vtt/episode_014_small.vtt

WEBVTT

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 The following is a conversation with Kyle Vogt.

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 He's the president and the CTO of Cruise Automation,

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 leading an effort to solve one of the biggest

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 robotics challenges of our time, vehicle automation.

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 He's a cofounder of two successful companies,

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 Twitch and Cruise, that have each sold for a billion dollars.

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 And he's a great example of the innovative spirit

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 that flourishes in Silicon Valley.

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 And now is facing an interesting and exciting challenge

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 of matching that spirit with the mass production

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 and the safety centered culture of a major automaker,

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 like General Motors.

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 This conversation is part of the MIT

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 Artificial General Intelligence series

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 and the Artificial Intelligence podcast.

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 If you enjoy it, please subscribe on YouTube, iTunes,

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 or simply connect with me on Twitter at Lex Friedman,

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 spelled F R I D.

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 And now here's my conversation with Kyle Vogt.

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 You grew up in Kansas, right?

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 Yeah, and I just saw that picture you had to hit know

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

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 So in high school in Kansas City,

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 you joined Shawnee Mission North High School Robotics Team.

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 Now that wasn't your high school.

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 That's right.

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 That was the only high school in the area that had a teacher

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 who was willing to sponsor our first robotics team.

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 I was gonna troll you a little bit.

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 Jog your mouth a little bit with that kid.

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 I was trying to look super cool and intense.

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 You did?

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 Because this was BattleBots, this is serious business.

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 So we're standing there with a welded steel frame

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 and looking tough.

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 So go back there.

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 What does that drew you to robotics?

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 Well, I think, I've been trying to figure this out

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 for a while, but I've always liked building things

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 with Legos.

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 And when I was really, really young,

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 I wanted the Legos that had motors and other things.

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 And then, you know, Lego Mindstorms came out

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 and for the first time you could program Lego contraptions.

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 And I think things just sort of snowballed from that.

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 But I remember seeing, you know, the BattleBots TV show

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 on Comedy Central and thinking that is the coolest thing

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 in the world, I wanna be a part of that.

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 And not knowing a whole lot about how to build

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 these 200 pound fighting robots.

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 So I sort of obsessively poured over the internet forums

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 where all the creators for BattleBots would sort of hang out

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 and talk about, you know, document their build progress

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 and everything.

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 And I think I read, I must have read like, you know,

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 tens of thousands of forum posts from basically everything

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 that was out there on what these people were doing.

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 And eventually, like sort of triangulated how to put

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 some of these things together and ended up doing BattleBots,

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 which was, you know, it was like 13 or 14,

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 which was pretty awesome.

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 I'm not sure if the show's still running,

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 but so BattleBots is, there's not an artificial intelligence

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 component, it's remotely controlled.

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 And it's almost like a mechanical engineering challenge

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 of building things that can be broken.

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 They're radio controlled.

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 So, and I think that they allowed some limited form

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 of autonomy, but, you know, in a two minute match,

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 you're, in the way these things ran,

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 you're really doing yourself a disservice by trying

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 to automate it versus just, you know,

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 do the practical thing, which is drive it yourself.

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 And there's an entertainment aspect,

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 just going on YouTube.

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 There's like some of them wield an axe, some of them,

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 I mean, there's that fun.

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 So what drew you to that aspect?

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 Was it the mechanical engineering?

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 Was it the dream to create like Frankenstein

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 and sentient being?

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 Or was it just like the Lego, you like tinkering stuff?

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 I mean, that was just building something.

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 I think the idea of, you know,

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 this radio controlled machine that can do various things.

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 If it has like a weapon or something was pretty interesting.

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 I agree, it doesn't have the same appeal as, you know,

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 autonomous robots, which I, which I, you know,

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 sort of gravitated towards later on,

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 but it was definitely an engineering challenge

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 because everything you did in that competition

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 was pushing components to their limits.

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 So we would buy like these $40 DC motors

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 that came out of a winch,

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 like on the front of a pickup truck or something.

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 And we'd power the car with those

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 and we'd run them at like double or triple

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 their rated voltage.

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 So they immediately start overheating,

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 but for that two minute match, you can get, you know,

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 a significant increase in the power output

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 of those motors before they burn out.

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 And so you're doing the same thing for your battery packs,

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 all the materials in the system.

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 And I think there was something,

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 something intrinsically interesting

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 about just seeing like where things break.

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 And did you offline see where they break?

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 Did you take it to the testing point?

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 Like, how did you know two minutes?

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 Or was there a reckless, let's just go with it and see.

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 We weren't very good at battle bots.

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 We lost all of our matches the first round.

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 The one I built first,

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 both of them were these wedge shaped robots

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 because the wedge, even though it's sort of boring

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 to look at is extremely effective.

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 You drive towards another robot

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 and the front edge of it gets under them

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 and then they sort of flip over,

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 it's kind of like a door stopper.

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 And the first one had a pneumatic polished stainless steel

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 spike on the front that would shoot out about eight inches.

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 The purpose of which is what?

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 Pretty ineffective actually, but it looked cool.

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 And was it to help with the lift?

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 No, it was just to try to poke holes in the other robot.

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 And then the second time I did it,

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 which is the following, I think maybe 18 months later,

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 we had a titanium axe with a hardened steel tip on it

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 that was powered by a hydraulic cylinder,

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 which we were activating with liquid CO2,

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 which had its own set of problems.

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 So great, so that's kind of on the hardware side.

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 I mean, at a certain point,

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 there must have been born a fascination

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 on the software side.

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 So what was the first piece of code you've written?

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 If you didn't go back there, see what language was it?

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 What was it, was it EMAX, VAM?

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 Was it a more respectable, modern ID?

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 Do you remember any of this?

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 Yeah, well, I remember, I think maybe when I was in

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 third or fourth grade, I was at elementary school,

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 had a bunch of Apple II computers,

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 and we'd play games on those.

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 And I remember every once in a while,

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 something would crash or wouldn't start up correctly,

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 and it would dump you out to what I later learned

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 was like sort of a command prompt.

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 And my teacher would come over and type,

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 I actually remember this to this day for some reason,

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 like PR number six, or PR pound six,

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 which is peripheral six, which is the disk drive,

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 which would fire up the disk and load the program.

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 And I just remember thinking, wow, she's like a hacker,

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 like teach me these codes, these error codes,

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 that is what I called them at the time.

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 But she had no interest in that.

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 So it wasn't until I think about fifth grade

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 that I had a school where you could actually

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 go on these Apple II's and learn to program.

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 And so it was all in basic, you know,

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 where every line, you know, the line numbers are all,

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 or that every line is numbered,

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 and you have to like leave enough space

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 between the numbers so that if you want to tweak your code,

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 you go back and if the first line was 10

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 and the second line is 20, now you have to go back

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 and insert 15.

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 And if you need to add code in front of that,

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 you know, 11 or 12, and you hope you don't run out

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 of line numbers and have to redo the whole thing.

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 And there's go to statements?

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 Yeah, go to and is very basic, maybe hence the name,

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 but a lot of fun.

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 And that was like, that was, you know,

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 that's when, you know, when you first program,

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 you see the magic of it.

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 It's like, just like this world opens up with,

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 you know, endless possibilities for the things

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 you could build or accomplish with that computer.

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 So you got the bug then, so even starting with basic

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 and then what, C++ throughout, what did you,

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 was there a computer programming,

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 computer science classes in high school?

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 Not, not where I went, so it was self taught,

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 but I did a lot of programming.

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 The thing that, you know, sort of pushed me in the path

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 of eventually working on self driving cars

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 is actually one of these really long trips

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 driving from my house in Kansas to, I think, Las Vegas,

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 where we did the BattleBots competition.

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 And I had just gotten my, I think my learners permit

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 or early drivers permit.

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 And so I was driving this, you know, 10 hour stretch

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 across Western Kansas where it's just,

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 you're going straight on a highway

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 and it is mind numbingly boring.

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 And I remember thinking even then

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 with my sort of mediocre programming background

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 that this is something that a computer can do, right?

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 Let's take a picture of the road,

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 let's find the yellow lane markers

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 and, you know, steer the wheel.

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 And, you know, later I'd come to realize

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 this had been done, you know, since the 80s

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 or the 70s or even earlier, but I still wanted to do it.

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 And sort of immediately after that trip,

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 switched from sort of BattleBots,

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 which is more radio controlled machines

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 to thinking about building, you know,

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 autonomous vehicles of some scale,

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 start off with really small electric ones

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 and then, you know, progress to what we're doing now.

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 So what was your view of artificial intelligence

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 at that point?

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 What did you think?

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 So this is before there's been waves

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 in artificial intelligence, right?

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 The current wave with deep learning

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 makes people believe that you can solve

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 in a really rich, deep way,

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 the computer vision perception problem.

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 But like before the deep learning craze,

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 you know, how do you think about

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 how would you even go about building a thing

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 that perceives itself in the world,

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 localize itself in the world, moves around the world?

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 Like when you were younger, I mean,

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 as what was your thinking about it?

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 Well, prior to deep neural networks

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 or convolutional neural nets,

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 these modern techniques we have,

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 or at least ones that are in use today,

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 it was all heuristic space.

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 And so like old school image processing,

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 and I think extracting, you know,

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 yellow lane markers out of an image of a road

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 is one of the problems that lends itself

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 reasonably well to those heuristic base methods, you know,

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 like just do a threshold on the color yellow

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 and then try to fit some lines to that

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 using a huff transform or something

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 and then go from there.

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 Traffic light detection and stop sign detection,

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 red, yellow, green.

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 And I think you can, you could,

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 I mean, if you wanted to do a full,

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 I was just trying to make something that would stay

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 in between the lanes on a highway,

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 but if you wanted to do the full,

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 the full, you know, set of capabilities

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 needed for a driverless car,

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 I think you could, and we've done this at cruise,

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 you know, in the very first days,

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 you can start off with a really simple,

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 you know, human written heuristic

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 just to get the scaffolding in place

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 for your system, traffic light detection,

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 probably a really simple, you know,

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 color thresholding on day one

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 just to get the system up and running

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 before you migrate to, you know,

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 a deep learning based technique or something else.

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 And, you know, back in, when I was doing this,

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 my first one, it was on a Pentium 203,

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 233 megahertz computer in it.

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 And I think I wrote the first version in basic,

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 which is like an interpreted language.

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 It's extremely slow because that's the thing

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 I knew at the time.

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 And so there was no, no chance at all of using,

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 there's no computational power to do

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 any sort of reasonable deep nets like you have today.

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 So I don't know what kids these days are doing.

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 Are kids these days, you know, at age 13

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 using neural networks in their garage?

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 I mean, that would be awesome.

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 I get emails all the time from, you know,

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 like 11, 12 year olds saying, I'm having, you know,

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 I'm trying to follow this TensorFlow tutorial

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 and I'm having this problem.

