The following is a conversation with Kyle Vogt.

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

leading an effort to solve one of the biggest robotics challenges of our time,

vehicle automation. He's a cofounder of two successful companies, Twitch

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

And he's a great example of the innovative spirit that flourishes

in Silicon Valley, and now is facing an interesting and exciting challenge of

matching that spirit with the mass production and the safety centric

culture of a major automaker like General Motors. This conversation is

part of the MIT Artificial General Intelligence series

and the Artificial Intelligence podcast. If you enjoy it,

please subscribe on YouTube, iTunes, or simply connect with me on Twitter

at Lex Friedman, spelled F R I D. And now here's my conversation with Kyle Vogt.

You grew up in Kansas, right? Yeah, and I just saw that picture you had hidden

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

Yeah, so in high school in Kansas City, you joined Shawnee Mission

North high school robotics team. Yeah. Now that wasn't your high school.

That's right, that was that was the only high school in the area that had a

like a teacher who was willing to sponsor our first robotics team.

I was gonna troll you a little bit. Jog your memory a little bit.

Yeah, I was trying to look super cool and intense, because you know this

was BattleBots. This is serious business. So we're standing there with a welded

steel frame and looking tough. So go back there. What is that drew you

to robotics? Well, I think I've been trying to figure

this out for a while, but I've always liked building things with Legos. And

when I was really, really young, I wanted the Legos that had motors and

other things. And then, you know, Lego Mindstorms came out, and for the

first time you could program Lego contraptions. And I think things

just sort of snowballed from that. But I remember

seeing, you know, the BattleBots TV show on Comedy Central and thinking that is

the coolest thing in the world. I want to be a part of that.

And not knowing a whole lot about how to build these

200 pound fighting robots. So I sort of obsessively poured over

the internet forums where all the creators for BattleBots would sort of

hang out and talk about, you know, document their build progress and

everything. And I think I read, I must have read like,

you know, tens of thousands of forum posts from basically

everything that was out there on what these people were doing. And eventually

like sort of triangulated how to put some of these things together.

And I ended up doing BattleBots, which was, you know, I was like 13 or 14, which

was pretty awesome. I'm not sure if the show is still

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

intelligence component. It's remotely controlled. And it's

almost like a mechanical engineering challenge of building things

that can be broken. They're radio controlled. So,

and I think that they allowed some limited form of autonomy.

But, you know, in a two minute match, you're, in the way these things ran,

you're really doing yourself a disservice by trying to automate it

versus just, you know, do the practical thing, which is drive it yourself.

And there's an entertainment aspect, just going on YouTube, there's like an,

some of them wield an axe, some of them, I mean, there's that fun.

So what drew you to that aspect? Was it the mechanical engineering?

Was it the dream to create like Frankenstein and

sentient being? Or was it just like the Lego,

you like tinkering with stuff? I mean, that was just building something.

I think the idea of, you know, this radio controlled machine that

can do various things, if it has like a weapon or something was pretty

interesting. I agree it doesn't have the same

appeal as, you know, autonomous robots, which I,

which I, you know, sort of gravitated towards later on. But it was definitely

an engineering challenge because everything you did in that

competition was pushing components to their limits. So we would

buy like these $40 DC motors that came out of a

winch, like on the front of a pickup truck or something, and we'd

power the car with those and we'd run them at like double or triple their

rated voltage. So they immediately start overheating,

but for that two minute match you can get,

you know, a significant increase in the power output of those motors

before they burn out. And so you're doing the same thing for your battery packs,

all the materials in the system. And I think there's something, something

intrinsically interesting about just seeing like where things break.

And did you offline see where they break? Did you

take it to the testing point? Like how did you know two minutes? Or was there a

reckless let's just go with it and see? We weren't very good at

BattleBots. We lost all of our matches the first round.

The one I built first, both of them were these wedge shaped robots because

wedge, even though it's sort of boring to look

at, is extremely effective. You drive towards another robot and

the front edge of it gets under them and then they sort of flip over,

kind of like a door stopper. And the first one had a

pneumatic polished stainless steel spike on the front that would shoot out about

eight inches. The purpose of which is what? Pretty,

pretty ineffective actually, but it looks cool.

And was it to help with the lift? No, it was, it was just to try to poke holes

in the other robot. And then the second time I did it, which is the following,

I think maybe 18 months later, we had a, well a titanium axe with a, with a

hardened steel tip on it that was powered by a hydraulic

cylinder which we were activating with liquid CO2, which was,

had its own set of problems. So great, so that's kind of on the hardware side.

I mean at a certain point there must have been born a fascination

on the software side. So what was the first piece of code you've written?

Go back there, see what language was it?

What, what was that? Was it Emacs? Vim? Was it a more

respectable modern IDE? Do you, do you remember any of this?

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

third or fourth grade, the school I was at, elementary school, had a bunch of

Apple II computers and we'd play games on those. And I

remember every once in a while something would,

would, would crash or wouldn't start up correctly and it would dump you out to

what I later learned was like sort of a command prompt.

And my teacher would come over and type, I actually remember this to this day for

some reason, like PR number six or PR pound six, which is

peripheral six, which is the disk drive, which would fire up the disk and load the

program. And I just remember thinking wow, she's

like a hacker, like teach me these, these codes, these error codes, that is what

I called them at the time. But she had no interest in that, so it

wasn't until I think about fifth grade that I had a school where you could

actually go on these Apple IIs and learn to program. And so it was all in basic,

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

line is numbered and you have to like leave enough space

between the numbers so that if you want to tweak your code you go back and

the first line was 10 and the second line is 20. Now you have to go back and

insert 15 and if you need to add code in front of

that, you know, 11 or 12 and you hope you don't run out of line numbers and have

to redo the whole thing. And there's go to statements? Yeah, go to

and it's very basic, maybe hence the name, but a lot of fun.

