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Kyle Vogt: Cruise Automation | Lex Fridman Podcast #14

Lex Fridman and Kyle Vogt on kyle Vogt on Cruise, startups, and building safe self-driving cars.

Lex FridmanhostKyle Vogtguest
Feb 7, 201955mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 2:36

    Kansas City robotics roots: BattleBots, LEGOs, and early engineering instincts

    1. LF

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

    2. KV

      Yeah. And I just saw that picture you had hidden-

    3. LF

      Oh, boy.

    4. KV

      ... under there, so I'm a little bit- a little bit worried about that now.

    5. LF

      Nervous.

    6. KV

      Yeah.

    7. LF

      So in high school, in Kansas City, you joined Shawnee Mission North High School robotics team.

    8. KV

      Yeah.

    9. LF

      Now, that wasn't your high school.

    10. KV

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

    11. LF

      I was gonna troll you a little bit, jog your memory-

    12. KV

      Yep.

    13. LF

      ... a little bit-

    14. KV

      Yep.

    15. LF

      ... about that kid.

    16. KV

      I was trying to look super cool and intense-

    17. LF

      You did.

    18. KV

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

    19. LF

      So go back there. What is it that drew you to robotics?

    20. KV

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

  2. 2:36 – 5:23

    BattleBots design realities: wedges, weapons, and pushing hardware past ratings

    1. LF

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

    2. KV

      Yeah.

    3. LF

      ... building things that can't be broken.

    4. KV

      They're- they're radio controlled. So-

    5. LF

      Okay.

    6. KV

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

    7. LF

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

    8. KV

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

    9. LF

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

    10. KV

      Uh, we- we weren't very good at BattleBots. We lost all of our matches before-

    11. LF

      What did yours look like?

    12. KV

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

    13. LF

      The purpose of which is what?

    14. KV

      Pretty- pretty ineffective actually, but it looked cool. (laughs) And, uh-

    15. LF

      Was it to help with the lift so that-

    16. KV

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

  3. 5:23 – 7:20

    First programming experiences: Apple II, BASIC, and the “magic” of code

    1. LF

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

    2. KV

      Oh, man. So-

    3. LF

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

    4. KV

      (laughs)

    5. LF

      ... IDE? Do you re- do you remember any of this?

    6. KV

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

    7. LF

      Mm-hmm.

    8. KV

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

    9. LF

      Yeah.

    10. KV

      And I just remember thinking, "Wow, she's like a hacker." Like, "Teach me-"

    11. LF

      Yeah.

    12. KV

      "... these, these codes, these error codes."

    13. LF

      Yeah.

    14. KV

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

    15. LF

      And there's go-to statements?

    16. KV

      Yeah, go-to and ... It's, it's very basic, maybe hence-

    17. LF

      Yeah.

    18. KV

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

    19. LF

      So you got the bug then? So the even-

    20. KV

      Yeah.

    21. LF

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

  4. 7:20 – 12:53

    First self-driving spark: boring highways and heuristic computer vision thinking

    1. KV

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

    2. LF

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

    3. KV

      Yeah.

    4. LF

      Is there ... What was your thinking about it?

    5. KV

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

    6. LF

      Traffic light detection and stop sign detection, red, yellow, green. We're good.

    7. KV

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

    8. LF

      Yeah.

    9. KV

      ... on day one just to get the system up and running-

    10. LF

      Yeah.

    11. KV

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

    12. LF

      (laughs)

    13. KV

      And it ... And I, I think I wrote the first version in Basic, which is-

    14. LF

      Yeah.

    15. KV

      ... like an interpreted language.

    16. LF

      Right.

    17. KV

      It's extremely slow-

    18. LF

      Yeah.

    19. KV

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

    20. LF

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

    21. KV

      Mm-hmm.

    22. LF

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

    23. KV

      Yeah.

    24. LF

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

    25. KV

      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.

    26. LF

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

    27. KV

      Mm-hmm.

    28. LF

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

    29. KV

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

    30. LF

      Mm-hmm.

