Speaker 1: Pushkin. In two thousand and three, Chris Armsen was a
Speaker 1: grad student at Carnegie Mellon University and he was working
Speaker 1: on a research project in Chile in the Ata Kama Desert.
Speaker 2: Beautiful place, very desolate, the closest thing we have to
Speaker 2: Mars in many ways on Earth, which is why you
Speaker 2: were there. We were building a robot to go look
Speaker 2: for signs of life, and I was developing the navigation
Speaker 2: system for it, and my PhD advisor came down and said, Hey,
Speaker 2: the Defense Departments has this competition. They want to build
Speaker 2: a robot to drive fifty miles an hour across the
Speaker 2: desert between Los Angeles Las Vegas. And I thought that
Speaker 2: just sounded awesome. So we bought a Humvy and we
Speaker 2: cut the top of it, put a big electronics box
Speaker 2: in a lot of computers. All of the stuff that's
Speaker 2: in a self driving car today. The high performs computing,
Speaker 2: the lasers, the radars, the cameras, the hd map apps
Speaker 2: were part of this. It's kind of the first time
Speaker 2: when all this stuff came together into a vehicle, and
Speaker 2: then we developed it for six or nine months, and
Speaker 2: then we tried to compete with it. The first darper challenge.
Speaker 2: The team I was a technical director for we actually
Speaker 2: had the highest performing thing there. It was supposed to
Speaker 2: go one hundred and twenty six miles across the desert.
Speaker 2: It ended up going about seven when it got hung
Speaker 2: up on a berm and almost literally burst into flames.
Speaker 1: And you, I mean, nobody won. But if anybody won,
Speaker 1: you won, right, that was more than nobody. That was
Speaker 1: farther than Yeah. The key thing is nobody won.
Speaker 2: If you imagine a marathon where you're supposed to go
Speaker 2: twenty six miles and the best runner went too right,
Speaker 2: it's yes.
Speaker 1: Yes, fair, and then burst into flames and then burst
Speaker 1: into flames.
Speaker 2: Yeah.
Speaker 1: I'm Jacob Goldstein, and this is What's your problem? The
Speaker 1: show where I talk to people who are trying to
Speaker 1: make technological progress. My guest today is Chris Ermson. He's
Speaker 1: the co founder and CEO of Aurora, a company that's
Speaker 1: trying to bring autonomous driving to commercial trucks. As you
Speaker 1: just heard, Chris started working on autonomous vehicles more than
Speaker 1: twenty years ago, and in fact, his career traces the
Speaker 1: really the entire modern arc of self driving cars, from
Speaker 1: that first DARPA challenge to a top job at Google's
Speaker 1: self driving car project, the project that later became Waimo
Speaker 1: to founding Aurora, the company where he works now. We
Speaker 1: talked a lot about the problems Chris is trying to
Speaker 1: solve in autonomous trucking, and we also talked about the
Speaker 1: future of autonomous driving more generally. But before we got
Speaker 1: to that, we talked about the earlier work he did,
Speaker 1: work that really helped get the field, the broader field,
Speaker 1: to where it is today. So we'll pick up the
Speaker 1: interview back in two thousand and four at that moment
Speaker 1: when his team's autonomous car had failed and almost burst
Speaker 1: into flames.
Speaker 2: It was crushing, yeah, right, Like, you know, on the
Speaker 2: one hand, it was called a Grand challenge, right because
Speaker 2: you know, a lot of people didn't think it could
Speaker 2: be solved. It was impossible. Because on the one hand,
Speaker 2: it's kind of like, okay, it was really hard. On
Speaker 2: the other hand, you've you know, we're sleeping on the
Speaker 2: floor in the lab, in the you know, in this
Speaker 2: garage in the middle of the desert for weeks and
Speaker 2: months trying to get this thing to go, and then
Speaker 2: you launch it out there and you have this this
Speaker 2: epic moment where you're watching this thing that you've built,
Speaker 2: tear out across the desert and you can just see
Speaker 2: the top of it, and these helicopters that the military
Speaker 2: is using to track them as they're out in the
Speaker 2: desert doing this, and then you hear you know, it's
Speaker 2: supposed to one hundred and twenty miles, and they're like, oh,
Speaker 2: it's stuck. And then you hear like billowing smoke and
Speaker 2: broken and poor thing comes back looking very sad, and
Speaker 2: you know, it was crushing. But at the same time,
Speaker 2: we did set records. Right, if you look at the
Speaker 2: kind of the product of the speed and the distance
Speaker 2: it went, this was an order of magnitude better than
Speaker 2: anyone had done before, and so it was a big step.
Speaker 2: And I think that was partly what encouraged the Defense
Speaker 2: Department to say, hey, come back again and try it
Speaker 2: in a year.
Speaker 1: And then you did, and then you did it again
Speaker 1: and again. And what year do we go to? Is
Speaker 1: it two thousand and seven? Is that at the next
Speaker 1: big moment?
Speaker 2: Yeah? Two thousand and five was another one of these
Speaker 2: in the desert. Two thousand and seven they did what
Speaker 2: they called the Urban Challenge. So two thousand and five
Speaker 2: we're able to drive it around the desert not hit
Speaker 2: things too much. Two thousand and seven, they say, okay,
Speaker 2: that's great, but now let's stay on the right side
Speaker 2: of the road. Let's move in kind of pseudo urban
Speaker 2: environments and show that that can work. And that was
Speaker 2: the last of Grand challenges, because that year we actually
Speaker 2: accomplished it. We drove sixty miles around this air base
Speaker 2: and interacted with traffic. They hired a bunch of stunt
Speaker 2: drivers out of Hollywood to come and drive cars around
Speaker 2: these robots to make traffic for them, and it was
Speaker 2: a heck of a day. And then the Defense Department,
Speaker 2: or at least darpas said, you know, effectively, problem solved.
Speaker 2: We've got this close enough. Now hand it off to
Speaker 2: industry or whoever to take it from here.
Speaker 1: And how did how did your car do?
Speaker 2: In two thousand and seven, we won, which was nice.
Speaker 2: It was a good change.
