Kyle Vogt: Cruise Automation | Lex Fridman Podcast #14
the following is a conversation with convoked he is the president and the CTO of Cruz Automation leading an effort to solve one of the biggest robotics challenges of our time vehicle automation he's a co-founder of two successful companies twitch and crews 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 Fri D and now here's my conversation with Kyle vote grew up in Kansas right yeah and I just saw that picture you had you know there's them a little bit a little bit worried about that yeah 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 that was the only high school in the area that had a like a teacher who was willing to sponsor a FIRST Robotics team I was gonna troll you a little bit jog your mess trying to look super cool and intense because you know this was BattleBots it's a serious business so we're standing there with a welded steel frame and looking tough so go back there what is that jury to robotics well I think I've been trying to figure this out for a while but I've always liked building things with Legos and when I was really really young I wanted the Legos I had motors and other things and then you know Lego Mindstorms came out and for the first time you could program Lego contraptions and I think things just sort of snowballed from that but I remember seeing you know the battle bots 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 pored over the internet forums where all the creator's 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 from basically everything that was out there on what these people were doing and eventually like sort of triangulated how to how to put some of these things together and and ended up doing battle bots which was you know I was like 13 or 14 which is pretty awesome I'm not sure if the show is still running but the battle bots is there's not an artificial intelligence component it's remotely controlled and yeah it's an almost like a mechanical generic challenge yeah I think things that can be broken they're radio-controlled so and I think that they allowed some limited form of autonomy but you know in a two-minute match you're in and the way these things ran you're really doing yourself a disservice by trying to automate it versus just you know do the practical thing which is drive it yourself the entertainment aspect just going on YouTube there's like and some of them wield an axe some of them I mean there's that fun so what drew you to that aspect it wasn't the mechanical engineering was it the dream to create like Frankenstein and sentient being I was just like the Lego you like tinkering with stuff I mean that that was just building something I think the the idea of you know this this radio-controlled machine that that can do various things if it has like a weapon or something was pretty interesting I agree it doesn't have the same appeal as you know autonomous robots which I which I you know sort of gravitated towards later on but it was definitely an engineering challenge because everything you did in in that competition was pushing components to their limits so we would buy like these $40 DC motors that came out of a 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 2-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 was something something intrinsically interesting about just seeing like where things break and did you all fly and see where they break did you take it to the testing point like how did you know two minutes or was there a reckless let's just go with it and see we weren't very good at BattleBots we lost all of our matches that woody first round like the one I built first both of them were these wedge-shaped robots because a wedge even though it's sort of boring to look at is extremely effective you drive towards another robot and the front edge of it gets under him 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 helpful to 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 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 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 coal you've written go back there see what language was it what what was that was a Emacs vim was it a more respectable modern ID do you remember any of this yeah well I remember I think maybe when I was in third or fourth grade school I was at elementary school had a bunch of Apple 2 computers and we'd play games on those and I remember every once in a while something mood would 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 this day for some reason like PR number six or PR pound six which is peripheral 6 which is the disk drive which would fire up the disk and load the program and I just remember thinking wow she's like a hacker like teach me these these codes these error codes what I called him 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 to program and so it's all in basic you know where every line you know the line numbers are all number 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 and 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 there's go-to statements yeah go to and it's very basic maybe it's a name but a lot of fun and that was like that was you know it's fun that's when you know when your 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 it was a computer program in computer science classes in high school not not where I went so it was a 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 Battle Watts competition and I had just gotten my I think my learner's permit or early driver's permit and so I was driving this you know 10 hour stretch across western Kansas where it's just you're going straight on a highway and it is mind-numbing ly 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've 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 uh before there's been ways in artificial intelligence right the the current wave with deep learning makes people believe that you can solve in a really rich deep way the computer vision perception problem but like in 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 local as itself in the world moves around the world like when you were younger and yeah as what was your thinking about it well prior to deep neural networks our convolutional neural as these modern techniques we have or at least ones that are in use today it was all heuristic space and so like old-school image processing and I think extracting you know yellow lane markers out of an image of a road is one of the problems that lends itself reasonably well to those heuristic based methods you know like just do a threshold on the color yellow and then try to fit some lines to that using a Hough transform or something and then go from there traffic like detection and then stop signs 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 thing that would stay in between the lanes on a highway but if you wanted to do the full the full you know set of capabilities needed for a driverless car I think you could and we 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 threshold injustice system up and running before you migrate to you know a deep learning based technique or something else and you know back in when I was doing this my first one it was on Pentium 203 233 megahertz computer in it and I 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 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 I also I get emails all the time from you know like 11 12 year old 