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 And the general approach in the deep learning community

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 is of extreme optimism of, as opposed to,

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 you mentioned like heuristics, you can,

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 you can, you can separate the autonomous driving problem

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 into modules and try to solve it sort of rigorously,

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 where you can just do it end to end.

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 And most people just kind of love the idea that,

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 you know, us humans do it end to end,

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 we just perceive and act.

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 We should be able to use that,

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 do the same kind of thing with your own nets.

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 And that, that kind of thinking,

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 you don't want to criticize that kind of thinking

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 because eventually they will be right.

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 Yeah. And so it's exciting.

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 And especially when they're younger to explore that

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 is a really exciting approach.

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 But yeah, it's, it's changed the, the language,

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 the kind of stuff you're tinkering with.

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 It's kind of exciting to see when these teenagers grow up.

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 Yeah, I can only imagine if you, if your starting point

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 is, you know, Python and TensorFlow at age 13,

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 where you end up, you know,

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 after 10 or 15 years of that, that's, that's pretty cool.

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 Because of GitHub, because the state tools

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 for solving most of the major problems

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 that are artificial intelligence

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 are within a few lines of code for most kids.

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 And that's incredible to think about,

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 also on the entrepreneurial side.

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 And, and, and at that point, was there any thought

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 about entrepreneurship before you came to college

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 is sort of doing your building this into a thing

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 that impacts the world on a large scale?

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 Yeah, I've always wanted to start a company.

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 I think that's, you know, just a cool concept

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 of creating something and exchanging it

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 for value or creating value, I guess.

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 So in high school, I was, I was trying to build like,

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 you know, servo motor drivers, little circuit boards

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 and sell them online or other, other things like that.

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 And certainly knew at some point I wanted to do a startup,

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 but it wasn't really, I'd say until college until I felt

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 like I had the, I guess the right combination

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 of the environment, the smart people around you

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 and some free time and a lot of free time at MIT.

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 So you came to MIT as an undergrad 2004.

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 That's right.

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 And that's when the first DARPA Grand Challenge

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 was happening.

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 Yeah.

13:00.720 --> 13:03.360
 The timing of that is beautifully poetic.

13:03.360 --> 13:05.680
 So how'd you get yourself involved in that one?

13:05.680 --> 13:07.080
 Originally there wasn't a

13:07.080 --> 13:07.920
 Official entry?

13:07.920 --> 13:09.520
 Yeah, faculty sponsored thing.

13:09.520 --> 13:12.760
 And so a bunch of undergrads, myself included,

13:12.760 --> 13:14.160
 started meeting and got together

13:14.160 --> 13:17.800
 and tried to, to haggle together some sponsorships.

13:17.800 --> 13:20.120
 We got a vehicle donated, a bunch of sensors

13:20.120 --> 13:21.600
 and tried to put something together.

13:21.600 --> 13:24.640
 And so we had, our team was probably mostly freshmen

13:24.640 --> 13:26.800
 and sophomores, you know, which, which was not really

13:26.800 --> 13:30.960
 a fair, fair fight against maybe the, you know, postdoc

13:30.960 --> 13:32.840
 and faculty led teams from other schools.

13:32.840 --> 13:35.000
 But we, we got something up and running.

13:35.000 --> 13:37.400
 We had our vehicle drive by wire and, you know,

13:37.400 --> 13:42.440
 very, very basic control and things, but on the day

13:42.440 --> 13:46.800
 of the qualifying, sort of pre qualifying round,

13:46.800 --> 13:50.840
 the one and only steering motor that we had purchased,

13:50.840 --> 13:52.600
 the thing that we had, you know, retrofitted to turn

13:52.600 --> 13:55.760
 the steering wheel on the truck died.

13:55.760 --> 13:58.440
 And so our vehicle was just dead in the water, couldn't steer.

13:58.440 --> 13:59.880
 So we didn't make it very far.

13:59.880 --> 14:00.920
 On the hardware side.

14:00.920 --> 14:03.000
 So was there a software component?

14:03.000 --> 14:06.200
 Was there, like, how did your view of autonomous vehicles

14:06.200 --> 14:08.040
 in terms of artificial intelligence

14:09.520 --> 14:10.720
 evolve in this moment?

14:10.720 --> 14:12.400
 I mean, you know, like you said,

14:12.400 --> 14:14.080
 from the 80s has been autonomous vehicles,

14:14.080 --> 14:16.720
 but really that was the birth of the modern wave.

14:16.720 --> 14:20.080
 The, the thing that captivated everyone's imagination

14:20.080 --> 14:21.520
 that we can actually do this.

14:21.520 --> 14:26.000
 So how, were you captivated in that way?

14:26.000 --> 14:27.600
 So how did your view of autonomous vehicles

14:27.600 --> 14:29.000
 change at that point?

14:29.000 --> 14:33.760
 I'd say at that point in time, it was, it was a curiosity

14:33.760 --> 14:35.840
 as in like, is this really possible?

14:35.840 --> 14:38.440
 And I think that was generally the spirit

14:38.440 --> 14:43.280
 and the purpose of that original DARPA Grand Challenge,

14:43.280 --> 14:45.520
 which was to just get a whole bunch

14:45.520 --> 14:48.680
 of really brilliant people exploring the space

14:48.680 --> 14:49.880
 and pushing the limits.

14:49.880 --> 14:51.960
 And, and I think like to this day,

14:51.960 --> 14:54.160
 that DARPA challenge with its, you know,

14:54.160 --> 14:57.120
 million dollar prize pool was probably one

14:57.120 --> 15:00.840
 of the most effective, you know, uses of taxpayer money,

15:00.840 --> 15:03.320
 dollar for dollar that I've seen, you know,

15:03.320 --> 15:06.720
 because that, that small sort of initiative

15:06.720 --> 15:10.440
 that DARPA put put out sort of, in my view,

15:10.440 --> 15:12.560
 was the catalyst or the tipping point

15:12.560 --> 15:14.800
 for this, this whole next wave

15:14.800 --> 15:16.120
 of autonomous vehicle development.

15:16.120 --> 15:17.160
 So that was pretty cool.

15:17.160 --> 15:20.240
 So let me jump around a little bit on that point.

15:20.240 --> 15:23.240
 They also did the urban challenge where it was in the city,

15:23.240 --> 15:25.920
 but it was very artificial and there's no pedestrians

15:25.920 --> 15:27.640
 and there's very little human involvement

15:27.640 --> 15:30.480
 except a few professional drivers.

15:30.480 --> 15:31.640
 Yeah.

15:31.640 --> 15:33.560
 Do you think there's room, and then there was

15:33.560 --> 15:35.360
 the robotics challenge with human robots?

15:35.360 --> 15:36.200
 Right.

15:36.200 --> 15:38.720
 So in your now role as looking at this,

15:38.720 --> 15:41.640
 you're trying to solve one of the, you know,

15:41.640 --> 15:43.120
 autonomous driving, one of the harder,

15:43.120 --> 15:45.480
 more difficult places in San Francisco.

15:45.480 --> 15:47.320
 Is there a role for DARPA to step in

15:47.320 --> 15:49.680
 to also kind of help out, like,

15:49.680 --> 15:54.000
 challenge with new ideas, specifically pedestrians

15:54.000 --> 15:55.880
 and so on, all these kinds of interesting things?

15:55.880 --> 15:57.680
 Well, I haven't thought about it from that perspective.

15:57.680 --> 15:59.280
 Is there anything DARPA could do today

15:59.280 --> 16:00.680
 to further accelerate things?

16:00.680 --> 16:04.880
 And I would say my instinct is that that's maybe not

16:04.880 --> 16:07.040
 the highest and best use of their resources in time

16:07.040 --> 16:10.640
 because, like, kick starting and spinning up the flywheel

16:10.640 --> 16:12.720
 is I think what they did in this case

16:12.720 --> 16:14.200
 for very, very little money.

16:14.200 --> 16:15.800
 But today this has become,

16:16.880 --> 16:19.000
 this has become, like, commercially interesting

16:19.000 --> 16:20.680
 to very large companies and the amount of money

16:20.680 --> 16:23.040
 going into it and the amount of people,

16:23.040 --> 16:24.840
 like, going through your class and learning

16:24.840 --> 16:27.200
 about these things and developing these skills

16:27.200 --> 16:29.120
 is just, you know, orders of magnitude

16:29.120 --> 16:30.840
 more than it was back then.

16:30.840 --> 16:33.080
 And so there's enough momentum and inertia

16:33.080 --> 16:36.520
 and energy and investment dollars into this space right now

16:36.520 --> 16:39.960
 that I don't, I don't, I think they're,

16:39.960 --> 16:42.200
 I think they're, they can just say mission accomplished

16:42.200 --> 16:44.320
 and move on to the next area of technology

16:44.320 --> 16:45.360
 that needs help.

16:46.280 --> 16:49.120
 So then stepping back to MIT,

16:49.120 --> 16:50.880
 you left MIT Junior Junior year,

16:50.880 --> 16:53.080
 what was that decision like?

16:53.080 --> 16:55.680
 As I said, I always wanted to do a company

16:55.680 --> 16:59.080
 or start a company and this opportunity landed in my lap

16:59.080 --> 17:01.960
 which was a couple of guys from Yale

17:01.960 --> 17:04.240
 were starting a new company and I Googled them

17:04.240 --> 17:06.720
 and found that they had started a company previously

17:06.720 --> 17:10.640
 and sold it actually on eBay for about a quarter million bucks

17:10.640 --> 17:12.880
 which was a pretty interesting story.

17:12.880 --> 17:15.760
 But so I thought to myself, these guys are, you know,

17:15.760 --> 17:19.080
 rock star entrepreneurs, they've done this before,

17:19.080 --> 17:20.720
 they must be driving around in Ferraris

17:20.720 --> 17:22.320
 because they sold their company.

17:23.320 --> 17:26.000
 And, you know, I thought I could learn a lot from them.

17:26.000 --> 17:28.320
 So I teamed up with those guys and, you know,

17:28.320 --> 17:32.000
 went out during, went out to California during IAP

17:32.000 --> 17:36.440
 which is MIT's month off on one way ticket

17:36.440 --> 17:38.040
 and basically never went back.

17:38.040 --> 17:39.280
 We were having so much fun,

17:39.280 --> 17:42.040
 we felt like we were building something and creating something

17:42.040 --> 17:44.440
 and it was gonna be interesting that, you know,

17:44.440 --> 17:46.800
 I was just all in and got completely hooked

17:46.800 --> 17:49.640
 and that business was Justin TV

17:49.640 --> 17:52.400
 which is originally a reality show about a guy named Justin

17:53.720 --> 17:57.120
 which morphed into a live video streaming platform

17:57.120 --> 18:00.320
 which then morphed into what is Twitch today.

18:00.320 --> 18:03.720
 So that was quite an unexpected journey.

18:04.720 --> 18:07.000
 So no regrets?

