focusing on driverless cars and all the technology needed to enable that for a little over 4 and 1 1⁄2 years now. 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 double parked vehicle into oncoming traffic or navigate through construction zones, the scaffolding and the building blocks was there pretty early on. And so the challenge is not any one scenario or situation for which we fail at 100% of those. It's more we're benchmarking against a pretty good or pretty high standard, which is human driving. All things considered, humans are excellent at handling edge cases and unexpected scenarios, whereas computers are the opposite. And so beating that 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 the most uncomfortable or the things that could lead to a safety issue or other things, all these events. And then we categorize them and rework parts of our system to make incremental improvements and do that over and over and over again. And we just see the overall performance of the system actually increasing in a pretty steady clip. But there's no one thing. There's actually thousands of little things and just polishing functionality and making sure that it handles every version and possible permutation of a situation by either applying more deep learning systems or just by adding more test coverage or new scenarios that we develop against and just grinding on that. We're sort of in the unsexy phase of development right now, which is doing the real engineering work that it takes to go from prototype to production. You're basically scaling the grinding, instead of taking seriously the process of all those edge cases, both with human experts and machine learning methods to cover all those situations. Yeah. And the exciting thing for me is I don't think that grinding ever stops. Because there's a moment in time where you've crossed that threshold of human performance and become superhuman. But there's no first principles reason that AV capability will tap out anywhere near humans. There's no reason it couldn't be 20 times better, whether that's 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 forever to build the best possible product. Better and better and better. And always new edge cases come up and new experiences. And you want to automate that process as much as possible. So what do you think in general in society? 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 the leading people trying to define that future. But even then, you still don't know. But if you think about hundreds of thousands of vehicles, so a significant fraction of vehicles in major cities are autonomous. Do you think, are you with Rodney Brooks, who is 2050 and beyond? Or are you more with Elon Musk, who is, we should have had that two years ago? Well, I mean, I'd love to have it two years ago, but we're not there yet. So I guess the way I would think about that is, let's flip that question around. So what would prevent you to reach hundreds of thousands of vehicles? And that's a good rephrasing. Yeah, so the, I'd say that it seems the consensus among the people developing self-driving cars today is to sort of start with some form of an easier environment, whether it means lacking inclement weather, or mostly sunny or whatever it is, and then add capability for more complex situations over time. And so if you're only able to deploy in areas that meet sort of your criteria or the current operating domain of the software you developed, 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 capability to drive really well and safely in heavy rain or snow, that probably opens up the market by two or threefold in terms of the cities you can expand into and so on. And so the real question is, I know that the city is a very, very, very, very, I know today if we wanted to, we could produce that many autonomous vehicles, but we wouldn't be able to make use of all of them yet, because we would sort of saturate the demand 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. Maybe a range is better. I would say less than five years. Less than five years. Yeah. And of course you're working hard to make that happen. So you started two companies that were eventually acquired for each $4 billion. So you're a pretty good person to ask, what does it take to build a successful startup? I think there's sort of survivor bias here a little bit, but I can try to find some common threads for the things that worked for me, which is, in both of these companies, I was really passionate about the core technology. I actually like lay awake at night thinking about these problems 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 these crazy ups and downs. Like one day you think the business is just on, 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 just going south and it's gonna be over tomorrow. And so I think like having a true passion that you can fall back on and knowing that you would be doing it even if you weren't getting paid for it helps you weather those tough times. So that's one thing. I think the other one is really good people. So I've always been surrounded by really good co-founders that are logical thinkers, are always pushing their limits and have very high levels of integrity. So that's Dan Kahn and 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, I guess persistence or perseverance. Like, and that can apply to sticking to sort of, or having conviction around the original premise of your idea and sticking around to do all the unsexy work to actually make it come to fruition, including dealing with whatever it is that you're not passionate about, whether that's finance or HR or operations or those things. As long as you are grinding away and working towards that North Star for your business, whatever it is, and you don't give up and you're making progress every day, it seems like eventually you'll end up in a good place. And the only things that can slow you down are, running out of money or I suppose your competitor is destroying you, but I think most of the time it's people giving up or somehow destroying things themselves rather than being beaten by their competition or running out of money. Yeah, if you never quit, eventually you'll arrive. So- It's a much more concise version of what I was trying to say. Yeah, it was good. So you went the Y Combinator out twice. Yeah. What do you think, in a quick question, do you think is the best way to raise funds in the early days? Or not just funds, but just community, develop your idea and so on. Can you do it solo or maybe with a co-founder with like self-funded? Do you think Y Combinator is 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? There's no one size fits all answer, but if your ambition I think is to see how big you can make something or rapidly expand and capture a market or solve a problem or whatever it is, then going the venture back route is probably a good approach so that capital doesn't become your primary constraint. Y Combinator, I love because it puts you in this sort of competitive environment where you're surrounded by the top maybe 1% of other really highly motivated peers who are in the same place. And that environment I think just breeds success. If you're surrounded by really brilliant, hardworking people, you're gonna feel sort of compelled or inspired to try to emulate them or beat them. And so even though I had done it once before and I felt like 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 surrounding myself with other