Transcript for Kyle Vogt: Cruise Automation

You grew up in Kansas, right? Yeah, and I just saw that picture you had to hit no. There's some a little bit a little bit worried about that now. Yeah.

That's right. That was the only high school in the area that had a, like a teacher who was willing to sponsor a first robotics team.

Jog your map a little bit. Yep. That kid was trying to look super cool and intense because, you know, this was battle bots a serious business. So we're standing there where the welded steel frame and looking tough.

Well, 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 Battlebot's 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, you know, document their build progress and everything. And I think I read, I must have read like, you know, tens of thousands of forum posts from basically everything that was out there on what these people were doing. And eventually, like sort of triangulated how to put some of these things together and ended up doing battle bots, which was, you know, I was like 13 or 14, which was pretty awesome.

The radio controlled. And I think that they allowed some limited form of autonomy. But in a two minute match, in 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.

I mean, that was just building something. I think the idea of, you know, this radio controlled machine that 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, 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 power the car with those and we'd run them at like double or triple the 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's 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 battle bots we lost all of our matches that the one I built first both of them were these wedge shaped robots because of wedge even though it's sort of boring to look at is extremely effective you drive towards another robot and the front edge of it gets under them and then they sort of flip over kind of 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 a 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 is just a following, I think maybe 18 months later. We had, well, a titanium axe with a, 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.

Yeah, well, I remember, I think maybe when I was in third or fourth grade, the school I was at elementary school, I had a bunch of Apple to computers, and we'd play games on those. And I remember everyone once a while, something would crash, 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 the state for some reason, like PR number six, or PR pound six, which is peripheral six, which is the disk drive, which is fire up the disk and load the program. And I just remember thinking, wow, she's like a hacker, like teaching me these code, these error codes, is what I call 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 tools and learn to program. And so it was all in basic, you know, where every line, you know, the line numbers are all number. 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 in with the first line was 10 and the second line is 20 now you have to go back and insert 15 and if you need to add code in front of that you know 11 or 12 and you hope you don't run out of line numbers and have to redo the whole thing. 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 accomplish with that computer.

Not not where I went, so it was self-taught, but I did a lot of programming. The thing that You know, sort of pushing 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 Battle Wads 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 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 and 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 the 80s or the 70s are even earlier, but I still wanted to do it and sort of immediately after that trip switched from sort of battle bots, 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.

Well, prior to deep neural networks or convolutional neural networks, 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 lens 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 science 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 lanes on a highway, but if you wanted to do the full the full set of capabilities needed for a driverless car. I think you could, and we've done this at cruise, you know, in the very first days, you can start off with a really simple, you know, human written heuristic, just to get the scaffolding in place for your system, traffic light detection, probably a really simple, you know, color thresholding on day one, just to get the system up and running before you migrate to, you know, a deep learning based technique or something else. And, you know, back when I was doing this, my first one, it was on a pinium 233, 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's no computational power to do any sort of reasonable. deep nuts 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.

Yeah, I can only imagine if you're 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.

Yeah, I've always wanted to start a company. I think that's, you know, 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 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.

It originally there wasn't a official injury. Yeah, faculty sponsored thing. And so a bunch of undergrads, myself included, started meeting and got together and tried to to haggle together some sponsorships. We got a vehicle donated a bunch of sensors and tried to put something together. And so we had our team was probably mostly freshmen and sophomores, which was not really a fair fight against maybe the You know, postdoc and faculty led teams from other schools, but we got something up and running. We had our vehicle drive by wire and you know, very, very basic control and 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.

I'd say at that point in time it was it was a Curiosity as in like is is is really possible and I think that was generally the spirit and the the purpose of of that original DARPA grand challenge which was to just get a whole bunch of really brilliant people exploring the space and pushing the limits. And I think to this day that DARPA challenge with its, you know, million dollar prize pool was probably one of the most effective, you know, uses of taxpayer money, dollar for dollar that I've seen, you know, because that small sort of 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.