18:07.000 --> 18:07.840
 No.

18:07.840 --> 18:09.120
 Looking back, it was just an obvious,

18:09.120 --> 18:10.720
 I mean, one way ticket.

18:10.720 --> 18:12.760
 I mean, if we just pause on that for a second,

18:12.760 --> 18:17.680
 there was no, how did you know these were the right guys?

18:17.680 --> 18:19.520
 This is the right decision.

18:19.520 --> 18:22.640
 You didn't think it was just follow the heart kind of thing?

18:22.640 --> 18:24.520
 Well, I didn't know, but, you know,

18:24.520 --> 18:26.520
 just trying something for a month during IAP

18:26.520 --> 18:28.240
 seems pretty low risk, right?

18:28.240 --> 18:30.760
 And then, you know, well, maybe I'll take a semester off.

18:30.760 --> 18:32.280
 MIT's pretty flexible about that.

18:32.280 --> 18:33.840
 You can always go back, right?

18:33.840 --> 18:35.680
 And then after two or three cycles of that,

18:35.680 --> 18:36.960
 I eventually threw in the towel.

18:36.960 --> 18:41.920
 But, you know, I think it's, I guess in that case,

18:41.920 --> 18:44.880
 I felt like I could always hit the undo button if I had to.

18:44.880 --> 18:45.720
 Right.

18:45.720 --> 18:49.600
 But nevertheless, from when you look in retrospect,

18:49.600 --> 18:51.680
 I mean, it seems like a brave decision.

18:51.680 --> 18:53.200
 You know, it would be difficult

18:53.200 --> 18:54.320
 for a lot of people to make.

18:54.320 --> 18:55.440
 It wasn't as popular.

18:55.440 --> 18:58.120
 I'd say that the general, you know,

18:58.120 --> 19:01.480
 flux of people out of MIT at the time was mostly

19:01.480 --> 19:04.120
 into, you know, finance or consulting jobs

19:04.120 --> 19:05.720
 in Boston or New York.

19:05.720 --> 19:07.840
 And very few people were going to California

19:07.840 --> 19:09.080
 to start companies.

19:09.080 --> 19:12.240
 But today, I'd say that's probably inverted,

19:12.240 --> 19:15.400
 which is just a sign of a sign of the times, I guess.

19:15.400 --> 19:16.080
 Yeah.

19:16.080 --> 19:21.560
 So there's a story about midnight of March 18, 2007,

19:21.560 --> 19:25.720
 where TechCrunch, I guess, announced Justin TV earlier

19:25.720 --> 19:29.080
 than it was supposed to a few hours.

19:29.080 --> 19:30.360
 The site didn't work.

19:30.360 --> 19:32.520
 I don't know if any of this is true, you can tell me.

19:32.520 --> 19:36.200
 And you and one of the folks at Justin TV,

19:36.200 --> 19:39.240
 Emma Shear, coded through the night.

19:39.240 --> 19:41.440
 Can you take me through that experience?

19:41.440 --> 19:47.160
 So let me say a few nice things that the article I read quoted

19:47.160 --> 19:49.600
 Justin Khan said that you were known for bureau coding

19:49.600 --> 19:53.520
 through problems and being a creative genius.

19:53.520 --> 19:59.440
 So on that night, what was going through your head?

19:59.440 --> 20:01.400
 Or maybe I put another way, how do you

20:01.400 --> 20:02.520
 solve these problems?

20:02.520 --> 20:05.480
 What's your approach to solving these kind of problems

20:05.480 --> 20:07.080
 where the line between success and failure

20:07.080 --> 20:09.680
 seems to be pretty thin?

20:09.680 --> 20:10.680
 That's a good question.

20:10.680 --> 20:13.400
 Well, first of all, that's nice of Justin to say that.

20:13.400 --> 20:16.880
 I think I would have been maybe 21 years old then

20:16.880 --> 20:18.800
 and not very experienced at programming.

20:18.800 --> 20:22.680
 But as with everything in a startup,

20:22.680 --> 20:24.720
 you're sort of racing against the clock.

20:24.720 --> 20:27.320
 And so our plan was the second we

20:27.320 --> 20:32.600
 had this live streaming camera backpack up and running

20:32.600 --> 20:33.600
 where Justin could wear it.

20:33.600 --> 20:35.320
 And no matter where he went in the city,

20:35.320 --> 20:36.400
 it would be streaming live video.

20:36.400 --> 20:37.960
 And this is even before the iPhones,

20:37.960 --> 20:40.880
 this is like hard to do back then.

20:40.880 --> 20:41.800
 We would launch.

20:41.800 --> 20:45.160
 And so we thought we were there and the backpack was working.

20:45.160 --> 20:47.080
 And then we sent out all the emails

20:47.080 --> 20:49.920
 to launch the company and do the press thing.

20:49.920 --> 20:53.000
 And then we weren't quite actually there.

20:53.000 --> 20:55.880
 And then we thought, oh, well, they're

20:55.880 --> 21:00.160
 not going to announce it until maybe 10 AM the next morning.

21:00.160 --> 21:01.880
 And it's, I don't know, it's 5 PM now.

21:01.880 --> 21:03.640
 So how many hours do we have left?

21:03.640 --> 21:08.000
 What is that, like 17 hours to go?

21:08.000 --> 21:10.440
 And that was going to be fine.

21:10.440 --> 21:11.440
 Was the problem obvious?

21:11.440 --> 21:13.280
 Did you understand what could possibly be?

21:13.280 --> 21:16.520
 Like how complicated was the system at that point?

21:16.520 --> 21:18.840
 It was pretty messy.

21:18.840 --> 21:22.760
 So to get a live video feed that looked decent working

21:22.760 --> 21:25.680
 from anywhere in San Francisco, I

21:25.680 --> 21:28.600
 put together this system where we had like three or four

21:28.600 --> 21:29.880
 cell phone data modems.

21:29.880 --> 21:32.200
 And they were like, we take the video stream

21:32.200 --> 21:35.600
 and sort of spray it across these three or four modems

21:35.600 --> 21:38.080
 and then try to catch all the packets on the other side

21:38.080 --> 21:39.480
 with unreliable cell phone networks.

21:39.480 --> 21:41.080
 Pretty low level networking.

21:41.080 --> 21:41.720
 Yeah.

21:41.720 --> 21:44.760
 And putting these sort of protocols

21:44.760 --> 21:47.560
 on top of all that to reassemble and reorder the packets

21:47.560 --> 21:49.720
 and have time buffers and error correction

21:49.720 --> 21:50.960
 and all that kind of stuff.

21:50.960 --> 21:53.960
 And the night before, it was just

21:53.960 --> 21:56.280
 staticky. Every once in a while, the image would go

21:56.280 --> 21:59.640
 staticky and there would be this horrible like screeching

21:59.640 --> 22:02.080
 audio noise because the audio was also corrupted.

22:02.080 --> 22:04.600
 And this would happen like every five to 10 minutes or so.

22:04.600 --> 22:08.080
 And it was a really, you know, off of putting to the viewers.

22:08.080 --> 22:08.880
 Yeah.

22:08.880 --> 22:10.200
 How do you tackle that problem?

22:10.200 --> 22:13.280
 What was the, you're just freaking out behind a computer.

22:13.280 --> 22:16.880
 There's the word, are there other folks working on this problem?

22:16.880 --> 22:18.120
 Like were you behind a whiteboard?

22:18.120 --> 22:22.000
 Were you doing a hair coding?

22:22.000 --> 22:23.840
 Yeah, it's a little lonely because there's four of us

22:23.840 --> 22:26.880
 working on the company and only two people really wrote code.

22:26.880 --> 22:29.200
 And Emmett wrote the website in the chat system

22:29.200 --> 22:32.400
 and I wrote the software for this video streaming device

22:32.400 --> 22:34.280
 and video server.

22:34.280 --> 22:36.240
 And so, you know, it was my sole responsibility

22:36.240 --> 22:37.320
 to figure that out.

22:37.320 --> 22:39.440
 And I think it's those, you know,

22:39.440 --> 22:42.200
 setting deadlines, trying to move quickly and everything

22:42.200 --> 22:44.200
 where you're in that moment of intense pressure

22:44.200 --> 22:46.960
 that sometimes people do their best and most interesting work.

22:46.960 --> 22:48.800
 And so even though that was a terrible moment,

22:48.800 --> 22:50.760
 I look back on it fondly because that's like, you know,

22:50.760 --> 22:54.720
 that's one of those character defining moments, I think.

22:54.720 --> 22:59.480
 So in 2013, October, you founded Cruise Automation.

22:59.480 --> 23:00.200
 Yeah.

23:00.200 --> 23:04.200
 So progressing forward, another exceptionally successful

23:04.200 --> 23:09.920
 company was acquired by GM in 2016 for $1 billion.

23:09.920 --> 23:14.120
 But in October 2013, what was on your mind?

23:14.120 --> 23:16.360
 What was the plan?

23:16.360 --> 23:19.840
 How does one seriously start to tackle

23:19.840 --> 23:22.800
 one of the hardest robotics, most important impact

23:22.800 --> 23:24.960
 for robotics problems of our age?

23:24.960 --> 23:28.760
 After going through Twitch, Twitch was,

23:28.760 --> 23:31.480
 and is today pretty successful.

23:31.480 --> 23:36.880
 But the work was, the result was entertainment mostly.

23:36.880 --> 23:39.840
 Like the better the product was, the more we would entertain

23:39.840 --> 23:42.760
 people and then, you know, make money on the ad revenues

23:42.760 --> 23:43.760
 and other things.

23:43.760 --> 23:45.000
 And that was a good thing.

23:45.000 --> 23:46.320
 It felt good to entertain people.

23:46.320 --> 23:49.120
 But I figured like, you know, what is really the point

23:49.120 --> 23:51.120
 of becoming a really good engineer

23:51.120 --> 23:53.160
 and developing these skills other than, you know,

23:53.160 --> 23:53.960
 my own enjoyment.

23:53.960 --> 23:55.760
 And I realized I wanted something that scratched

23:55.760 --> 23:57.680
 more of an existential itch, like something

23:57.680 --> 23:59.440
 that truly matters.

23:59.440 --> 24:03.680
 And so I basically made this list of requirements

24:03.680 --> 24:06.160
 for a new, if I was going to do another company.

24:06.160 --> 24:08.000
 And the one thing I knew in the back of my head

24:08.000 --> 24:12.320
 that Twitch took like eight years to become successful.

24:12.320 --> 24:14.880
 And so whatever I do, I better be willing to commit,

24:14.880 --> 24:17.000
 you know, at least 10 years to something.

24:17.000 --> 24:20.400
 And when you think about things from that perspective,

24:20.400 --> 24:21.760
 you certainly, I think, raise the bar

24:21.760 --> 24:23.200
 on what you choose to work on.

