Lex Fridman Podcast - Kyle Vogt: Cruise Automation

Episode Date: February 7, 2019

Kyle Vogt is the President and CTO of Cruise Automation, leading an effort in trying to solve one of the biggest robotics challenges of our time: vehicle autonomy. He is the co-founder of 2 successful companies (Cruise and Twitch) that were each acquired for 1 billion dollars. Video version is available on YouTube. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations.

The following is a conversational co-voked. He is the president and the CTO of cruise automation, leading an effort to solve one of the biggest robotic 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 Vogue. You grew up in Kansas, right? Yeah, and I just saw that picture you had hidden over there. There's some a little bit worried about that now. So in high school in Kansas City, you joined Shawnee Mission North High School Robotics team.

Yeah. 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 a first robotics team. I was gonna troll you a little bit. Jog your mental a little bit. Yep.

That kid was trying to look super cool and intense. He did. Because this was Battlebots, this is a serious business. So we're standing there with a weld is to deal frame and looking tough. So go back there. What is that jewelry to robotics? Well, I think I've been trying to free this up for a while, but I've always liked building things with Legos.

And when I was really, really young, I wanted the Legos. I had motors and other things. And then Lego mind storms 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 want to 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 poured over the internet forums where all the creators for battle bots 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, like sort of try and gillated how to put some of these things together and ended up doing battlebots, which was, you know, like, 13 or 14, which is 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, jeaniering 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 wheeled in acts, 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 essentially in 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, you know, 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, you know, autonomous robots, which I, which I, you know, sort of gravitated towards later on, but it was definitely an engineering challenge because everything you did in 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 you know a significant increase in the power output of those motors before they burn out. And so you're doing the same thing for your battery packs, all the materials in the system. And I think there's something intrinsically interesting about just seeing where things break.

And did you offline see where they break? Did you take it to the testing point? Like how did you know the 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 one in first round.

The one I built for both of them were these wedge-shaped robots, because of wedge, even though it's sort of boring to look at as extremely effective, you drive towards another robot and the front edge of it gets under them and then they sort of flip over, kind of like a door stopper.

And the first one had a pneumatic polished stainless steel spike on the front that would shoot out about eight inches. The purpose of which is what pretty pretty ineffective actually, but it looks cool. And was it a help with the lift? No, it was, it was just to try to poke holes in the other robot. And then the second time I did it, which is the following, I think maybe 18 months later, robot. And then the second time I did it, which is the following, I think maybe 18 months later, we had a, well, 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 was 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 co-euvre in? If you go back there see what language was it? What was that? Was it EMAX? Van 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 distri, which is to fire up the disc and load the program. And I just remember thinking, wow, she's like a hacker. Like teach me these codes, these error codes,

that 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 twos and learn a program. And so it was all in basic, you know, where every line, you know, the line numbers are all number, or that every line is numbered. And you have to like leave enough space between the numbers so that if you want to tweak your code, you go back and the first line was 10. And the second line is 20. Now you have to go back and insert 15. And if you need to add code in front of that, you know, 11 11 or 12 and you hope you don't run out of line numbers and have to redo the whole thing.

And let'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, you know, that's when, you know, when you first program, you see the magic of it. It's like it just, just like this world opens up with, you know, endless possibilities for the things you could build or or accomplish with that computer. So you got the bug then. So even starting with basic and then what C++ throughout. What did you was there computer programming computer science classes in high school? Not not where I went. So it was self-taught, but I did a lot of programming. The thing that you know sort of pushed me in the path of eventually working

on self-driving cars is actually one of these really long trips driving from my house in Kansas to, I think, Las Vegas where we did the Battlewads competition. And I had just gotten my, I think my learners permit or early drivers permit. And so I was driving this, you know, 10 hours stretch across Western Kansas where it's just you're going straight on a highway And it is mind numbingly boring and I remember thinking even then with my sort of mediocre programming background that this is something that a computer can do right? Let's take a picture of the road. Let's find the yellow lane markers and you know steer the wheel and you know later I'd come to realize this had been done you know since 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, you know, autonomous vehicles of some scale start off with really small electric ones and then, you know, progress to what we're doing now. So what was your view of artificial intelligence at that point? What did you think? So this is before there's been ways in artificial intelligence, right? The the current way with deep learning makes people believe they can solve in a really rich, deep way the computer vision perception problem. But like in 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 it on the world, like when you were younger,