And that was like, that was, you know, that's when, you know,

when you first program you see the magic of it.

It's like, it just, just like this world opens up with, you know, endless

possibilities for the things you could build or

or accomplish with that computer. So you got the bug then, so

even starting with basic and then what C++ throughout,

what did you, was there computer programming, computer science classes in

high school? Not, not where I went, so it was self

taught, but I did a lot of programming. The thing that, you know, sort of

pushed me in the path of eventually working on self driving cars is actually

one of these really long trips driving from my

house in Kansas to, to I think Las Vegas where we did the BattleBots competition

and I had just gotten my, I think my learner's permit or

early driver's permit and so I was driving this,

you know, 10 hour stretch across western Kansas where it's just,

you're going straight on a highway and it is mind numbingly boring. And I

remember thinking even then with my sort of mediocre programming

background that this is something that a computer can do, right? Let's take a

picture of the road, let's find the yellow lane markers and,

you know, steer the wheel. And, you know, later I'd come to realize this had been

done, you know, since, since the 80s or the 70s or even

earlier, but I still wanted to do it and sort of

immediately after that trip switched from sort of BattleBots, which is more

radio controlled machines, to thinking about building,

you know, autonomous vehicles of some scale. Start off with really small

electric ones and then, you know, progress to what we're

doing now. So what was your view of artificial intelligence at that point?

What did you think? So this is before, there's been waves in artificial

intelligence, right? The current wave with deep learning

makes people believe that you can solve in a really rich deep way the computer

vision perception problem, but like in

before the deep learning craze, you know, how do you think about,

how would you even go about building a thing that perceives itself in the

world, localizes itself in the world, moves around the world?

Like when you were younger, I mean, what was your thinking about it? Well,

prior to deep neural networks or convolutional neural

analysis, these modern techniques we have, or at least

ones that are in use today, it was all a heuristic space and so like old school

image processing and I think extracting, you know, yellow lane markers out of an

image of a road is one of the problems that lends itself

reasonably well to those heuristic based methods, you know, like

just do a threshold on the color yellow and then try to

fit some lines to that using a Huff transform or something and then

go from there. Traffic light detection and stop sign detection, red, yellow, green.

And I think you could, I mean, if you wanted to do a full,

I was just trying to make something that would stay in between the lanes on a

highway, but if you wanted to do the full,

the full, you know, set of capabilities needed for a driverless car,

I think you could, and we'd done this at cruise, you know, in the very first days,

you can start off with a really simple, you know, human written heuristic just to

get the scaffolding in place for your system. Traffic light detection,

probably a really simple, you know, color thresholding on day one just to

get the system up and running before you migrate to, you know, a deep

learning based technique or something else.

And, you know, back in when I was doing this, my first one, it was on a Pentium

203, 233 megahertz computer in it and I think I wrote the first version in

basic, which is like an interpreted language. It's

extremely slow because that's the thing I knew at the time.

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

there was no, no computational power to do any sort of reasonable

deep nets like you have today. So I don't know what kids these days are doing. Are

kids these days, you know, at age 13 using neural networks in

their garage? I mean, that would be awesome.

I get emails all the time from, you know, like 11, 12 year olds

saying I'm having, you know, I'm trying to follow this TensorFlow tutorial

and I'm having this problem. And the general approach

in the deep learning community is of extreme optimism of, as opposed to,

you mentioned like heuristics, you can, you can, you can

separate the autonomous driving problem into modules and try to solve it sort of

rigorously, or you can just do it end to end.

And most people just kind of love the idea that, you know, us humans do it end

to end. We just perceive and act. We should be able to use that, do the

same kind of thing when you're on nets. And that,

that kind of thinking, you don't want to criticize that kind of thinking because

eventually they will be right. Yeah. And so it's exciting and especially

when they're younger to explore that as a really exciting approach. But yeah,

it's, it's changed the, the language,

the kind of stuff you're tinkering with. It's kind of exciting to see when these

teenagers grow up. Yeah. I can only imagine if you,

if your starting point is, you know, Python and TensorFlow at age 13

where you end up, you know, after 10 or 15 years of that,

that's, that's pretty cool. Because of GitHub, because the state tools for

solving most of the major problems in artificial intelligence

are within a few lines of code for most kids.

And that's incredible to think about also on the entrepreneurial side.

And, and on that point, was there any thought about entrepreneurship before

you came to college? Is sort of doing, you're building this

into a thing that impacts the world on a large scale? Yeah. I've always

wanted to start a company. I think that's, you know, just a cool concept of

creating something and exchanging it for value or creating value, I guess.

So in high school, I was, I was trying to build like, you know, servo motor

drivers, little circuit boards and sell them online

or other, other things like that. And certainly knew at some point I wanted to

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

like I had the,

I guess the right combination of the environment, the smart people around you

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

So you came to MIT as an undergrad 2004. That's right. And that's when the first

DARPA Grand Challenge was happening. Yeah. The, the timing of that is

beautifully poetic. So how did you get yourself involved in that one?

Originally there wasn't a official entry. Yeah, faculty sponsored thing. And so a

bunch of undergrads, myself included, started meeting and got together and

tried to haggle together some sponsorships. We got a vehicle donated,

a bunch of sensors and tried to put something together. And so we had,

our team was probably mostly freshmen and sophomores, you know, which, which was

not really a fair, fair fight against maybe the,

you know, postdoc and faculty led teams from other schools. But

we, we got something up and running. We had our vehicle drive by wire and

you know, very, very basic control and things. But

on the day of the qualifying, sort of pre qualifying round, the one and

only steering motor that we had purchased,

the thing that we had retrofitted to turn the steering wheel on the truck

died. And so our vehicle was just dead in the water, couldn't steer.