24:23.200 --> 24:24.320
 So for me, the three things where

24:24.320 --> 24:27.120
 it had to be something where the technology itself

24:27.120 --> 24:28.960
 determines the success of the product,

24:28.960 --> 24:31.840
 like hard, really juicy technology problems,

24:31.840 --> 24:33.600
 because that's what motivates me.

24:33.600 --> 24:36.280
 And then it had to have a direct and positive impact

24:36.280 --> 24:37.640
 on society in some way.

24:37.640 --> 24:39.200
 So an example would be like, you know,

24:39.200 --> 24:41.560
 health care, self driving cars because they save lives,

24:41.560 --> 24:43.600
 other things where there's a clear connection to somehow

24:43.600 --> 24:45.200
 improving other people's lives.

24:45.200 --> 24:47.160
 And the last one is it had to be a big business

24:47.160 --> 24:50.200
 because for the positive impact to matter,

24:50.200 --> 24:51.240
 it's got to be a large scale.

24:51.240 --> 24:52.080
 Scale, yeah.

24:52.080 --> 24:53.840
 And I was thinking about that for a while

24:53.840 --> 24:55.960
 and I made like a, I tried writing a Gmail clone

24:55.960 --> 24:57.640
 and looked at some other ideas.

24:57.640 --> 24:59.480
 And then it just sort of light bulb went off

24:59.480 --> 25:00.440
 like self driving cars.

25:00.440 --> 25:02.360
 Like that was the most fun I had ever had

25:02.360 --> 25:04.040
 in college working on that.

25:04.040 --> 25:05.960
 And like, well, what's the state of the technology

25:05.960 --> 25:08.440
 has been 10 years, maybe times have changed

25:08.440 --> 25:10.800
 and maybe now is the time to make this work.

25:10.800 --> 25:13.320
 And I poked around and looked at the only other thing

25:13.320 --> 25:15.480
 out there really at the time was the Google self driving

25:15.480 --> 25:16.680
 car project.

25:16.680 --> 25:19.600
 And I thought surely there's a way to, you know,

25:19.600 --> 25:21.600
 have an entrepreneur mindset and sort of solve

25:21.600 --> 25:23.520
 the minimum viable product here.

25:23.520 --> 25:25.200
 And so I just took the plunge right then and there

25:25.200 --> 25:26.680
 and said, this, this is something I know

25:26.680 --> 25:27.840
 I can commit 10 years to.

25:27.840 --> 25:30.760
 It's probably the greatest applied AI problem

25:30.760 --> 25:32.000
 of our generation.

25:32.000 --> 25:34.240
 And if it works, it's going to be both a huge business

25:34.240 --> 25:37.040
 and therefore like probably the most positive impact

25:37.040 --> 25:38.280
 I can possibly have on the world.

25:38.280 --> 25:40.920
 So after that light bulb went off,

25:40.920 --> 25:43.000
 I went all in on cruise immediately

25:43.000 --> 25:45.560
 and got to work.

25:45.560 --> 25:47.360
 Did you have an idea how to solve this problem?

25:47.360 --> 25:49.640
 Which aspect of the problem to solve?

25:49.640 --> 25:53.720
 You know, slow, like we just had Oliver from voyage here

25:53.720 --> 25:56.560
 slow moving retirement communities,

25:56.560 --> 25:58.080
 urban driving, highway driving.

25:58.080 --> 26:00.400
 Did you have like, did you have a vision

26:00.400 --> 26:03.560
 of the city of the future or, you know,

26:03.560 --> 26:06.400
 the transportation is largely automated,

26:06.400 --> 26:07.240
 that kind of thing.

26:07.240 --> 26:12.240
 Or was it sort of more fuzzy and gray area than that?

26:12.240 --> 26:16.640
 My analysis of the situation is that Google's putting a lot,

26:16.640 --> 26:19.200
 had been putting a lot of money into that project.

26:19.200 --> 26:20.760
 They had a lot more resources.

26:20.760 --> 26:23.720
 And so, and they still hadn't cracked

26:23.720 --> 26:26.200
 the fully driverless car.

26:26.200 --> 26:28.520
 You know, this is 2013, I guess.

26:29.480 --> 26:33.360
 So I thought, what can I do to sort of go from zero

26:33.360 --> 26:35.600
 to, you know, significant scale

26:35.600 --> 26:37.280
 so I can actually solve the real problem,

26:37.280 --> 26:38.640
 which is the driverless cars.

26:38.640 --> 26:40.480
 And I thought, here's the strategy.

26:40.480 --> 26:44.080
 We'll start by doing a really simple problem

26:44.080 --> 26:45.560
 or solving a really simple problem

26:45.560 --> 26:48.080
 that creates value for people.

26:48.080 --> 26:50.040
 So it eventually ended up deciding

26:50.040 --> 26:51.800
 on automating highway driving,

26:51.800 --> 26:54.240
 which is relatively more straightforward

26:54.240 --> 26:56.440
 as long as there's a backup driver there.

26:56.440 --> 26:58.480
 And, you know, the go to market

26:58.480 --> 27:00.240
 will be able to retrofit people's cars

27:00.240 --> 27:02.240
 and just sell these products directly.

27:02.240 --> 27:04.520
 And the idea was, we'll take all the revenue

27:04.520 --> 27:08.320
 and profits from that and use it to do the,

27:08.320 --> 27:10.920
 to sort of reinvest that in research for doing

27:10.920 --> 27:12.600
 fully driverless cars.

27:12.600 --> 27:13.960
 And that was the plan.

27:13.960 --> 27:15.720
 The only thing that really changed along the way

27:15.720 --> 27:17.360
 between then and now is,

27:17.360 --> 27:19.000
 we never really launched the first product.

27:19.000 --> 27:21.680
 We had enough interest from investors

27:21.680 --> 27:24.120
 and enough of a signal that this was something

27:24.120 --> 27:25.000
 that we should be working on,

27:25.000 --> 27:28.400
 that after about a year of working on the highway autopilot,

27:28.400 --> 27:31.040
 we had it working, you know, at a prototype stage,

27:31.040 --> 27:33.120
 but we just completely abandoned that

27:33.120 --> 27:34.960
 and said, we're gonna go all in on driverless cars

27:34.960 --> 27:36.480
 now is the time.

27:36.480 --> 27:38.120
 Can't think of anything that's more exciting.

27:38.120 --> 27:39.720
 And if it works more impactful,

27:39.720 --> 27:41.360
 so we're just gonna go for it.

27:41.360 --> 27:43.440
 The idea of retrofit is kind of interesting.

27:43.440 --> 27:44.280
 Yeah.

27:44.280 --> 27:46.880
 Being able to, it's how you achieve scale.

27:46.880 --> 27:47.880
 It's a really interesting idea,

27:47.880 --> 27:51.120
 is it's something that's still in the back of your mind

27:51.120 --> 27:52.800
 as a possibility?

27:52.800 --> 27:53.640
 Not at all.

27:53.640 --> 27:57.080
 I've come full circle on that one after trying

27:57.080 --> 27:58.880
 to build a retrofit product.

27:58.880 --> 28:01.240
 And I'll touch on some of the complexities of that.

28:01.240 --> 28:04.240
 And then also having been inside an OEM

28:04.240 --> 28:05.400
 and seeing how things work

28:05.400 --> 28:08.320
 and how a vehicle is developed and validated.

28:08.320 --> 28:09.360
 When it comes to something

28:09.360 --> 28:11.280
 that has safety critical implications,

28:11.280 --> 28:12.520
 like controlling the steering

28:12.520 --> 28:15.280
 and other control inputs on your car,

28:15.280 --> 28:17.720
 it's pretty hard to get there with a retrofit.

28:17.720 --> 28:20.520
 Or if you did, even if you did,

28:20.520 --> 28:23.280
 it creates a whole bunch of new complications around

28:23.280 --> 28:25.400
 liability or how did you truly validate that?

28:25.400 --> 28:27.480
 Or, you know, something in the base vehicle fails

28:27.480 --> 28:29.880
 and causes your system to fail, whose fault is it?

28:31.560 --> 28:34.080
 Or if the car's anti lock brake systems

28:34.080 --> 28:36.680
 or other things kick in or the software has been,

28:36.680 --> 28:38.240
 it's different in one version of the car.

28:38.240 --> 28:40.080
 You retrofit versus another and you don't know

28:40.080 --> 28:43.000
 because the manufacturer has updated it behind the scenes.

28:43.000 --> 28:45.400
 There's basically an infinite list of long tail issues

28:45.400 --> 28:46.240
 that can get you.

28:46.240 --> 28:47.760
 And if you're dealing with a safety critical product,

28:47.760 --> 28:48.960
 that's not really acceptable.

28:48.960 --> 28:52.160
 That's a really convincing summary of why

28:52.160 --> 28:53.160
 it's really challenging.

28:53.160 --> 28:54.360
 But I didn't know all that at the time.

28:54.360 --> 28:55.480
 So we tried it anyway.

28:55.480 --> 28:57.160
 But as a pitch also at the time,

28:57.160 --> 28:58.400
 it's a really strong one.

28:58.400 --> 29:00.720
 That's how you achieve scale and that's how you beat

29:00.720 --> 29:03.360
 the current, the leader at the time of Google

29:03.360 --> 29:04.720
 or the only one in the market.

29:04.720 --> 29:06.840
 The other big problem we ran into,

29:06.840 --> 29:08.240
 which is perhaps the biggest problem

29:08.240 --> 29:10.280
 from a business model perspective,

29:10.280 --> 29:15.280
 is we had kind of assumed that we started with an Audi S4

29:15.440 --> 29:16.880
 as the vehicle we retrofitted

29:16.880 --> 29:18.760
 with this highway driving capability.

29:18.760 --> 29:21.040
 And we had kind of assumed that if we just knock out

29:21.040 --> 29:23.360
 like three make and models of vehicle,

29:23.360 --> 29:25.880
 that'll cover like 80% of the San Francisco market.

29:25.880 --> 29:27.400
 Doesn't everyone there drive, I don't know,

29:27.400 --> 29:30.240
 a BMW or a Honda Civic or one of these three cars?

29:30.240 --> 29:32.040
 And then we surveyed our users and we found out

29:32.040 --> 29:33.480
 that it's all over the place.

29:33.480 --> 29:36.680
 We would, to get even a decent number of units sold,

29:36.680 --> 29:39.880
 we'd have to support like 20 or 50 different models.

29:39.880 --> 29:42.200
 And each one is a little butterfly that takes time

29:42.200 --> 29:44.800
 and effort to maintain that retrofit integration

29:44.800 --> 29:47.120
 and custom hardware and all this.

29:47.120 --> 29:49.240
 So it was a tough business.

29:49.240 --> 29:54.240
 So GM manufactures and sells over nine million cars a year.