I mean, yeah, so what was your thinking about it? Well, prior to deep neural networks or convolutional neural LEDs, these modern techniques we have, or at least ones that are in use today, it was all a heuristic space. And so, like, old school image processing. And I think extracting, you know, yellow lane markers out of an image of a road is one of the problems that lends itself reasonably well to those heuristic base methods. You know, like, just do a threshold on the color yellow and then try to fit some lines to that using a puff transform or something and then go from there traffic like detection and stop sign detection red yellow green. And I think you can you could I mean if you wanted to do a full I was just trying to make something that would stay in between the lands on a highway, but if you wanted to do the full.

But if you wanted to do the full set of capabilities needed for a driverless car, I think you could, and we've 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 was on Opinion 203, 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. Our kids these days, you know, at age 13, using neural networks in their garage. I mean, that would be awesome.

I get emails all the time from, you know, like 11, 12-year-olds saying I'm having, you know, I'm trying to follow this TensorFlow tutorial, and I'm having this problem. And their general approach in the deep learning community is of extreme optimism of, as opposed to, you mentioned, like, heuristics, you can, you can, you can separate the autonomous driving problem into modules and try to solve it sort of rigorously, where you can just do it end- end. And most people just kind of love the idea that, you know, us humans do it end to end, we just perceive and act. We should be able to do the same kind

of thing when you're on that. And that kind of thinking, you don't want to criticize that kind of thinking because eventually, they will be right. Yeah. 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, you know, Python and TensorFlow at age 13, where you end up, you know, after 10 or 15 years of that, that's pretty cool. Because of GitHub, because the state of 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 doing your 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 like, you know, a 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, I guess, the right combination of the environment, the smart people around you, and some free time, and 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.

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. And 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 a wire and very basic control in things. But on the day of the qualifying sort of pre-qualifying round, the one and only steering motor that we had purchased, the thing that we had 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? Was there, like, how did your view of autonomous vehicles in terms of artificial intelligence evolve in this moment? I mean, you know, like you said, from the 80s, there's been autonomous vehicles, but really that was the birth of the modern wave, the thing that captivated everyone's imagination that we can actually do this.

So how were you captivated in that way? So how did your view of a times 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 the space and pushing the limits. 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 initiative that DARPA put out sort of, in my view, was the catalyst or the tipping point for this whole next wave of autonomous vehicle development. So that was pretty cool. So 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? Right. So in your novel, it's looking at this, you're trying to solve one of the, you know, autonomous driving one of the harder, more difficult places in San Francisco. Is there a role for DARPA to step in to also kind of help out that 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 kickstarting and spinning up the flywheel is I think what they did in this case for a very little money. But today, this has become commercially interesting

to very large companies in the amount of money going into it and the amount of people going through your class and learning about these things and developing their 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 don't I think they're 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, junior year.

What was that decision like? As I said, I always wanted to do a company in or start a company. And this opportunity landed in my lap, which was a couple guys from Yale. We're starting a new company. And I googled them and found that they had started a company previously and sold it actually on eBay for about a quarter million bucks, which was a pretty interesting story. But so I thought to myself, these guys are rockstar 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-on-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, I mean, one way to get it. I mean, if we just pause in 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, you know, just trying something for a month during IAP seems pretty little risk, right? And then, you know, well, maybe I'll take a semester off.

MIT is pretty flexible about that. You can always go back, right? And then after two or three cycles of that, I eventually threw in the towel. But, you know, 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 a sign of the times, I guess.

Yeah. So there's a story about midnight of March 18, 2007, where we're a tech crunch, I guess, announced Justin TV earlier than we're supposed to a few hours. This 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 Emmett share quoted through the night. Can you take me through that experience? So let me let me say a few nice things that the article I read quoted Justin Khan said that you were known for your

coding through problems and being a creative, 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 with the line between success and failure seems to be pretty thin. That's a good question. Well, first of all, that's a nice adjustment to say that. I think, you know, I would have been maybe 21 years old then and not very experienced at programming. But as with with everything and 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 a city, it would be streaming live video. And this is even before the iPhones. This is like hard to do back then. We would launch. And so we thought we were there. And the backpack was working. And then we sent out all the emails

to launch the company and do the press thing. 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 a.m. the next morning and it's, I don't know, it's 5 p.m. now. So how many hours do we have left? What is that? You know, 17 hours to go. And that was going gonna be fine. Was the problem obvious. Did you understand what could possibly like how complicated was the system at that point?

