The following is a conversation with Kyle Vogt.
He's the president and the CTO of Cruise Automation,
leading an effort to solve one of the biggest
robotics challenges of our time, vehicle automation.
He's a co-founder of two successful companies,
Twitch and Cruise, that have each sold for a billion dollars.
And he's a great example of the innovative spirit
that flourishes in Silicon Valley.
And now is facing an interesting and exciting challenge
of matching that spirit with the mass production
and the safety-centric culture of a major automaker,
like General Motors.
This conversation is part of the MIT
Artificial General Intelligence series
and the Artificial Intelligence podcast.
If you enjoy it, please subscribe on YouTube, iTunes,
or simply connect with me on Twitter at Lex Freedman,
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 to hit know
there, so I'm a little bit worried about that now.
So in high school in Kansas City,
you joined Shawnee Mission North High School Robotics Team.
Now, that wasn't your high school.
That's right.
That was the only high school in the area that had a teacher
who was willing to sponsor our first robotics team.
I was going to troll you a little bit.
Jog your mouth a little bit with that kid.
I was trying to look super cool and intense.
You did.
Because 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 LEGO Mindstorms came out.
And for the first time, you could program LEGO contraptions.
And I think things just snowballed from that.
But I remember seeing 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 obsessively poured over the internet forums
where all the creators for BattleBots
would hang out and talk about, document their build progress
and everything.
And I think I read, I must have read tens of thousands
of forum posts from basically everything
that was out there on what these people were doing.
And eventually, I sort of triangulated
how to put some of these things together.
And I ended up doing BattleBots, which 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.
It's almost like a mechanical engineering challenge
of building things that can be broken.
They're radio-controlled.
And I think that they allowed some limited form of autonomy.
But in a two-minute match, and the way these things ran,
you're really doing yourself a disservice
by trying to automate it versus just do
the practical thing, which is drive it yourself.
And there's an entertainment aspect, just going on YouTube.
There's some of them wield and 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 Frankenstein and sentient
being, or was it just the Lego, the tinkering stuff?
I mean, that was just building something.
I think the idea of this radio-controlled machine
that can do various things, if it has a weapon or something,
was pretty interesting.
I agree it doesn't have the same appeal
as autonomous robots, which I 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 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 double or triple their rated voltage.
So they immediately start overheating.
But for that two-minute match, you
can get a significant increase in the power output
of those motors before they burn out.
And so you're doing the same thing for your battery packs,
all the materials in the system.
And I think there is something intrinsically interesting
about just seeing where things break.
And did you offline see where they break?
Did you take it to the testing point?
How did you know in two minutes?
Or was there a reckless, let's just go with it and see?
We weren't very good at battle bots.
We lost all of our matches the first round.
The one I built first, both of them
were these wedge-shaped robots.
Because a wedge, even though it's sort of boring to look at,
is extremely effective.
You drive towards another robot, and the front edge of it
gets under 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 8 inches.
The purpose of which is what?
Pretty ineffective, actually, but it looked cool.
Was it to help with the lift?
No, it was just to try to poke holes in the other robot.
And then the second time I did it, which is the following,
I think maybe 18 months later, we
had a titanium axe with a hardened steel tip on it
that was powered by a hydraulic cylinder, which we were
activating with liquid CO2, which 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?
If you didn't go back there, see what language was it?
So what was it, was it Emacs, Vim?
Was it a more respectable, modern ID?
Do you remember any of this?
Yeah, well, I remember, I think maybe when
I was in third or fourth grade, 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 crash or wouldn't start up correctly.
And it would dump you out to what I later
learned was sort of a command prompt.
And my teacher would come over and type, actually.
I 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.
Teach me these codes, these error codes,
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, where every line, the line
numbers are all number, or that every line is numbered.
And you have to leave enough space between the numbers
so that if you want to tweak your code,
you go back and if the first line was 10 and the second line
is 20, now you have to go back and insert 15.
And if you need to add code in front of that, 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 when you first program, you see the magic of it.
It's like this world opens up with endless possibilities
for the things you could build or accomplish
with that computer.
So you got the bug then.
So even starting with basic and then what?
C++ throughout, was there a computer programming,
computer science classes in high school?