So we didn't make it very far. On the hardware side. So was there a software

component? Was there, like, how did your view of autonomous

vehicles in terms of artificial intelligence

evolve in this moment? I mean, you know, like you said from the 80s has been

autonomous vehicles, but really that was the birth of the modern wave.

The, the thing that captivated everyone's imagination that we can actually do this.

So what, how were you captivated in that way?

So how did your view of autonomous vehicles change at that point?

I'd say at that point in time it was, it was a

curiosity as in, like, is this really possible?

And I think that was generally the spirit and

the purpose of that original DARPA Grand Challenge, which was to just get

a whole bunch of really brilliant people exploring the space and pushing the

limits. And I think, like, to this day that

DARPA Challenge with its, you know, million dollar prize pool

was probably one of the most effective, you know, uses of taxpayer

money dollar for dollar that I've seen, you know, because that,

that small sort of initiative that DARPA put,

put out sort of, in my view, was the catalyst or the tipping point

for this, this whole next wave of autonomous vehicle development. So that

was pretty cool. So let me jump around a little bit on that point.

They also did the Urban Challenge where it was in the city, but it was very

artificial and there's no pedestrians and there's very little human

involvement except a few professional drivers. Yeah.

Do you think there's room, and then there was the Robotics Challenge with

humanoid robots. Right. So in your now role is looking at this,

you're trying to solve one of the, you know, autonomous driving, one of the

harder, more difficult places in San Francisco.

Is there a role for DARPA to step in to also kind of help out,

like, challenge with new ideas, specifically pedestrians and so on, all

these kinds of interesting things? Well, I haven't, I haven't thought about it

from that perspective. Is there anything DARPA could do today to further

accelerate things? And I would say, my instinct is that that's maybe not the

highest and best use of their resources and time,

because, like, kick starting and spinning up the flywheel is, I think, what

what they did in this case for very, very little money. But today this has become,

this has become, like, commercially interesting to very large companies and

the amount of money going into it and the amount of

people, like, going through your class and learning about these things and

developing these skills is just, you know, orders of magnitude

more than it was back then. And so there's enough momentum and inertia

and energy and investment dollars into this space right now that

I don't, I don't, I think they're, I think they're, they can just say mission

accomplished and move on to the next area of technology that needs help.

So then stepping back to MIT, you left MIT during your junior year.

What was that decision like? As I said, I always wanted to do

a company in, or start a company, and this opportunity landed in my lap, which

was a couple guys from Yale were starting a

new company, and I googled them and found that they had

started a company previously and sold it actually on eBay for

about a quarter million bucks, which was a pretty interesting story, but

so I thought to myself, these guys are, you know, rock star entrepreneurs, they've

done this before, they must be driving around in Ferraris

because they sold their company, and, you know, I thought I could learn

a lot from them, so I teamed up with those guys and,

you know, went out during, went out to California during IAP, which is MIT's

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

We were having so much fun, we felt like we were building something and creating

something, and it was going to be interesting

that, you know, I was just all in and got completely hooked, and that

that business was Justin TV, which is originally a reality show about a guy

named Justin, which morphed into a live video

streaming platform, which then morphed into what is Twitch

today, so that was, that was quite an unexpected journey.

So no regrets? No. Looking back, it was just an obvious, I mean,

one way ticket. I mean, if we just pause on that for a second,

there was no, how did you know these are the right guys, this is the

right decision, you didn't think it was just follow the

heart kind of thing? Well, I didn't know, but, you know, just trying something for a

month during IAP seems pretty low risk, right?

And then, you know, well, maybe I'll take a semester off, MIT's pretty flexible

about that, you can always go back, right? And then after two or three cycles of

that, I eventually threw in the towel, but, you know, I think it's,

I guess in that case I felt like I could always hit the undo button if I had to.

Right. But nevertheless, from when you look

in retrospect, I mean, it seems like a brave decision,

you know, it would be difficult for a lot of people to make. It wasn't as

popular, I'd say that the general, you know, flux of people

out of MIT at the time was mostly into, you know, finance or consulting jobs in

Boston or New York, and very few people were going to

California to start companies, but today I'd say that's,

it's probably inverted, which is just a sign of,

a sign of the times, I guess. Yeah. So there's a story about

midnight of March 18, 2007, where TechCrunch, I guess, announced

Justin.TV earlier than it was supposed to, a few hours.

The site didn't work. I don't know if any of this is true, you can tell me.

And you and one of the folks at Justin.TV,

Emmett Shearer, coded through the night. Can you take me through that experience?

So let me, let me say a few nice things that,

the article I read quoted Justin Kahn said that you were known for bureau

coding through problems and being a creative, quote, creative

genius. So on that night,

what, what was going through your head, or maybe I'd put another way,

how do you solve these problems? What's your approach to solving these kinds of

problems where the line between success and failure seems to be pretty

thin? That's a good question. Well, first of all, that's, that's a

nice of Justin to say that. I think, you know, I would have been

maybe 21 years old then and not very experienced at programming,

but as with, with everything in a startup, you're sort of racing against

the clock. And so our plan was the second we had

this live streaming camera backpack up and running, where Justin could wear it

and no matter where he went in a city, it

would be streaming live video. And this is

even before the iPhones. This is like hard to do back then.