Speaker 1: So you know, we went and did it catch on fire?
Speaker 2: No fire? This time, it just kind of worked. We
Speaker 2: had a little bit of nerves at the very beginning,
Speaker 2: because you know, our system was pretty reliable, robust, it
Speaker 2: worked all the time. We go and bring it out
Speaker 2: to the start line and we can't get it to
Speaker 2: boot properly because it can't see this GPS satellites that
Speaker 2: it needed, and we never had this problem before. We're like,
Speaker 2: you know, frantically trying to figure out what's going on.
Speaker 2: And then we look over and realized that for race
Speaker 2: day they brought a JumboTron and they've set that up
Speaker 2: right where the robots are at the starting place. And
Speaker 2: it turns out they don't really check these mobile jumbotrons
Speaker 2: for like how much interference they're generating, I guess. So
Speaker 2: we had this panic moment we said, you know, can
Speaker 2: you turn off the jumbo tron? And sure enough they
Speaker 2: turn off that and suddenly everything works again. So you know,
Speaker 2: it was it wasn't without its challenges, so so but it.
Speaker 1: Works right, So it works, and it works in a
Speaker 1: like quasi real environment, a pseudo real environment. So I
Speaker 1: mean when you at that moment, when you were thinking
Speaker 1: about the future, the future of self driving cars, how
Speaker 1: did it look to you? What did you think?
Speaker 2: Yeah, I think at that point was starting to believe
Speaker 2: there was a thing that could happen here, right, and
Speaker 2: probably you know, certainly naively thought oh yeah, we've got
Speaker 2: this right.
Speaker 1: This maybe everybody kind of thought that, right, Like.
Speaker 2: Yeah, I think, And that's been part of what's made
Speaker 2: the journey really fun for me. It's just there's just
Speaker 2: so many things to learn, so many new problems to
Speaker 2: overcome along the way of taking something from a it
Speaker 2: worked on this day in a race, to hey, we
Speaker 2: can go do something transformational that will save lives and
Speaker 2: improve the economy and you know, do all these other
Speaker 2: great things.
Speaker 1: It seems like that the distance between it worked in
Speaker 1: a race and we can do something transformational has been longer,
Speaker 1: certainly than a lot of people thought. Then a lot
Speaker 1: of like smart, well informed people.
Speaker 2: Thought, yeah, me among them, right. And you know, it's
Speaker 2: one of these things where I think anyone who sets
Speaker 2: out and does something interest and exciting, often I think
Speaker 2: if they understood how hard it was going to be
Speaker 2: before they started, may not have actually taken the first step.
Speaker 2: And so I feel a little lucky to have not
Speaker 2: understood that, because it's caused me to kind of stick
Speaker 2: with it and really enjoy the journey along the way.
Speaker 1: I mean, entrepreneurs basically always say that thing that you
Speaker 1: just said, but in the case of autonomous driving, I
Speaker 1: really believe it. Right, It seems like extra true. Like
Speaker 1: two thousand and seven was basically like the year the
Speaker 1: iPhone came out, right, like, which is to say, in
Speaker 1: technology long time ago, not to mention that there was
Speaker 1: like no computer vision as we know it today, right,
Speaker 1: which has become important, I mean so much.
Speaker 2: No, it is. You know, the waves of technological evolution
Speaker 2: that we've seen since then are profound, and we've been
Speaker 2: able to kind of surf those as we've been building it.
Speaker 2: And it's been Yeah, it is, it's been a long time.
Speaker 1: So then you go to Google, right, you go to
Speaker 1: Google a couple couple of years after that two thousand
Speaker 1: and nine. So you're at Google from twenty nine to
Speaker 1: twenty sixteen, Right, what do you and the team figure out?
Speaker 1: And then yeah, when do you leave?
Speaker 2: So we get there, and we originally want to show
Speaker 2: that we could go from what we've shown at the
Speaker 2: Darker Challenge to something actually worked on public roads, and
Speaker 2: so we had this challenge of driving one hundred thousand
Speaker 2: miles in public roads and miles of really interesting, challenging
Speaker 2: roads in the Bay Area, things like driving down Highway
Speaker 2: wand from San Francisco to La and across all the
Speaker 2: Bay bridges and things like that, with the idea that
Speaker 2: if we could prove we could do these things, then yeah,
Speaker 2: this technology probably has legs. And it was kind of
Speaker 2: a dread pirate Roberts setup right in that, you know,
Speaker 2: we were told that you've got you know, two years
Speaker 2: to do this, and you know then we'll probably fire
Speaker 2: you the day after, right, and you kind of this
Speaker 2: was kind of with us for the time. And so
Speaker 2: we worked with urgency, and it turns out we did
Speaker 2: in eighteen months, and through that we had some really
Speaker 2: interesting technological evolution. You know, we started to see some
Speaker 2: of the you know, early legs of deep learning started
Speaker 2: to show up in that point.
Speaker 1: During that time, so this is like two thousand and ten,
Speaker 1: twenty eleven, yeah.
Speaker 2: Exactly those times, yep.
Speaker 1: Yeah.
Speaker 2: And then but I think almost more importantly really started
Speaker 2: we really started to understand what this would mean to people.
Speaker 2: Right that we you know, as somebody who had worked
Speaker 2: on this because the technology was cool and initially and
Speaker 2: enjoyed the challenge of it, and seeing the benefit to
Speaker 2: the military as we thought about you know, as we
Speaker 2: started to get into the space and started to talk
Speaker 2: to people in the public sector about the number of
Speaker 2: lives lost on America's roads, you know, forty plus thousand
Speaker 2: people every year. The challenge that people, whether because they
Speaker 2: have physical limitation to prevent them from driving or because
Speaker 2: they've aged out no longer feel comfortable driving, the implications
Speaker 2: that has for the quality of life, and how we
Speaker 2: can make that better for folks, you know, the accessibility
Speaker 2: that we could bring to transportation that's kind of lost
Speaker 2: with the public trans system we have in the US.
