title: Search Engine Presents: Are you a good driver?
author: Odd Lots
content_type: podcast
publication: Odd Lots
published: 2026-04-08T21:00:00+00:00
word_count: 12044
[0:00] Introduction
00:00:00 Speaker 1: Hey, there are odd lots listeners. I'm Tracy Alloway and I'm Jill. 00:00:03 Speaker 2: Why isn't though? 00:00:03 Speaker 1: And we want to welcome you to a special presentation of the podcast search Engine. We all know that artificial intelligence might replace all sorts of jobs humans do today, but for most of us that's still mostly theoretical. There's one job, though, where robots are already taking the wheel, and that is driving. 00:00:21 Speaker 3: In fact, it's one of the most common jobs in America for young men without college degrees, and over the course of two episodes, Search Engine tackles both the promise and the peril this growing technology. 00:00:31 Speaker 1: In part one, Are You a Good Driver? The Search Engine team tells the story of how a small secret team at Google spent fifteen years teaching a computer to drive from a failed robot in the Mojave Desert to a vehicle that might actually be the safest on the road. This episode tracks the engineering breakthroughs the nearer catastrophes, and takes a skeptical look at the safety data behind Weaymo's claim that its cars are ninety percent safer than human drivers in serious crashes. 00:00:57 Speaker 3: All of it boils down to one big question. All the robots actually say for drivers than we are. Enjoy this presentation of Search Engine, and be sure to catch part two, titled The Trial of the Driverless Car, available wherever you get your podcasts. 00:01:37 Speaker 2: Before we start the story today, I want to ask you to imagine a different version of your life. You're you, but it's almost two hundred years ago, and unfortunately and our hypothetical, it's Monday morning. It's Monday morning, and it's very early pre dawn. You wake up to this really hard wrapping at your window. That's the knocker Upper here to get you up for where we're in the eighteen hundreds before the invention of the adjustable alarm clock. The knocker Upper is a job. The knocker Upper walks the neighborhood with a long stick and taps it on the windows of people's houses early in the morning to wake them up for work. Who wakes up the locker rupper for work? Nobody knows. But this is a job, a job that'll actually exist for another century. Outside the gas street lamps are still burning. The lamplighter lit them the night before. He's supposed to come at dawn to extinguish them, but it's so early that he has it yet. Your lamplighter is one of those neighbors. You have a deep fondness for a fixture. Every day you watch him make the rounds at dusk with his ladder and his light. You yourself are a driver. Professional driver two hundred years ago is also a job. You're a person who sits on a coach and holds the reins of a horse. You take passengers where they want to go. You start your workday. Okay, hypothetical. Over two of those jobs are obviously so long disappeared that most people don't know about them. The knocker upper is your iPhone alarm. The lamplighter is the electric street light. The third one driver has persisted as a job for some, as a routine human task for nearly everyone else. This is a story about whether that's about to change. It's about how the word driver, which right now makes me picture a human, could soon transform to refer to a machine, the same way the words dishwasher, printer, and computer all did. I've thought about this, maybe too much, in the year I've been working on this story. In conversations constantly, I'd asked the humans I meant the same question, Are you a good driver? Are you do you consider yourself a good driver. 00:03:54 Speaker 4: I do within limits. I think I'm a good driver because I understand the limitations of my driving. 00:04:03 Speaker 2: This is Alex Davies. He wrote an excellent book called Driven, the Race to create the Autonomous Car. Alex, like me, thinks a lot about human driving about his own personal limitations. What are the limitations? 00:04:15 Speaker 4: The limitations are that I can't always pay attention to everything that I get tired. I've been trying really hard to be calmer in the road. My husband and I are expecting our first baby this fall. Congratulations, thank you, and I thought that, along with reading all the baby books, a good project to work on is just be calmer in the car. 00:04:39 Speaker 2: A very good resolution, because, of course, for most of us, driving is the riskiest behavior we routinely engage in. In fact, even Alex, despite his good intentions, would actually get in a car accident just a few months after we first spoke. He was okay, it was the car that was totaled. Safety is the entire pitch for the driver of this car, which is really a car and by a computer driver. LESE cars don't get drunk, tired, or distracted. They never text or feel road rage, and these drivers cars, they aren't the future. They're actually already here. But it's funny. If you just don't happen to live in a place that already has them, it's easy to not see how fast things are changing. Robo taxis like Waimo are operating in ten American cities, providing millions of rides to Americans. In China, the rollout is happening even more widely. They're in twice as many cities. But here, if you live in a place like San Francisco or Austin, today, a driver's car is about as exotic as an uber. A passenger in those cities opens up their phone and decides who should drive them, a human driver or a robot driver. How that happened is a story, a story we are living through right now, whose ending promise is to totally reshape the places we live. And today we're going to tell you how we got here. In chapters, Chapter one dreams without drivers. So it turns out this dream that inventors have had to replace the human driver with some kind of machine. That dream is about as old as the Lamplighters. 00:06:16 Speaker 4: People have been thinking about a self driving car for so it's about as long as there's been a human driven car. Why, there's this funny thing you lose when you move from the horse to the human driven car, which is that in a horse drawn carriage, the horse is not just going to run off a cliff. If you let go of the reins, you lose sentience in your vehicle. 