We would launch. And so we thought we were there and the backpack was working

and then we sent out all the emails to launch the,

launch the company and do the press thing. And then, you know,

we weren't quite actually there. And then

we thought, oh, well, you know, they're not going to announce it until

maybe 10 a.m. the next morning. And it's, I don't know, it's 5 p.m. now. So

how many hours do we have left? What is that? Like, you know, 17 hours to go.

And, and that was, that was going to be fine.

Was the problem obvious? Did you understand

what could possibly, like, how complicated was the system at that point?

It was, it was pretty messy. So to get a live video feed that looked decent

working from anywhere in San Francisco, I put together this system where we had

like three or four cell phone data modems and

they were, like, we take the video stream and,

you know, sort of spray it across these three or four modems and then try to

catch all the packets on the other side, you know, with unreliable cell phone

networks. It's pretty low level networking.

Yeah, and putting these, like, you know, sort of

protocols on top of all that to, to reassemble and reorder the packets and

have time buffers and error correction and all that kind of stuff.

And the night before it was just staticky. Every once in a while the image

would, would go to staticky and there would be this horrible,

like, screeching audio noise because the audio was also corrupted.

And this would happen, like, every five to ten minutes or so and it was

a really, you know, off putting to the viewers.

How do you tackle that problem? What was the, uh, you're just freaking out behind a

computer. There's, are there other, other folks working

on this problem? Like, were you behind a whiteboard? Were you doing, uh,

Yeah, it was a little, it was a little, yeah, it's a little lonely because there's four of us

working on the company and only two people really wrote code.

And Emmett wrote the website and the chat system and I wrote the

software for this video streaming device and video server.

And so, you know, it's my sole responsibility to figure that out.

And I think, I think it's those, you know, setting,

setting deadlines, trying to move quickly and everything where you're in that

moment of intense pressure that sometimes people do their

best and most interesting work. And so even though that was a terrible moment,

I look back on it fondly because that's like, you know, that's one of those

character defining moments, I think. So in 2013, October, you founded

Cruise Automation. Yeah. So progressing forward, another

exceptionally successful company was acquired by GM in 16

for $1 billion. But in October 2013, what was on your mind?

What was the plan? How does one seriously start to tackle

one of the hardest robotics, most important impact for robotics

problems of our age? After going through Twitch, Twitch was,

was, and is today, pretty successful. But the, the work was,

the result was entertainment, mostly. Like, the better the product was,

the more we would entertain people and then, you know, make money on the ad

revenues and other things. And that was, that was a good thing. It

felt, felt good to entertain people. But I figured like, you know, what is really

the point of becoming a really good engineer and

developing these skills other than, you know, my own enjoyment? And I

realized I wanted something that scratched more of an existential

itch, like something that, that truly matters. And so I

basically made this list of requirements for a new, if I was going to

do another company, and the one thing I knew in the back of

my head that Twitch took like eight years to become successful.

And so whatever I do, I better be willing to commit, you know, at least 10 years

to something. And when you think about things from that perspective,

you certainly, I think, raise the bar on what you choose to work on. So for me,

the three things were it had to be something where the technology

itself determines the success of the product,

like hard, really juicy technology problems, because that's what

motivates me. And then it had to have a direct and positive impact on society in

some way. So an example would be like, you know,

health care, self driving cars, because they save lives, other things where

there's a clear connection to somehow improving other people's lives.

And the last one is it had to be a big business, because

for the positive impact to matter, it's got to be a large scale.

And I was thinking about that for a while, and I made like, I tried

writing a Gmail clone and looked at some other ideas.

And then it just sort of light bulb went off, like self driving cars, like that

was the most fun I had ever had in college working on that.

And like, well, what's the state of the technology? It's been 10 years.

Maybe times have changed, and maybe now is the time to make this work.

And I poked around and looked at, the only other thing out there really at the

time was the Google self driving car project.

And I thought, surely there's a way to, you know, have an entrepreneur mindset

and sort of solve the minimum viable product here.

And so I just took the plunge right then and there and said, this is something I

know I can commit 10 years to. It's the probably the greatest

applied AI problem of our generation. And if it works, it's going to be both a

huge business and therefore like, probably the most positive impact I can

possibly have on the world. So after that light bulb went off, I went

all in on cruise immediately and got to work.

Did you have an idea how to solve this problem? Which aspect of the problem to

solve? You know, slow, like we just had Oliver

from Voyage here, slow moving retirement communities,

urban driving, highway driving. Did you have, like, did you have a vision of the

city of the future where, you know, the transportation is

largely automated, that kind of thing? Or was it sort of

more fuzzy and gray area than that? My analysis of the situation is that

Google is putting a lot, had been putting a lot of money into that project. They

had a lot more resources. And so, and they still hadn't cracked

the fully driverless car. You know, this is 2013, I guess.

So I thought, what can I do to sort of go from zero to,

you know, significant scale so I can actually solve the real problem, which is

the driverless cars. And I thought, here's the strategy. We'll

start by doing a really simple problem or solving a

really simple problem that creates value for people. So,

eventually ended up deciding on automating highway driving,

which is relatively more straightforward as long as there's a

backup driver there. And, you know, the go to market will be able to retrofit

people's cars and just sell these products directly. And

the idea was, we'll take all the revenue and profits from that and use it

to do the, so sort of reinvest that in research for

doing fully driverless cars. And that was the plan.

The only thing that really changed along the way between then and now is

we never really launched the first product. We had enough interest from

investors and enough of a signal that this was

something that we should be working on, that after about a year of working on

the highway autopilot, we had it working, you know, on a

prototype stage. But we just completely abandoned that

and said, we're going to go all in on driverless cars now is the time.

Can't think of anything that's more exciting and if it works more impactful,

so we're just going to go for it. The idea of retrofit is kind of

interesting. Yeah. Being able to, it's how you achieve scale.