Speaker 2: All of this was like, oh wow, there is not
Speaker 2: just it's not just a cool, interesting technology. It's not
Speaker 2: that I get to work with good people. It's not
Speaker 2: just that you know, this could be valuable. It's that, like,
Speaker 2: this is socially really important to do as well. And then,
Speaker 2: you know, the course of the program, we got to
Speaker 2: the point where we launched for the first time a
Speaker 2: vehicle operating on public roads with nobody in it, well,
Speaker 2: actually with somebody at it, with Steve Mahon, who is
Speaker 2: a blind gentleman who'd worked with us on some of
Speaker 2: the early kind of concepts and understanding of this, and
Speaker 2: he took a road to Austin, Texas by himself and
Speaker 2: seeing the emotional impact that had on him of you know,
Speaker 2: once again having the freedom to get from me to
Speaker 2: be without having asked somebody else. It was just like, wow,
Speaker 2: this this actually matters. So that was very cool.
Speaker 1: So so you mentioned the machine learning piece starting to
Speaker 1: come in. It's I think I was looking back at
Speaker 1: I think it's twenty twelve that alex net comes along, right,
Speaker 1: the first thing. I think that's right, kind of big
Speaker 1: computer vision breakthrough. And so it is interesting. I mean
Speaker 1: when I I think as a sort of lay person
Speaker 1: about autonomous cars, now, I think of computer vision as
Speaker 1: like central like, how could you even do it without
Speaker 1: that right, without computers being able to quote unquote understand
Speaker 1: what they see. But this is actually emerging just as
Speaker 1: you're building this car, right, this system, So how like
Speaker 1: tell me about that? Tell me sort of building in
Speaker 1: parallel with this technological enabling wave.
Speaker 2: Yeah, there was a bunch of these things. So Moore's
Speaker 2: law was continued in a march along and the amount
Speaker 2: of computation we had in the vehicles improving the cell
Speaker 2: phone was, believe it or not, was a big boon
Speaker 2: to us because it drove the performance of cameras.
Speaker 1: So, oh, interesting thing about like all those selfies everybody
Speaker 1: just wanting to make better cheaper cameras and the processing
Speaker 1: for images, yeah.
Speaker 2: One hundred, but even just the physical imager itself, right,
Speaker 2: went from you know, a one megapixel three twenty two
Speaker 2: forty camera, which is what if you bought a top
Speaker 2: the line you know, Kodak digital camera back in the day,
Speaker 2: was now my iPhone's got forty eight megapixel, right, and so,
Speaker 2: and the quality and color performance and everything that's improved dramatically.
Speaker 2: And then light ar is another thing that has really
Speaker 2: evolved over the course of the last twenty years, going
Speaker 2: from things where we could get a single point along
Speaker 2: the line to now full three D fields of view
Speaker 2: and allows us to combine the camera data with the
Speaker 2: light our data with the computation. And then the final
Speaker 2: big thing that's moved forward is in the sensing side
Speaker 2: is radar and the automotive radar. The quality that you
Speaker 2: can get out of that, the price point that comes
Speaker 2: in at now, you know, when you combine that set
Speaker 2: of advances, there's orders of magnitude improvement in both the
Speaker 2: quality of data we can consume and the amount of
Speaker 2: data that we can perform computation over that really is
Speaker 2: unlocked our ability to understand the world around these vehicles.
Speaker 1: So you leave in twenty sixteen, like, are people getting
Speaker 1: rides in wherever?
Speaker 2: It was?
Speaker 1: Chandler, Arizona yet at that time? Like, is that is
Speaker 1: it out in the world yet when you leave?
Speaker 2: Okay, No, we've been doing demos and we were doing development.
Speaker 2: I think we had vehicles in Chandler at that time,
Speaker 2: and we're starting to operate, but they were still with
Speaker 2: a person behind the steering wheel, and we were still
Speaker 2: we were building towards what ultimately became the initial channel launch.
Speaker 1: Why'd you leave?
Speaker 2: It was time? You know, I had been there. I
Speaker 2: really valued and left the experience, but at some point
Speaker 2: I kind of lost confidence that we were going to
Speaker 2: make the decisions that we needed to make to be successful.
Speaker 2: And as a person leading the organization that you know,
Speaker 2: Google had been incredibly generous to me, give me this
Speaker 2: incredible opportunity, and if I just didn't believe it, it
Speaker 2: wasn't my place to lead the team. And and so
Speaker 2: you know, I'm a big believer that in business, you
Speaker 2: you know, kind of you have three options. You if
Speaker 2: you see something you don't like, you try and fix it.
Speaker 2: If you can't fix it, you get in line. And
Speaker 2: if you can't get in line, you get out of
Speaker 2: the way, and you know, I did what I could
Speaker 2: to try and you know, kind of move things away
Speaker 2: that I thought they needed to go, and ultimately said,
Speaker 2: you know, this company's been incredibly galous to me. Let
Speaker 2: me get out of the way and let it gets
Speaker 2: on its own path.
Speaker 1: When you say you lost confidence that they were going
Speaker 1: to make the decisions they needed to make, I mean,
Speaker 1: obviously we should evaluate decisions based on the information available
Speaker 1: at the time, but it is the case that you know,
Speaker 1: you can get in Awai Mo in San Francisco in
Speaker 1: one minute and have a beautiful ride right now, Like,
Speaker 1: were you wrong?
Speaker 2: I apparently yes, right, Like you know, like you think,
Speaker 2: there's no way to judge it other than they they've
Speaker 2: ultimately been successful. You know, maybe would have taken a different,
Speaker 2: shorter path, but you know, I think I think it's
Speaker 2: it's yeah. I don't know if you're a parent, but
Speaker 2: one of the things you hope as a parent is
Speaker 2: that you you you embody your kids with things and
Speaker 2: then they go off from the world and succeed. And
Speaker 2: the same is true when you lead teams or build something,
Speaker 2: is that you know you do your part and you
Speaker 2: hope it outlives you and that you know, outperforms what
Speaker 2: you might have hoped for it. And I think it's
Speaker 2: been awesome to see way most succeed.
Speaker 1: So you started in twenty seventeen, right, uh, And as
Speaker 1: I understand it, you didn't start focused on trucks, Like,
Speaker 1: tell me about what you're thinking when you start the company?