00:06:45 Speaker 2: When automobiles first arrived, these powerful and nonsensient cars, there's actually a passionate fight to keep them off the streets. It was the eighteen hundreds, and people feared these new things, the steam powered vehicles thundering down the roads that soon evolved into gas powered vehicles, also thundering down the roads. The fear was partly about jobs. These vehicles were seen as a huge threat to a whole network of working class jobs. Horse breeders and horse farriers, horse feed suppliers, horse manure haulers, horse carriage manufacturers. Not to mention the teamsters Teamsters today the word makes me think of the Teamsters union, But originally the teamsters were the workers who drove teams of horses. Teamsters were like truckers before we had trucks. Cars seemed to imperil all these horse related jobs. And even if you weren't worried about these workers, the cars were also less safe. Some anti car activists battled to stop or slow the new technology, mainly with regulations. There were red flag laws, which said if you had an automobile, you had to hire a person to walk in front of it, waving a giant red flag to warn people. In Pennsylvania, a law was prepared requiring horseless carriage drivers who encountered livestock to stop, disassemble their car, and hide the parts behind the bushes. The governor vetoed it. But to think about these crazy anti car activists is that directionally they were right. Those cars did initially wipe out a lot of jobs, even if they created more, and cars were very unsafe. The cities that threw their doors open to cars without regulation were rewarded with astonishing death rates. Detroit let drivers pretty much run wild. In the early nineteen hundreds, deaths accumulated in a Detroit without drivers licenses, stop lights, or turn signals. Many of those deaths were children. It took decades for society to mostly learn to live with cars. The rest of the story is just the world you grew up in. We invented laws, licenses, drivers ed, We learned to better design roads. We invented the highway, the seatbelt, the airbag. All those things made driving less debts, although the smartphone reverse some of that progress. Nationally, Today, deaths from cars are about as common in America as deaths from guns or opioids, about one in one hundred. It'll probably happen to someone you know in your life, maybe several someone's. Whether or not you see that as an urgent problem to solve depends on you. But as long as there have been cars, there have been people who wanted to truly solve what's left of the safety problem the best way we knew how. They wanted to make the car more like the horse it replaced, make the car more sentient. 00:09:36 Speaker 4: So that thought is there early and like early visions have hit include, oh well, we'll have radio controlled cars, because they had radios at the time. There's a real effort at one point to build magnets under the road, and at each stage what a self driving car can be is dictated by the technology that's available at the time. For the most part, no one's thinking that much about a vehicle that thinks for itself. They're just thinking about a vehicle that the person in it doesn't have to drive. 00:10:13 Speaker 2: Many different attempts, many different failures, as many wonders as we invented, we could not approach nature's most majestic creation, a horse's brain, at least not until the turn of the millennium. 00:10:30 Speaker 5: Are to. 00:10:38 Speaker 6: Deep within the Department of Defense, there's a little known military agency that has created some of the most innovative technology of the twentieth century. This is the story of Dark. 00:10:49 Speaker 2: Chapter two, DARPA's Million Dollar Prize. 00:10:52 Speaker 6: DARPA's current goal is to develop autonomous military vehicles machines that can operate on their own without drivers. 00:11:00 Speaker 4: Darpe has always been intrigued with him. 00:11:01 Speaker 2: This is from a documentary called The Million Dollar Challenge. Honestly less a doc more an ad for DARPA, the Pentagon's research arm. DARPA's mission is to try to keep American technology one generation ahead of everybody else. It doesn't always work, but DARPA has invented or funded a lot GPS and the M sixteen, the early Internet, and the Predator drone. In two thousand and two, DARPA decided to pursue the driver's car in a very unusual way. 00:11:29 Speaker 4: The director of DARPA at the time, a guy named Tony Tether, who had been a door to door salesman in his use definitely has that flare in that way of thinking, says, let's have a contest. Let's see who can put all of these ingredients that we've developed together into a proper self driving car. His original idea is we'll drive him down the Las Vegas Strip that's almost immediately next because it's insane. 00:11:59 Speaker 7: Oh right, you would have to like literally gridlock a huge American city so people could put robot cars on it exactly. 00:12:09 Speaker 4: So he says, Okay, do you know what, We'll do it in the desert. We'll do it in the desert outside Las Vegas, and anyone who wants to can make a team build a self driving car, bring it to the desert, and we'll race them. 00:12:22 Speaker 2: The driver that DARPA wanted to replace was the American soldier. DARPA wanted a vehicle that could drive itself down roads that might be filled with hidden explosive devices. So, in this moment at the tail end of the dot com boom, darpest trying to inspire tech to build something besides another website darpest. Tony Tether announces that the prize for whoever can win its Grand Challenge will be one million dollars. 00:12:46 Speaker 4: The rules for very open there were little rules like you couldn't have two vehicles communicating with one another, but you could build any kind of vehicle you wanted, could have six wheels. It could be a truck, it could be a motorcycle, could be a trice. It just couldn't attack other vehicles. That was rolled out early on. 00:13:04 Speaker 2: Oh, was that a concern that people would just like sort of battlebot the thing you're auto's vehicle would have like a little shredder that would take out somebody else's. 00:13:12 Speaker 4: Someone asked in the first Q and A at this like they said, can we attack other vehicles? And they said no. And it's funny you bring up BattleBots because a lot of teams who entered this had BattleBots history interesting. They were used to building robots for interesting purposes, and when they caught wind of this, they said, we can do this. We can scrap together some money and this will just be fun. 00:13:41 Speaker 2: I'm going to tell you what happened in this robot race in the desert, not because I care so much about these early robot vehicles, but because I care a lot about the engineers who were making them. These would be the people who would later go