I'm a 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 like kick starting and spinning up the flywheel is I think what what they did in this case for a very very little money but today this has become This has become like commercially interesting to very large companies in the amount of money going into it and the amount of people like going through your class and learning about these things and developing skills is just you know orders of magnitude more than it was back then And so there's enough momentum in inertia and energy and investment dollars into this space right now that I don't I don't I think they're I think they're they can just say mission accomplished and move on to the next area of technology that that need help.

As I said, I would always wanted to do a company in a or start a company in 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 pretty interesting story. But so I thought to myself, these guys are, you know, rock star entrepreneurs. They've done this before. They must be driving around in Ferrari 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 during, 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.

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 and my T's pretty flexible about that. You can always go back, right? And then after two or three cycles of that, I eventually threw in the towel. But, 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.

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

That's a good question. Well, first of all, that's a nice of Justin 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, uh, with everything and I start up your sort of racing against the clock. And so our plan was the second we had this live streaming camera backpack up and running. We're just and 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 launch the company and do the press thing and then you know we weren't quite actually there and then we thought oh well you know 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 to it? Is that like you know 17 hours to go and And that was that was gonna be fine was the problem obvious did you understand what could possibly like 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 the system where we had like three or four cell phone data modems and they were like we take the video stream and you know 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. 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. And the night before it was just static. Every once in a while the image would go to static and there would be this horrible like 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.

It's a little, it's a little, it's a little, it's a little, it's a little, it's 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, you know, it's my soul responsibility to figure that out. 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 look back on it finally because that's like, you know, that's one of those character defining moments, I think.

After going through Twitch, Twitch was in a pretty successful, but the result was entertainment. The better the product was, the more we would entertain people and then make money on the ad revenues and other things. And that was a good thing. It felt good to entertain people, but I figured like, you know, what is really the point of becoming a really good engineer and developing these skills other than, you know, my own enjoyment. 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 like eight years to become successful. And so whatever I do, I better be willing to commit at least ten 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, example, would be like, you know, healthcare, 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. And the thing 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 have an entrepreneur mindset and sort of solve the minimum viable product here. And so I just took the plunge right then in 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. After that light bulb went off, I went all in on crews immediately and got to work.

My analysis of the situation is that Google is putting a lot. It 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 20, 20, 13, I guess. So I thought what what can I do to sort of go from zero to you know significant scale so I can actually solve the real problem which is the driverless cars and I thought here's the strategy will start by doing a really simple problem or solving a really simple problem that increase 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 I'll 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 full 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 an prototype stage. but we just completely abandoned that and said we're going to go all in on driverless cars now as 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.

I'm 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 controlling puts 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 to truly validate that or you know if something in the base vehicle fails and causes your system to fail whose fault is it Or if the cars anti-lock break 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

But I did more than the time. So try it anyway.

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 Honda Civic or one of these three cars. And then we surveyed 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 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 is it, is it tough business?

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 flowing 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 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 sort of value systems. Like in a car company, the thing that gets you promoted and sort of 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, it's, you know, you can count the millions of dollars that burn by pretty quickly. Whereas I think in a most Silicon Valley companies and and and and and cruise in 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, so, meshing that culture of sort of continuous improvement and experimentation with one where everything needs to be rigorously defined up front so that you never slip a deadline or miss a budget was a pretty big challenge. We're over three years in now after the acquisition. And I'd say like, you know, the investment we made in figuring out how to work together successfully and who should do what and 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 crews were very steep on the learning curve.

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, now these great processes and this huge infrastructure and supply base that we can tap into is just remarkable because the scope in 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 and assemble and build the cars themselves, I mean, we were close to a gym 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

I would have been nice if you could focus on the software and not the hardware platform.

Well, the things that drive the economics for fleets of self-driving cars or they're sort of a handful of variables. One is, you know, the cost to build the vehicle itself. So the material cost, how many, you know, what's the cost of all your sensors plus the cost of the vehicle and every 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 falls apart versus, you know, 2 million. And then, you know, if you have a fleet, it's kind of like an airplane where or a 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, you know, the ride sharing business because that's pretty clear that there's demand for that. There's existing markets you can tap into and larger urban areas that kind of thing. Yeah, yeah, and and I think that there are some real benefits to having. cars without drivers compared to sort of the status quo for people who use red share services today. You know, you get privacy, consistency, hopefully significantly improved safety, all these benefits versus the current product. But it's a, it's a credit market and then other opportunities which you've seen a lot of activity in the last really in the last six, 12 months is, you know, delivery, whether that's parcels and packages, food or groceries. Those are all sort of, I think, opportunities 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 better, that are as good or better than humans, and that's sort of where the entire industry is sort of in this holding patent right now.