29:54.280 --> 29:58.560
 And what you with crews are trying to do

29:58.560 --> 30:01.160
 some of the most cutting edge innovation

30:01.160 --> 30:03.000
 in terms of applying AI.

30:03.000 --> 30:06.040
 And so how do those, you've talked about it a little bit

30:06.040 --> 30:07.760
 before, but it's also just fascinating to me,

30:07.760 --> 30:09.360
 we work a lot of automakers.

30:10.560 --> 30:12.880
 The difference between the gap between Detroit

30:12.880 --> 30:14.680
 and Silicon Valley, let's say,

30:14.680 --> 30:17.320
 just to be sort of poetic about it, I guess.

30:17.320 --> 30:18.680
 How do you close that gap?

30:18.680 --> 30:21.480
 How do you take GM into the future

30:21.480 --> 30:24.840
 where a large part of the fleet would be autonomous perhaps?

30:24.840 --> 30:28.520
 I wanna start by acknowledging that GM is made up of

30:28.520 --> 30:30.240
 tens of thousands of really brilliant,

30:30.240 --> 30:32.720
 motivated people who wanna be a part of the future.

30:32.720 --> 30:35.240
 And so it's pretty fun to work with them.

30:35.240 --> 30:37.480
 The attitude inside a car company like that

30:37.480 --> 30:41.240
 is embracing this transformation and change

30:41.240 --> 30:42.360
 rather than fearing it.

30:42.360 --> 30:45.440
 And I think that's a testament to the leadership at GM

30:45.440 --> 30:47.680
 and that's flown all the way through to everyone

30:47.680 --> 30:49.280
 you talk to, even the people in the assembly plants

30:49.280 --> 30:51.200
 working on these cars.

30:51.200 --> 30:52.040
 So that's really great.

30:52.040 --> 30:55.160
 So starting from that position makes it a lot easier.

30:55.160 --> 30:59.160
 So then when the people in San Francisco

30:59.160 --> 31:01.400
 but cruise interact with the people at GM,

31:01.400 --> 31:02.960
 at least we have this common set of values,

31:02.960 --> 31:05.000
 which is that we really want this stuff to work

31:05.000 --> 31:06.040
 because we think it's important

31:06.040 --> 31:07.440
 and we think it's the future.

31:08.360 --> 31:11.520
 That's not to say those two cultures don't clash.

31:11.520 --> 31:12.440
 They absolutely do.

31:12.440 --> 31:14.760
 There's different sort of value systems.

31:14.760 --> 31:17.960
 Like in a car company, the thing that gets you promoted

31:17.960 --> 31:22.600
 and sort of the reward system is following the processes,

31:22.600 --> 31:26.080
 delivering the program on time and on budget.

31:26.080 --> 31:30.440
 So any sort of risk taking is discouraged in many ways

31:30.440 --> 31:34.000
 because if a program is late

31:34.000 --> 31:36.200
 or if you shut down the plant for a day,

31:36.200 --> 31:37.560
 you can count the millions of dollars

31:37.560 --> 31:39.600
 that burn by pretty quickly.

31:39.600 --> 31:43.800
 Whereas I think most Silicon Valley companies

31:43.800 --> 31:48.280
 and in cruise and the methodology we were employing,

31:48.280 --> 31:50.080
 especially around the time of the acquisition,

31:50.080 --> 31:53.800
 the reward structure is about trying to solve

31:53.800 --> 31:56.120
 these complex problems in any way, shape or form

31:56.120 --> 31:59.640
 or coming up with crazy ideas that 90% of them won't work.

31:59.640 --> 32:02.920
 And so meshing that culture

32:02.920 --> 32:05.480
 of sort of continuous improvement and experimentation

32:05.480 --> 32:07.400
 with one where everything needs to be

32:07.400 --> 32:08.480
 rigorously defined up front

32:08.480 --> 32:12.760
 so that you never slip a deadline or miss a budget

32:12.760 --> 32:13.600
 was a pretty big challenge

32:13.600 --> 32:16.960
 and that we're over three years in now

32:16.960 --> 32:18.360
 after the acquisition.

32:18.360 --> 32:20.480
 And I'd say like the investment we made

32:20.480 --> 32:23.600
 in figuring out how to work together successfully

32:23.600 --> 32:24.440
 and who should do what

32:24.440 --> 32:26.360
 and how we bridge the gaps

32:26.360 --> 32:27.680
 between these very different systems

32:27.680 --> 32:29.520
 and way of doing engineering work

32:29.520 --> 32:30.920
 is now one of our greatest assets

32:30.920 --> 32:32.320
 because I think we have this really powerful thing

32:32.320 --> 32:35.560
 but for a while it was both GM and cruise

32:35.560 --> 32:37.440
 were very steep on the learning curve.

32:37.440 --> 32:38.920
 Yeah, so I'm sure it was very stressful.

32:38.920 --> 32:39.960
 It's really important work

32:39.960 --> 32:43.680
 because that's how to revolutionize the transportation.

32:43.680 --> 32:46.640
 Really to revolutionize any system,

32:46.640 --> 32:48.200
 you look at the healthcare system

32:48.200 --> 32:49.680
 or you look at the legal system.

32:49.680 --> 32:52.040
 I have people like Laura's come up to me all the time

32:52.040 --> 32:53.920
 like everything they're working on

32:53.920 --> 32:55.960
 can easily be automated.

32:55.960 --> 32:57.480
 But then that's not a good feeling.

32:57.480 --> 32:58.320
 Yeah.

32:58.320 --> 32:59.160
 Well, it's not a good feeling,

32:59.160 --> 33:01.200
 but also there's no way to automate

33:01.200 --> 33:06.200
 because the entire infrastructure is really based

33:06.360 --> 33:08.360
 is older and it moves very slowly.

33:08.360 --> 33:11.560
 And so how do you close the gap between?

33:11.560 --> 33:13.880
 I haven't, how can I replace?

33:13.880 --> 33:15.720
 Of course, Laura's the one be replaced with an app

33:15.720 --> 33:17.920
 but you could replace a lot of aspect

33:17.920 --> 33:20.160
 when most of the data is still on paper.

33:20.160 --> 33:23.400
 And so the same thing with automotive.

33:23.400 --> 33:26.080
 I mean, it's fundamentally software.

33:26.080 --> 33:28.560
 So it's basically hiring software engineers.

33:28.560 --> 33:30.320
 It's thinking of software world.

33:30.320 --> 33:32.560
 I mean, I'm pretty sure nobody in Silicon Valley

33:32.560 --> 33:34.640
 has ever hit a deadline.

33:34.640 --> 33:36.000
 So and then on GM.

33:36.000 --> 33:37.400
 That's probably true, yeah.

33:37.400 --> 33:39.920
 And GM side is probably the opposite.

33:39.920 --> 33:42.720
 So that's that culture gap is really fascinating.

33:42.720 --> 33:45.160
 So you're optimistic about the future of that.

33:45.160 --> 33:47.440
 Yeah, I mean, from what I've seen, it's impressive.

33:47.440 --> 33:49.400
 And I think like, especially in Silicon Valley,

33:49.400 --> 33:51.440
 it's easy to write off building cars

33:51.440 --> 33:53.120
 because people have been doing that

33:53.120 --> 33:54.960
 for over a hundred years now in this country.

33:54.960 --> 33:57.080
 And so it seems like that's a solved problem,

33:57.080 --> 33:58.840
 but that doesn't mean it's an easy problem.

33:58.840 --> 34:02.280
 And I think it would be easy to sort of overlook that

34:02.280 --> 34:06.080
 and think that we're Silicon Valley engineers,

34:06.080 --> 34:08.960
 we can solve any problem, building a car,

34:08.960 --> 34:13.200
 it's been done, therefore it's not a real engineering

34:13.200 --> 34:14.600
 challenge.

34:14.600 --> 34:17.480
 But after having seen just the sheer scale

34:17.480 --> 34:21.360
 and magnitude and industrialization that occurs

34:21.360 --> 34:23.280
 inside of an automotive assembly plant,

34:23.280 --> 34:25.840
 that is a lot of work that I am very glad

34:25.840 --> 34:28.200
 that we don't have to reinvent

34:28.200 --> 34:29.480
 to make self driving cars work.

34:29.480 --> 34:31.680
 And so to have partners who have done that for a hundred

34:31.680 --> 34:32.960
 years and have these great processes

34:32.960 --> 34:35.720
 and this huge infrastructure and supply base

34:35.720 --> 34:38.760
 that we can tap into is just remarkable

34:38.760 --> 34:43.760
 because the scope and surface area of the problem

34:44.560 --> 34:47.400
 of deploying fleets of self driving cars is so large

34:47.400 --> 34:50.320
 that we're constantly looking for ways to do less

34:50.320 --> 34:52.920
 so we can focus on the things that really matter more.

34:52.920 --> 34:55.360
 And if we had to figure out how to build and assemble

34:55.360 --> 35:00.120
 and test and build the cars themselves,

35:00.120 --> 35:01.640
 I mean, we work closely with GM on that,

35:01.640 --> 35:03.240
 but if we had to develop all that capability

35:03.240 --> 35:08.240
 in house as well, that would just make the problem

35:08.320 --> 35:10.200
 really intractable, I think.

35:10.200 --> 35:14.880
 So yeah, just like your first entry at the MIT DARPA

35:14.880 --> 35:17.680
 challenge when it was what the motor that failed

35:17.680 --> 35:19.000
 and somebody that knows what they're doing

35:19.000 --> 35:20.040
 with the motor did it.

35:20.040 --> 35:22.080
 It would have been nice if we could focus on the software

35:22.080 --> 35:23.880
 and not the hardware platform.

35:23.880 --> 35:24.800
 Yeah, right.

35:24.800 --> 35:27.080
 So from your perspective now,

35:28.080 --> 35:29.960
 there's so many ways that autonomous vehicles

35:29.960 --> 35:34.280
 can impact society in the next year, five years, 10 years.

35:34.280 --> 35:37.080
 What do you think is the biggest opportunity

35:37.080 --> 35:39.360
 to make money in autonomous driving,

35:40.560 --> 35:44.720
 sort of make it a financially viable thing in the near term?

35:44.720 --> 35:49.120
 What do you think would be the biggest impact there?

35:49.120 --> 35:52.160
 Well, the things that drive the economics

35:52.160 --> 35:53.600
 for fleets of self driving cars

35:53.600 --> 35:56.440
 are there's sort of a handful of variables.

35:56.440 --> 36:00.400
 One is the cost to build the vehicle itself.

36:00.400 --> 36:03.720
 So the material cost, what's the cost of all your sensors,

36:03.720 --> 36:05.200
 plus the cost of the vehicle

36:05.200 --> 36:07.560
 and all the other components on it.

36:07.560 --> 36:09.520
 Another one is the lifetime of the vehicle.

36:09.520 --> 36:12.480
 It's very different if your vehicle drives 100,000 miles

36:12.480 --> 36:14.800
 and then it falls apart versus 2 million.