Not where I went.
So I was self-taught, but I did a lot of programming.
The thing that sort of pushed me in the path of eventually
working on self-driving cars was actually one of these
really long trips driving from my house in Kansas
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 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 steer the wheel.
And later I'd come to realize this had been done
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 autonomous vehicles
of some scale, start off with really small electric ones
and then 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 before the deep learning craze,
how do you think about,
how would you even go about building a thing
that perceives itself in the world,
localizes itself in the world, moves around in the world?
Like when you were younger,
what was your thinking about it?
Well, prior to deep neural networks
or convolutional neural nets,
these modern techniques we have,
or at least ones that are in use today,
it was all heuristic space.
And so like old school image processing,
and I think extracting yellow lane markers
out of an image of a road is one of the problems
that lends itself reasonably well
to those heuristic base methods.
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 set of capabilities needed for a driverless car,
I think you could,
and we've done this at cruise in the very first days,
you can start off with a really simple,
human written heuristic just to get the scaffolding
in place for your system.
Traffic light detection,
probably a really simple color thresholding on day one
just to get the system up and running
before you migrate to a deep learning base technique
or something else.
And back when I was doing this,
my first one was on a Pentium 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.
Cause that's the thing I knew at the time.
And so there was no, no chance at all of using,
there's 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.
Our 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 their 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
with your own 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
is 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, and at that point, was there any thought
about entrepreneurship before you came to college
is sort of doing your building this into a thing
that impacts the world on 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 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,
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 freshman
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,
you know, retrofitted to turn the steering wheel
on the truck, died.
And so our vehicle was just dead in the water, couldn't
steer.
So we didn't make it very far.
On the hardware side.
So was there a software component?
Was there like, how did your view of autonomous vehicles
in terms of artificial intelligence?
How did it 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, of that original DARPA Grand Challenge,
which was to just get a whole bunch of really brilliant
people exploring the space and pushing the limits.
And, and I think like to this day, that DARPA Challenge
with its, you know, million dollar prize pool was probably
one of the most effective, you know, uses of taxpayer money,
dollar for dollar that I've seen, you know,
because that, that small sort of initiative that DARPA put,
put out sort of in my view was the catalyst
or the tipping point for this, this whole next wave
of autonomous vehicle development.
So that was pretty cool.
So let me jump around a little bit on that point.
They also did the urban challenge where 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 as 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 in time,
because like kick starting and spinning up the flywheel
is I think 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 your 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 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 Junior, Junior year,
what was that decision like?
As I said, I always wanted to do a company
or start a company and this opportunity landed in my lap,
which was a couple of 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 went out to California during IAP,
which is MIT's month off on one-way ticket
and basically never went back.
We were having so much fun,
we felt like we were building something
and creating something and it was gonna be interesting
that I was just all in and got completely hooked.
And that business was Justin TV,
which is originally a reality show
about a guy named Justin,
which morphed into a live video streaming platform,
which then morphed into what is Twitch today.
So that was quite an unexpected journey.
So no regrets?
No.
Looking back, it was just an obvious,
I mean, one-way ticket.
I mean, if we just pause on that for a second,
there was no,
how did you know these were the right guys,
this is the right decision?
You didn't think it was just follow the heart kind of thing?
Well, I didn't know,
but, you know, just trying something for a month during 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 that,
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 probably inverted,
which is just 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,
Emma Shear, coded through the night.
Can you take me through that experience?
So let me say a few nice things that,
the article I read quoted Justin Khan said
that you were known for bureau coding through problems
and being a creative genius.
So on that night, what was going through your head?
Or maybe put another way, how do you solve these problems?
What's your approach to solving these kinds of problems
where the line between success and failure
seems to be pretty thin?
That's a good question.
Well, first of all, that's 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 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 the 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 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 gonna 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 that was gonna be fine.
Was the problem obvious?
Did you understand what could possibly,
like how complicated was the system at that point?
It was pretty messy.
So to get a live video feed that looked decent
working from anywhere in San Francisco,
I put together the system where we had like
three or four cell phone data modems.
And they were like, we take the video stream
and sort of sprayed across these three or four modems
and then try to catch all the packets on the other side,
you know, with unreliable cell phone networks.