It's a really interesting idea. Is it something that's still in the

in the back of your mind as a possibility?

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

trying to build a retrofit product, and I'll touch on some of the complexities

of that, and then also having been inside an OEM

and seeing how things work and how a vehicle is developed and

validated. When it comes to something that has

safety critical implications like controlling the steering and

other control inputs on your car, it's pretty hard to get there

with a retrofit. Or if you did, even if you did, it creates a whole bunch

of new complications around liability or how did you truly validate

that. Or you know, something in the base vehicle fails and causes your system to

fail, whose fault is it?

Or if the car's anti lock brake systems or other things kick in

or the software has been, it's different in one version of the car you retrofit

versus another and you don't know because

the manufacturer has updated it behind the scenes. There's basically an

infinite list of long tail issues that can get you.

And if you're dealing with a safety critical product, that's not really

acceptable. That's a really convincing summary of why

that's really challenging. But I didn't know all that at the time, so we tried it

anyway. But as a pitch also at the time, it's a

really strong one. Because that's how you achieve scale and that's how you beat

the current, the leader at the time of Google or the only one in the market.

The other big problem we ran into, which is perhaps the biggest problem from a

business model perspective, is we had kind of assumed that we

started with an Audi S4 as the vehicle we

retrofitted with this highway driving capability.

And we had kind of assumed that if we just knock out like three

make and models of vehicles, that'll cover like 80% of the San Francisco

market. Doesn't everyone there drive, I don't know, a BMW or a Honda Civic or

one of these three cars? And then we surveyed our users and we found out that

it's all over the place. We would, to get even a decent number of

units sold, we'd have to support like, you know, 20 or 50 different models.

And each one is a little butterfly that takes time and effort to maintain,

you know, that retrofit integration and custom hardware and all this.

So it was a tough business. So GM manufactures and sells over 9 million

cars a year. And what you with Cruise are trying to do

some of the most cutting edge innovation in terms of applying AI.

And so how do those, you've talked about a little bit before, but it's also just

fascinating to me. We work a lot of automakers,

you know, the difference between the gap between Detroit

and Silicon Valley, let's say, just to be sort of poetic about it, I guess.

How do you close that gap? How do you take GM into the future

where a large part of the fleet will be autonomous, perhaps?

I want to start by acknowledging that GM is made up of,

you know, tens of thousands of really brilliant, motivated people who want to

be a part of the future. And so it's pretty fun to work

within the attitude inside a car company like that is, you

know, embracing this transformation and change

rather than fearing it. And I think that's a testament to

the leadership at GM and that's flown all the way through to everyone you

talk to, even the people in the assembly plants working on these cars.

So that's really great. So starting from that position makes it a lot easier

so then when the people in San Francisco at Cruise

interact with the people at GM, at least we have this common set of values, which

is that we really want this stuff to work

because we think it's important and we think it's the future.

That's not to say, you know, those two cultures don't clash. They absolutely do.

There's different sort of value systems. Like in a

car company, the thing that gets you promoted and sort of the reward

system is following the processes, delivering

the program on time and on budget. So any sort of risk taking

is discouraged in many ways because if a program is late or if you shut down

the plant for a day, it's, you know, you can count the millions of dollars that

burn by pretty quickly. Whereas I think, you know, most Silicon

Valley companies and in Cruise and the methodology

we were employing, especially around the time of the acquisition,

the reward structure is about trying to solve

these complex problems in any way shape or form or coming up with crazy ideas

that, you know, 90% of them won't work. And so meshing that culture

of sort of continuous improvement and experimentation

with one where everything needs to be rigorously defined up front so that

you never slip a deadline or miss a budget

was a pretty big challenge. And that we're over three years in now

after the acquisition and I'd say like, you know, the investment we made in

figuring out how to work together successfully and

who should do what and how we bridge the gaps between these

very different systems and way of doing engineering work

is now one of our greatest assets because I think we have this really

powerful thing. But for a while it was both GM and Cruise were very

steep on the learning curve. Yeah, so I'm sure it was very stressful.

It's really important work because that's how

to revolutionize the transportation, really to revolutionize

any system. You know, you look at the health care system or you look at the

legal system. I have people like Loris come up to me all the time like

everything they're working on can easily be automated.

But then that's not a good feeling. Yeah, well it's not a good feeling but also

there's no way to automate because the entire infrastructure is really,

you know, based is older and it moves very slowly. And so

how do you close the gap between I have an

how can I replace, of course, Loris don't want to be replaced with an app, but you

could replace a lot of aspect when most of the data is still on paper.

And so the same thing was with automotive.

I mean, it's fundamentally software. It's basically hiring software

engineers. It's thinking in a software world.

I mean, I'm pretty sure nobody in Silicon Valley has ever hit a deadline.

So and then on GM. That's probably true, yeah. And GM side is probably the

opposite. Yeah. So that's that culture gap is really fascinating.

So you're optimistic about the future of that?

Yeah, I mean, from what I've seen, it's impressive. And I think like

especially in Silicon Valley, it's easy to write off building cars because,

you know, people have been doing that for over 100 years now in this country. And

so it seems like that's a solved problem, but that doesn't mean it's an easy

problem. And I think it would be easy to sort of

overlook that and think that, you know, we're

Silicon Valley engineers. We can solve any problem, you know, building a car.