36:16.720 --> 36:18.840
 And then if you have a fleet,

36:18.840 --> 36:22.920
 it's kind of like an airplane or an airline

36:22.920 --> 36:26.120
 where once you produce the vehicle,

36:26.120 --> 36:27.880
 you want it to be in operation

36:27.880 --> 36:30.760
 as many hours a day as possible producing revenue.

36:30.760 --> 36:32.480
 And then the other piece of that

36:32.480 --> 36:35.280
 is how are you generating revenue?

36:35.280 --> 36:36.880
 I think that's kind of what you're asking in.

36:36.880 --> 36:38.400
 I think the obvious things today

36:38.400 --> 36:40.080
 are the ride sharing business

36:40.080 --> 36:42.760
 because that's pretty clear that there's demand for that.

36:42.760 --> 36:46.240
 There's existing markets you can tap into and...

36:46.240 --> 36:47.960
 Large urban areas, that kind of thing.

36:47.960 --> 36:48.800
 Yeah, yeah.

36:48.800 --> 36:51.200
 And I think that there are some real benefits

36:51.200 --> 36:54.520
 to having cars without drivers

36:54.520 --> 36:56.040
 compared to sort of the status quo

36:56.040 --> 36:58.520
 for people who use ride share services today.

36:58.520 --> 37:01.040
 You know, your privacy, consistency,

37:01.040 --> 37:02.440
 hopefully significantly improve safety,

37:02.440 --> 37:05.120
 all these benefits versus the current product.

37:05.120 --> 37:06.520
 But it's a crowded market.

37:06.520 --> 37:08.000
 And then other opportunities

37:08.000 --> 37:09.600
 which you've seen a lot of activity in the last,

37:09.600 --> 37:12.560
 really in the last six or 12 months is delivery,

37:12.560 --> 37:17.560
 whether that's parcels and packages, food or groceries.

37:17.800 --> 37:20.320
 Those are all sort of, I think, opportunities

37:20.320 --> 37:23.640
 that are pretty ripe for these.

37:23.640 --> 37:26.000
 Once you have this core technology,

37:26.000 --> 37:28.080
 which is the fleet of autonomous vehicles,

37:28.080 --> 37:30.920
 there's all sorts of different business opportunities

37:30.920 --> 37:32.080
 you can build on top of that.

37:32.080 --> 37:34.520
 But I think the important thing, of course,

37:34.520 --> 37:36.440
 is that there's zero monetization opportunity

37:36.440 --> 37:37.520
 until you actually have that fleet

37:37.520 --> 37:39.160
 of very capable driverless cars

37:39.160 --> 37:41.040
 that are as good or better than humans.

37:41.040 --> 37:44.120
 And that's sort of where the entire industry

37:44.120 --> 37:45.920
 is sort of in this holding pattern right now.

37:45.920 --> 37:47.960
 Yeah, they're trying to achieve that baseline.

37:47.960 --> 37:51.520
 But you said sort of not reliability consistency.

37:51.520 --> 37:52.360
 It's kind of interesting.

37:52.360 --> 37:54.200
 I think I heard you say somewhere,

37:54.200 --> 37:55.440
 not sure if that's what you meant,

37:55.440 --> 37:58.240
 but I can imagine a situation

37:58.240 --> 38:01.200
 where you would get an autonomous vehicle.

38:01.200 --> 38:04.560
 And when you get into an Uber or Lyft,

38:04.560 --> 38:05.960
 you don't get to choose the driver

38:05.960 --> 38:07.320
 in a sense that you don't get to choose

38:07.320 --> 38:09.080
 the personality of the driving.

38:09.080 --> 38:12.040
 Do you think there's room

38:12.040 --> 38:14.120
 to define the personality of the car

38:14.120 --> 38:15.040
 the way it drives you,

38:15.040 --> 38:17.600
 in terms of aggressiveness, for example,

38:17.600 --> 38:21.120
 in terms of sort of pushing the boundaries.

38:21.120 --> 38:22.760
 One of the biggest challenges in autonomous driving

38:22.760 --> 38:27.760
 is the trade off between sort of safety and assertiveness.

38:28.600 --> 38:30.920
 And do you think there's any room

38:30.920 --> 38:35.920
 for the human to take a role in that decision?

38:36.040 --> 38:38.080
 Sort of accept some of the liability, I guess.

38:38.080 --> 38:41.000
 I wouldn't say, no, I'd say within reasonable bounds,

38:41.000 --> 38:42.280
 as in we're not gonna,

38:43.200 --> 38:44.360
 I think it'd be higher than likely

38:44.360 --> 38:46.600
 we'd expose any knob that would let you

38:46.600 --> 38:50.240
 significantly increase safety risk.

38:50.240 --> 38:53.080
 I think that's just not something we'd be willing to do.

38:53.080 --> 38:56.760
 But I think driving style or like,

38:56.760 --> 38:59.120
 are you gonna relax the comfort constraints slightly

38:59.120 --> 39:00.160
 or things like that?

39:00.160 --> 39:02.400
 All of those things make sense and are plausible.

39:02.400 --> 39:04.480
 I see all those as nice optimizations.

39:04.480 --> 39:06.760
 Once again, we get the core problem solved

39:06.760 --> 39:08.120
 in these fleets out there.

39:08.120 --> 39:10.440
 But the other thing we've sort of observed

39:10.440 --> 39:12.560
 is that you have this intuition

39:12.560 --> 39:15.400
 that if you sort of slam your foot on the gas

39:15.400 --> 39:16.680
 right after the light turns green

39:16.680 --> 39:18.160
 and aggressively accelerate,

39:18.160 --> 39:19.720
 you're gonna get there faster.

39:19.720 --> 39:22.080
 But the actual impact of doing that is pretty small.

39:22.080 --> 39:23.680
 You feel like you're getting there faster,

39:23.680 --> 39:26.680
 but so the same would be true for AVs.

39:26.680 --> 39:29.640
 Even if they don't slam the pedal to the floor

39:29.640 --> 39:31.000
 when the light turns green,

39:31.000 --> 39:32.520
 they're gonna get you there within,

39:32.520 --> 39:33.600
 if it's a 15 minute trip,

39:33.600 --> 39:36.400
 within 30 seconds of what you would have done otherwise

39:36.400 --> 39:37.800
 if you were going really aggressively.

39:37.800 --> 39:40.760
 So I think there's this sort of self deception

39:40.760 --> 39:44.440
 that my aggressive driving style is getting me there faster.

39:44.440 --> 39:46.640
 Well, so that's, you know, some of the things I study,

39:46.640 --> 39:48.760
 some of the things I'm fascinated by the psychology of that.

39:48.760 --> 39:50.640
 And I don't think it matters

39:50.640 --> 39:52.240
 that it doesn't get you there faster.

39:52.240 --> 39:55.520
 It's the emotional release.

39:55.520 --> 39:59.080
 Driving is a place, being inside our car,

39:59.080 --> 40:00.880
 somebody said it's like the real world version

40:00.880 --> 40:02.920
 of being a troll.

40:02.920 --> 40:04.960
 So you have this protection, this mental protection,

40:04.960 --> 40:06.640
 and you're able to sort of yell at the world,

40:06.640 --> 40:08.200
 like release your anger, whatever it is.

40:08.200 --> 40:10.040
 But so there's an element of that

40:10.040 --> 40:12.000
 that I think autonomous vehicles

40:12.000 --> 40:15.400
 would also have to, you know, giving an outlet to people,

40:15.400 --> 40:19.120
 but it doesn't have to be through driving or honking

40:19.120 --> 40:21.200
 or so on, there might be other outlets.

40:21.200 --> 40:24.040
 But I think to just sort of even just put that aside,

40:24.040 --> 40:26.880
 the baseline is really, you know, that's the focus,

40:26.880 --> 40:28.200
 that's the thing you need to solve,

40:28.200 --> 40:31.000
 and then the fun human things can be solved after.

40:31.000 --> 40:34.680
 But so from the baseline of just solving autonomous driving,

40:34.680 --> 40:36.000
 you're working in San Francisco,

40:36.000 --> 40:38.960
 one of the more difficult cities to operate in,

40:38.960 --> 40:42.080
 what is the, in your view currently,

40:42.080 --> 40:45.040
 the hardest aspect of autonomous driving?

40:46.880 --> 40:49.200
 Negotiating with pedestrians,

40:49.200 --> 40:51.400
 is it edge cases of perception?

40:51.400 --> 40:52.760
 Is it planning?

40:52.760 --> 40:54.520
 Is there a mechanical engineering?

40:54.520 --> 40:57.040
 Is it data, fleet stuff?

40:57.040 --> 41:01.200
 What are your thoughts on the more challenging aspects there?

41:01.200 --> 41:02.240
 That's a good question.

41:02.240 --> 41:03.520
 I think before we go to that though,

41:03.520 --> 41:05.080
 I just want to, I like what you said

41:05.080 --> 41:07.600
 about the psychology aspect of this,

41:07.600 --> 41:09.680
 because I think one observation I've made is,

41:09.680 --> 41:11.760
 I think I read somewhere that I think it's,

41:11.760 --> 41:13.880
 maybe Americans on average spend, you know,

41:13.880 --> 41:16.520
 over an hour a day on social media,

41:16.520 --> 41:18.280
 like staring at Facebook.

41:18.280 --> 41:20.080
 And so that's just, you know,

41:20.080 --> 41:21.600
 60 minutes of your life, you're not getting back.

41:21.600 --> 41:23.120
 It's probably not super productive.

41:23.120 --> 41:26.200
 And so that's 3,600 seconds, right?

41:26.200 --> 41:29.160
 And that's, that's time, you know,

41:29.160 --> 41:30.600
 it's a lot of time you're giving up.

41:30.600 --> 41:34.080
 And if you compare that to people being on the road,

41:34.080 --> 41:35.360
 if another vehicle,

41:35.360 --> 41:37.600
 whether it's a human driver or autonomous vehicle,

41:37.600 --> 41:39.840
 delays them by even three seconds,

41:39.840 --> 41:41.920
 they're laying in on the horn, you know,

41:41.920 --> 41:43.280
 even though that's, that's, you know,

41:43.280 --> 41:45.280
 one 1,000th of the time they waste

41:45.280 --> 41:46.360
 looking at Facebook every day.

41:46.360 --> 41:48.640
 So there's, there's definitely some,

41:48.640 --> 41:50.040
 you know, psychology aspects of this,

41:50.040 --> 41:50.880
 I think that are pretty interesting.

41:50.880 --> 41:51.720
 Road rage in general.

41:51.720 --> 41:52.960
 And then the question, of course,

41:52.960 --> 41:54.960
 is if everyone is in self driving cars,

41:54.960 --> 41:57.560
 do they even notice these three second delays anymore?

41:57.560 --> 41:58.920
 Because they're doing other things

41:58.920 --> 42:01.720
 or reading or working or just talking to each other.