It was it was pretty messy so to get a live video feed that looked decent working for anywhere in San Francisco I Put together the system where we had like three or four cell phone data modems and they were like we take the video stream and sort of spray it across these three or four modems and then try to catch all the packets on the other side, you know, with unreliable cell phone networks. It's pretty low level networking. Yeah.

And putting these like, you know, sort of 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. The night before, it was just static. Every once in a while, the image would go static-y, and there would be this horrible screeching audio noise because the audio was also corrupted, and this would happen like every five to ten minutes or so, and it was a really off-putting to the viewers. How do you tackle that problem? What was the, you just freaking out behind a computer?

Are there other folks working on this problem? Like, we behind a whiteboard, were you doing a... Yeah, it was a little lonely. Air coding. Yeah, it was a little lonely because there was four of us working on the company and only two people really wrote code. And Emmett wrote the website in the chat system and I wrote the software for this video streaming device and video server and

So I you know is my sole responsibility to figure that out Yeah, and I think I think it's those you know setting 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 I look back on it finally, because that's like, you know, that's one of those character defining moments, I think. So, in 2013, October, you founded Cruise Automation. Yeah. So, progressing forward, another exception successful company was acquired by GM in 16 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, the work was, the result was entertainment, mostly like the better the product was, the

more we would entertain people and then, you know, make money on the ad revenues and other things. And that was, that was a good thing. It felt, felt good to entertain people. But I figured like, you know, what is really the point of becoming a really good engineer and developing these skills other than, you know, my own enjoyment? 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 gonna do another company, and the one thing I knew in the back of my head that Twitch took like eight years to become successful. And so whatever I do, I better be willing to commit, you know, at least 10 years to 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 where 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, you know, healthcare or 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 a large scale. Scale, yeah. And it was thinking about that for a while

and I made like a tried writing a Gmail clone and looked at some other ideas. And then it just sort of light bulb went off like self-driving cars. Like that was the most fun I had ever had in college working on that. And like, well, what's the state of the technology has 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, you know, 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.

That's right. And if it works, it's going to be both a huge business and therefore, like, 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? You know, slow, like we just had Oliver from Boyage here, slow moving retirement communities, urban driving, highway driving. Did you have, like, did you have a vision of the city of the future, or, you know, the transportation is largely automated, that kind of thing, or was it sort of more fuzzy

in gray area than that? My analysis of the situation is that Google has been putting a lot of money into that project. They had a lot more resources. 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, you know, 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 it eventually ended up deciding on automating highway driving, which is relatively more straightforward as long as there's a backup driver there. And, you know, the go-to market will be a little retrofit people's cars

and just sell these products directly. And the idea was, we'll take all the revenue and profits from that and use it to do the, this is 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, you know, on a prototype stage, but we just completely abandoned that and said, we're going to go all in on driverless cars now as the time. I 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. Yeah. Being able to, it's how you achieve scale.

It's a really interesting idea is it's something that's still in the, 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. and 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 this 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 to truly validate that or something in the base vehicle fails and causes your system to fail, whose fault is it?

Or if the cars 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.

Yeah. That's how you achieve scale, and that's how you beat the current, 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 kind of assumed that we started with an Audi S4 as the vehicle we retrofitted with this highway driving capability, and we had kind of assumed that if we just knock out like three making models of vehicle,

that'll cover like 80% of the San Francisco market. Doesn't everyone there drive, I don't know, a BMW or a Honda Civic or one of these three cars. And then we serve it our users as we found out that it's all over the place. We would, to get even a decent number of units sold, we'd have to support like, you know, 20 or 50 different models.

And each one is a little butterfly that takes time and effort to maintain, you know, that retrofit integration and custom hardware and all this. So is it, it a tough business? So GM manufacturers and cells, over nine million cars a year. And what you with crews are trying to do some of the most cutting-edge innovation

in terms of applying AI. And so how do those, you've talked 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 So how do those, you've talked about a little bit before, but it's also just fascinating to me. We work a lot of automakers, you know, the difference between the gap, between Detroit and Silicon Valley. Let's 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 will be autonomous perhaps? I want to start by acknowledging that 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 this simply plants working on these cars. So that's really great. So that starting from that position makes it a lot easier. So then when the people in San Francisco at Cruz 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, you know, those two cultures don't clash. They absolutely do. There's different, different sort of value systems like in a car company, the thing that

gets you promoted and 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 crews 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 continuous improvement and experimentation with one where everything needs to be rigorously defined up front so that you never slip a deadline or a mis-a-budget was a pretty big challenge. We're over three years in now after the acquisition.