entrepreneurs is gonna make me work a little bit harder or push a little harder than is worth it. And so that's why I did it, for example, the second time. Let's go philosophical, existential. If you go back and do something differently in your life, starting in high school and MIT, leaving MIT, you could have gone the PhD route, doing startup, going to see about a startup in California, or maybe some aspects of fundraising. Is there something you regret, something you not necessarily regret, but if you go back, you could do differently? I think I've made a lot of mistakes, like pretty much everything you can screw up. I think I've screwed up at least once. But I don't regret those things. I think it's hard to look back on things, even if they didn't go well and call it a regret, because hopefully it took away some new knowledge or learning from that. So I would say there was a period, yeah, the closest I can come to is this. There was a period in Justin TV, I think, after seven years where the company was going one direction, which was towards Twitch and video gaming. And I'm not a video gamer. I don't really even use Twitch at all. And I was still working on the core technology there, but my heart was no longer in it, because the business that we were creating was not something that I was personally passionate about. It didn't meet your bar of existential impact. Yeah, and I'd say I probably spent an extra year or two working on that. And I'd say I would have just tried to do something different sooner, because those were two years where I felt like, from this philosophical or existential thing, I just felt that something was missing. And so I would have, if I could look back now and tell myself, I would have said exactly that. You're not getting any meaning out of your work personally right now. You should find a way to change that. And that's part of the pitch I use to basically everyone who joins crews today. It's like, hey, you've got that now, why come here? Well, maybe you needed the two years of that existential dread to develop the feeling that ultimately it was the fire that created crews. So you never know, you can't remember. Good theory, yeah. So last question, what does 2019 hold for crews? After this, I guess we're gonna go and I'll talk to your class, but one of the big things is going from prototype to production for autonomous cars. And what does that mean? What does that look like? And 2019 for us is the year that we try to cross over that threshold and reach superhuman level of performance to some degree with the software and have all the other of the thousands of little building blocks in place to launch our first commercial product. So that's what's 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 all up to us now. Yeah, from Charlie Miller and Chris Wells, like the people I've crossed paths with. It sounds like you have an amazing team. So like I said, it's one of the most, I think one of the most important problems in artificial intelligence of the century. It'll be one of the most defining. It's super exciting that you work on it. And the best of luck in 2019. I'm really excited to see what Cruz comes up with. Thank you. Thanks for having me today. Thanks for having me.
 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 Lex Friedman,
 spelled F-R-I-D.
 And now, here's my conversation with Kyle Vogt.
 You grew up in Kansas, right?
 Yeah, and I just saw that picture you had hidden over there,
 so I'm a little bit worried about that now.
 So in high school in Kansas City,
 you joined Shawnee Mission North High School robotics team.
 Now, that wasn't your high school.
 That's right.
 That was the only high school in the area
 that had a teacher who was willing to sponsor
 our first robotics team.
 I was gonna troll you a little bit.
 Jog you a little bit.
 I was trying to look super cool and intense,
 because this was BattleBots.
 This is serious business.
 So we're standing there with a welded steel frame
 and looking tough.
 So go back there.
 What is that jury to robotics?
 Well, I think 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 Legos that had motors and other things.
 And then Lego Mindstorms came out,
 and for the first time, you could program Lego contraptions.
 And I think things just sort of snowballed from that.
 But I remember seeing 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 not knowing a whole lot about how to build
 these 200 pound fighting robots.
 So I sort of obsessively pored over the internet forums
 where all the creators for BattleBots would sort of hang out
 and talk about, document their build progress and everything.
 And I think I read, I must have read like,
 tens of thousands of forum posts from basically,
 everything that was out there
 on what these people were doing.
 And eventually, sort of triangulated
 how to put some of these things together.
 And ended up doing BattleBots,
 which was like 13 or 14, which was pretty awesome.
 I'm not sure if the show's still running,
 but so BattleBots is,
 there's not an artificial intelligence component.
 It's remotely controlled.
 And it's almost like a mechanical engineering challenge
 of building things that can be broken.
 They're radio controlled.
 So, and I think that they allowed
 some limited form of autonomy,
 but in a two minute match,
 and the way these things ran,
 you're really doing yourself a disservice
 by trying to automate it versus just,
 do the practical thing, which is drive it yourself.
 And there's an entertainment aspect,
 just going on YouTube,
 there's like some of them wield an axe,
 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 Frankenstein
 and sentient being, or was it just like the Lego,
 you like tinkering with stuff?
 I mean, that was just building something.
 I think the idea of this radio controlled machine
 that can do various things,
 if it has like a weapon or something,
 was pretty interesting.
 I agree, it doesn't have the same appeal
 as autonomous robots,
 which I sort of gravitated towards later on,
 but it was definitely an engineering challenge,
 because everything you did in that competition
 was pushing components to their limits.
 So we would buy like these $40 DC motors
 that came out of 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 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 was something intrinsically interesting
 about just seeing like where things break.
 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 there a reckless, let's just go with it and see?
 We weren't very good at BattleBots.
 We lost all of our matches the first round.
 What did yours look like?
 The one I built first,
 both of them were these wedge shaped robots,
 because the 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,
 it's kind of like a door stopper.
 And the first one had a pneumatic,
 polished stainless steel spike on the front
 that would shoot out about eight inches.
 The purpose of which is what?
 Pretty ineffective actually, but it looks cool.
 And-
 Was it to help with the lift?
 No, it was just to try to poke holes in the other robot.
 And then the second time I did it,
 which was the following, I think maybe 18 months later,
 we had a titanium ax with a hardened steel tip on it
 that was powered by a hydraulic cylinder,
 which we were activating with liquid CO2,
 which had its own set of problems.
 So great, so that's kind of on the hardware side.
 I mean, 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?