42:01.720 --> 42:03.200
 So it'll be interesting to see where that goes.

42:03.200 --> 42:05.120
 In a certain aspect, people,

42:05.120 --> 42:06.360
 people need to be distracted

42:06.360 --> 42:07.360
 by something entertaining,

42:07.360 --> 42:09.160
 something useful inside the car

42:09.160 --> 42:10.960
 so they don't pay attention to the external world.

42:10.960 --> 42:14.240
 And then, and then they can take whatever psychology

42:14.240 --> 42:17.400
 and bring it back to Twitter and then focus on that

42:17.400 --> 42:19.640
 as opposed to sort of interacting,

42:20.920 --> 42:23.200
 sort of putting the emotion out there into the world.

42:23.200 --> 42:24.560
 So it's an interesting problem,

42:24.560 --> 42:26.960
 but baseline autonomy.

42:26.960 --> 42:28.760
 I guess you could say self driving cars,

42:28.760 --> 42:31.680
 you know, at scale will lower the collective blood pressure

42:31.680 --> 42:33.920
 of society probably by a couple of points

42:33.920 --> 42:35.760
 without all that road rage and stress.

42:35.760 --> 42:37.480
 So that's a good, good externality.

42:38.560 --> 42:41.760
 So back to your question about the technology

42:41.760 --> 42:43.760
 and the, I guess the biggest problems.

42:43.760 --> 42:45.560
 And I have a hard time answering that question

42:45.560 --> 42:48.680
 because, you know, we've been at this,

42:48.680 --> 42:51.440
 like specifically focusing on driverless cars

42:51.440 --> 42:53.520
 and all the technology needed to enable that

42:53.520 --> 42:55.160
 for a little over four and a half years now.

42:55.160 --> 42:58.080
 And even a year or two in,

42:58.080 --> 43:02.960
 I felt like we had completed the functionality needed

43:02.960 --> 43:04.800
 to get someone from point A to point B.

43:04.800 --> 43:07.280
 As in, if we need to do a left turn maneuver

43:07.280 --> 43:08.960
 or if we need to drive around a, you know,

43:08.960 --> 43:11.800
 a double parked vehicle into oncoming traffic

43:11.800 --> 43:13.840
 or navigate through construction zones,

43:13.840 --> 43:15.960
 the scaffolding and the building blocks

43:15.960 --> 43:17.800
 was there pretty early on.

43:17.800 --> 43:22.360
 And so the challenge is not any one scenario or situation

43:22.360 --> 43:25.520
 for which, you know, we fail at 100% of those.

43:25.520 --> 43:28.960
 It's more, you know, we're benchmarking against a pretty good

43:28.960 --> 43:31.320
 or pretty high standard, which is human driving.

43:31.320 --> 43:33.320
 All things considered, humans are excellent

43:33.320 --> 43:36.240
 at handling edge cases and unexpected scenarios

43:36.240 --> 43:38.400
 where it's computers are the opposite.

43:38.400 --> 43:43.080
 And so beating that baseline set by humans is the challenge.

43:43.080 --> 43:46.520
 And so what we've been doing for quite some time now

43:46.520 --> 43:50.760
 is basically it's this continuous improvement process

43:50.760 --> 43:55.000
 where we find sort of the most, you know, uncomfortable

43:55.000 --> 43:59.840
 or the things that could lead to a safety issue

43:59.840 --> 44:00.960
 or other things, all these events.

44:00.960 --> 44:02.520
 And then we sort of categorize them

44:02.520 --> 44:04.560
 and rework parts of our system

44:04.560 --> 44:06.200
 to make incremental improvements

44:06.200 --> 44:08.040
 and do that over and over and over again.

44:08.040 --> 44:10.160
 And we just see sort of the overall performance

44:10.160 --> 44:12.120
 of the system, you know,

44:12.120 --> 44:13.960
 actually increasing in a pretty steady clip.

44:13.960 --> 44:15.360
 But there's no one thing.

44:15.360 --> 44:17.360
 There's actually like thousands of little things

44:17.360 --> 44:19.880
 and just like polishing functionality

44:19.880 --> 44:21.640
 and making sure that it handles, you know,

44:21.640 --> 44:26.120
 every version and possible permutation of a situation

44:26.120 --> 44:29.200
 by either applying more deep learning systems

44:30.120 --> 44:32.960
 or just by, you know, adding more test coverage

44:32.960 --> 44:35.760
 or new scenarios that we develop against

44:35.760 --> 44:37.160
 and just grinding on that.

44:37.160 --> 44:40.120
 We're sort of in the unsexy phase of development right now

44:40.120 --> 44:41.800
 which is doing the real engineering work

44:41.800 --> 44:44.120
 that it takes to go from prototype to production.

44:44.120 --> 44:46.960
 You're basically scaling the grinding.

44:46.960 --> 44:50.560
 So sort of taking seriously the process

44:50.560 --> 44:54.040
 of all those edge cases, both with human experts

44:54.040 --> 44:57.520
 and machine learning methods to cover,

44:57.520 --> 44:59.320
 to cover all those situations.

44:59.320 --> 45:00.760
 Yeah, and the exciting thing for me is

45:00.760 --> 45:03.000
 I don't think that grinding ever stops

45:03.000 --> 45:04.840
 because there's a moment in time

45:04.840 --> 45:08.760
 where you've crossed that threshold of human performance

45:08.760 --> 45:10.000
 and become superhuman.

45:11.200 --> 45:13.560
 But there's no reason, there's no first principles reason

45:13.560 --> 45:17.560
 that AV capability will tap out anywhere near humans.

45:17.560 --> 45:20.280
 Like there's no reason it couldn't be 20 times better

45:20.280 --> 45:22.120
 whether that's, you know, just better driving

45:22.120 --> 45:24.240
 or safer driving or more comfortable driving

45:24.240 --> 45:26.800
 or even a thousand times better given enough time.

45:26.800 --> 45:31.480
 And we intend to basically chase that, you know, forever

45:31.480 --> 45:32.840
 to build the best possible product.

45:32.840 --> 45:33.960
 Better and better and better

45:33.960 --> 45:36.400
 and always new edge cases come up and new experiences.

45:36.400 --> 45:39.520
 So, and you want to automate that process

45:39.520 --> 45:40.720
 as much as possible.

45:42.680 --> 45:45.160
 So what do you think in general in society

45:45.160 --> 45:48.200
 when do you think we may have hundreds of thousands

45:48.200 --> 45:50.200
 of fully autonomous vehicles driving around?

45:50.200 --> 45:53.560
 So first of all, predictions, nobody knows the future.

45:53.560 --> 45:55.360
 You're a part of the leading people

45:55.360 --> 45:56.560
 trying to define that future,

45:56.560 --> 45:58.560
 but even then you still don't know.

45:58.560 --> 46:02.240
 But if you think about hundreds of thousands of vehicles,

46:02.240 --> 46:05.840
 so a significant fraction of vehicles

46:05.840 --> 46:07.600
 in major cities are autonomous.

46:07.600 --> 46:10.800
 Do you think, are you with Rodney Brooks

46:10.800 --> 46:13.960
 who is 2050 and beyond?

46:13.960 --> 46:17.200
 Or are you more with Elon Musk

46:17.200 --> 46:20.600
 who is, we should have had that two years ago?

46:20.600 --> 46:23.840
 Well, I mean, I'd love to have it two years ago,

46:23.840 --> 46:26.120
 but we're not there yet.

46:26.120 --> 46:28.480
 So I guess the way I would think about that

46:28.480 --> 46:31.240
 is let's flip that question around.

46:31.240 --> 46:34.200
 So what would prevent you to reach hundreds

46:34.200 --> 46:36.320
 of thousands of vehicles and...

46:36.320 --> 46:38.200
 That's a good rephrasing.

46:38.200 --> 46:43.200
 Yeah, so the, I'd say that it seems the consensus

46:43.200 --> 46:45.200
 among the people developing self driving cars today

46:45.200 --> 46:49.200
 is to sort of start with some form of an easier environment,

46:49.200 --> 46:52.200
 whether it means lacking, inclement weather,

46:52.200 --> 46:55.200
 or mostly sunny or whatever it is.

46:55.200 --> 46:59.200
 And then add capability for more complex situations

46:59.200 --> 47:00.200
 over time.

47:00.200 --> 47:05.200
 And so if you're only able to deploy in areas

47:05.200 --> 47:07.200
 that meet sort of your criteria

47:07.200 --> 47:09.200
 or that the current don't meet,

47:09.200 --> 47:13.200
 operating domain of the software you developed,

47:13.200 --> 47:16.200
 that may put a cap on how many cities you could deploy in.

47:16.200 --> 47:19.200
 But then as those restrictions start to fall away,

47:19.200 --> 47:22.200
 like maybe you add capability to drive really well

47:22.200 --> 47:25.200
 and safely and have you rain or snow,

47:25.200 --> 47:28.200
 that probably opens up the market by two or three fold

47:28.200 --> 47:31.200
 in terms of the cities you can expand into and so on.

47:31.200 --> 47:33.200
 And so the real question is,

47:33.200 --> 47:35.200
 I know today if we wanted to,

47:35.200 --> 47:39.200
 we could produce that many autonomous vehicles,

47:39.200 --> 47:41.200
 but we wouldn't be able to make use of all of them yet

47:41.200 --> 47:44.200
 because we would sort of saturate the demand in the cities

47:44.200 --> 47:47.200
 in which we would want to operate initially.

47:47.200 --> 47:49.200
 So if I were to guess what the timeline is

47:49.200 --> 47:51.200
 for those things falling away

47:51.200 --> 47:54.200
 and reaching hundreds, thousands of vehicles.

47:54.200 --> 47:55.200
 Maybe a range is better.

47:55.200 --> 47:57.200
 I would say less than five years.

47:57.200 --> 47:58.200
 Less than five years.

47:58.200 --> 47:59.200
 Yeah.

47:59.200 --> 48:02.200
 And of course you're working hard to make that happen.

48:02.200 --> 48:05.200
 So you started two companies that were eventually acquired

48:05.200 --> 48:08.200
 for each $4 billion.

48:08.200 --> 48:10.200
 So you're a pretty good person to ask,

48:10.200 --> 48:13.200
 what does it take to build a successful startup?

48:13.200 --> 48:18.200
 I think there's sort of survivor bias here a little bit,

48:18.200 --> 48:20.200
 but I can try to find some common threads

48:20.200 --> 48:22.200
 for the things that worked for me, which is...

48:24.200 --> 48:26.200
 In both of these companies,

48:26.200 --> 48:28.200
 I was really passionate about the core technology.

48:28.200 --> 48:31.200
 I actually lay awake at night thinking about these problems

48:31.200 --> 48:33.200
 and how to solve them.