Speaker 2: Yeah, how you get to trucks? So you know, I
Speaker 2: didn't leave Google to start the company. I left Goog
Speaker 2: because it was time, and I then spent some time
Speaker 2: trying to figure out what to do and kind of
Speaker 2: came to the conclusion that, you know, it was worth
Speaker 2: taking another shot at this and trying to build something.
Speaker 2: And so found two great co founders in Sterling and True,
Speaker 2: and we wanted to build a driver and we wanted
Speaker 2: to apply that driver and passenger cars and trucks and
Speaker 2: wherever it could go. And we thought at the time,
Speaker 2: we didn't see how it's going to be possible to
Speaker 2: do trucking because based on the experience I had and
Speaker 2: others had, we looked at how far you had to
Speaker 2: see down the road to do that safely, and we
Speaker 2: came to the conclusion you just couldn't do that with
Speaker 2: the technology that exists all the time, and you have.
Speaker 1: To see farther just because it takes longer for a
Speaker 1: big truck to stop.
Speaker 2: This is physics. There's just way more kinetic energy. Right,
Speaker 2: They're heavier, and they're moving at seventy miles an hour
Speaker 2: instead of in a city you're moving at fifteen miles
Speaker 2: an hour. So the combination of the speed and the
Speaker 2: way it means that it just takes longer to stop
Speaker 2: farther distance down the road. And then you add to that,
Speaker 2: it's much easier drive around a light vehicle than a
Speaker 2: big truck, so you don't need a special license. They're
Speaker 2: much smaller, you don't need special thing maintenance programs and whatnot.
Speaker 2: And so we're like, okay, let's focus on light vehicles.
Speaker 2: We see there's a real opportunity there. If we can
Speaker 2: crack the can you see far enough problem, then trucking
Speaker 2: to be a great application to go to. Yeah. Yeah uh.
Speaker 2: And so part of what I spent my time doing
Speaker 2: during the first couple of years of the company was looking,
Speaker 2: you know, basically turning over every rock we could with
Speaker 2: with Bart, one of our early team members, to find
Speaker 2: a technology that would allow us to see for far
Speaker 2: enough that we could actually do trucking. And ultimately we
Speaker 2: found that in this little company in Montana called Blackmore
Speaker 2: that had built this really special kind of laser range
Speaker 2: finder that because of the way it did measurements and
Speaker 2: what's called frequency modulated continuous wave it it could see
Speaker 2: basically twice as far as the rest of the light
Speaker 2: our stuff could see. Uh, And so we're like, huh,
Speaker 2: we now we we have some really interesting driving capability.
Speaker 2: We've now got this pseudomagical light our sensor that can
Speaker 2: see far enough trucking for like a really great application
Speaker 2: to go take on.
Speaker 1: Let's talk about a few of the things you had
Speaker 1: to figure out. So you decide you're going to do trucking. Yeah,
Speaker 1: and presumably by trucking, Like I mean, I know where
Speaker 1: you are now, Like, so presumably by trucking. You don't
Speaker 1: mean like the bread truck that goes to the little
Speaker 1: market by my house. You mean the great, big truck
Speaker 1: that goes on the freeway.
Speaker 2: Yeah, that's where we thought there was the biggest opportunity
Speaker 2: was working what we call class eight tractor trailers, big
Speaker 2: you know, semi trucks. You know, they drive a lot
Speaker 2: of miles. We don't have enough people who want to
Speaker 2: do that job, and yet it's absolutely essential to American
Speaker 2: and worldwide way of life, and there's a real need.
Speaker 2: Right as we've dug into it, we realized that there's
Speaker 2: five thousand people killed every year in heavy truck accidents,
Speaker 2: five hundred thousand people injured. And then we can move
Speaker 2: goods more efficiently, which you know is important, particularly in
Speaker 2: the you know, this age of e commerce where people
Speaker 2: expect things next day or you know, be able to
Speaker 2: move those goods efficiently the customers is really valuable.
Speaker 1: Okay, this April, if I've got my dates right, you
Speaker 1: actually have a truck drive for real stuff on the
Speaker 1: freeway without someone behind the wheel, right.
Speaker 2: That's exactly right. It was awesome, right. We've as a
Speaker 2: company been building towards that for almost eight and a
Speaker 2: half years, eight and fourty years, and you know, we
Speaker 2: take doing these things safely, very seriously, and really a
Speaker 2: lot of the last couple of years has been making
Speaker 2: sure we could have confidence that when we let the
Speaker 2: thing go, that it was actually going to be safe.
Speaker 2: And then we got to a point where we're like, yeah,
Speaker 2: this is safe. We can go send this out in
Speaker 2: the daylight and dry weather and have it drive back
Speaker 2: and forth between Dallas and Houston. And I had the
Speaker 2: privilege of right along in the back seat. And it
Speaker 2: was awesome right in that it worked. Incredibly boring, right,
Speaker 2: It turns out it's like it's.
Speaker 1: Wanted to be so boring. You wanted to be very boring.
Speaker 2: But but it just kind of worked. And I went
Speaker 2: down and back that day in the truck and were.
Speaker 1: You hauling stuff at that point? Was it was it?
Speaker 2: We were? We were all the histories.
Speaker 1: Yeah, oh pastries, Okay, pastries both ways, that would be
Speaker 1: a bit. There's some pastries. It just that they want
Speaker 1: to know.
Speaker 2: I think there is a pastry imbalance. I think we
Speaker 2: for the first trip, I think we may have actually
Speaker 2: been empty. But on the way back, I'm pretty sure
Speaker 2: from for whatever reasons, the pastries diffused from Houston to Dallas.
Speaker 1: So then what happens in May and may you put
Speaker 1: a person back behind the wheel, right? Tell me about that.