  5. 12:53 – 14:28

    MIT and the DARPA Grand Challenge: undergrad ambition meets hardware failure

    1. LF

      ... MIT as an undergrad, 2004.

    2. KV

      That's right.

    3. LF

      And that's when the first DARPA Grand Challenge was happening.

    4. KV

      Yeah.

    5. LF

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

    6. KV

      Originally there wasn't a sort of-

    7. LF

      Official entry?

    8. KV

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

    9. LF

      (laughs)

    10. KV

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

    11. LF

      On the hardware side. So-

    12. KV

      Yeah.

    13. LF

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

  6. 14:28 – 16:46

    Why DARPA mattered—and why it may not need to re-enter the AV race today

    1. KV

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

    2. LF

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

    3. KV

      Mm-hmm.

    4. LF

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

    5. KV

      Yeah.

    6. LF

      Do you think there's room... and then there was the Robotics Challenge with humanoid robots.

    7. KV

      Right.

    8. LF

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

    9. KV

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

    10. LF

      Right.

    11. KV

      Because, like, kickstarting and, and spinning up the flywheel is, I think, what, what they-

    12. LF

      Mm-hmm.

    13. KV

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

    14. LF

      Success.

    15. KV

      ... area of technology that, that needs help.

  7. 16:46 – 19:15

    Leaving MIT for Justin.tv: the one-way ticket and startup addiction

    1. LF

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

    2. KV

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

    3. LF

      Mm-hmm.

    4. KV

      ... uh, month off, uh, on a, on a one-way ticket and basically never went back.

    5. LF

      (laughs)

    6. KV

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

    7. LF

      S- so no regrets?

    8. KV

      No.

    9. LF

      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?

    10. KV

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

    11. LF

      Right.

    12. KV

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

    13. LF

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

    14. KV

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

  8. 19:15 – 22:54

    Launch-night crisis engineering: building reliable live streaming before iPhones

    1. LF

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

    2. KV

      (laughs) .

    3. LF

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

    4. KV

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

    5. LF

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

    6. KV

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

    7. LF

      Yeah.

    8. KV

      ... you know, with unreliable cell phone networks and-

    9. LF

      That's pretty low level networking you were doing.

    10. KV

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

    11. LF

      Right. Sure.

    12. KV

      ... you know, off-putting to the viewers.

    13. LF

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

    14. KV

      Yeah. It's-

    15. LF

      ... uh, pair coding?

    16. KV

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

    17. LF

      Yeah.

    18. KV

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

  9. 22:54 – 25:45

    Founding Cruise: choosing a 10-year mission with real societal impact

    1. LF

      So in, uh, 2013, October, you founded Cruise Automation.

    2. KV

      Yeah.

    3. LF

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

    4. KV

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

    5. LF

      Right.

    6. KV

      And I realized I wanted something that scratched more of an existential itch, like something that, that truly matters.

    7. LF

      Yeah.

    8. KV

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

    9. LF

      Scale, yeah.

    10. KV

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

    11. LF

      Mm-hmm.

    12. KV

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

    13. LF

      Mm-hmm.

    14. KV

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

    15. LF

      Whoosh.

    16. KV

      ... probably the greatest applied AI problem of our generation.

    17. LF

      That's right.

    18. KV

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

  10. 25:45 – 29:49

    Early Cruise strategy and the retrofit dead-end: from highway assist to full autonomy

    1. LF

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

    2. KV

      Yeah.

    3. LF

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

    4. KV

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

    5. LF

      Mm-hmm.

    6. KV

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

    7. LF

      Fully auto.

    8. KV

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

    9. LF

      The, the idea of retrofit is kind of interesting.

    10. KV

      Yeah.

    11. LF

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

    12. KV

      Not at all. I've come full circle on that one.

    13. LF

      Uh-huh.

    14. KV

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

    15. LF

      That's a really convincing summary of why it's really challenging ............................ Yeah.

    16. KV

      But I didn't know all that at the time.

    17. LF

      Right.

    18. KV

      So we tried it anyway. Uh-

    19. LF

      But as a pitch also at the time-

    20. KV

      Yeah.

    21. LF

      ... it's a really strong one.