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 separate the autonomous driving problem into modules and try to solve it sort of rigorously or you just do it end to end and most people just kind of love the idea that you know us humans do a tenth and we just perceive and act we should be able to use that do the same kind of thing when you're on that's and that 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 is a really exciting approach but yeah it's it's changed the the language the kind of stuff you turned green with it it's kind of exciting to see when they seniors grow up yeah I can only imagine if you if your starting point is you know Python and tensorflow at age 13 where you end up you know after 10 or 15 years of that that's that's pretty cool because of github because this they're tools for solving most of the major problems and 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 and and at 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 the large scale 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 I was so 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 so you came to MIT as an undergrad 2004 that's right and that's when the first DARPA Grand Challenge was happening yeah the 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 I 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 we had our team was probably mostly freshmen and sophomores you know which was not really a fair fair fight against maybe the you know postdoc and faculty-led teams from other schools but we we got something up and running we had our vehicle drive by a wire and you know very very basic control and things but on the day of the qualifying for pre qualifying round the one and only steering motor that we had purchased the thing that we had you know retrofitted to turn the steering wheel on the truck died and so our vehicle was just dead in the water couldn't steer so we didn't make it very far 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 has been autonomous vehicles but really that was the birth of the modern wave the the thing that captivated everyone's imagination that we can actually do this so what how were you captivated in that way so how did your view of autonomous vehicles change at that point I'd say at that point in time it was it was a the curiosity as in like is this really possible and I think that was generally the spirit and the 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 and and I think like to this day that DARPA challenge with its you know million dollar prize pool was probably one of the most effective you know uses of taxpayer money dollar for dollar that I've seen you know because that that small sort of initiative that DARPA put put out sort of in my view was the catalyst or the tipping point for this 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 I 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 yeah do you think there's room and then there was the Robotics Challenge with humanoid robots right so in your now role is looking at this you're trying to solve one of the you know autonomous driving one of the harder more difficult places of San Francisco is there a role for DARPA to step in to also kind of help out they challenge with new ideas specifically a pedestrians and so on all these kinds of interesting things well I haven't I haven't thought about it from that perspective is there anything DARPA could do today to further accelerate things and I would say my instinct is that that's maybe not the highest and best use of their resources 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 and the amount of money going into it and the amount of people like going through your class and learning about these things and developing these skills is just you know orders of magnitude more than it was back then and so there's enough momentum and inertia and energy and investment dollars into this space right now that 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 needs help so then stepping back to MIT you left on my teaching a 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 you know rock star entrepreneurs they've done this before they must be driving around in Ferraris because they sold their company and you know I thought I could learn a lot from them so I teamed up with those guys and you know went out during went out to California during IIP which is my tease month off on 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 you know I was just all in and got completely hooked and that 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 that was quite a an unexpected journey so no regrets no looking back it was just an obvious I mean one-way ticket I mean if we just pause on that for a second there was no how did you know these were the right guys this is the right decision you didn't think it was just follow the heart kind of thing well I didn't know but you know just trying something for a month during IEP he seems pretty little risk right right and then you know well maybe I'll take a semester off and my teas 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 it never lasts from from when you look in retrospect I mean it seems like a brave decision that you know it's difficult it would be difficult for a lot of people to make 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 financier consulting jobs in Boston or New York and very few people were going to California to start companies but today I'd say that's it's probably inverted which is just a sign of a sign of the times I guess yeah so there's a story about midnight of March 18 2007 where whether we're TechCrunch I guess and I was just in TV earlier than was supposed to a few hours the site didn't work I don't know if any of this is true you can tell me and I you and one of the folks adjusted to e I'm a shear coated 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 Kahn said that you were known for mural coding through problems and being a creative quote creative genius so on that night what was going through your head or maybe I put another way how do you solve these problems what's your approach to solving these kinds of problems were the line between success and failure seems to be pretty thin that's a good question well first of all that's that's a nice of Justin to say that I think you know I would have been maybe twenty-one years old then and not very experienced at programming but as with with everything in a start-up you're sort of racing against the clock and so our plan was the second we had this live streaming camera backpack up and running where Justin could wear it and no matter where he went in a city it would be streaming live video and this is even before the iPhones this is like hard to do back then we would launch and so we thought we were there and and the backpack was working and then we sent out all the emails to launch the launch the company and do the press thing and then you know we weren't quite actually there and then 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 what is that like you have 17 hours to go and 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 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 and putting his 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 staticky every once in while the image would would go staticky 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 you know off-putting to the viewers how do you tackle that problem what was the just