 Oh man, so-
 Go back there, see what language was it,
 what was it, was it EMAX, VIM?
 Was it a more respectable modern ID,
 do you remember any of this?
 Yeah, well, I remember,
 I think 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 crash or it wouldn't start up correctly
 and it would dump you out to,
 what I later learned was like sort of a command prompt.
 And my teacher would come over and type,
 actually remember this to this day for some reason,
 like PR number six or PR pound six,
 which is peripheral six, which is the disk drive,
 which would fire up the disk and load the program.
 And I just remember thinking, wow, she's like a hacker,
 like teach me these codes, these error codes,
 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 school where you could actually
 go on these Apple IIs and learn to program.
 And so it was all in basic,
 where every line numbers are all,
 every line is numbered and you have to leave enough space
 between the numbers so that if you wanna tweak your code,
 you go back and 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,
 11 or 12 and you hope you don't run out of line numbers
 and have to redo the whole thing.
 And there's go to statements.
 Yeah, go to and it's very basic, maybe hence the name,
 but a lot of fun.
 And that was like, that was,
 that's when when you first program,
 you see the magic of it.
 It's like this world opens up with endless possibilities
 for the things you could build
 or accomplish with that computer.
 So you got the bug then, so even starting with basic
 and then what C++ throughout,
 what did you, was there a computer program
 and computer science classes in high school?
 Not where I went, so I was self-taught,
 but I did a lot of programming.
 The thing that 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 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 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 steer the wheel.
 And later I'd come to realize this had been done
 since 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 autonomous vehicles
 of some scale, start off with really small electric ones
 and then progress to what we're doing now.
 So what was your view of artificial intelligence
 at that point?
 What did you think?
 So this is before, there's been waves
 in artificial intelligence, right?
 The current wave with deep learning
 makes people believe that you 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,
 localize itself in the world, moves around the world?
 Like when you were younger, I mean,
 what was your thinking about it?
 Well, prior to deep neural networks
 or convolutional neural analysis,
 modern techniques we have, or at least ones
 that are in use today, it was all heuristic space.
 And so like old school image processing,
 and I think extracting 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 Huff Transform or something,
 and then go from there.
 Traffic light detection and stop sign detection,
 red, yellow, green.
 And I think you could, I mean, if you wanted to do a full,
 I was just trying to make something that would stay
 in between the lanes on a highway,
 but if you wanted to do the full set of capabilities
 needed for a driverless car, I think you could,
 and we'd done this at Cruise in the very first days,
 you can start off with a really simple
 human written heuristic just to get the scaffolding
 in place for your system.
 Traffic light detection, probably a really simple
 color thresholding on day one just to get the system
 up and running before you migrate to a deep learning
 based technique or something else.
 And back when I was doing this, my first one,
 it was on a Pentium 233 megahertz computer,
 and I think I wrote the first version in basic,
 which is like an interpreted language,
 it's extremely slow,
 because that's the thing I knew at the time.
 And so there was no chance at all of using,
 there was no computational power to do
 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 at age 13 using neural networks
 in their garage?
 I mean, that would be awesome.
 I get emails all the time from 11, 12 year olds
 saying I'm trying to follow this TensorFlow tutorial,
 and I'm having this problem.
 And their general approach in the deep learning community
 is of extreme optimism of, as opposed to,
 you mentioned heuristics,
 you can separate the autonomous driving problem into modules
 and try to solve it rigorously,
 or you can just do it end to end.
 And most people just kind of love the idea that,
 us humans do it end to end, we just perceive and act.
 We should be able to do the same kind of thing
 with neural nets.
 And that kind of thinking,
 you don't want to criticize that kind of thinking,
 because eventually they will be right.
 And so it's exciting.
 And especially when they're younger,
 to explore that as a really exciting approach.
 But yeah, it's changed the language,
 the kind of stuff you're tinkering with.
 It's kind of exciting to see when these teenagers grow up.
 Yeah, I can only imagine if your starting point
 is Python and TensorFlow at age 13,
 where you end up after 10 or 15 years of that,
 that's pretty cool.
 Because of GitHub, because the state of the art tools
 for solving most of the major problems
 in artificial intelligence are within a few lines of code
 for most kids.
 And that's incredible to think about,
 also on the entrepreneurial side.
 And on that point, was there any thought
 about entrepreneurship before you came to college,
 is sort of building this into a thing
 that impacts the world on a large scale?
 Yeah, I've always wanted to start a company.
 I think that's just a cool concept of creating something
 and exchanging it for value, or creating value, I guess.
 So in high school, I was trying to build
 servo motor drivers, little circuit boards,
 and sell them online, or other things like that.
 And certainly knew at some point I wanted to do a startup,
 but it wasn't really, I'd say, until college,
 until I felt like I had the right combination
 of the environment, the smart people around you,
 and some free time.
 I had a lot of free time at MIT.
 So you came to MIT as an undergrad, 2004.
 That's right.
 And that's when the first DARPA Grand Challenge
 was happening.
 Yeah.
 The timing of that is beautifully poetic.
 So how did you get yourself involved in that one?
 Originally, there wasn't a official entry.
 Yeah, faculty sponsored thing.
 And so a bunch of undergrads, myself included,
 started meeting and got together and tried
 to haggle together some sponsorships.
 We got a vehicle donated, a bunch of sensors,
 and tried to put something together.
 So our team was probably mostly freshmen and sophomores,
 which was not really a fair fight against maybe
 the postdoc and faculty-led teams from other schools.