Pretty low-level networking.
Yeah, and putting these like, you know,
sort of protocols on top of all that
to reassemble and reorder the packets
and have time buffers and error correction
and all that kind of stuff.
And the night before, it was just staticky.
Every once in a while the image would go staticky
and there would be this horrible,
like screeching audio noise
because the audio was also corrupted.
And this would happen like every five to 10 minutes or so
and it was a really, you know, off-putting to the viewers.
Yeah.
How do you tackle that problem?
What was the, you just freaking out behind a computer.
There's the word, are there other folks working
on this problem, like were you behind a whiteboard?
Were you doing a hair coding?
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 was my sole responsibility
to figure that out.
And I think it's those, you know, 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 2016 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 impactful robotics problems of our age?
After going through Twitch,
Twitch was in its today pretty successful,
but 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 a good thing.
It felt good to entertain people,
but I figured like, you know,
what is really the point of becoming a really good engineer
and developing these skills other than, you know,
my own enjoyment.
And I realized I wanted something that scratched
more of an existential itch,
like something that truly matters.
And so I basically made this list of requirements
for, I knew if I was gonna do another company
and the one thing I knew in the back of my head
that Twitch took like eight years to become successful.
And so whatever I do, I better be willing to commit,
you know, at least 10 years to something.
And when you think about things from that perspective,
you certainly, I think,
raise the bar on what you choose to work on.
So for me, the three things where it had to be something
where the technology itself determines
the success of the product,
like hard, really juicy technology problems,
because that's what motivates me.
And then it had to have a direct and positive impact
on society in some way.
So an example would be like, you know,
healthcare or self-driving cars,
because they save lives.
There are 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 gotta be a large scale.
Scale, yeah.
And I was thinking about that for a while.
And I made like a, I tried writing a Gmail clone
and looked at some other ideas.
And then it just sort of light bulb went off
like self-driving cars.
Like that was the most fun I had ever had in college
working on that.
And like, well, what's the state of the technology
has been 10 years.
Maybe times have changed
and maybe now is the time to make this work.
And I poked around and looked at the only other thing
out there really at the time
was the Google self-driving car project.
And I thought surely there's a way to,
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 probably the greatest applied AI problem
of our generation.
And if it works, it's gonna 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?
Or, you know, the transportation is largely automated
that kind of thing.
Or was it sort of more fuzzy and gray area than that?
My analysis of the situation is that Google's putting a lot
it 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 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, at a prototype stage,
but we just completely abandoned that
and said we're gonna go all in on driverless cars
now is the time.
Can't think of anything that's more exciting
and if it works more impactful,
so we're just gonna go for it.
The idea of retrofit is kind of interesting.
Yeah. Being able to, it's how you achieve scale.
It's a really interesting idea
is it's something that's still in the 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,
it creates a whole bunch of new complications around
liability or how did you truly validate that
or 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 it'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 vehicle,
that'll cover like 80% of the San Francisco market.
Doesn't everyone there drive, I don't know, a BMW
or Honda Civic or one of these three cars?
And then we surveyed our users
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 20 or 50 different models
and each one is a little butterfly
that takes time and effort to maintain
that retrofit integration and custom hardware and all this.
So it was a tough business.
So GM manufactures and sells over nine million cars a year
and what you with crews are trying to do
some of the most cutting edge innovation
in terms of applying AI.
And so how do those, you've talked about it 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 would 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 with them.
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 that 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 crews
in the methodology we were employing,
especially around the time of the acquisition,
the reward structure is about trying to solve
these complex problems in any way, shape, or form
or coming up with crazy ideas that, you know,
90% of them won't work.
And so meshing that culture
of sort of continuous improvement and experimentation
with one where everything needs to be, you know,
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 crews
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 healthcare system
or you look at the legal system.
I have people like Laura has 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 haven't, how can I replace?
Of course, lawyers 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 with automotive,
I mean, it's fundamentally software.
So it's basically hiring software engineers.
It's thinking of 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.
That's that culture gap is really fascinating.
So you're optimistic about the future of that.
Yeah. I mean, from what I've seen, it's impressive.