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, it was both GM and CREAS were very steep on the learning curve. Yeah, so 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 know, you look at the healthcare system or you look at the legal system I have people like Laura's come up to me all the time like everything they're working on can easily be automated But then that's not a good feeling. Yeah, well, that's not a good feeling But also there's no way to automate because the entire infrastructure is really You know, based is older and it moves very slowly and And so, so how do you close the gap between, I have an, how can I replace, of course, lures 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 was with automotive, I mean, it's fundamentally software.

So, it's basically hiring software engineers, it's thinking a software world. I mean, I'm pretty sure nobody in Silicon Valley has ever hit a deadline. So, and then on GM. That's probably true, yeah. And GM size is probably the opposite. Yeah.

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, like, 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 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, you know, it's, it's, it's not, it's not a real engineering challenge. But after having seen just this 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 now, 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. If we had to figure out how to build and assemble and test and build the cars themselves, we were close to a gym on that, but if we had to develop all that capability and house as

well, that would just make the problem really intractable, I think. So yeah, just like your first entry, the MIT DARPA challenge, when it was what the motor that failed, somebody that knows what they're doing with the motor, did it. I would have been nice if we could focus on the software, not the hardware platform. Yeah. So from your perspective, now, you know, there's 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 will be the biggest impact there? Well, the things that drive the economics for fleets of self-driving cars are they're sort of a handful of variables. One is the cost to build the vehicle itself, so the material cost, how many, 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, you know, 2 million. And then, you know, 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 what you're asking in. 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. And 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 the status quo for people who use red share services today. You get privacy, consistency, hopefully you 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 really in the last six or 12 months is you know delivery,

whether that's parcels and packages, food or or groceries. Those are all sort of I think opportunities that are that are pretty ripe for these you know once you have this core technology which is the fleet of autonomous vehicles. there's all sorts of different business opportunities you can build on top of that. But I think the important thing of 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 do that baseline. So, but you said sort of 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 a lift, you don't get to choose the driver in a sense that you don't get to choose the personality of the driving. Do you think there's a 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 bottom. One of the biggest challenges in time is 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 to accept some of the liability, I guess? I wouldn't, no, I'd say within reasonable bounds as in we're not gonna, I think it'd be higher than likely we'd expose any knob that would let you significantly increase safety risk. I think that's 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 and these fleets out there. But the other thing we've sort of observed is that you have this intuition that if you sort of slam your foot on the gas right after the light turns green and aggressively accelerate, you're going to get there faster. But the actual impact of doing that is pretty small. You feel like you're getting there faster. But so the same would be true for AVs, even if they don't slam their, you know, the pedal to the floor when the light turns green They're gonna get you there within, you know, if it's a 15 minute trip within 30 seconds of what you would have done otherwise

If you were going really aggressively, so I think there's this sort of Self deception that that my aggressive driving style is getting me there faster. Well, so that's you know Some of the things I study some 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 our 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 sort of yell at the world,

like release your anger, whatever it is. So there's an element of that that I think autonomous equals, it also have to, you know, giving an outlet to people, but it doesn't have to be through driving or honking or so it might be other outlets. But I think to sort of even just put that aside, the baseline is really, you know, that's the focus, that's the thing you need to solve and then the fun human things can be solved after it. But 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 the interview currently, the hardest aspect of autonomous driving. Negotiating with pedestrians is that the edge cases of perception, is it planning, is there 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. So that's just 60 minutes of your life you're not getting back.