 But we got something up and running.
 We had our vehicle drive by wire and very, very basic control
 and things.
 But on the day of the qualifying, pre-qualifying round,
 the one and only steering motor that we had purchased,
 the thing that we had 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.
 On the hardware side, so was there a software component?
 How did your view of autonomous vehicles
 in terms of artificial intelligence
 evolve in this moment?
 I mean, like you said, from the 80s
 has been autonomous vehicles.
 But really, that was the birth of the modern wave,
 the thing that captivated everyone's imagination
 that we can actually do this.
 So were you captivated in that way?
 So how did your view of autonomous vehicles
 change at that point?
 I'd say at that point in time, it was a curiosity,
 as in like, is this really possible?
 And I think that was generally the spirit and the purpose
 of that original DARPA Grand Challenge, which
 was to just get a whole bunch of really brilliant people
 exploring this space and pushing the limits.
 And I think to this day, that DARPA challenge
 with its million dollar prize pool
 was probably one of the most effective uses of taxpayer money
 dollar for dollar that I've seen.
 Because that small sort of initiative that DARPA put out,
 in my view, was the catalyst or the tipping point
 for this whole next wave of autonomous vehicle development.
 So that was pretty cool.
 Let me jump around a little bit on that point.
 They also did the Urban Challenge,
 where it was in the city.
 But it was very artificial, and there's no pedestrians.
 And there's very little human involvement
 except a few professional drivers.
 Do you think there's room?
 And then there was the Robotics Challenge with humanoid robots.
 In your role as looking at this, you're
 trying to solve one of the autonomous driving,
 one of the harder, more difficult places in San Francisco.
 Is there a role for DARPA to step in to also help out,
 challenge with new ideas, specifically pedestrians
 and so on, all these kinds of interesting things?
 Well, 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 in time.
 Because kick-starting and spinning up the flywheel
 is, I think, what they did in this case for very little money.
 But today, this has become commercially interesting
 to very large companies.
 And the amount of money going into it,
 and the amount of people going through your class
 and learning about these things and developing these skills
 is just 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 I think they can just say mission accomplished
 and move on to the next area of technology that needs help.
 So then stepping back to MIT, you left MIT Junior Year.
 What was that decision like?
 As I said, I always wanted to do a company
 or start a company.
 And this opportunity landed in my lap,
 which was a couple guys from Yale
 were 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 rock star entrepreneurs.
 They've done this before.
 They must be driving around in Ferraris,
 because they sold their company.
 And I thought I could learn a lot from them.
 So I teamed up with those guys and went out to California
 during IAP, which is MIT's month off, one-way ticket,
 and basically never went back.
 We were having so much fun.
 We felt like we were building something and creating
 something.
 And it was going to be interesting that I was just
 all in and got completely hooked.
 And that business was Justin TV, which is 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 quite an unexpected journey.
 So no regrets?
 No.
 Looking back, it was just an obvious one-way ticket.
 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?
 Well, I didn't know.
 But just trying something for a month during IAP
 seems pretty low risk.
 And then, well, maybe I'll take a semester off.
 MIT's pretty flexible about that.
 You can always go back.
 And then after two or three cycles of that,
 I eventually threw in the towel.
 But I think it's, I guess in that case,
 I felt like I could always hit the undo button if I had to.
 Right.
 But nevertheless, from when you look in retrospect,
 I mean, it seems like a brave decision.
 It would be difficult for a lot of people to make.
 It wasn't as popular.
 I'd say the general flux of people out of MIT at the time
 was mostly into 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 probably inverted,
 which is just a sign of the times, I guess.
 So there's a story about midnight of March 18, 2007,
 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.
 And you and one of the folks at Justin TV, Emma Shear,
 coded through the night.
 Can you take me through that experience?
 So let me say a few nice things that the article I read
 quoted.
 Justin Khan said that you were known for bureau coding
 through problems and being a creative genius.
 So on that night, 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 thin?
 That's a good question.
 Well, first of all, that's nice of Justin to say that.
 I think I would have been maybe 21 years old then
 and not very experienced at programming.
 But as with everything in a startup,
 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 the city,
 it would be streaming live video.
 And this is even before the iPhones.
 This is hard to do back then.
 We would launch.
 And so we thought we were there.
 And the backpack was working.
 And then we sent out all the emails
 to launch the company and do the press thing.
 And then we weren't quite actually there.
 And then we thought, oh, well, they're
 not going to announce it until maybe 10 AM the next morning.
 And it's, I don't know, it's 5 PM now.
 So how many hours do we have left?
 What is that?
 Like 17 hours to go.
 And that was going to be fine.
 Was the problem obvious?
 Did you understand what could possibly be?
 How complicated was the system at that point?
 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 three or four cell phone
 data modems.
 And they were like, we take the video stream
 and sort of sprayed across these three or four modems
 and then try to catch all the packets on the other side
 with unreliable cell phone networks.
 It's pretty low level networking.
 Yeah.
 And putting these protocols on top of all that
 to reassemble and reorder the packets
 and have time buffers and error correction
 and all that kind of stuff.
 And the night before, it was just staticky.
 Every once in a while, the image would go staticky.
 And there would be this horrible screeching audio noise
 because the audio was also corrupted.
 And this would happen every 5 to 10 minutes or so.
 And it was really off putting to the viewers.
 How do you tackle that problem?
 You're just freaking out behind a computer.