It's been done. Therefore, it's, you know, it's not a real

engineering challenge. But after having seen just the sheer

scale and magnitude and industrialization

that occurs inside of an automotive assembly plant, that is a lot of work

that I am very glad that we don't have to reinvent

to make self driving cars work. And so to have, you know, partners who have done

that for 100 years now, these great processes and this huge infrastructure

and supply base that we can tap into is just remarkable

because the scope and surface area of

the problem of deploying fleets of self driving cars is so large

that we're constantly looking for ways to do less

so we can focus on the things that really matter more. And if we had to

figure out how to build and assemble and

you know, build the cars themselves. I mean, we work closely with GM on

that. But if we had to develop all that capability in house as well,

you know, that would just make the problem really intractable, I think.

So yeah, just like your first entry at the MIT DARPA challenge when

there was what the motor that failed, somebody that knows what they're

doing with the motor did it. That would have been nice if we could

focus on the software, not the hardware platform.

Yeah. Right. So from your perspective now,

you know, there's so many ways that autonomous vehicles can impact

society in the next year, five years, ten years.

What do you think is the biggest opportunity to make

money in autonomous driving, sort of make it a

financially viable thing in the near term?

What do you think will be the biggest impact there?

Well, the things that drive the economics for fleets of self driving

cars are, there's sort of a handful of variables. One is,

you know, the cost to build the vehicle itself. So the material cost, how many,

you know, what's the cost of all your sensors plus the cost of the vehicle and

every all the other components on it. Another one is the lifetime of the

vehicle. It's very different if your vehicle

drives 100,000 miles and then it falls apart versus,

you know, two million. And then, you know, if you have a fleet, it's

kind of like an airplane or an airline where

once you produce the vehicle, you want it to be in

operation as many hours a day as possible producing revenue.

And then, you know, the other piece of that is

how are you generating revenue? I think that's kind of what you're asking. And I

think the obvious things today are, you know, the ride sharing business

because that's pretty clear that there's demand for that,

there's existing markets you can tap into and

large urban areas, that kind of thing. Yeah, yeah. And I think that there are

some real benefits to having cars without

drivers compared to sort of the status quo for

people who use ride share services today.

You know, you get privacy, consistency, hopefully significantly improve safety,

all these benefits versus the current product.

But it's a crowded market. And then other opportunities, which you've

seen a lot of activity in the last, really in the last six or twelve months,

is, you know, delivery, whether that's parcels and packages,

food or groceries. Those are all sort of, I think, opportunities that are

pretty ripe for these, you know, once you have this

core technology, which is the fleet of autonomous vehicles, there's all sorts of

different business opportunities you can build on

top of that. But I think the important thing, of course, is that

there's zero monetization opportunity until you actually have that fleet of

very capable driverless cars that are that are as good or better than humans.

And that's sort of where the entire industry is

sort of in this holding pattern right now.

Yeah, they're trying to achieve that baseline. So, but you said sort of

not reliability, consistency. It's kind of interesting, I think I heard you say

somewhere, I'm not sure if that's what you meant, but

you know, I can imagine a situation where you would get an autonomous vehicle

and, you know, when you get into an Uber or Lyft,

you don't get to choose the driver in a sense that you don't get to choose the

personality of the driving. Do you think there's a, there's room

to define the personality of the car the way it drives you in terms of

aggressiveness, for example, in terms of sort of pushing the

bound? One of the biggest challenges of autonomous driving is the

is the trade off between sort of safety and

assertiveness. And do you think there's any room

for the human to take a role in that decision? Sort of accept some of the

liability, I guess. I wouldn't, no, I'd say within

reasonable bounds, as in we're not gonna, I think it'd be

highly unlikely we'd expose any knob that would let you, you know, significantly

increase safety risk. I think that's just not

something we'd be willing to do. But I think driving style or like, you

know, are you going to relax the comfort constraints slightly or things like that,

all of those things make sense and are plausible. I see all those as, you know,

nice optimizations. Once again, we get the core problem solved in these fleets

out there. But the other thing we've sort of

observed is that you have this intuition that if you

sort of slam your foot on the gas right after the light turns green and

aggressively accelerate, you're going to get there faster. But the

actual impact of doing that is pretty small.

You feel like you're getting there faster, but so the same would be

true for AVs. Even if they don't slam their, you know, the pedal to the floor

when the light turns green, they're going to get you there within, you

know, if it's a 15 minute trip, within 30 seconds of what you would have done

otherwise if you were going really aggressively.

So I think there's this sort of self deception that my aggressive

driving style is getting me there faster.

Well, so that's, you know, some of the things I've studied, some of the things

I'm fascinated by the psychology of that. I don't think it matters

that it doesn't get you there faster. It's the emotional release.

Driving is a place, being inside of a car,

somebody said it's like the real world version of being a troll.

So you have this protection, this mental protection, you're able to sort of yell

at the world, like release your anger, whatever.

So there's an element of that that I think autonomous vehicles would also

have to, you know, giving an outlet to people, but it doesn't have to be

through, through, through driving or honking or so on.

There might be other outlets, but I think to just sort of even just put that aside,

the baseline is really, you know, that's the focus.

That's the thing you need to solve.

And then the fun human things can be solved after.

But so from the baseline of just solving autonomous driving, you're working in

San Francisco, one of the more difficult cities to operate in.

What is, what is the, in your view, currently the hardest

aspect of autonomous driving?

Is it negotiating with pedestrians?

Is it edge cases of perception?

Is it planning?

Is there a mechanical engineering?

Is it data, fleet stuff?

What are your thoughts on the challenge, the more challenging aspects there?

That's a, that's a good question.

I think before, before we go to that, though, I just want to, I like what you

said about the psychology aspect of this,

because I think one observation I've made is I think I read somewhere that I

think it's maybe Americans on average spend, you know, over an hour a day on

social media, like staring at Facebook.

And so that's just, you know, 60 minutes of your life, you're not getting back.