48:33.200 --> 48:35.200
 And I think that's helpful because when you start a business,

48:35.200 --> 48:37.200
 there are...

48:37.200 --> 48:40.200
 To this day, there are these crazy ups and downs.

48:40.200 --> 48:43.200
 One day you think the business is just on top of the world

48:43.200 --> 48:45.200
 and unstoppable and the next day you think,

48:45.200 --> 48:47.200
 okay, this is all going to end.

48:47.200 --> 48:50.200
 It's just going south and it's going to be over tomorrow.

48:52.200 --> 48:55.200
 And so I think having a true passion that you can fall back on

48:55.200 --> 48:57.200
 and knowing that you would be doing it

48:57.200 --> 48:58.200
 even if you weren't getting paid for it

48:58.200 --> 49:00.200
 helps you weather those tough times.

49:00.200 --> 49:02.200
 So that's one thing.

49:02.200 --> 49:05.200
 I think the other one is really good people.

49:05.200 --> 49:07.200
 So I've always been surrounded by really good cofounders

49:07.200 --> 49:09.200
 that are logical thinkers,

49:09.200 --> 49:11.200
 are always pushing their limits

49:11.200 --> 49:13.200
 and have very high levels of integrity.

49:13.200 --> 49:15.200
 So that's Dan Kahn in my current company

49:15.200 --> 49:17.200
 and actually his brother and a couple other guys

49:17.200 --> 49:19.200
 for Justin TV and Twitch.

49:19.200 --> 49:23.200
 And then I think the last thing is just,

49:23.200 --> 49:26.200
 I guess, persistence or perseverance.

49:26.200 --> 49:29.200
 And that can apply to sticking to

49:29.200 --> 49:33.200
 having conviction around the original premise of your idea

49:33.200 --> 49:36.200
 and sticking around to do all the unsexy work

49:36.200 --> 49:38.200
 to actually make it come to fruition,

49:38.200 --> 49:41.200
 including dealing with whatever it is

49:41.200 --> 49:43.200
 that you're not passionate about,

49:43.200 --> 49:47.200
 whether that's finance or HR or operations or those things.

49:47.200 --> 49:49.200
 As long as you are grinding away

49:49.200 --> 49:52.200
 and working towards that North Star for your business,

49:52.200 --> 49:54.200
 whatever it is and you don't give up

49:54.200 --> 49:56.200
 and you're making progress every day,

49:56.200 --> 49:58.200
 it seems like eventually you'll end up in a good place.

49:58.200 --> 50:00.200
 And the only things that can slow you down

50:00.200 --> 50:01.200
 are running out of money

50:01.200 --> 50:03.200
 or I suppose your competitor is destroying you,

50:03.200 --> 50:06.200
 but I think most of the time it's people giving up

50:06.200 --> 50:08.200
 or somehow destroying things themselves

50:08.200 --> 50:10.200
 rather than being beaten by their competition

50:10.200 --> 50:11.200
 or running out of money.

50:11.200 --> 50:14.200
 Yeah, if you never quit, eventually you'll arrive.

50:14.200 --> 50:16.200
 It's a much more concise version

50:16.200 --> 50:18.200
 of what I was trying to say.

50:18.200 --> 50:21.200
 So you went the Y Combinator out twice.

50:21.200 --> 50:23.200
 What do you think, in a quick question,

50:23.200 --> 50:25.200
 do you think is the best way to raise funds

50:25.200 --> 50:27.200
 in the early days?

50:27.200 --> 50:30.200
 Or not just funds, but just community,

50:30.200 --> 50:32.200
 develop your idea and so on.

50:32.200 --> 50:37.200
 Can you do it solo or maybe with a cofounder

50:37.200 --> 50:39.200
 like self funded?

50:39.200 --> 50:40.200
 Do you think Y Combinator is good?

50:40.200 --> 50:41.200
 Is it good to do VC route?

50:41.200 --> 50:43.200
 Is there no right answer or is there,

50:43.200 --> 50:45.200
 from the Y Combinator experience,

50:45.200 --> 50:47.200
 something that you could take away

50:47.200 --> 50:49.200
 that that was the right path to take?

50:49.200 --> 50:50.200
 There's no one size fits all answer,

50:50.200 --> 50:54.200
 but if your ambition I think is to see how big

50:54.200 --> 50:57.200
 you can make something or rapidly expand

50:57.200 --> 50:59.200
 and capture a market or solve a problem

50:59.200 --> 51:02.200
 or whatever it is, then going the venture

51:02.200 --> 51:04.200
 back route is probably a good approach

51:04.200 --> 51:07.200
 so that capital doesn't become your primary constraint.

51:07.200 --> 51:10.200
 Y Combinator, I love because it puts you

51:10.200 --> 51:13.200
 in this sort of competitive environment

51:13.200 --> 51:16.200
 where you're surrounded by the top,

51:16.200 --> 51:19.200
 maybe 1% of other really highly motivated

51:19.200 --> 51:22.200
 peers who are in the same place.

51:22.200 --> 51:26.200
 In that environment I think just breeds success.

51:26.200 --> 51:28.200
 If you're surrounded by really brilliant

51:28.200 --> 51:30.200
 hardworking people, you're going to feel

51:30.200 --> 51:32.200
 sort of compelled or inspired to try

51:32.200 --> 51:35.200
 to emulate them or beat them.

51:35.200 --> 51:37.200
 So even though I had done it once before

51:37.200 --> 51:41.200
 and I felt like I'm pretty self motivated,

51:41.200 --> 51:43.200
 I thought this is going to be a hard problem,

51:43.200 --> 51:45.200
 I can use all the help I can get.

51:45.200 --> 51:46.200
 So surrounding myself with other entrepreneurs

51:46.200 --> 51:48.200
 is going to make me work a little bit harder

51:48.200 --> 51:51.200
 or push a little harder then it's worth it.

51:51.200 --> 51:54.200
 That's why I did it, for example, the second time.

51:54.200 --> 51:57.200
 Let's go full soft, go existential.

51:57.200 --> 52:00.200
 If you go back and do something differently in your life,

52:00.200 --> 52:06.200
 starting in high school and MIT, leaving MIT,

52:06.200 --> 52:08.200
 you could have gone to the PhD route,

52:08.200 --> 52:13.200
 doing startup, going to see about a startup in California

52:13.200 --> 52:15.200
 or maybe some aspects of fundraising.

52:15.200 --> 52:17.200
 Is there something you regret,

52:17.200 --> 52:20.200
 not necessarily regret, but if you go back,

52:20.200 --> 52:22.200
 you could do differently?

52:22.200 --> 52:24.200
 I think I've made a lot of mistakes,

52:24.200 --> 52:26.200
 pretty much everything you can screw up,

52:26.200 --> 52:28.200
 I think I've screwed up at least once.

52:28.200 --> 52:30.200
 But I don't regret those things.

52:30.200 --> 52:32.200
 I think it's hard to look back on things,

52:32.200 --> 52:34.200
 even if they didn't go well and call it a regret,

52:34.200 --> 52:37.200
 because hopefully it took away some new knowledge

52:37.200 --> 52:39.200
 or learning from that.

52:42.200 --> 52:45.200
 I would say there's a period,

52:45.200 --> 52:47.200
 the closest I can come to this,

52:47.200 --> 52:49.200
 there's a period in just in TV,

52:49.200 --> 52:54.200
 I think after seven years where the company was going

52:54.200 --> 52:57.200
 one direction, which is towards Twitch and video gaming.

52:57.200 --> 52:58.200
 I'm not a video gamer.

52:58.200 --> 53:01.200
 I don't really even use Twitch at all.

53:01.200 --> 53:04.200
 I was still working on the core technology there,

53:04.200 --> 53:06.200
 but my heart was no longer in it,

53:06.200 --> 53:08.200
 because the business that we were creating

53:08.200 --> 53:10.200
 was not something that I was personally passionate about.

53:10.200 --> 53:12.200
 It didn't meet your bar of existential impact.

53:12.200 --> 53:16.200
 Yeah, and I'd say I probably spent an extra year or two

53:16.200 --> 53:20.200
 working on that, and I'd say I would have just tried

53:20.200 --> 53:22.200
 to do something different sooner.

53:22.200 --> 53:26.200
 Because those were two years where I felt like,

53:26.200 --> 53:29.200
 from this philosophical or existential thing,

53:29.200 --> 53:31.200
 I just felt that something was missing.

53:31.200 --> 53:34.200
 If I could look back now and tell myself,

53:34.200 --> 53:35.200
 I would have said exactly that.

53:35.200 --> 53:38.200
 You're not getting any meaning out of your work personally

53:38.200 --> 53:39.200
 right now.

53:39.200 --> 53:41.200
 You should find a way to change that.

53:41.200 --> 53:44.200
 And that's part of the pitch I used

53:44.200 --> 53:46.200
 to basically everyone who joins Cruise today.

53:46.200 --> 53:48.200
 It's like, hey, you've got that now by coming here.

53:48.200 --> 53:51.200
 Well, maybe you needed the two years of that existential dread

53:51.200 --> 53:53.200
 to develop the feeling that ultimately

53:53.200 --> 53:55.200
 it was the fire that created Cruise.

53:55.200 --> 53:56.200
 So you never know.

53:56.200 --> 53:57.200
 You can't repair.

53:57.200 --> 53:58.200
 Good theory, yeah.

53:58.200 --> 53:59.200
 So last question.

53:59.200 --> 54:02.200
 What does 2019 hold for Cruise?

54:02.200 --> 54:05.200
 After this, I guess we're going to go and talk to your class.

54:05.200 --> 54:08.200
 But one of the big things is going from prototype to production

54:08.200 --> 54:09.200
 for autonomous cars.

54:09.200 --> 54:10.200
 And what does that mean?

54:10.200 --> 54:11.200
 What does that look like?

54:11.200 --> 54:14.200
 2019 for us is the year that we try to cross over

54:14.200 --> 54:17.200
 that threshold and reach superhuman level of performance

54:17.200 --> 54:20.200
 to some degree with the software and have all the other

54:20.200 --> 54:23.200
 of the thousands of little building blocks in place

54:23.200 --> 54:27.200
 to launch our first commercial product.

54:27.200 --> 54:30.200
 So that's what's in store for us.

54:30.200 --> 54:32.200
 And we've got a lot of work to do.

54:32.200 --> 54:35.200
 We've got a lot of brilliant people working on it.

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 So it's all up to us now.

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 Yeah.

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 So Charlie Miller and Chris Vell is like the people I've

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 crossed paths with.

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 Oh, great, yeah.

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 It sounds like you have an amazing team.

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 So like I said, it's one of the most, I think, one of the most

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 important problems in artificial intelligence of this century.

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 It'll be one of the most defining.

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 It's super exciting that you work on it.

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 And the best of luck in 2019.

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 I'm really excited to see what Cruise comes up with.

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 Thank you.

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 Thanks for having me today.

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 Thank you.