[15:30] Lead development for the billion dollar
on to lead development for the billion dollar companies creating today's drive those cars. The people had very different views about how to get that technology ready, different values when it came to things like the acceptability of risking human life, abstract differences that would become very concrete later on, to the point where people would be charged with federal crimes. That's the future. But listening to this part of the story, what I listen for is how much of it can you detect already? How much of the differences already present. The first engineer I want you to pay attention to is a man named Chris Armson, and way back in two thousand and two, how did you end up being part of the Dark Program challenge? 00:14:39 Speaker 8: It sounded like fun. 00:14:43 Speaker 2: Chris these days, the CEO of a large tech company back then, a PhD student at Carnegie Mellon University. When he first got recruited for the race, he was out in the field observing a robot as it crept across the Autacama Desert training for its future deployment on the surface of Mars. 00:15:00 Speaker 8: H advisor came down and was really excited about this Darker Grind challenge thing, and the idea that you'd have a robot run across the desert at fifty miles an hour just sounded exciting having spent the last couple of weeks walking behind a robot at very low speed. 00:15:20 Speaker 2: So Chris would join Carnegie Mellon's Red Team and help build a car called Sandstorm, a bright red humvey with the top lopped off, a plethora of futuristic sensors mounted to it like scanners a crackpot would use to search for aliens. You can see Chris back in that documentary. He explains to the filmmaker at the time that the hard part, of course, isn't the vehicle, it's the driver. How do you even begin to teach a computer to operate a hum vy at all? How does a. 00:15:45 Speaker 4: Computer make the steering wheel turn? 00:15:46 Speaker 5: How does a computer change the. 00:15:48 Speaker 4: Pressure on the break and the throttle? Those are the issues that we're fighting through right now. 00:15:53 Speaker 2: The answer Sandstorm represented the best entry from the contest's traditional academic crowd, but there's a different crowd there too. Represented best by a man named Anthony Lewandowski. Can you tell me about Anthony Lewandowski? 00:16:07 Speaker 4: Anthony Lewandowski. Where to begin? So Anthony is like an entrepreneur. He's a really charming guy. He's six foot six, He's gangly as all get down. He grew up mostly in Belgium because his mom was working for the EU. For high school, he moved to Marin to live with his dad. And he's a hustler. 00:16:39 Speaker 9: My name is Anthony Lewandowski. 00:16:42 Speaker 4: I was a grad student at Berkeley. 00:16:44 Speaker 9: Instead of continuing
[18:48] Finish my PhD I decided it
on to finish my PhD, I decided it was much better to do the Grand Challenge. 00:16:50 Speaker 2: We asked Anthony for an interview. He didn't respond, but here he is in the footage from back then. Anthony did not have the engineering experience or resources of a team like Carnegie Mellons Red Team. So you tried something very different, a vehicle that had almost no chance of winning the race, but which was also perfectly designed to stand out to get him a lot of attention, maybe a job. The race's only self driving motorcycle, it was named ghost Rider, a stubby little thing covered in stickers with then inten on the back and cameras on the front. 00:17:21 Speaker 9: There's a steering actuator on the top here, which allows us to modify the steering angle. So basically, if you're driving, you start to follow the left, you steer left. That makes you turn the left, and then you get the tripleal acceleration to put you back up to the right. And you're monitoring that in real time and making small adjustments, and you stay bounced. 00:17:42 Speaker 10: Stroll Blight is on. The command from the tower is to move, ladies and gentlemen Sandstorm. 00:17:50 Speaker 2: The race happens on a Saturday in March of two thousand and. 00:17:52 Speaker 10: Four, autonomous vehicle traversing the desert with the goal of keeping our young military personnel out of harm's way. 00:18:08 Speaker 2: Oh yeah, what happens the first time they try to do this competition? 00:18:14 Speaker 4: The two thousand and four Grand Challenge is an utter hysterical disaster. 00:18:22 Speaker 2: Disaster Number one ghost Rider the motorcycle Anthony Lewandowski forgot to flip on the switch for the stabilization system. The bike immediately topples ghost Rider down. 00:18:34 Speaker 4: Anthony good effort, and then every vehicle after it fails miserably. Like one vehicle drives up onto a burm flips off. One vehicle, drives straight out, does an inexplicable U turn and just drives back to the starting line. And the rules are that once your vehicle starts, you can't do anything. 00:18:57 Speaker 2: Even Sandstorm got stuck on a burm. Chris Urmson just standing there, unable to help his robot. 00:19:03 Speaker 8: Poor thing was trying to get going, but its wheels were just spinning on the gravel and tried so hard that it actually melted the rubber of the tires. 00:19:12 Speaker 5: And so there's this plums of. 00:19:13 Speaker 8: Black squoke before they killed it. 00:19:16 Speaker 2: For the roboticists, this was obviously very disappointing. Chris Urmson compared it to an Olympic marathon where the best runner only makes it two of the twenty six miles. What this contest had done, though, was it had flushed all these inventors out. It had jumpstarted the scene that would develop this technology. One of the most important people there that day, actually just watching, was someone I haven't mentioned yet, a legendary roboticist named Sebastian Thrun. 00:19:43 Speaker 4: Sebastian Thrun, he was at the first Grand Challenge. He didn't bring a team, he wasn't participating. DARPA wanted to show off some other projects they'd been funding, including one of his robots. So he brings the robot and so he's there and he watches this disaster or anything's can do better for mess. 00:20:03 Speaker 11: I looked at the very first iteration of this quan challenge, but it didn't participate. It was a spectator. 