It's probably not super productive.

And so that's 3,600 seconds, right?

And that's, that's time, you know, it's a lot of time you're giving up.

And if you compare that to people being on the road, if another vehicle, whether

it's a human driver or autonomous vehicle, delays them by even three

seconds, they're laying in on the horn, you know, even though that's, that's, you

know, one, one thousandth of the time they waste looking at Facebook every day.

So there's, there's definitely some.

You know, psychology aspects of this, I think that are pretty interesting road

rage in general.

And then the question of course is if everyone is in self driving cars,

do they even notice these three second delays anymore?

Cause they're doing other things or reading or working or just talking to

each other.

So it'll be interesting to see where that goes.

In a certain aspect, people, people need to be distracted by something

entertaining, something useful inside the car.

So they don't pay attention to the external world.

And then, and then they can take whatever psychology and bring it back to

Twitter and then focus on that as opposed to sort of interacting, sort of putting

the emotion out there into the world.

So it's a, it's an interesting problem, but baseline autonomy.

I guess you could say self driving cars, you know, at scale will lower the

collective blood pressure of society probably by a couple of points without

all that road rage and stress.

So that's a good, good external.

So back to your question about the technology and the, I guess the biggest

problems.

And I have a hard time answering that question because, you know, we've been

at this like specifically focusing on driverless cars and all the technology

needed to enable that for a little over four and a half years now.

And even a year or two in, I felt like we had completed the functionality needed

to get someone from point A to point B.

As in, if we need to do a left turn maneuver, or if we need to drive around

at, you know, a double parked vehicle into oncoming traffic or navigate

through construction zones, the scaffolding and the building blocks was

there pretty early on.

And so the challenge is not any one scenario or situation for which, you

know, we fail at 100% of those.

It's more, you know, we're benchmarking against a pretty good or pretty high

standard, which is human driving.

All things considered, humans are excellent at handling edge cases and

unexpected scenarios where computers are the opposite.

And so beating that baseline set by humans is the challenge.

And so what we've been doing for quite some time now is basically, it's this

continuous improvement process where we find sort of the most, you know,

uncomfortable or the things that could lead to a safety issue or other

things, all these events.

And then we sort of categorize them and rework parts of our system to make

incremental improvements and do that over and over and over again.

And we just see sort of the overall performance of the system, you know,

actually increasing in a pretty steady clip.

But there's no one thing.

There's actually like thousands of little things and just like polishing functionality

and making sure that it handles, you know, every version and possible

permutation of a situation by either applying more deep learning systems or

just by, you know, adding more test coverage or new scenarios that we

develop against and just grinding on that.

We're sort of in the unsexy phase of development right now, which is doing

the real engineering work that it takes to go from prototype to production.

You're basically scaling the grinding, sort of taking seriously that the

process of all those edge cases, both with human experts and machine

learning methods to cover all those situations.

Yeah.

And the exciting thing for me is I don't think that grinding ever stops because

there's a moment in time where you've crossed that threshold of human

performance and become superhuman.

But there's no reason, there's no first principles reason that AV capability

will tap out anywhere near humans.

Like there's no reason it couldn't be 20 times better, whether that's, you

know, just better driving or safer driving or more comfortable driving or

even a thousand times better given enough time.

And we intend to basically chase that, you know, forever to build the best

possible product.

Better and better and better.

And always new edge cases come up and new experiences.

So, and you want to automate that process as much as possible.

So what do you think in general in society?

When do you think we may have hundreds of thousands of fully autonomous

vehicles driving around?

So first of all, predictions, nobody knows the future.

You're a part of the leading people trying to define that future, but even

then you still don't know.

But if you think about hundreds of thousands of vehicles, so a significant

fraction of vehicles in major cities are autonomous.

Do you think, are you with Rodney Brooks, who is 2050 and beyond, or are you

more with Elon Musk, who is, we should have had that two years ago?

Well, I mean, I'd love to have it two years ago, but we're not there yet.

So I guess the way I would think about that is let's flip that question

around.

So what would prevent you to reach hundreds of thousands of vehicles?

And that's a good, that's a good rephrasing.

Yeah.

So the, I'd say the, it seems the consensus among the people developing

self driving cars today is to sort of start with some form of an easier

environment, whether it means, you know, lacking inclement weather or, you

know, mostly sunny or whatever it is.

And then add, add capability for more complex situations over time.

And so if you're only able to deploy in areas that meet sort of your

criteria or the current domain, you know, operating domain of the

software you developed, that may put a cap on how many cities you could

deploy in.

But then as those restrictions start to fall away, like maybe you add

capability to drive really well and safely in heavy rain or snow, you

know, that, that probably opens up the market by two, two or three fold

in terms of the cities you can expand into and so on.

And so the real question is, you know, I know today if we wanted to, we

could produce that, that many autonomous vehicles, but we wouldn't be

able to make use of all of them yet.

Cause we would sort of saturate the demand in the cities in which we

would want to operate initially.

So if I were to guess like what the timeline is for those things falling

away and reaching hundreds of thousands of vehicles, I would say that

thousands of vehicles, maybe a range is better, I would say less than

five years, less than five years.

Yeah.

And of course you're working hard to make that happen.

So you started two companies that were eventually acquired for each

four billion dollars.

So you're a pretty good person to ask, what does it take to build a

successful startup?

I think there's, there's sort of survivor bias here a little bit, but

I can try to find some common threads for the things that worked for

me, which is, you know, in, in both of these companies, I was really

passionate about the core technology.

I actually like, you know, lay awake at night thinking about these

problems and how to solve them.

And I think that's helpful because when you start a business, there

are like to this day, there are these crazy ups and downs.