freaking out behind a computer there's the word are there other other folks working on this problem like we behind a whiteboard were you doing uh yes a little hair coding it has a little only because there's 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 it's 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 look back on it fondly 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 dollars 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 was and it is today pretty successful but the the work was the result was entertainment mostly like the the better the product was the more we would entertain people and then you know make money on them 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 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 you know at least ten years to something and when you think about things from that perspective you certainly I think raised the bar on weight 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 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 I was thinking about that for a while and I made like I tried writing a gmail clone and looked at some other ideas and then it just sort of light bulb went off like self-driving cars like that was the most fun I had ever had in college working on that and like well what's the state of the technology has been ten years maybe maybe times have changed and maybe now is the time to make this work and I poked around and looked at the only other thing out there really at the time was the Google self-driving car project 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 in there and said this this is something I know I can commit ten years to it's the probably the greatest applied AI problem of our generation it'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 crews 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 what we just had Oliver for voyage 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 and gray area than that my analysis of the situation is that Google is putting a lot it had been putting a lot of money into that project that a lot more resources and so and they still hadn't cracked the fully driverless car you know this is 20 2013 I guess so I thought what what can I do to sort of go from zero to you know significant scale so I can actually solve the real problem which is the driverless cars and I thought here's the strategy we'll start by doing a really simple problem or solving a really simple problem that creates value for people so eventually ended up deciding on automating highway driving which is relatively more straightforward as long as there's a backup driver there and I'll you know the go-to-market will be able 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 social reinvest that in research for doing fully fabulous 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 in 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 gonna go all in on driverless cars now is the time can't think of anything that's more exciting and if it works more impactful so we're just gonna 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 trying to build a retrofit product and I'll touch on some of the complexities of that and then also having been inside in 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 the other control inputs on your car it's pretty hard to get there with with a retrofit or if you did even if you did it it creates a whole bunch of new complications around liability or how did you truly validate that or you know 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 longtail 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 at the time so we tried it anyway but it's a pitch also at the time it's a really strong one yes that's how you achieve scale and that's how you beat the current the the leader at the time of Google or the only one in the market 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'd we started with an Audi s4 as the vehicle we retrofitted with his highway driving capability and we had kind of assumed that if we just knock out like three make and models of vehicle that'll cover like eighty percent of a 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 surveyed our users 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 there's a tough business so GM manufactures and sells 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 hot out of those you've talked about a little bit before but it's also just fascinating to me we'll work a lot of automakers you know the difference between the gap between Detroit and Silicon Valley let's say just to be sort of poetic about it I guess what how do you close that gap how do you take GM into the future where a large part of the fleet would be autonomous perhaps I want to start by acknowledging that that GM is made up of you know 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 within the attitude inside a car company like that is you know embracing this this transformation and change rather than fearing it and I think that's a testament to the leadership at GM and that's flown all the way through to to everyone you talk to even the people in this in blue plants working on these cars so that's really great so that starting from that position makes a lot easier so then when the 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 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 so the reward system is following the processes delivering the 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 you know most Silicon Valley companies and crews 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 you know 90% of them won't work and and so so meshing that culture of sort of continuous improvement and experimentation with one where everything needs to be you know rigorously defined upfront so that you never slip a deadline or miss a budget was a pretty big challenge and that 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 both GM and crews were very steep on the learning curve yes I'm sure it was very stressful it's really important work because that's that's how to revolutionize the transportation it really to revolutionize any system you know you look at the healthcare system or you look at the legal system I have people like lawyers come up to me all the time like everything they're working on can easily be automated but then that's not a good feeling yeah that was it's not a good feeling but also there's no way to automate because the the the entire infrastructure is really you know based is older and it moves very slowly and so how do you close the gap between I haven't how can I replace of course lawyers don't wanna 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 is basically hiring software engineers it's thinking a software world I mean I'm pretty sure nobody in Silicon Valley's ever hit a deadline so and then it's probably true yeah and GSI is probably the opposite yeah so that's 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 you know people have been doing that for over a hundred 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 you know we're Silicon Valley engineers we can solve any problem you know building a car it's been done therefore it's you know it's it's it's not 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 you know partners who have done that for a hundred 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 an assemble in you know test and build the cars themselves I mean we work