It's probably not super productive. So that's 3,600 seconds. 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 1 1 1 1 1 1 1 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 is psychology and bring it back to Twitter and 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, good, extra-no-led. So back to your question about the technology and the biggest problems. And I have a hard time answering that question because we've been at this, like specifically focusing on driverless cars and all the technology needed to enable that for a little over four and a half years now. And even a year or two in, I felt like we had

completed the functionality needed to get someone from point A to point B as in, if we need to do a left turn maneuver or if we need to drive around a, you know, a double parked vehicle into oncoming traffic or navigate through construction zones, the scaffolding and the building blocks were there pretty early on. And so the challenge is not any one scenario or situation for which, you know, we fail at 100% of those. It's more, you know, we're benchmarking against a pretty good, a pretty high standard, which is human driving. All things considered, humans are excellent at handling edge cases and unexpected scenarios where 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 sort of the most uncomfortable or the things that could lead to a safety issue, other things, all these events, and then we sort of 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, you know, actually increasing in a pretty steady clip. But there's no one thing. There's actually like thousands of little things and just like polishing functionality

and making sure that it handles, you know, 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, So it's sort of taking seriously that the process of all those edge cases, both with human experts and machine learning methods to cover, to cover all those situations. Yeah, and the exciting thing for me is I don't

think that grinding ever stops, right? Because there's a moment in time where you cross that threshold of human performance and become superhuman. But there's no reason, there's no first principles reason that AV capability will tap out anywhere near humans. Like there's no reason it couldn't be 20 times better whether that's just better driving or safer driving or more comfortable driving or even a thousand 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 you experience this. So, and you want to automate that process as much as possible. So what do you think in general 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? Are you more with Elon Musk who is we should have had that two years ago?

Well, I mean, I don't 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, there's a good, the rephrasing it. Yeah. So the, I'd say the, it seems the consensus among the people developing self-driving cars today is to start with some form of an easier environment, whether it means lacking in climate weather, or mostly sunny year, whatever it is, and then add capability for more complex situations over time. 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 and have your rain or snow, that probably opens up the market by two or three-fold in terms of the cities you can expand into and so on.

And so the real question is, you know, 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 time one 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. 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, you know, in both of these companies, I was really passionate about the core technology. I actually, like, you know, lay awake at night thinking about these problems and 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 top of the world and unstoppable. And the next day, you think, okay, this is all going to end, you know, it's just going south and it's going to be over tomorrow. And so I think 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 whether 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 Khan in my current company and actually his brother and a couple other guys for Justin Tv and Twitch. And then I think the last thing is just, I guess, persistence or perseverance. And that can apply to 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 competitors 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 much more concise version of what I was trying to say. It was good. So you went to a 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, developer ideas and so on. Can you do it solo or maybe with a co-founder with like self-funded? Do you think why combinators good is a good to do VC route? Is there no right answer? Or is there from the why 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 you're in vision, 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 to venture background is probably a good approach, so that capital doesn't become your primary constraint. Y-combinator, I love because it puts you in this competitive environment where you're surrounded by the top 1% of other really highly motivated peers who are in the same place. That environment, I think, just breeds success. If you're surrounded by really brilliant, hardworking people, you're going to feel, you know, 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, you know, pretty self-motivated,

I thought like, look, this is going to be a hard problem. I can use all the help I can get. So surrounding myself with other entrepreneurs is going to make me work a little bit harder or push a little harder than it's worth it. And that's why I did it, you know, for example, the second time. Let's go for a soft go existential. If you go back and do something differently in your life, starting in high school and MIT leaving MIT, you could have gone to PhD or out doing the start-up, going to see about a start-up in California, or maybe some aspects of fundraising. Is there something

you regret, something you, not necessarily to regret, but if you go back, you could do definitely. I think I've made a lot of mistakes, like, you know, pretty much everything you can screw up, I think I've screwed up at least once. But I, you know, I don't regret those things. I think it's hard to look back on things, even if they didn't go well and call it a regret, because hopefully, you know, it took away some new knowledge or learning from that. So, I would say there was a period, yeah, the closest I can come to is this. There's a period 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 in video gaming. 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 like, 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 like something was missing. And so I would have, I would have,

if I could look back now and tell myself, it's like I would have said exactly that. Like 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 used to basically everyone who joins Cruz today. It's like, hey, you've got that now by coming here. Well, maybe you needed the two years of that existential dread to develop the feeling that ultimately was the fire that created cruise. So, you

know, you can't remember. Yeah, good theory. So last question, what does 2019 hold for cruise? After this, I guess we're going to go and 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? 2019 for us is the year that we try to crash 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 score for us, or in the 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 Vells,

like the people I have crossed paths with. Oh, great. And if you, 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 this century. It'll be one of the most defining. That'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, Carl. Thank you.