And I think like, especially in Silicon Valley,
it's easy to write off building cars
because, you know, people have been doing that
for over a hundred years now in this country.
And so it seems like that's a solved problem,
but that doesn't mean it's an easy problem.
And I think it would be easy to sort of overlook that
and think that, you know,
we're Silicon Valley engineers, we can solve any problem,
you know, building a car, it's been done.
Therefore, it's, you know, it's not a real
engineering challenge.
But after having seen just the sheer scale
and magnitude and industrialization
that occurs inside of an automotive assembly plant,
that is a lot of work that I am very glad
that we don't have to reinvent
to make self-driving cars work.
And so to have partners who have done that for a hundred years
and have these great processes
and this huge infrastructure and supply base
that we can tap into is just remarkable
because the scope and surface area
of 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,
the MIT DARPA challenge,
when it was what the motor that failed,
if somebody that knows what they're doing
with the motor did it.
It would have been nice if we could focus on the software
and not the hardware platform.
Yeah.
Right.
So from your perspective, now, you know,
there's so many ways that autonomous vehicles
can impact society in the next year, five years, 10 years.
What do you think is the biggest opportunity
to make money in autonomous driving,
sort of make it a financially viable thing in the near term?
What do you think would be the biggest impact there?
Well, the things that 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 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, 2 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?
And I think that's kind of what you're asking in.
I think the obvious things today
are, you know, the ride-sharing business
because that's pretty clear that there's demand for that.
There's existing markets you can tap into and...
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, your 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 12 months is, you know, delivery,
whether that's parcels and packages, food or groceries.
Those are all sort of, I think, opportunities
that are pretty ripe for these, you know,
once you have this core technology,
which is the fleet of autonomous vehicles,
there's all sorts of different business opportunities
you can build on top of that.
But I think the important thing, of course,
is that there's zero monetization opportunity
until you actually have that fleet
of very capable driverless cars
that are 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,
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 about the,
one of the biggest challenges in autonomous driving
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 balance,
as in we're not gonna, I think it'd be higher than likely
we'd expose any knob that would let you,
you know, significantly increase safety risk.
I think that's just not something
you'd be willing to do.
But I think driving style or like, you know,
are you gonna relax the comfort constraints slightly
or things like that?
All of those things make sense and are plausible.
I see all those as, you know, nice optimizations.
Once again, we get the core problem solved
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 gonna get there faster.
But the actual impact of doing that is pretty small.
You feel like you're getting there faster,
but so the same would be true for AVs.
Even if they don't slam their, you know,
the pedal to the floor when the light turns green,
they're gonna get you there within, you know,
if it's a 15 minute trip within 30 seconds
of what you would 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 study,
some of the things I'm fascinated by the psychology of that.
And 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 our 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 it is.
But 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 driving or honking
or so on, there might be other outlets.
But I think to 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 the interview 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 more challenging aspects there?
That's a good question.
I think 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 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 1,000th 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 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 I guess the biggest problems.
And I have a hard time answering that question
because, you know, we've been at this,
like specifically focusing on driverless cars
and all the technology needed to enable that
for a little over four and a half years now.
And even a year or two in,
I felt like we had completed the functionality needed
to get someone from point A to point B.
As in, if we need to do a left turn maneuver
or if we need to drive around a, you know,
a double parked vehicle into oncoming traffic
or navigate through construction zones,
the 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
for computers of 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.
So sort of taking seriously the process
of all those edge cases, both with human experts
and machine learning methods to cover,
to cover all those situations.
Yeah, and the exciting thing for me is
I don't think that grinding ever stops
because there's a moment in time
where you cross that threshold of human performance
and become superhuman, but there's no reason,
there's no first principles reason
that AV capability will tap out anywhere near humans.
Like there's no reason it couldn't be 20 times better,
whether that's, you know, just better driving
or safer driving 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,
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 rephrasing.
Yeah, so the,
I'd say that 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 lacking, inclement weather,
or mostly sunny or whatever it is,
and then 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 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 and heavy rain or snow,
that probably opens up the market by two or three fold
in terms of the cities you can expand into and so on.
And so the real question is,
I know today if we wanted to,
we could produce that many autonomous vehicles,
but we wouldn't be able to make use of all of them yet
because we would sort of saturate the demand
in the cities in which we would want to operate initially.