00:20:08 Speaker 2: This, of course is Sebastian Thrun. He grew up in West Germany, moved to the US Toddic Carnegie Mellon before moving to Stanford. Watching that day, he saw this fundamental error. He believed all the entrance had made. 00:20:21 Speaker 11: I saw that all the teams treated this like a hardware problem. They looked at this and say, we have to build a bigger wheels and bigger chassis and so on. And I looked at this and said, about wait a minute. The challenge really is to build a self driving car. They can drive for the desert. I can get a rental car. They can do it just fine, provided as a person insight and the challenges we need to take the person out of the driver's seat and replace it by computer. That is not a problem with bigger tires. That's actually be a software problem. 00:20:55 Speaker 2: Sebastian Thrun had a dual background robotics and artificial intelligence, which probably explains his focus here on the robot driver's mind. He was thinking about something else too. The military wanted this tech to replace a relatively small number of drivers in its war zones, but Sebastian was already imagining something bigger. What would happen to traffic deaths worldwide? If one day everyone had access to a driverless car. 00:21:21 Speaker 11: I had experiences of losing people in my life to traffic accidents, and I felt we lost over the million people in the world to traffic accidents. Wouldn't it be amazing if Dabok invented something that would save a million lives a year. 00:21:33 Speaker 12: In October of two thousand and five, forty three teams have brought their vehicles to compete in a unique event, a race driven not by testosterone but computer. 00:21:44 Speaker 2: Coke Chapter three Machine Learning. 00:21:54 Speaker 12: The race course is a circular maze that zigzags for one hundred and thirty two. 00:21:58 Speaker 2: Eighteen months later this second Grand Challenge, DARPA doubled the bounty two million dollars. This footage is from a PBS documentary called The Great Robot Race, narrated to My Mild Joy by John Lithgow. Familiar faces have returned. Chris Earmsen back with the Carnegie Mellon team, the Sign with two vehicles, Highlander and Sandstorm. Anthony Lewandowski back with his motorcycle, which still doesn't work. He's knocked out in the qualifiers. And now there's also Stanford's entrant compared to Sandstorm, the bulked up hummer. The car looks easily a blue suv donated by Volkswagen. A baby face, Run smiles next to his soccer mom looking vehicle. 00:22:38 Speaker 13: The Vika's name is Stanley, so Stanley is nothing else but Stanford, but it also gives the vehicle a personality. If you think of the Vega more and more as an intelligent decision maker. 00:22:50 Speaker 4: Run is a computer scientist, and Thrun really broad more artificial intelligence, which at the time we're talking two thousand and five was still rather primitive, especially compared to what we have today. But he could use it to teach his vehicle how to recognize the road and how to do it much faster. They found a dirt road out near Stanford, and they drive it down a dirt road and have the car's cameras record what they were seeing. 00:23:20 Speaker 11: The robot Standy was able to train itself as it and the way it worked. Its eyes looked way ahead and it could see stuff way at distance. When it drives over the stuff, you could tell it wasn't a good place to drive or not, because it could measure how slippery or how bumpy the vote was. And they could then retroactively train and say, say, this green stuff over there, it's something good to drive on aka grass, and this browner stuff aka mutt is not so good to drive. 00:23:49 Speaker 2: And so it was able to detect patterns and generalize from what it had learned. 00:23:55 Speaker 4: Yeah. 00:23:55 Speaker 11: Absolutely, and this is like thirty times a second, I mean, just like a person. 00:24:00 Speaker 2: The race kicks off with Stanley Sandwich between Carnegie Mountains tow behemoths. 00:24:05 Speaker 12: Highlander leads the path, followed by Stanley and Sandstorm. 00:24:10 Speaker 2: What happens in the second race? 00:24:12 Speaker 4: The second race is as successful as the first race is disastrous. 00:24:20 Speaker 2: Nearly every entrance in the second race would go further than Sandstorm had in the first. Multiple vehicles would finish the course. The real question was who would do it fastest? And so at what point was it clear to you that you were going to win well. 00:24:34 Speaker 11: Once we passed the front running team, we kind of saw the vehicle descend into what was the hardest part of the race course, a very treachery mountain pass, and we saw at a distance a dust cloud. We saw a helicopter. We so a little features that must believe ow there's something happening that's magical, and this dust cloud then all of a sudden turned bluish because the cover was blue, and came closer, and then it came first to the finish line and was unbelievably magical. 00:25:04 Speaker 2: At the end of the dock over some criminally corny piano music. Sebastian Thron gives his post race interview. He's dressed a lot like a race car driver. Watching you could forget he wasn't in the car. 00:25:14 Speaker 11: It was just amazing to see this community of people, that community succeeded. 00:25:20 Speaker 4: Today. 00:25:21 Speaker 11: Behind me, there are three vobos that made it all the way through the desert, and all three of them did be unthinkable. It's such a fantastic successful this community. 00:25:30 Speaker 2: I think we all win a made for TV Kumbaya moment. Still years before the race to build driverless cars would enter its cutthroat phase. What would happen next is that a small band of lunatics would take driverless cars out of the desert start secretly driving them on public roads in the state of California. They would do this at the behest of a man who had been observing from the stands that day, disguised and hat and sunglasses, who watched the challenge while his mind spun this after a short break, Welcome back to the show. Chapter four, something actually useful for the world. The race in the Desert had been designed as a spectacle, something flashy to dry out America's smartest roboticists, but it had drawn another person who come for his own reasons. Google's Larry Page arrived at the Darker Grand Challenge in a baseball hat and sunglasses disguise. He found Sebastian Throne and buttonhold him, asking him a million highly specific questions about things like the wavelength his light our system used. But this meeting in the desert, this was not actually their first introduction. 