Speaker 2: Yeah. We so as a company, we've really focused on
Speaker 2: do what we do best, and what we do best
Speaker 2: is building a safe, capable driving system. The oral driver
Speaker 2: and we worked with other companies like Peterbilt at Pacar
Speaker 2: and Volvo Trucks, and our partners at peter Bolt called
Speaker 2: up and said, hey, we'd like you to have an
Speaker 2: observer in the seat because there's some prototype parts in
Speaker 2: our truck and it matters to us that you put
Speaker 2: an observer in. And so we said, okay, uh, you know,
Speaker 2: we had a conversation. We respect them, and we ultimately
Speaker 2: put the observer in the seat, and we have a
Speaker 2: person sat on board who is sat in the driver's seat,
Speaker 2: but they're an observer and they're really just there because
Speaker 2: our partner asked them to put them there.
Speaker 1: The VIHA presumably they are a license to drive an
Speaker 1: eighteen year It's not just some n week watching them.
Speaker 1: What's going on. That's the truck driver.
Speaker 2: You know. We we we don't want to put anyone
Speaker 2: in place where you know, the high control has to
Speaker 2: debate whether you know, it's just like, okay, let's just
Speaker 2: put a truck driver there. They sit there. If you
Speaker 2: go to YouTube dot com slash or row driver, you
Speaker 2: can see a live video between eight and five o'clock
Speaker 2: every day of our trucks drive down the road and
Speaker 2: you can see that you know, this person sat there
Speaker 2: and sometimes they're twiddling their sums, sometimes they're eating Fredo's,
Speaker 2: you know, just observing.
Speaker 1: I mean, at least from a narrative standpoint, it's a
Speaker 1: bummer to have to put the person back behind the wheel.
Speaker 2: Surely, absolutely, from a narrative standpoint, it's a bummer, right,
Speaker 2: And it creates a nuance that is, you know, certainly
Speaker 2: in today's.
Speaker 1: You don't want a person buy the wheel. It's the
Speaker 1: whole point. It's the whole point. It's you don't want
Speaker 1: a person buy the wheel, right.
Speaker 2: Totally agree. But in terms of the development and delivery
Speaker 2: of what we're building, it just doesn't matter, right, it's
Speaker 2: a complete nothing.
Speaker 1: Well, I mean that's some margin. It matters at some point,
Speaker 1: it matters, right. The whole premise is you can do
Speaker 1: it without a person there, and so like at some point,
Speaker 1: like relatively soon, I would imagine you need to do
Speaker 1: it without a person there.
Speaker 2: That's right, and uh, look forward to doing that soon.
Speaker 3: We'll be back in just a minute.
Speaker 1: Yeah, So where are you now, Like, give me where
Speaker 1: we're basically to the present now after our twenty year journey,
Speaker 1: Like what's like, are you are people paying you to
Speaker 1: haul stuff.
Speaker 2: Yeah, absolutely, So to like today is an incredibly exciting moment.
Speaker 2: So we have a technology that can drive a truck.
Speaker 2: It has the skills necessary to drive on the freeway
Speaker 2: and drive to sites off the freeway in these industrial
Speaker 2: park areas, and it does that safely. And then internally
Speaker 2: we've got the capability say, okay, what are the new
Speaker 2: features we need to make this more useful for our customers.
Speaker 2: And so we launched in April. About three months later
Speaker 2: we were able to show, you know, be able to
Speaker 2: launch the next version that now doesn't just operate day
Speaker 2: in the daytime, but operates at night as well. And
Speaker 2: of the course the rest of this year, it's really
Speaker 2: about taking and building the you know, in enhancing the
Speaker 2: skills that it has to add the few new skills
Speaker 2: it needs to be able to drive between Fort Worth
Speaker 2: and Phoenix and El Paso. And so at that point, just.
Speaker 1: To be clear, the route you have now is Houston Dallas,
Speaker 1: Is that right?
Speaker 2: That's right? I'm sorry, We drive tween thousand Houston and
Speaker 2: by the end of the year we expect to be
Speaker 2: driving between Fort Worth and Phoenix. And what's exciting about
Speaker 2: and fort Worth and El Paso and El Paso and Phoenix.
Speaker 2: And what's exciting about that is fort worth of Phoenix
Speaker 2: is a thousand miles and a person is not able
Speaker 2: to legally drive that in a day, And so we'll
Speaker 2: be able to start doing things that are superhuman in
Speaker 2: more and more dimensions.
Speaker 1: Presumably the value preposition is really long drives, right, Like
Speaker 1: it makes absolutely no sense for whatever one hundred miles
Speaker 1: or something. And the longer it gets, the more the
Speaker 1: economics makes sense.
Speaker 2: Is that right? I mean, that's think it makes sense
Speaker 2: across all different kinds of scales smiles. But I think
Speaker 2: we're the biggest impact we'll ultimately is on these long
Speaker 2: call trips.
Speaker 1: And like, I get the freeway part in Broadway, How
Speaker 1: does the not on the freeway part work? How constrained
Speaker 1: are you as to where you can go when you're
Speaker 1: not on the freeway.
Speaker 2: Yeah, So the capability we're building is to be able
Speaker 2: to drive everywhere. Sure, but like now, so today we
Speaker 2: drive from a place that's off the freeway onto the freeway,
Speaker 2: and then when we get in at the Dallas end
Speaker 2: of the route, and then at the Houston end, we
Speaker 2: drive about five miles from where we get off the
Speaker 2: freeway to our terminal side, and along the way we
Speaker 2: drive past customer locations. So we're making the choice today
Speaker 2: to go to our terminals, but it's relatively straightforward to
Speaker 2: stop and go to a different terminal instead or a
Speaker 2: different See.
Speaker 1: Like, how hard is that off the freeway part, the
Speaker 1: sort of marginal you know, mile when not on the freeway?
Speaker 2: Not, right, would be the short answer. Right, it's you know,
Speaker 2: we've put a lot of for for a lot of time,
Speaker 2: the primary focus what we're building was the freeway part, because,
Speaker 2: as you pointed out, that's where the most kinetic energy is.
Speaker 2: That's where you know, that's where the bad things could happen. Yeah.
Speaker 1: I mean it's also simpler in certain ways, right, Like
Speaker 1: it seems like turning a big truck around or just
Speaker 1: getting a truck around. Like I live in New York
Speaker 1: City and I see eighteen wheelers in New York City,
Speaker 1: and I don't know, maybe that's maybe that's not that
Speaker 1: hard to engineer for autonomy. You tell me it seems hard.