So if I were to guess what the timeline is
for those things falling away
and reaching hundreds of 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 $4 billion.
So you're a pretty good person to ask,
what does it take to build a successful startup?
I think there's sort of survivor bias here a little bit,
but I can try to find some common threads
for the things that worked for me,
which is in both of these companies,
I was really passionate about the core technology.
I actually lay awake at night thinking about these problems
and how to solve them.
And I think that's helpful
because when you start a business,
there are like to this day,
there are these crazy ups and downs.
Like one day you think the business is just on top
of the world and unstoppable.
And the next day you think, okay, this is all gonna end.
It's just going south and it's gonna be over tomorrow.
And so I think like having a true passion
that you can fall back on and knowing that you would
be doing it even if you weren't getting paid for it,
helps you weather those tough times.
So that's one thing.
I think the other one is really good people.
So I've always been surrounded by really good co-founders
that our 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 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're not passionate about,
whether that's finance or HR or operations or those things.
As long as you are grinding away and working towards,
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 competitor is destroying you,
but I think most of the time it's people giving up
or somehow destroying things themselves
rather than being beaten by their competition
or running out of money.
Yeah, if you never quit, eventually you'll arrive.
So...
It's a much more concise version
of what I was trying to say, yeah, that's 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
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 see how big
you can make something or rapidly expand
and capture a market or solve a problem or whatever it is,
then going the venture back route
is probably a good approach
so that capital doesn't become your primary constraint.
Y Combinator, I love because it puts you
in this sort of competitive environment
where you're surrounded by the top,
maybe 1% of other really highly motivated peers
who are in the same place.
And that environment, I think, just breeds success.
If you're surrounded by really brilliant,
hardworking people, you're gonna feel sort of compelled
or inspired to try to emulate them or beat them.
And so even though I had done it once before
and I felt like I'm pretty self-motivated,
I thought, look, this is gonna be a hard problem.
I can use all the help I can get.
So surrounding myself with other entrepreneurs
is gonna make me work a little bit harder
or push a little harder, then it's worth it.
And so that's why I did it, for example, the second time.
Let's go full soft, go existential.
If you go back and do something differently in your life,
starting in high school and MIT, leaving MIT,
you could have gone the PhD route, doing the startup,
going to see about a startup in California
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, pretty much everything you can screw up,
I think I've screwed up at least once.
But I don't regret those things.
I think it's hard to look back on things,
even if they didn't go well and call it a regret,
because hopefully it took away some new knowledge
or learning from that.
So, I would say there's a period,
yeah, the closest I can come to is this.
There's a period in Justin TV, I think,
after seven years where the company was going one direction,
which is towards Twitch in video gaming.
I'm not a video gamer.
I don't really even use Twitch at all.
And I was still working on the core technology there,
but my heart was no longer in it,
because the business that we were creating
was not something that I was personally passionate about.
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 I'd say I would have just tried
to do something different sooner,
because those were two years where I felt like,
from this philosophical or existential thing,
I just felt that something was missing.
And so, I would have, if I could look back now
and tell myself, I would have said exactly that.
You're not getting any meaning out of your work personally
right now, you should find a way to change that.
And that's part of the pitch I used
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 repair it.
Good theory, yeah.
So, last question, what does 2019 hold for Cruise?
After this, I guess we're gonna go and talk to your class,
but one of the big things is going from prototype
to production for autonomous cars.
And what does that mean?
What does that look like?
And 2019 for us is the year that we try
to cross over that threshold and reach,
superhuman level of performance to some degree
with the software and have all the other
of the thousands of little building blocks in place
to launch our first commercial product.
So, that's what's 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 all up to us now.
Yeah, from Charlie Miller and Chris Vells,
like the people I've crossed paths with.
Oh, great, yeah.
It sounds like you have an amazing team.
So, like I said, it's one of the most,
I think one of the most important problems
in artificial intelligence of this century.
You'll be one of the most defining.
That's super exciting that you work on it.
And the best of luck in 2019.
I'm really excited to see what Cruise comes up with.
Thank you, thanks for having me today.
Thanks, Kyle.