00:27:20 Speaker 11: Well, the first time I met Larry was a bit earlier. He had built a small little robot that acted as a tailor presence for meetings, and he was trying to drive it around the Google officers instead of himself going to meeting with a robot. And he sent me a message and said, I'm going to show you the vote I've built. And I, in a spur of like craziness, I sent the message Breck saying, Larry, I'm so glad that Google it he used twenty percent of time. It was something useful for the world. I couldn't. I either expected a rapid response or never hear from him again. It turns out I was lucky. He responded immediately. I took his role, would fix it next fenty four hours. 00:28:01 Speaker 2: And he was very heavy. Larry Page, it turned out, had actually been interested in autonomous vehicles since at least grad school. That's what he'd wanted to do his thesis on before being guided by some wise PhD advisor towards search engines instead. Now as a spectator at DARPA's second Grand Challenge, he could see real world evidence that autonomous vehicles might actually be a thing. At first, Larry Page hires Sebastian's run along with fellow Darbik contestant Anthony Lewandowski, just to build what will become Google street View. They'll actually modify the system that Stanley the car's roof mounted cameras had used to begin photographing American streets. But before long, Larry Page returns to Sebastian with his dream of a driverless car, and so how soon after arriving at Google this project chauffeur again, like Larry Page says to you, I have a mission, like how does this happen? 00:29:00 Speaker 11: And this is an embarrassing moment for me. It's about two years later, two thousand and nine, where I sit in a cubicle and like Page comes by and says, Sebastian, I think you should build a self diving car that can drive anywhere in the world. And my immediate reaction was, no, taking the technology we build for this empty desert and put it in the middle of Market Street in San Francisco is going to kill somebody. And Larry would come back the next day with the same idea, and I would give them the same answer, and both of us got increasingly more frustrated. God damn it, it can't be done, and eventually came and said, look, Sebastian, OK, care, I get it. You can't do it. I want to explain to Erk Schmidt the CEO at the time and Sergey Britt my cofounder, why it can't be done? Can you give me the technical reason why it can't be done? And that's the moment of incredible pain, because I go home and I can't think of a technical reason why not. It was this kind of moment where I felt, look, I'm the world expert on self diving cars, and I'm the person who denies that it can be done. Like that taught me an incredibly important lesson about experts that for the rest of my life, I decided experts I usually explore. 00:30:10 Speaker 4: The past and not the future. 00:30:12 Speaker 11: And if you ask an expert about innovation, something crazy new, they're the least likely person to say, yes, it can be done. 00:30:20 Speaker 2: So this is where the Google's self driving car project begins in two thousand and nine. It's led by Sebastian, joined by others from the Darker Challenges. The methodical Chris Armsen was running most things day to day. Anthony Lewandowski, the flashy motorcycle guy, would work on hardware. Dmitriy Dolgov, another darker veteran, would be responsible for planning and optimization. It was a secret project did report directly to Larry Page, a small enough team that there'd be no bureaucracy, few emails, fewer meetings, just eleven engineers, who writer Alex Davies says, represented some of the best young talent in the country. 00:30:54 Speaker 4: And so Google builds this very quiet team and it says to them, buildness a self driving car. And because that goal is super nebulous, they give them two challenges. They say, safely log one hundred thousand miles on public roads. But they also give them a challenge called the Larry one K. 00:31:19 Speaker 11: So Larry and Serge and I said together and the two of them carved out one thousand total miles of road surface in California. 00:31:27 Speaker 4: They open up Google Maps and they just click around and they look for ten separate one hundred mile routes that are really tricky. 00:31:37 Speaker 11: Absolutely everything like the Bay Bridge and Lake Tao and Highway one to Los Angeles and Market Street and even crooked Lambas Street. 00:31:46 Speaker 4: And they say to the team, you have to drive each of these one hundred mile routes without one human takeover of the system, without one failure of the car to. 00:31:56 Speaker 2: Get off to your running start. The team licenses the code from Sanford darpa Urban Challenge vehicle. Anthony Lewandowski goes to a local Toyota dealership and buys eight priuses, takes them back to Google and retrofits them to accept a computer as a driver. He hooks that computer driver electronically into the brakes, the gas, the steering. These Priuses get a radar system behind the bumper cameras alied Our system spenning three hundred and sixty degrees on topli like radar, but it shoots lasers instead of sound waves. At first, the team gives each Prius a cool name, like night Rider. 00:32:33 Speaker 14: But I think we quickly realized that we're not going to be able to name all these vehicles as we scale up our fleet, and so we just started to number them, like you know, Prius twenty seven. 00:32:42 Speaker 2: This is Don Burnett. He'd been a researcher working on autonomous submarines. He lost a friend in a car accident, separately gotten a bad accident himself, and decided he wanted to do work on self driving cars. That's how he eventually ended up on the team. 00:32:55 Speaker 14: In its early days, I was on the motion planning and behavior decision making team, and my responsibility was to work on the nudging behavior. 00:33:05 Speaker 2: Nudging what a big truck passes a human driver on the right, The driver will nudge a little to the left. For us, it's an instinct. Don's job was to teach a computer to nudge. 00:33:15 Speaker 14: They're trying to encode the behavior that you would use as a driver under kind of partially good perception. 00:33:22 Speaker 15: And it's a really tricky problem. 