I wouldn't, 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, you know, significantly increase, you know, safety risk. I think that's not something we'd be willing to do. But I think driving style or like, you know, are you going to relax the comfort constraints slightly or things like that? All of those things make sense in our plausible. I see all those is, you know, nice optimizations once again. We get the core problem solved and these fleets out there. 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 going to 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.

That's a good question. Before we go to that though, I like what you said about the psychology aspect of this because I think one observation I 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. It's probably not super productive. And so that's 3,600 seconds. that's that's time you know it'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 you know even though that's that's you know one one thousandth of the time they waste looking at Facebook every day so there's There's definitely some, you know, psychology aspects of this. I think that are pretty interesting. Road rage in general, and then the question, of course, is if everyone is in self-driving cars, do they even notice these three second delays anymore? 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.

I guess you could say self-driving cars at scale will lower the collective blood pressure of society probably by a couple of points with all that road rage and stress so that's a good extra So back to your your question about the technology and the I guess the biggest problems and I have a hard time answering that question because you know we've been at this like specifically focusing on driverless cars and all the technology needed to enable that for a little over four and a half years now 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 were 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, you know, we're benchmarking against a pretty good or pretty high standard, which is human driving. All things considered humans are excellent at handling edge cases and unexpected scenarios where computers are the opposite. And so beating that 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, you know, 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 sort of 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. It's 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.

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 you've crossed that threshold of of human performance and become superhuman. But there's no reason. There's no first principles reason that AV capability will tap out anywhere in their humans. Like there's no reason it couldn't be 20 times better, whether that's, you know, just better driving or safer driving or comfortable driving, or even a thousand times better given enough time. And we intend to basically chase that, you know, forever to build the best possible product.

Well, I mean, I don't want 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

Yeah, so I'd say that it seems the consensus Among the people developing self-driving cars today is just sort of start with some form of an easier environment, whether it means, you know, lacking, implement weather or, you know, mostly sunny or whatever it is. And then add, add capability from 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 domain, you know, 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, you know, capability to drive really well and safely and have your rain or snow, you know, that 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, you know, I know today if we wanted to, we could produce that 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 time one is for those things falling away and reaching hundreds of thousands of vehicles? Maybe a range is, but I would say less than five years.

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, it 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 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, it helps you weather those stuff 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 our logical thinkers are always pushing their limits and have very high levels of integrity. So that's Dan Khan 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 that can apply to sticking to sort of a or having conviction around the original premise of your idea and and sticking around to do all the you know the unsexy work to actually make it come to fruition including dealing with you know 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.

It's just more concise version of what I was trying to say. That was good.

There's no one size fits all answer, but if you're 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 background is probably a good approach so that capital doesn't become your primary constraint. Why commentator 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 that environment I think just breeds pre-success, right? 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, yeah, 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 so that's why I did it, you know, for example, the second time.

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 there's a period in Justin TV. I think after seven years where You know, the company was going one direction, which is sort of 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.

Yeah, and I'd say I I probably spent an extra year to working on that and and I'd say like I would have just tried to do something different sooner. Because those were two years where I felt like. you know 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 it's like I would have said exactly that like you're not getting any meeting out of your work personally right now you should you should find a way to change that and that's that's part of the pitch I used to basically everyone who joins crews today it's like hey you've got that now by coming here

I guess we're going to go and 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 in? 2019 for us is the year that we try to cross over that threshold and reach, you know, superhuman level of performance to some degree with the software and have all the other of the thousands of little building blocks in place to launch, you know, our first commercial product. So that's, that's what's in score for us 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, it's all up to us now.

Thank you. Thanks for having me today.