closely with Jim on that but if we had to develop all that capability in-house as well you know that that would just make make the problem really intractable I think mmm so yeah just like your first entry mit DARPA challenge when there was what the motor that failed and somebody that knows what they're doing with the motor did it that would have been nice if you focus on the software and not the hardware platform yeah right so from your perspective now you know there's so many ways that autonomous vehicles can impact Society in the next year five years ten years what do you think is the biggest opportunity to make money in autonomous driving sort of make it a financially viable thing in the near-term what do you think would be the biggest impact there well the things that 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 one hundred thousand miles and then it falls apart versus you know two million and then you know if you have a fleet it's kind of like an airplane where or 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 a you know the other piece of that is how are you generating revenue I think that's kind what you're asking and 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 and I think that there are some real benefits to having cars without drivers compared to through the status quo for people who use ride share services today you know you get privacy consistency hopefully significant improve safety all these benefits versus the current product but it's it's a crowded market and then other opportunities which you've seen a lot of activity in the last really in last six to twelve 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 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 the trend achieved that baseline so but you said sort of rely not reliability consistency it's kind of interesting I think I heard you say somewhere I'm not sure if that's what you meant but you know I can imagine a situation where you would get an autonomous vehicle and you know when you get into an uber or lyft you don't get to choose the driver in a sense that you don't get to choose the personality of the driving do you think there's a there's room to define the personality of the car the way drives you in terms of aggressiveness for example in terms of sort of pushing the bomb the one of the biggest challenges in Toms driving is the is a trade-off between sort of safety and and do you think there's any room for the human to take a role in that decision to accept the liability I guess we III wouldn't it no I'd say within reasonable bounds as in we're not gonna I think it'd be highly unlikely we did expose any nob that would let you you know significantly increase safety risk I think that's that's just not something we'd be willing to do but I think driving style or like you know are you gonna relax the comfort constraints slightly or things like that all of those things make sense and are plausible I see all those is you know nice optimizations once again we get the core problem solved and these fleets out there but the other thing we've sort of observed is that you have this intuition that if you sort of slam your foot on the gas right after the light turns green and aggressively accelerate you're gonna get there faster but the actual impact of doing that is pretty small you feel like you're getting there faster but so that so the same would be true for ABS even if they don't slam there you know the pedal to the floor when the light turns green they're gonna get you they're 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 it's the emotional release driving is is a place being inside or a car somebody said it's like the real world version of being a troll so you have this protection this mental protection you're able to sort of yell at the world like release your anger whatever is but so there's an element of that that I think autonomous vehicles would also have to you know have giving an outlet to people but it doesn't have to be through through through driving or honking or so on there might be other outlets but I think to just sort of even just put that aside the baseline is really you know that's the focus that's the thing you need to solve and then the fun human things can be solved after but so from the baseline of just solving autonomous driving and you're working in San Francisco one of the more difficult cities to operate in what what is what is the any of you currently the hardest aspect of autonomous driving and negotiated with pedestrians is that edge cases of perception is it planning is there a mechanical engineering is it data fleet stuff like what are your thoughts on the challenge the more challenging aspects there that's a good that's a good question I think before before we go to that though I just wanted 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 you know over an hour a day on social media like staring at Facebook and so that's just you know 60 minutes of your life you're not getting back and it's probably not super productive and so that's 3,600 seconds right and 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 pre 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 people need to be distracted by something entertaining something useful inside the car so they don't pay attention to the external world and then and then and it can take whatever psychology and bring it back to Twitter and then focus on that as opposed to sort of interacting sort of putting the emotion out there into the world so it's a it's an interesting problem but baseline autonomy I guess you could say self-driving cars you know at scale will lower the collective blood pressure of society probably by a couple points yeah without all that road rage and stress so that's a good good externality so back to your question about the technology in the the I guess the biggest problems and I have a hard time answering that question because you know we've been at this like specifically focusing on driverless cars and all the technology needed to enable that for a little over four and a half years now and even a year or two in I felt like we had completed the functionality needed to get someone from point A to point B as in if we need to do a left turn maneuver or if we need to drive around a you know a double parked vehicle into oncoming traffic or navigate through construction zones the the scaffolding and the building blocks where it was there pretty early on and so the challenge is not any one scenario or situation for which you know we fail at 100% of those it's more you know we're benchmarking against a pretty good or pretty high standard which is human driving all things considered humans are excellent at handling the edge cases and unexpected scenarios whereas computers 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 the most you know uncomfortable or the things that 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 impossible permutation of a situation by either applying more deep learning systems or just by you know adding more tests coverage or new scenarios that that we develop against and just grinding on that it's we're sort of in the the unsexy phase of development right now which is doing the real engineering work that it takes to go from prototype to production you're basically scaling the the grinding so has 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 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 you know just