00:33:24 Speaker 2: A team of academic roboticists, some of whom had had friends die in cars, spending Google's money to see if they could make driving safer. It was a weird era. There's this big concert venue near Google's offices called the Shoreline Amphitheater. In two thousand and nine, you could have seen Cheryl Crow there the Killers Fish. But the most interesting show that year was one almost nobody knew about. In the venue parking lot. On days when there was no concert, no tour buses around to see them, the Google team would run its first test runs of their driverless cars, essentially hiding in plane sight a prius driving itself around the Amphitheater parking lot with an attentive safety driver sitting behind the wheel just in case. The team was making sure the basics functioned that the censors could really recognize another car that the computer in the car was abiding by their orders. These were the baby steps that happened in this parking lot and at an empty airplane runway that was close to their offices. Spring two thousand and nine, the team tries actual real road driving for the first time. Chris Armson takes one of the priuses out on the Central Expressway, speed limit forty five miles per hour. There are humans driving here and immediately outside the confines of the empty parking lot and empty airplane runway. Here's what's clear. They had a real problem. The car was swerving wildly. 00:34:51 Speaker 8: It was weaving around like a drunken sailor. And we realized that the scale of the runway was such that you didn't notice the one or two foot kind of oscillation it had in lateral control, and you put it on Central Expressway and suddenly, you know, yep. Turns out, actually, that's a problem. 00:35:13 Speaker 2: One more problem to fix. Listening to the story, it's funny because I can imagine it giving me a totally different feeling than it does. A tech company with nobody's permission was testing driverless cars on public roads in California. I don't know why that strikes me as being about invention instead of just hubris and impunity. Maybe it's because I know that Google would be one of the few tech companies whose driverless cars would not cause any fatal accidents in testing, and that the team would just take more safety precautions than the other companies who'd rush in later to catch up with them once. This was an arms race. The way these cars were designed, the safety driver set behind the steering wheel, ready to take over when the other seat was their partner watching the monitor displaying a graphical interface designed by Dmitri Dolgov. The people watching the screen would call out problems ahead, some discrepancy between what the sensors were seeing and what was actually in the road. This is what teaching a car to drive actually looked like. Two person teams spanning the cars, logging errors, going back to the office to troubleshoot, and then updating the code. I asked Don Burnette about this era, and while you're doing this and then like you leave work and you get in your car that you drive as a human, did you find yourself thinking more carefully, like, how do I know what I know when I'm driving, like you're trying to teach a machine by day, did it affect how you thought about human driving? By night? 00:36:41 Speaker 14: Almost obnoxiously so to any passengers in the car with me. I was obsessed with one big question, which is why do humans drive the way they drive? And it turns out there were no good answers, and I still think they're not great answers. And instead of actually answering that question, we've just turned to machine learning to infer the deep truths behind why humans do what they do. Then, so there's some basic principles that you can understand, Like we try to minimize lateral acceleration, meaning you don't want to be thrown to the outside of your car when you're making a turn. 00:37:16 Speaker 15: So you're going to slow down, but how much do you slow down? 00:37:19 Speaker 4: Right? 00:37:19 Speaker 15: And it turns out that's contextual. 00:37:23 Speaker 2: Don gave me an example. So you're trying to figure out the right speed and angle for the car on one of those tight curvy on ramps onto the highway. You want it to feel comfortable for a passenger. Don says, you can work out the math. The lateral acceleration is two meters per second squared but the surprising thing is that number only applies on the on ramp. 00:37:45 Speaker 14: If I put you at a col de sac in a neighborhood and you were going to do a U turn at the end of the cold de sac, even though the speed is significantly slower, if you did two meters per second squared of lateral acceleration around a cul de sac, you would tell your driver they were crazy. It would be incredibly uncomfortable, like incredibly uncomfortable. 00:38:10 Speaker 2: You would feel like you're in Mario Kart. 00:38:12 Speaker 15: Yes, it would feel Mario Kart. 00:38:14 Speaker 14: And remember this is a force, so it's a physical feeling on your body is exactly the same. But the contextual awareness of the situation of speeding up to get on the highway versus making a U turn in a residential street tricks your brain into feeling opposite about the situation. And so it turns out the limit for a cul de sac is around point seventy five. It's almost three times less than you would be willing to tolerate as you accelerate onto a highway. 00:38:44 Speaker 15: And so there were. 00:38:45 Speaker 14: Things like that where you couldn't just say humans have specific physical restrictions right from a force's perspective, the context matters, and when the context matters, now all of a sudden, anything is game. 00:39:00 Speaker 15: So things like that is where. 00:39:02 Speaker 14: I spent my time as a researcher trying to figure out, Okay, how are we going to make this comfortable for passengers? 00:39:08 Speaker 2: All these little problems to solve. But there's one gift, which is that the team at this point had an overarching goal uniting them. The Darba Challenge told them drive across this patch of desert Valaria. One k Challenge told them drive these ten roots without human intervention. The specificity of the mission meant they never had to squabble about why they were there. By twenty ten, just a year in, the team was really on a roll. 00:39:35 Speaker 4: They start knocking out roots. 00:39:37 Speaker 14: Each one of the routes was unique and distinct and different and had its own challenges. 00:39:42 Speaker 4: Down Route one Silicon Valley to Car Mount. 00:39:46 Speaker 14: The Bridges run where we had to go across all of the bridges in the Bay area, starting in Mountain View, finishing crossing the Golden Gate Bridge. 00:39:54 Speaker 4: It's Chris Hermsen in the car. It's Anthony Lewandowski in the car. 00:39:58 Speaker 14: I was in the car with Dimitri, Chris and Anthony. It was the four of us in the prius. 00:40:03 Speaker 4: They're figuring out the technology much faster than they thought they could. 00:40:07 Speaker 2: The Larry one K was set up like a video game, meaning they'd get to try the route over and over until they could complete it without a single human takeover. Then they'd move