Speaker 1: It looks hard.
Speaker 2: Yeah, I think New York City is hard, right, And
Speaker 2: I think there'll be a time where we get to
Speaker 2: that where I'm really focused and we're really focused on
Speaker 2: in the near term is how do we get to
Speaker 2: the place where most freight moves right, And a lot
Speaker 2: of that will go to these industrial parks in regions
Speaker 2: of a city. And the reason why it's hard to
Speaker 2: drive a truck in New York City right, and it's slow.
Speaker 2: And so if you're trying to build a distribution center
Speaker 2: for these Class eight tractors and trailers, you put them
Speaker 2: in places where it's convenient because you're trying to run
Speaker 2: a business, not build a technology showcase.
Speaker 1: So what can you not do now ya?
Speaker 2: So today we can't drive in the rain. Well, in fact,
Speaker 2: that's not actually true. So today we drive in the
Speaker 2: rain really well, but we haven't validated that fully for
Speaker 2: driverless operations. And this is really an important idea, right,
Speaker 2: is if you came down to Texas and got a
Speaker 2: ride in one of our trucks and it was raining out,
Speaker 2: it would work, and you would go, huh, why if
Speaker 2: you guys not launched this, and we would say, yeah,
Speaker 2: it basically works. But that's not good enough. It's not
Speaker 2: good enough that it feels like it works. What we
Speaker 2: want to have done is.
Speaker 1: All basically it makes me nervous with a ten roil truck.
Speaker 2: Exactly and it should right, And so we put a
Speaker 2: lot of effort into making sure that it doesn't just
Speaker 2: seem like it works, but we have conviction that it
Speaker 2: will work right. And you know, as an example, two
Speaker 2: of the last things that we we got checked off
Speaker 2: when we launch, where it's one really high speed motorbikes
Speaker 2: running red lights in the city. So we had done
Speaker 2: a bunch of testing, I think up to let me say,
Speaker 2: I'm gonna say some numbers here. These are probably not
Speaker 2: quite right, but I'll use some numbers, you know, at
Speaker 2: ninety miles an hour, because we figure, you know, ninety
Speaker 2: miles an hour in a surface road, nobody.
Speaker 1: Need the motorcycles going ninety.
Speaker 2: Motorcycles going ninety miles an hour and they're going to
Speaker 2: run a red light in front of us, and we
Speaker 2: want the truck to not hit that motorbike. And then
Speaker 2: we're on the freeway and we see a motorbike doing
Speaker 2: one hundred and fifty miles an hour, and we're like, okay,
Speaker 2: that is let's go make sure that that works right,
Speaker 2: because you know, at some point we just want to
Speaker 2: have comps. And so we went through and we found
Speaker 2: out that when motorbike's going that extremely fast. First, we
Speaker 2: found that our simulation wasn't quite up to snuff to
Speaker 2: deal with that, so we had to go fix that.
Speaker 2: And then we found that our perception system wasn't able
Speaker 2: to quite track it as well as we'd want, so
Speaker 2: we had some things to fix there, and so ultimately
Speaker 2: we got to the point where if you're a motorbike,
Speaker 2: please don't go test this run in a red light
Speaker 2: at one hundred and fifty miles an hour. We're going
Speaker 2: to see you and assuming you know, we can actually
Speaker 2: see you, because there's not a building in the way,
Speaker 2: we're going to stop and you know, not have an
Speaker 2: event with you.
Speaker 1: So is the rain analog of that, like more rain
Speaker 1: than I can imagine and flooding and it's dark or something.
Speaker 1: I mean, yeah, maybe frozen frozen also.
Speaker 2: And so this is this is the answers. We we
Speaker 2: have some good ideas of what these challenges are, and
Speaker 2: so we're in the process of putting place the last
Speaker 2: of the tests that we need to say yeah, across
Speaker 2: the things that might be a problem, we actually handle
Speaker 2: those in a way that's safe.
Speaker 1: Once you got rain, are you good? I mean, I
Speaker 1: guess you have the whole southern US without snow and ice? Right, Well,
Speaker 1: think it not freezes in Texas? Right? Does it freeze in?
Speaker 2: Does we get we get snow in Texas? And but
Speaker 2: the good news is, like the reason why a lot
Speaker 2: of freight moves wrong the sun belt is because trucks
Speaker 2: don't like driving in snow. Right, It's just, you know,
Speaker 2: much like people in general don't like driving in snow.
Speaker 2: But there is snow. But if you've ever been in
Speaker 2: Texas when there's a snowstorm, it basically shuts down.
Speaker 1: Yeah, so you don't you don't have to solve snow.
Speaker 1: You don't have to solve snow for now.
Speaker 2: For now. But we will solve snow, right because all
Speaker 2: of the stuff that works for rain mostly works for snow.
Speaker 1: To me, all of these problems we can stipulate will
Speaker 1: be solved by somebody, right, Yeah, plausibly by you, if
Speaker 1: not you, by somebody else. But like I feel like
Speaker 1: there's some sense of urgency. Like I'll say so interestingly,
Speaker 1: just purely coincidentally, I talked to uh Worris softman?
Speaker 2: Is it softman? Yeah?
Speaker 1: Have his name is Med and so he said, and
Speaker 1: we were not talking about you. This was not a
Speaker 1: dig on you. It was more talking about why he's
Speaker 1: doing what he's doing. He said, I don't think Open
Speaker 1: Road Autonomy is a startup game. Uh. And he basically
Speaker 1: you probably know he thinks, I mean, he basically thinks
Speaker 1: it's too expensive.
Speaker 2: Yeah.
Speaker 1: In short, too expensive and too hard in that way
Speaker 1: for a company that is capital constrained, for a company
Speaker 1: that you know doesn't have a monopoly on search.
Speaker 2: Let's see.
Speaker 1: And so I do feel like in talking to you
Speaker 1: like I, you plausibly may solve them. But there must
Speaker 1: be some clock for you, right, You must have to
Speaker 1: get to a point where you're making money relatively soon.