better driving or safer driving a more 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 better and better and better and always new educators come up and you experiences so and you want to automate that process as much as possible mhm so what do you think in general in society when do you think we may have hundreds of thousands of fully autonomous vehicles driving around so first of all predictions nobody knows the future you're a part of the leading people trying to define that future but even then you still don't know but if you think about a hundreds of thousands of heat so a significant fraction of vehicles in major cities are autonomous do you think I would 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 want me to have it two years ago but we're not there yet so I guess the the way I would think about that is let's let's flip that question around so what would prevent you to reach hundreds of thousands of vehicles and that's a goodness a good rephrasing yeah so the I'd say the it seems the consensus among the people developing self-driving cars today is to sort of start with some form of an easier environment whether it means you know lacking inclement weather or you know mostly sunny or whatever it is and then add add capability for more complex situations over time and so if you're only able to deploy in areas that that meet sort of your criteria or that 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 and safely in heavy rain or snow you know that 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 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 the timeline is for those things falling away and reaching hundreds of thousands of vehicles maybe a range is but I would I would say less than five years that's in five years yeah and of course you're working hard to make that happen so you started two companies that were eventually acquired for each for a billion dollars so you're pretty good person to ask what does it take to build a successful startup mmm-hmm I think there's there sort of survivor bias here a little bit but I can try to find some common threads for the the things that worked for me which is you know in 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 they're they're these crazy ups and downs like one day you think the business is just on you're just on top of the world and unstoppable and the next day you think okay this is all gonna and you know it's it's just it's just going south and it's gonna be over tomorrow and and so I think like having a true passion that you can fall back on and knowing that you would be doing it even if you weren't getting paid for it helps you whether those 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 uh I guess persistence or perseverance like and 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 that you're not passionate about whether that's finance or or HR or or operations or those things as long as you are grinding away in working towards you know that North Star for your business whatever it is and you don't give up and you're making progress every day it seems like eventually you'll end up in a good place and the only things that can slow you down are you know running out of money or I suppose your competitors destroying you but I think most of the time it's people giving up or 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 working size version of what I was trying to say yeah so you want the Y Combinator out twice yeah what do you think in a quick question do you think is the best way to raise funds in the early days or not just funds but just community develop your idea and so on can you do it solo or maybe with a co-founder with like self-funded do you think Y Combinator is good it's good to do VC route is there no right answer was there for the Y Combinator experience something that you could take away that that was the right path to take there's no one-size-fits-all answer but if your ambition I think is to you know see how big you can make something or or or rapidly expand and capture market or solve a problem or whatever it is then then you know going to venture back route is probably a good approach so that so that capital doesn't become your primary constraint Y Combinator I love because it puts you in this sort of competitive environment while you're where you're surrounded by you know the top maybe one percent of other really highly motivated you know peers who are in the same same place and that that environment I think just breeds breed success right if you're surrounded by really brilliant hard-working people you're gonna feel you know sort of compelled or inspired to try to emulate them and/or beat them and so even though I had done it once before and I felt like yeah I'm pretty self-motivated I thought like I look this is gonna be a hard problem I can use all the help I can get so surrounding myself with other entrepreneurs is gonna make me work a little bit harder or push a little harder than it's worth it when Saba white why I did it you know for example a second time let's let's go philosophical existential if you'd go back and do something differently in your life starting in high school than MIT leaving MIT you could have gone the PG route doing startup I'm gonna see about a start-up in California and youth or maybe some aspects of fundraising is there something you'll regret something you need not necessarily grab but if you go back it could do differently 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 hard to look back on things even if they didn't go well and call it a regret because hopefully took away some new knowledge or learning from that so I would say there was a period yeah the closest I can I can come to us is there's a period in in justin.tv I think after seven years where that the company was going one direction which is sorts twitch in video gaming and I'm not a video gamer I don't really even use twitch at all and I was still working on the core technology there but my heart was no longer in it because the business that we were creating was not something that I was personally passionate about it didn't meet your bar of existential impact yeah and I'd say III probably spent an extra year or two working on that and and I'd say like I would have just tried to do something different sooner because those are those were two years where I felt like you know from this philosophical or existential thing I I just I just felt 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 you should find a way to change that and that's part of the pitch I use to basically everyone who joins crews today it's like hey you've got that now 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 crews so you never know you can be good theory yeah so last question what does 2019 hold for crews after this I guess we're gonna go and I'll talk to your class but one of the big things is going from prototype to production for autonomous cars and what does that mean once 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 are 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 yeah from Charlie Miller and Chris fells like the people I have crossed paths with if you know it sounds like you have an amazing team so I'm like I said it's one of the most I think one of the most important problems in artificial intelligence of the century you'll be one of the most defining the 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 nice call you