[46:36] The next one 00:40:17 Speaker 8
on to the next one. 00:40:17 Speaker 8: It was really a proof of concept exercise. Can you even make this happen? 00:40:24 Speaker 4: Once? When they fail a route, they know what the car can't handle, so they go back and say they have to be better at doing XYZ. 00:40:32 Speaker 14: And then we got back to the office, we regrouped, we went back out I think at like eleven PM, and by one am we had completed the route. 00:40:41 Speaker 4: They buy a bottle of Corbel champagne. They all write their names on it. 00:40:46 Speaker 2: Corbell thirteen ninety nine, a bottle the champagne they have at Trader Joe's. They had won for every route they completed. 00:40:52 Speaker 4: And one by one they pick off the Larry one K routes. And they think this is going to take them about two years when they start out, and they do it in a little bit more than a year, nearly twice as fast as they had expected. 00:41:07 Speaker 2: By fall of twenty ten. They're done. Here's Chris Armsen. 00:41:10 Speaker 8: And I think we had a big party up at Sebastian's house and Los Athos Hills. So you know, it was pretty spectacular, right. 00:41:17 Speaker 4: They throw each other in the pool, they celebrate, and then they're not entirely sure what to do next. 00:41:25 Speaker 8: It was kind of a okay, And now. 00:41:27 Speaker 2: What the team had pulled off a kind of miracle in a year, a driverless car with human supervision, with lots of human coding, but still a driverless car successfully navigating some very tricky roads in California. They've done this safely, they've done it quickly, and now things would begin to wobble. Competition would arrive, the team itself would begin to schism, and one member, a person who believed the team was moving too slowly, would actually take matters into his own hands in a particularly extreme way after the break mutiny. Welcome back to the show. As early as twenty ten, Google's Driver's car project had developed some very impressive self driving technology, but what they were struggling to decide was this, what was the actual product they were developing. Here, here's a Bastian throne. 00:42:46 Speaker 11: We had a lot of debates inside Google what the right business model was. At some point, v actually had a big debate Beato just by Tesla, and Tesla was worth two billion dollars at the time. I remember this, maybe you should have been hindsight, but joking is idea. There was a debate whether this is more of an assistive technology or a disruptive replacements anology. 00:43:11 Speaker 2: Basically, should they follow the route that Tesla ultimately d design self driving as a feature in your car, something that could take over sometimes but still need human monitoring, or was it better to wait until the car could fully drive itself. Thron would eventually come around to this version of self driving. Specifically, he'd come around to the idea of self driving robotaxis. 00:43:33 Speaker 11: A taxi service type system is way more capital efficient than ownership. An owned car is being used for four percent of the time, and it's parked ninety six plent a time. Imagine a city without parked cars, where every car is being utilized called it fifty percent of the time, which means we have like only ten percent number of cars needed that we need today when we own own cars, that's going to happen. There's no absolut question. 00:43:57 Speaker 2: What Sebastian is describing here, as a matter of fact, is a fairly radical reimagination of American cities. The idea that robotaxis would be so cheap and widely available that most people just wouldn't own cars, that we could put something else, anything else, in the places where we put most of our parking lots and parking spaces. That is a far fetched idea, just given how much of American identity is tied into personal car ownership. A farvetched idea, and for it to begin to happen, Google would have to bring a product to market. But the years passed and they didn't, and some people who were there felt stuck. Don Burnette says he believes life at Google got dangerously cushy. The food was great, the money was too, these former academics making much more than they'd ever expected. 00:44:49 Speaker 14: There was a lack of urgency on the team to actually make something viable. We had a funding supply that effectively felt infinite, and maybe it was, maybe it wasn't, but it certainly felt infinite. And when you have infinite funding, you're not forced to make hard decisions, You're not forced to focus, you're not forced to look at the opportunity, the market, the customer and be the best. It was more like, hey, let's take our time, let's make sure we do it right, which is on its face a good principle, but at the end of the day, I think the lack of urgency wasn't for everyone. 00:45:27 Speaker 4: And within the team you got team Chris and team Anthony, and they start butting heads all the time. 00:45:35 Speaker 2: Chris and Anthony meaning Chris Armsen, official head of the project, versus Anthony Lewandowski, who I still think of as the motorcycle guy. 00:45:42 Speaker 4: The main difference in their approach is how quickly they want to move. Anthony is very okay with risk. 00:45:50 Speaker 2: We'll say. 00:45:53 Speaker 4: He gets one of these cars and he's driving it back, and he lives in Berkeley, works in Palauto. He's just using this car like the Bay Bridge every day, probably outside the bounds of what the team actually wanted, and he's not necessarily logging data. He's just enjoying his self driving car and taking it all over the place. Chris comes from an academic background. He's that Canadian, very nice, very careful, very risk averse. 00:46:21 Speaker 2: When I asked Chris Armson about all this, his memory was slightly different. In his memory, Team Anthony was pretty much just Anthony and Anthony, he said, was a move fast and break things kind of guy. Move fast and break things a motto famously coined by Mark Zuckerberg. It defines a way of developing technology which once might have felt cute and revolutionary, but which today, at least to me, feels pretty irresponsible. Chris didn't think that philosophy was an option for their team, even if their cars were statistically safer than human drivers. He knew that the first news story about a self driving car in a fatal accident, it was going to be a huge deal. Anecdote was going to demolish data if they weren't extremely careful. By all accounts, Anthony Lewandowski felt differently, but he actually wasn't the only one. Here's Don Burnett. 