 Are there other folks working on this problem?
 Were you behind a whiteboard?
 Were you doing hair coating?
 Yeah, it's a little lonely because there's four of us
 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 it was my sole responsibility
 to figure that out.
 And I think it's those setting deadlines, trying
 to move quickly and everything 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 because that's
 one of those character defining moments, I think.
 So in 2013, October, you founded Cruise Automation.
 So progressing forward, another exceptionally successful
 company was acquired by GM in 2016 for $1 billion.
 But in October 2013, what was on your mind?
 What was the plan?
 How does one seriously start to tackle
 one of the hardest robotics, most important impact
 for robotics problems of our age?
 After going through Twitch, Twitch was, and is today,
 pretty successful.
 But the result was entertainment, mostly.
 The better the product was, the more
 we would entertain people and then make money on them,
 add revenues and other things.
 And that was a good thing.
 It felt good to entertain people.
 But I figured, what is really the point
 of becoming a really good engineer
 and developing these skills other than my own enjoyment?
 And I realized I wanted something that scratched
 more of an existential itch, like something
 that truly matters.
 And so I basically made this list of requirements
 for a new, if I was going to do another company.
 And the one thing I knew in the back of my head
 that Twitch took eight years to become successful.
 And so whatever I do, I'd better be
 willing to commit at least 10 years to something.
 And when you think about things from that perspective,
 you certainly, I think, raise 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, because 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 health care, self-driving cars,
 because 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 at large scale.
 And I was thinking about that for a while.
 And 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.
 That was the most fun I had ever had in college working on that.
 And well, what's the state of the technology?
 It's been 10 years.
 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.
 And I thought, surely there's a way
 to have an entrepreneur mindset and sort of solve
 the minimum viable product here.
 And so I just took the plunge right then and there and said,
 this is something I know I can commit 10 years to.
 It's probably the greatest applied AI
 problem of our generation.
 And if it works, it's going to be both a huge business
 and therefore probably the most positive impact
 I can possibly have on the world.
 So after that light bulb went off,
 I went all in on cruise immediately.
 And got to work.
 Did you have an idea how to solve this problem?
 Which aspect of the problem to solve?
 Like we just had Oliver from Voyage here,
 slow moving retirement communities, urban driving,
 highway driving.
 Did you have a vision of the city of the future
 where the transportation is largely automated,
 that kind of thing?
 Or was it sort of more fuzzy and gray area than that?
 My analysis of the situation is that Google
 had been putting a lot of money into that project.
 They had a lot more resources.
 And they still hadn't cracked the fully driverless car.
 This is 2013, I guess.
 So I thought, what can I do to sort of go from zero
 to significant scale so I can actually
 solve the real problem, which is the driverless cars?
 And I thought, here's the strategy.
 We'll start by doing a really simple problem
 or solving a really simple problem that
 creates value for people.
 So eventually ended up deciding on automating highway driving,
 which is relatively more straightforward as long
 as there's a backup driver there.
 And the go-to-market will be able to 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 sort of reinvest that in research
 for doing fully 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 after about a year of working on the highway autopilot,
 we had it working at a prototype stage.
 But we just completely abandoned that and said,
 we're going to go all in on driverless cars.
 Now is the time.
 Can't think of anything that's more exciting.
 And if it works, more impactful.
 So we're just going to go for it.
 The idea of retrofit is kind of interesting.
 Being able to, it's how you achieve scale.
 It's a really interesting idea.
 Is it something that's still in the back of your mind
 as a possibility?
 Not at all.
 I've come full circle on that one.
 After 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 other control inputs
 on your car, it's pretty hard to get there with a retrofit.
 Or if you did, even if you did, it
 creates a whole bunch of new complications around liability
 or how did you truly validate that.
 Or 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.
 That's a really convincing summary
 of why it's really challenging.
 But I didn't know all that at the time,
 so we tried it anyway.
 But as a pitch also, at the time,
 it's a really strong one.
 That's how you achieve scale and that's
 how you beat the leader at the time of Google
 or the only one in the market.
 The other big problem we ran into,
 which is perhaps the biggest problem from a business model
 perspective, is we had assumed that we started with an Audi
 S4 as the vehicle we retrofitted with this highway driving
 capability.
 And we had assumed that if we just knock out
 three make and models of vehicle, that'll cover 80%
 of the San Francisco market.
 Doesn't everyone there drive 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.
 To get even a decent number of units sold,
 we'd have to support 20 or 50 different models.
 And each one is a little butterfly
 that takes time and effort to maintain that retrofit
 integration and custom hardware and all this.
 So it was a tough business.
 So GM manufactures and sells over 9 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 do those, you've talked about it a little bit
 before, but it's also just fascinating to me.
 We work a lot of automakers.
 The difference between the gap between Detroit and Silicon
 Valley, let's say, just to be sort of poetic about it,
 I guess, how do you close that gap?
 How do you take GM into the future
 where a large part of the fleet would be autonomous perhaps?
 I want to start by acknowledging that GM is made up
 of tens of thousands of really brilliant, motivated people
 who want to be a part of the future.
 And so it's pretty fun to work with them, the attitude
 inside a car company like that is embracing 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 everyone you talk to,
 even the people in the assembly plants working on these cars.
 So that's really great.
 So starting from that position makes it a lot easier.
 So then when 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
 because we think it's important and we think it's the future.
 That's not to say those two cultures don't clash.
 They absolutely do.