Speaker 2: Is that right? Yeah, Well we've been at the game
Speaker 2: for a while. You know, we went into an eyes
Speaker 2: open right and looked at the you know, having spent
Speaker 2: the time with Waymo, having the history I have, I
Speaker 2: was like, this is not one hundred million dollar problem
Speaker 2: or a ten million dollar problem. This is a multi
Speaker 2: billion dollar problem. And so as we've built Aurora, it's
Speaker 2: been Okay, we need to set ourselves up with the staff,
Speaker 2: we need to set ourselves up with the capital partners,
Speaker 2: we need to set ourselves up with the technology partners
Speaker 2: that allow us to take that run. And I think
Speaker 2: we've been very careful about how we've done that, and
Speaker 2: we've put ourselves in a position to succeed. And I
Speaker 2: agree with with Boris this is you know, I hear
Speaker 2: people throwing out, oh, we're gonna, you know, spend one
Speaker 2: hundred million dollars to do this. I think, you know,
Speaker 2: you haven't even met the ante, right, And so for us,
Speaker 2: we look at say, driving in the rain, we expect
Speaker 2: to be solving that by the end of this year, right,
Speaker 2: And at that point, as you point out, we've basically
Speaker 2: unlocked the Southern freight belt. The amount of truck travel
Speaker 2: that's there is gigantic, and so you know, from now
Speaker 2: where we have a small number of trucks in the road,
Speaker 2: by the end of next year, end of twenty six,
Speaker 2: we expect to have a few hundred trucks on the road,
Speaker 2: and then the year after that we expect to have
Speaker 2: one thousand plus trucks on the road, and then we'll
Speaker 2: built from there. And so it's you know, the conviction
Speaker 2: we're starting to build internally, like yeah, this is just
Speaker 2: just turn over the kind of the way we want it,
Speaker 2: and we expect it is happening now and so it's
Speaker 2: not a hypothetical. It some day we'll solve it. It's like, no,
Speaker 2: we're you know, four or five months kind of time frame. So, well,
Speaker 2: three four months timeframe.
Speaker 1: What's the business model?
Speaker 2: Yeah, so our business model is to work with the
Speaker 2: ecosystem that's there today. So today, if you're a truck
Speaker 2: and company, you buy a truck and then you pay
Speaker 2: somebody to drive that truck for you. Uh, and so
Speaker 2: that's what our company's going to do. You'll if you're
Speaker 2: a company and wants some Automas truck, you're gonna go
Speaker 2: to pac r and peter Bilt, or you're going to
Speaker 2: go in Evolve and say I want to buy a truck,
Speaker 2: and they buy the truck stuff and then it'll roll
Speaker 2: off the line from our partner and it'll have the
Speaker 2: row driver installed on it and you'll pay a subscription
Speaker 2: to Aurora to drive that truck for you.
Speaker 1: I see. Okay, So so you guys get paid by
Speaker 1: the by the mile or by the by the hall
Speaker 1: or something like that.
Speaker 2: Yeah, that's exactly right. You know, it's at the risk
Speaker 2: of at a service as a services you know, it's
Speaker 2: driver as a service. That's the way we were thinking
Speaker 2: about the business.
Speaker 1: And the trucking company is buying the truck, So that's
Speaker 1: good for you. You don't have to When you say
Speaker 1: you'll have a thousand trucks out there, you don't mean
Speaker 1: you're going to own a thousand trucks. You mean you'll
Speaker 1: have essentially a thousand autonomous drivers in trucks that other
Speaker 1: people own.
Speaker 2: That's right, And this is because that's what our customers
Speaker 2: want right that there Also it.
Speaker 1: Seems easier as a business, and I still not have
Speaker 1: to get into the trucking business.
Speaker 2: I agreed again back to the philosophy of we think
Speaker 2: we're really good at building the driver. And I look
Speaker 2: at our partners, whether it's Werner or Hirshbock or Federal Express,
Speaker 2: they know what they're doing. Why don't we help them
Speaker 2: make their business a bit better and work together?
Speaker 1: Can we talk about autonomy a little bit more broadly,
Speaker 1: just since you're at it so long and I'm curious
Speaker 1: what you think, So, like, what do you think are
Speaker 1: the important constraints on autonomous vehicles right now? Sort of
Speaker 1: broadly technologically, you know, in terms of policy.
Speaker 2: I think it's primarily technological right now. Okay, right, And
Speaker 2: I've said that. You know, people for for the better
Speaker 2: part of a decade have been you know, when when
Speaker 2: it's convenient hiding behind you know, regulation or policy. And
Speaker 2: you know, the US policy of regulatory environment is permissive.
Speaker 2: It's one of the things that's allowed innovation to flourish
Speaker 2: in this country, certainly in the automotive space.
Speaker 1: So what are the what are the important technological constraints
Speaker 1: right now? Like, I mean, obviously WEIMO is thriving where
Speaker 1: it's living, but like, also there are no autonomous cars
Speaker 1: where I live and people aren't buying them, and so
Speaker 1: like what's the what's what are the important bottlenecks right now?
Speaker 2: Uh? Execution madwidth? Right? Like, So so at least where
Speaker 2: I sit at a roar today, I don't out and see,
Speaker 2: Oh my gosh, I don't know how we're going to
Speaker 2: solve that. It's hey, we have this many people, we
Speaker 2: have this much leadership band with we just need to
Speaker 2: go execute right.
Speaker 1: Like thinking industry wide and not to be like petty
Speaker 1: or something, but like why aren't autonomous cars everywhere?
Speaker 2: Right? Yeah? Because it's really hard, right, And I think
Speaker 2: the right metaphor for this is commercial aviation, like the
Speaker 2: physics are really in grasp and like you can both
Speaker 2: things together. And yet you know, we basically have two
Speaker 2: commercial aviation manufacturers globally, right, we have Boeing and Airbus.
Speaker 1: Because it's hard to build big jet aircraft.
Speaker 2: It's hard to build them, it's hard to have conviction
Speaker 2: they're going to be safe, it's hard to produce them right.