    22. KV

      Yeah.

    23. LF

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

    24. KV

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

    25. LF

      Right.

    26. KV

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

  11. 29:49 – 35:24

    Cruise + GM: bridging Silicon Valley experimentation with automaker safety/process culture

    1. LF

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

    2. KV

      Mm-hmm.

    3. LF

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

    4. KV

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

    5. LF

      Right.

    6. KV

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

    7. LF

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

    8. KV

      That's not a good feeling, yeah.

    9. LF

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

    10. KV

      Mm-hmm.

    11. LF

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

    12. KV

      (laughs) That's, that's probably true, yeah.

    13. LF

      (laughs) On GM's side, it's probably the opposite.

    14. KV

      Yeah.

    15. LF

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

    16. KV

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

    17. LF

      Tests and s- yeah.

    18. KV

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

    19. LF

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

    20. KV

      Mm-hmm.

    21. LF

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

    22. KV

      It would've been nice if we could focus on the software and not the-

    23. LF

      Yeah. (laughs)

    24. KV

      ... not the hardware platform. Yeah.

  12. 35:24 – 37:46

    AV business models: unit economics, ride-hailing, delivery, and the “no fleet, no revenue” reality

    1. LF

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

    2. KV

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

    3. LF

      Right.

    4. KV

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

    5. LF

      Right.

    6. KV

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

    7. LF

      Large urban areas, that kinda thing.

    8. KV

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

  13. 37:46 – 40:19

    Driving “personality,” road rage psychology, and what autonomy changes about human behavior

    1. LF

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

    2. KV

      Mm-hmm.

    3. LF

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

    4. KV

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

    5. LF

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

    6. KV

      (laughs)

    7. LF

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

    8. KV

      Yeah.

  14. 40:19 – 45:42

    The hardest part of autonomy: continuous improvement against a very high human baseline

    1. LF

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

    2. KV

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

    3. LF

      Sure.

    4. KV

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

    5. LF

      Oh.

    6. KV

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

    7. LF

      Right.

    8. KV

      ... they're laying in on the horn.

    9. LF

      Yeah.

    10. KV

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

    11. LF

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

    12. KV

      Yeah. (laughs)

    13. LF

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

    14. KV

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

    15. LF

      Yeah.

    16. KV

      ... without all that road rage and stress. So that- that's a good- good externality.

    17. LF

      Mm-hmm.

    18. KV

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

    19. LF

      Right.

    20. KV

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

    21. LF

      Yep.

    22. KV

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

    23. LF

      Mm-hmm.

    24. KV

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

    25. LF

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

    26. KV

      Yeah, and the exciting thing for me is I don't think that grinding ever stops-

    27. LF

      Right.

    28. KV

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

    29. LF

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

    30. KV

      Mm-hmm.

  15. 45:42 – 55:08

    Timeline to scale and closing advice: deployment constraints, startup lessons, and 2019 goals

    1. LF

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

    2. KV

      Well, I mean, I- I would've loved-

    3. LF

      I, I don't mean to use those people.

    4. KV

      ... to have it two years ago, but-

    5. LF

      (laughs)

    6. KV

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

    7. LF

      That's a good, that's a good, uh, rephrasing it.

    8. KV

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

    9. LF

      Mm-hmm.

    10. KV

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

    11. LF

      Maybe a range is better?

    12. KV

      ... I would, I would say less than five years.

    13. LF

      Less than five years?

    14. KV

      Yeah.

    15. LF

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

    16. KV

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

    17. LF

      Yeah.

    18. KV

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

    19. LF

      Right.

    20. KV

      ... things themselves rather than being beaten by their competition or running out of money.

    21. LF

      Yeah, if you never quit, eventually you'll arrive. S- so, uh-

    22. KV

      That was a much more concise version of what I was trying to say.

    23. LF

      (laughs)

    24. KV

      That was good.

    25. LF

      So you went the Y Combinator route twice.

    26. KV

      Yeah.

    27. LF

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

    28. KV

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

    29. LF

      Mm-hmm.

    30. KV

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

Episode duration: 55:23

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