 There's different sort of value systems.
 Like in a car company, the thing that gets you promoted
 and sort of the reward system is following the processes,
 delivering the program on time and on budget.
 So any sort of risk taking is discouraged in many ways
 because if a program is late or if you shut down
 the plant for a day, you can count the millions of dollars
 that burn by pretty quickly.
 Whereas I think most Silicon Valley companies and 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 90% of them won't work.
 And so meshing that culture of sort of continuous improvement
 and experimentation with one where everything needs
 to be rigorously defined upfront so that you never slip
 a deadline or miss a budget was a pretty big challenge.
 And we're over three years in now after the acquisition.
 And I'd say the investment we made in figuring out
 how to work together successfully and who should do what
 and how we bridge the gaps between these very
 different systems and way of doing engineering work
 is now one of our greatest assets
 because I think we have this really powerful thing.
 But for a while, both GM and Cruise
 were very steep on the learning curve.
 Yes, I'm sure it was very stressful.
 It's really important work because that's
 how to revolutionize the transportation.
 Really, to revolutionize any system,
 you look at the health care system
 or you look at the legal system.
 I have people like Laura come up to me all the time.
 Everything they're working on can easily be automated.
 But then that's not a good feeling.
 Yeah.
 That's not a good feeling, but also there
 is no way to automate because the entire infrastructure is
 really based, is older, and it moves very slowly.
 And so how do you close the gap between I have an app?
 How can I replace?
 Of course, lawyers don't want to be replaced with an app,
 but you could replace a lot of aspect
 when most of the data is still on paper.
 And so the same thing with automotive.
 I mean, it's fundamentally software.
 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.
 And then on GM?
 That's probably true, yeah.
 And GM side is probably the opposite.
 So that culture gap is really fascinating.
 So you're optimistic about the future of that?
 Yeah, I mean, from what I've seen, it's impressive.
 And I think, especially in Silicon Valley,
 it's easy to write off building cars because 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 I think it would be easy to sort of overlook that
 and think that we're Silicon Valley engineers,
 we can solve any problem.
 Building a car, it's been done.
 Therefore, it's not a real engineering challenge.
 But after having seen just the sheer scale and magnitude
 and 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 to make self-driving cars work.
 And so to have 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 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 a symbol and build
 the cars themselves, I mean, we work closely with GM on that.
 But if we had to develop all that capability in-house
 as well, that would just make the problem really
 intractable, I think.
 So yeah, just like your first entry at the MIT DARPA
 challenge when there was the motor that failed,
 if somebody that knows what they're doing with the motor
 did it, they could just focus on the software.
 Not the hardware platform.
 Yeah.
 Right.
 So from your perspective now, 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,
 sort of make it a financially viable thing in the near term?
 What do you think would be the biggest impact there?
 Well, the things that drive the economics for fleets
 of self-driving cars, there's sort
 of a handful of variables.
 One is the cost to build the vehicle itself.
 So the material cost, what's the cost of all your sensors
 plus the cost of the vehicle and 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 2 million.
 And then if you have a fleet, it's kind of like an airplane
 or an airline where once you produce the vehicle,
 you want it to be in operation as many hours a day
 as possible producing revenue.
 And then the other piece of that is,
 how are you generating revenue?
 I think that's kind of what you're asking.
 And I think the obvious things today
 are the ride sharing business because that's pretty clear
 that there's demand for that.
 There's existing markets you can tap into.
 Large urban areas, that kind of thing.
 Yeah, yeah.
 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 ride share
 services today.
 You get privacy, consistency, hopefully significantly
 improve safety, all these benefits
 versus the current product.
 But it's a crowded market.
 And then other opportunities, which
 you've seen a lot of activity in the last six or 12 months,
 is delivery, whether that's parcels and packages, food,
 or groceries.
 Those are all sort of, I think, opportunities
 that are pretty ripe for these.
 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 course,
 is that there's zero monetization opportunity
 until you actually have that fleet of very capable driverless
 cars 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.
 Yeah, they're trying to achieve that baseline.
 But you said sort of not reliability, consistency.
 It's kind of interesting.
 I think I heard you say somewhere.
 I'm not sure if that's what you meant.
 But I can imagine a situation where
 you would get an autonomous vehicle.
 And 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 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 bound?
 One of the biggest challenges in autonomous driving
 is the trade-off between sort of safety and assertiveness.
 And do you think there's any room for the human
 to take a role in that decision?
 Sort of accept some of the liability, I guess.
 I wouldn't say.
 No, I'd say within reasonable bounds, as in we're not going
 to, I think it'd be highly unlikely we'd expose any knob
 that would let you significantly increase safety risk.
 I think that's just not something
 we'd be willing to do.
 But I think driving style or are you
 going to relax the comfort constraints slightly
 or things like that?
 All of those things make sense and are plausible.
 I see all those as nice optimizations.
 Once again, we get the core problem solved
 in 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
 going to get there faster.
 But the actual impact of doing that is pretty small.
 You feel like you're getting there faster.
 So the same would be true for AVs.
 Even if they don't slam the pedal to the floor when
 the light turns green, they're going
 to get you there within, if it's a 15 minute trip,
 within 30 seconds of what you would have done otherwise
 if you were going really aggressively.
 So I think there's this sort of self-deception
 that my aggressive driving style is getting me there faster.
 Well, so that's some of the things I've studied,
 some of the things I'm fascinated
 by the psychology of that.