Speaker 2: And when I look at automated driving, I think it's
Speaker 2: in a very similar space in that a lot of
Speaker 2: things have to work well. You have to have the
Speaker 2: discipline to be able to work across all of them,
Speaker 2: make sure they all fit together, and then you have
Speaker 2: to have the processes. And process in Silicon Valley is
Speaker 2: often kind of a four letter word, like you know,
Speaker 2: you've got to move past and break things, but when
Speaker 2: you're building something safety critical, you actually have to get
Speaker 2: that stuff right, and the art is getting it right,
Speaker 2: we'll also be able to do it quickly.
Speaker 1: So so when you think about the spread of autonomy
Speaker 1: from here, again not just within your company, but sort
Speaker 1: of more broadly, like how do you think it'll play out?
Speaker 2: I think it's going to usher incredible new age all right,
Speaker 2: that we should be able to dramatically reduce traffic fatalities.
Speaker 2: The fact that you know, it had been a decade
Speaker 2: without a fatality on civil aviation, and yet we had
Speaker 2: forty thousand people killed on the road every year in
Speaker 2: the US. Like, we can bring a technology to bear
Speaker 2: to help solve this, right, and I think that's profound.
Speaker 2: I think we can have a huge sustainability impact all
Speaker 2: while actually kind of power up the US economy. And
Speaker 2: so for us, you know, we've got this mission right
Speaker 2: now that's focused on trucking, but what we're really building
Speaker 2: is this capability to release safety critical software and systems
Speaker 2: and this ability to understand the world. And so you
Speaker 2: combine those two things, the places we can go with
Speaker 2: that are profound, right, whether it is you know, last
Speaker 2: mile delivery or ride hailing applications, or mining or farming
Speaker 2: or aviation. Like there's it's just going to be a
Speaker 2: lot of fun over the next decade. So yeah, I'm
Speaker 2: psyched for it.
Speaker 1: Next decade sounds fast given the pace so far. I
Speaker 1: was surprised when you said next decade at the end there.
Speaker 2: Yeah, I think that like anything, there's a point where
Speaker 2: you have built a foundational base and then suddenly you're like, oh,
Speaker 2: now I have this power. Now I have the superpower
Speaker 2: to go do these things.
Speaker 1: It's basically the package of hardware plus software at a
Speaker 1: reductive level, and you can sort of put it on
Speaker 1: other things and it'll more or less work.
Speaker 2: More importantly the process and the data and again does
Speaker 2: not sound sexy.
Speaker 1: But the data sounds sexy, Like the more you talk
Speaker 1: to machine learning people, the more sexy data sounds. Right.
Speaker 2: Yeah, And you know, and we joke internally, you know,
Speaker 2: if you if we have three kind of big artifacts,
Speaker 2: being our software, our data, and our process, if you
Speaker 2: held my gun in my head and said delete one
Speaker 2: of them, I'd say delete the software, right, Please do
Speaker 2: not delete our software.
Speaker 1: Yeah, that's really interesting. Yeah, we'll be back in a
Speaker 1: minute with the life around. We're gonna finish with the
Speaker 1: lightning round. I appreciate your time, and we were almost done.
Speaker 2: Cool. Thanks for the conversation.
Speaker 1: So right that your father was a prison warden he was,
Speaker 1: in fact, Yeah, did you learn any management and were
Speaker 1: parenting tips from him?
Speaker 2: Lock them up? Uh? No, absolutely not. You know. One
Speaker 2: of the things that that stuck with me is, you know,
Speaker 2: it's it's a difficult job being a prison warden because
Speaker 2: you have the staff and then you have the inmates
Speaker 2: and right, and and they're often at odds. And one
Speaker 2: of the things he said was, you know, make sure
Speaker 2: you treat everyone with respect. Right that he would, you know,
Speaker 2: these are people who had made a mistake in life,
Speaker 2: you know, and you treat them with respect. Uh. And
Speaker 2: you know, I think that's really powerful and important.
Speaker 1: What's one thing you learned working at Google?
Speaker 2: I think big, right, I think I think that was,
Speaker 2: you know, Larry and Sergey never lacked for vision, and
Speaker 2: I think you can often find yourself constrained by kind
Speaker 2: of your own kind of sense of what's a limit,
Speaker 2: and they just thinking like, no, what, why can't you
Speaker 2: do that? Why can't you do that? Yeah?
Speaker 1: I remember, just as a consumer, just as a as
Speaker 1: an ordinary person in the world, when they started what
Speaker 1: became Google street View, I was like, yeah, surely you
Speaker 1: can't take a picture of every bit of every road
Speaker 1: in America, and actually you can and they did.
Speaker 2: Yeah. Right. Like I think that that sense of like
Speaker 2: you don't need to be intimidated by this, like you
Speaker 2: need to think rationally, you need to figure it out.
Speaker 2: But you know, I think it's a power one of
Speaker 2: my professors at Carnegie Mellon said, dream like an amateur,
Speaker 2: execute like professional, right, And I think that's just a
Speaker 2: really kind of profound sentiment.
Speaker 1: What's one thing your time at Google taught you not
Speaker 1: to do?
Speaker 2: Oh? What it teged me not to do?
Speaker 1: Uh?
Speaker 2: Maybe kind of adjacent that. Like, so when I went
Speaker 2: to Google, I thought I was a pretty good programmer,
Speaker 2: you know, I was, Yeah, Carnie Melon, I'm just good
Speaker 2: at what I did, and I'm not a bad programmer.
Speaker 2: But like, there are truly exceptional people out in the world, right,
Speaker 2: And so I think, you know, maybe don't overestimate yourself
Speaker 2: and kind of again kind of think, kind of think big,
Speaker 2: don't underestimate what's out there.
Speaker 1: Chris Sumson is the co founder and CEO of Aurora.
Speaker 1: Please email us at problem at pushkin dot fm. We
Speaker 1: are always looking for new guests for the show. Today's
Speaker 1: show was produced by Trinamnino and Gabriel Hunter Chang. It
Speaker 1: was edited by Alexander Garretson and engineered by Sarah Burgher.
Speaker 1: I'm Jacob Goldstein and we'll be back next week with
Speaker 1: another episode of What's Your Problem.