Like one day you think the business is just on, you're just on top of

the world and unstoppable.

And the next day you think, okay, this is all going to end, you know,

it's just, it's just going south and it's going to be over tomorrow.

And and so I think like having a true passion that you can fall back

on and knowing that you would be doing it, even if you weren't getting

paid for it, helps you weather those, those tough times.

So that's one thing.

I think the other one is really good people.

So I've always been surrounded by really good cofounders that are

logical thinkers are always pushing their limits and have very high

levels of integrity.

So that's Dan Kahn and my current company and actually his brother and

a couple other guys for Justin TV and Twitch.

And then I think the last thing is just I guess persistence or

perseverance, like, and, and that, that can apply to sticking to sort

of, or having conviction around the original premise of your idea and

sticking around to do all the, you know, the unsexy work to actually

make it come to fruition, including dealing with, you know, whatever

it is that you, that you're not passionate about, whether that's

finance or, or HR or, or operations or those things, as long as you

are grinding away and working towards, you know, that North star

for your business, whatever it is, and you don't give up and you're

making progress every day, it seems like eventually you'll end up in a

good place.

And the only things that can slow you down are, you know, running out

of money or I suppose your competitors destroying you.

But I think most of the time it's, it's people giving up or, or somehow

destroying things themselves rather than being beaten by their competition

or running out of money.

Yeah.

If you never quit, eventually you'll arrive.

So, uh, it's a much more concise version of what I was trying to say.

Yeah, that was good.

So you went the Y Combinator route twice.

Yeah.

What do you think in a quick question, do you think is the best way to

raise funds in the early days or not just funds, but just community

develop your idea and so on.

Can you do it solo or maybe with a co founder with like self funded?

Do you think Y Combinator is good?

Is it good to do VC route?

Is there no right answer or is there from the Y Combinator experience

something that you could take away that that was the right path to take?

There's no one size fits all answer, but if your ambition I think is to, you

know, see how big you can make something or, or, or rapidly expand and capture

a market or solve a problem or whatever it is, then, then, you know, going to

venture back route is probably a good approach so that, so that capital doesn't

become your primary constraint.

Y Combinator I love because it puts you in this, uh, sort of competitive

environment where you're, where you're surrounded by, you know, the top, maybe

1% of other really highly motivated, you know, peers who are in the same, same

place and that, uh, that environment I think just breeds breed success, right?

If you're surrounded by really brilliant, hardworking people, you're going to

feel, you know, sort of compelled or inspired to, to try to emulate them and

or beat them.

And, uh, so even though I had done it once before and I felt like, yeah, I'm

pretty self motivated.

I thought like, look, this is going to be a hard problem.

I can use all the help I can get.

So surrounding myself with other entrepreneurs is going to make me work a

little bit harder or push a little harder than it's worth it.

And so that's why I, why I did it, you know, for example, the second time.

Let's, uh, let's go philosophical existential.

If you go back and do something differently in your life, starting in the

high school and MIT leaving MIT, you could have gone the PhD route doing the

startup, going to see about a startup in California and you, or maybe some

aspects of fundraising.

Is there something you regret, something you not necessarily regret, but if

you go back, you could do differently.

I think I've made a lot of mistakes, like, you know, pretty much everything

you can screw up.

I think I've screwed up at least once, but I, you know, I don't regret those

things.

I think it's, it's hard to, it's hard to look back on things, even if it didn't

go well and call it a regret, because hopefully it took away some new knowledge

or learning from that.

So I would say there was a period.

Yeah.

The closest I can, I can come to is there's a period, um, in, in Justin

TV, I think after seven years where, you know, the company was going one

direction, which is towards Twitch, uh, in video gaming.

I'm not a video gamer.

I don't really even use Twitch at all.

And I was still, uh, working on the core technology there, but my, my heart

was no longer in it because the business that we were creating was not something

that I was personally passionate about.

It didn't meet your bar of existential impact.

Yeah.

And I'd say I probably spent an extra year or two working on that.

And, uh, and I'd say like, I would have just tried to do something different

sooner because those, those were two years where I felt like, um, you know,

from this philosophical or existential thing, I just, I just felt that

something was missing.

And so I would have, I would have, if I could look back now and tell myself,

it's like, I would have said exactly that.

Like, you're not getting any meaning out of your work personally right now.

You should, you should find a way to change that.

And that's, that's part of the pitch I use to basically everyone who joins

Cruise today, it's like, Hey, you've got that now by coming here.

Well, maybe you needed the two years of that existential dread to develop

the feeling that ultimately it was the fire that created Cruise.

So, you never know.

You can't, good theory.

So last question, what does 2019 hold for Cruise?

After this, I guess we're going to go and I'll talk to your class.

But one of the big things is going from prototype to production, uh, for

autonomous cars and what does that mean?

What does that look like?

And 2019 for us is the year that we try to cross over that threshold and reach,

you know, superhuman level of performance to some degree with the software and,

uh, have all the other of the thousands of little building blocks in place to,

to launch, um, you know, our, our first, uh, commercial product.

So that's, that's, what's in store for us or in store for us.

And we've got a lot of work to do.

We've got a lot of brilliant people working on it.

So it's, it's all up to us now.

Yeah.

From Charlie Miller and Chris Vells, like the people I've crossed paths with.

Oh, great.

If you, it sounds like you have an amazing team.

So, um, like I said, it's one of the most, I think one of the most important

problems in artificial intelligence of the century.

It'll be one of the most defining, the super exciting that you work on it.

And, uh, the best of luck in 2018, I'm really excited to see what

Cruz comes up with.

Thank you.

Thanks for having me today.

Thanks, Carl.