 I don't think it matters that it doesn't get you there faster.
 It's the emotional release.
 Driving is a place.
 Being inside of a car, somebody said
 it's like the real world version of being a troll.
 So you have this protection, this mental protection.
 You're able to yell at the world,
 release your anger, whatever.
 So there's an element of that that I
 think autonomous vehicles would also
 have to give an outlet to people.
 But it doesn't have to be through driving or honking
 or so on.
 There might be other outlets.
 But I think to even just put that aside, the baseline
 is really, that's the focus.
 That's the thing you need to solve.
 And then the fun human things can be solved after.
 So from the baseline of just solving autonomous driving,
 you're working in San Francisco, one of the more difficult
 cities to operate in.
 What is, in your view, currently the hardest
 aspect of autonomous driving?
 Is it negotiating with pedestrians?
 Is it edge cases of perception?
 Is it planning?
 Is there a mechanical engineering?
 Is it data, fleet stuff?
 What are your thoughts on the more challenging aspects
 there?
 That's a good question.
 I think before we go to that, though, I
 like what you said about the psychology aspect of this.
 Because I think one observation I've made is,
 I think I read somewhere that I think
 it's maybe Americans on average spend over an hour a day
 on social media, like staring at Facebook.
 And so that's just 60 minutes of your life
 you're not getting back.
 And it's probably not super productive.
 And so that's 3,600 seconds.
 And 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,
 they're laying in on the horn, even though that's
 1 1,000th of the time they waste looking at Facebook
 every day.
 So there's definitely some 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?
 Because they're doing other things, or reading,
 or working, or just talking to each other.
 So it'll be interesting to see where that goes.
 In a certain aspect, 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 they can take whatever psychology
 and bring it back to Twitter and then focus on that,
 as opposed to sort of interacting,
 sort of putting the emotion out there into the world.
 So it's an interesting problem, but baseline autonomy.
 I guess you could say self-driving cars at scale
 will lower the collective blood pressure of society
 probably by a couple of points, without all that road
 rage and stress.
 So that's a good externality.
 So back to your question about the technology
 and I guess the biggest problems.
 And I have a hard time answering that question,
 because we've been at this specifically focusing
 on driverless cars and all the technology needed
 to enable that for a little over 4 and 1 1⁄2 years now.
 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 double parked vehicle
 into oncoming traffic or navigate through construction
 zones, the scaffolding and the building blocks
 was there pretty early on.
 And so the challenge is not any one scenario or situation
 for which we fail at 100% of those.
 It's more we're benchmarking against a pretty good
 or pretty high standard, which is human driving.
 All things considered, humans are
 excellent at handling edge cases and unexpected scenarios,
 whereas computers are the opposite.
 And so beating that 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 the most uncomfortable or the things
 that could lead to a safety issue or other things,
 all these events.
 And then we categorize them and rework parts of our system
 to make incremental improvements and do that over and over
 and over again.
 And we just see the overall performance of the system
 actually increasing in a pretty steady clip.
 But there's no one thing.
 There's actually thousands of little things
 and just polishing functionality and making sure
 that it handles every version and possible permutation
 of a situation by either applying more deep learning
 systems or just by adding more test coverage or new scenarios
 that we develop against and just grinding on that.
 We're sort of in the unsexy phase of development
 right now, which is doing the real engineering work
 that it takes to go from prototype to production.
 You're basically scaling the grinding,
 instead of taking seriously the process of all those edge
 cases, both with human experts and machine learning methods
 to cover all those situations.
 Yeah.
 And the exciting thing for me is I don't think that grinding
 ever stops.
 Because there's a moment in time where
 you've crossed that threshold of human performance
 and become superhuman.
 But there's no first principles reason
 that AV capability will tap out anywhere near humans.
 There's no reason it couldn't be 20 times better,
 whether that's 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 forever
 to build the best possible product.
 Better and better and better.
 And always new edge cases come up and new experiences.
 And you want to automate that process as much as possible.
 So what do you think in general in society?
 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 the leading people
 trying to define that future.
 But even then, you still don't know.
 But if you think about hundreds of thousands of vehicles,
 so a significant fraction of vehicles in major cities
 are autonomous.
 Do you think, are you with Rodney Brooks, who
 is 2050 and beyond?
 Or are you more with Elon Musk, who is,
 we should have had that two years ago?
 Well, I mean, I'd love to have it two years ago,
 but we're not there yet.
 So I guess the way I would think about that is,
 let's flip that question around.
 So what would prevent you to reach
 hundreds of thousands of vehicles?
 And that's a good rephrasing.
 Yeah, so the,
 I'd say that it seems the consensus
 among the people developing self-driving cars today
 is to sort of start with some form of an easier environment,
 whether it means lacking inclement weather,
 or mostly sunny or whatever it is,
 and then add capability for more complex situations over time.
 And so if you're only able to deploy
 in areas that meet sort of your criteria
 or the current operating domain of the software you developed,
 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 capability to drive really well
 and safely in heavy rain or snow,
 that probably opens up the market by two or threefold
 in terms of the cities you can expand into and so on.
 And so the real question is,
 I know that the city is a very, very, very, very,
 I know today if we wanted to,
 we could produce that many autonomous vehicles,
 but we wouldn't be able to make use of all of them yet,
 because we would sort of saturate the demand
 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.
 Maybe a range is better.
 I would say less than five years.
 Less than five years.
 Yeah.
 And of course you're working hard to make that happen.
 So you started two companies that were eventually acquired
 for each $4 billion.
 So you're a pretty good person to ask,
 what does it take to build a successful startup?
 I think there's sort of survivor bias here a little bit,
 but I can try to find some common threads
 for the things that worked for me, which is,
 in both of these companies,
 I was really passionate about the core technology.
 I actually like lay awake at night
 thinking about these problems 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 these crazy ups and downs.
 Like one day you think the business is just on,
 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 just going south
 and it's gonna be over tomorrow.
 And so I think like having a true passion
 that you can fall back on
 and knowing that you would be doing it
 even if you weren't getting paid for it
 helps you weather those tough times.
 So that's one thing.
 I think the other one is really good people.
 So I've always been surrounded by really good co-founders
 that are logical thinkers,
 are always pushing their limits
 and have very high levels of integrity.
 So that's Dan Kahn and 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,
 I guess persistence or perseverance.
 Like, and that can apply to sticking to sort of,
 or having conviction around the original premise
 of your idea and sticking around to do all the unsexy work
 to actually make it come to fruition,
 including dealing with whatever it is
 that you're not passionate about,
 whether that's finance or HR or operations or those things.
 As long as you are grinding away
 and working towards that North Star for your business,
 whatever it is, and you don't give up
 and you're making progress every day,
 it seems like eventually you'll end up in a good place.
 And the only things that can slow you down are,
 running out of money
 or I suppose your competitor is destroying you,
 but I think most of the time it's people giving up
 or somehow destroying things themselves
 rather than being beaten by their competition
 or running out of money.
 Yeah, if you never quit, eventually you'll arrive.
 So-
 It's a much more concise version
 of what I was trying to say.
 Yeah, it was good.
 So you went the Y Combinator out twice.
 Yeah.
 What do you think, in a quick question,
 do you think is the best way to raise funds in the early days?
 Or not just funds, but just community,
 develop your idea and so on.
 Can you do it solo or maybe with a co-founder
 with like self-funded?
 Do you think Y Combinator is 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?
 There's no one size fits all answer,
 but if your ambition I think is to
 see how big you can make something
 or rapidly expand and capture a market
 or solve a problem or whatever it is,
 then going the venture back route
 is probably a good approach
 so that capital doesn't become your primary constraint.
 Y Combinator, I love because it puts you
 in this sort of competitive environment
 where you're surrounded by the top maybe 1%
 of other really highly motivated peers
 who are in the same place.
 And that environment I think just breeds success.
 If you're surrounded by really brilliant,
 hardworking people, you're gonna feel
 sort of compelled or inspired to try
 to emulate them or beat them.
 And so even though I had done it once before
 and I felt like 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 surrounding myself with other entrepreneurs
 is gonna make me work a little bit harder
 or push a little harder than is worth it.
 And so that's why I did it, for example, the second time.
 Let's go philosophical, existential.
 If you go back and do something differently in your life,
 starting in high school and MIT, leaving MIT,
 you could have gone the PhD route, doing startup,
 going to see about a startup in California,
 or maybe some aspects of fundraising.
 Is there something you regret,
 something you not necessarily regret,
 but if you go back, you could do differently?
 I think I've made a lot of mistakes,
 like pretty much everything you can screw up.
 I think I've screwed up at least once.
 But I don't regret those things.
 I think it's hard to look back on things,
 even if they didn't go well and call it a regret,
 because hopefully it took away some new knowledge
 or learning from that.
 So I would say there was a period, yeah,
 the closest I can come to is this.
 There was a period in Justin TV, I think,
 after seven years where the company was going one direction,
 which was towards Twitch and video gaming.
 And I'm not a video gamer.
 I don't really even use Twitch at all.
 And I was still working on the core technology there,
 but my heart was no longer in it,
 because the business that we were creating
 was not something that I was personally passionate about.
 It didn't meet your bar of existential impact.
 Yeah, and I'd say I probably spent an extra year
 or two working on that.
 And I'd say I would have just tried
 to do something different sooner,
 because those were two years where I felt like,
 from this philosophical or existential thing,
 I just felt that something was missing.
 And so I would have, if I could look back now
 and tell myself, I would have said exactly that.
 You're not getting any meaning out of your work
 personally right now.
 You should find a way to change that.
 And that's part of the pitch I use
 to basically everyone who joins crews today.
 It's like, hey, you've got that now,
 why come here?
 Well, maybe you needed the two years
 of that existential dread to develop the feeling
 that ultimately it was the fire that created crews.
 So you never know, you can't remember.
 Good theory, yeah.
 So last question, what does 2019 hold for crews?
 After this, I guess we're gonna go
 and I'll talk to your class,
 but one of the big things is going from prototype
 to production for autonomous cars.
 And what does that mean?
 What does that look like?
 And 2019 for us is the year that we try
 to cross over that threshold and reach
 superhuman level of performance to some degree
 with the software and have all the other
 of the thousands of little building blocks in place
 to launch our first commercial product.
 So that's what's 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 all up to us now.
 Yeah, from Charlie Miller and Chris Wells,
 like the people I've crossed paths with.
 It sounds like you have an amazing team.
 So like I said, it's one of the most,
 I think one of the most important problems
 in artificial intelligence of the century.
 It'll be one of the most defining.
 It's super exciting that you work on it.
 And the best of luck in 2019.
 I'm really excited to see what Cruz comes up with.
 Thank you.
 Thanks for having me today.
 Thanks for having me.