00:47:14 Speaker 14: There were some people on the team, very famously including myself, that started to get the itch kind of towards the three to four year mark, the itch of like, Okay, where is this going, who is it for? 00:47:27 Speaker 15: How are they going to use it? Where are they going to use it? 00:47:30 Speaker 14: And I felt like the leadership didn't have great answers to that. There was no commercial race, right. We had no competition and there was no market for the product. 00:47:38 Speaker 2: But competition would soon arrive in the form of Uber. This was the oh shit moment for me. 00:47:48 Speaker 14: Uber announced their self driving program, and I remember like it was yesterday, waking up, reading the news, going to my desk in the morning, and thinking, Oh crap, these guys are going to eat our lunch. 00:48:02 Speaker 2: In twenty thirteen, then CEO of Uber, Travis Kalanek, had gotten a ride in one of Google's prototype driverless cars, sitting in a taxi without a human driver. He'd understood that this could mean the end of his company, and so Uber had plunged headlong into the driverless car race. The company hired nearly half of Carnegie Mailn's top Robotics lab, and not long after we also know through court records and emails that Uber also began communicating with Anthony Lewandowski, who in twenty sixteen would leave Google, quitting just before he could be fired for a recruiting team members away, including Don Burnett. Anthony would then start his own autonomous vehicle company. Uber would soon buy that company for almost seven hundred million dollars, even though the company had no product and was only months old, which raised a mystery. Why would Uber pay so much for a company whose only assets seemed to be its people. 00:49:00 Speaker 4: This is where. 00:49:00 Speaker 16: Google goes into its computer security logs and realizes that not long before he left, Anthony Lewandowski downloaded something like fourteen thousand technical files onto his. 00:49:12 Speaker 4: Computer and moved them onto an external disc. 00:49:16 Speaker 2: Obviously you can't do that. I mean, I'm assuming obviously you can't do that. 00:49:19 Speaker 4: No, you definitely cannot see And this is the kind of thing that maybe if he had stayed there, this is the kind of thing Anthea would have done, and he would have been like, oh, it's just so I could have access to to it somewhere else. Then he probably would have gotten away with it. But when you then go and work for Uber and start running their direct competitor self driving car program, that's when you get in trouble. And that's when what's technically called WEIMO. At this point, Google's program sues UK and puts Anthony at the center of an enormous legal battle between these. 00:50:01 Speaker 15: Tech giants, secrets and subterfugia. 00:50:05 Speaker 4: In Silicon Valley, a former Google engineer has been charged with stealing files from Alphabet's self driving car project and taking them to Uber. 00:50:14 Speaker 11: Specifically, it involves a former lead engineer of Google's self driving car unit, Anthony Lewandowski. 00:50:21 Speaker 4: Now he's accused of using. 00:50:23 Speaker 11: His personal laptop and downloading more than fourteen. 00:50:26 Speaker 2: In twenty sixteen, Google had just spun its driverless car unit into a new entity, Weimo. Weimo sued Uber. Uber had to settled to the tune of two hundred and forty five million dollars, and in a separate criminal trial, Anthony Lewandowski pled guilty to stealing trade secrets. Afterwards, Uber continues their driverless car program without him, continuing to pursue its move fast, break things strategy, which in twenty eighteen leads to the death of a woman named Elaine Herzberg. 00:50:55 Speaker 8: Uber is sitting the brakes on its self driving cars after one of them hit and kill the woman in Arizona. 00:51:01 Speaker 1: The vehicle was in autonomous mode, but it did have a safety driver on board, but. 00:51:06 Speaker 17: A police report later indicating the safety driver was streaming TV shows on her phone for three hours that night, including at the time of the crash. 00:51:16 Speaker 2: The way this story was reported, nearly everyone blamed the safety driver. She was on her phone. She's streaming an episode. 00:51:22 Speaker 17: Of the Voice Tempe investigator saying, had Vasquez been paying attention to the road, she could have stopped the car forty two feet before impact the NTSB slamming. 00:51:33 Speaker 2: There were some important additional context, which is that Uber's robot driver was also just much worse than way Moo's, a statistic I found jaw dropping. At this point, Waymos's safety drivers were having to take over from the car once every five six hundred miles. Uber's safety drivers that year had to intervene more than once every thirteen miles. Despite that, five months before the crash, over employee objections, Uber had cut its safety crews. Instead of two humans, they just used one. One safety driver overseeing a robot driver that was arguably not ready to be on public roads. In the last moments of Alane Herzburg's life, the robot spent an indefensible five point six seconds trying and failing to guess the shape in the road there was a human body pushing a bike. Over those five point six seconds, the robot kept reclassifying our whishing an unknown object a vehicle a bicycle. During that time, spent wondering the car did not slow down. Soon after Elaine Hertzberg's death, Uber halted its testing program. 00:52:39 Speaker 17: Uber has temporarily suspended its driverless fleet nationwide, as the NTSB police, Uber and the National Highway Traffic Safety Administration investigate. 00:52:49 Speaker 2: We reached out to Uber for comment. A spokesperson said that the fatal collision was indeed a tragedy which had a significant impact on Uber and the entire industry. There'd be other competitors who would shut down after similar accidents. There would also be Tesla, which by twenty twenty was publicly marketing a product of the company called full self driving, but which absolutely was not. Meanwhile, Wimo had slowly continued develop its tech. Their robotaxis would be ready for riders by twenty twenty. The team had gotten an unexpected boost from a technology that was at the time very little understood. In twenty twenty six, when most people talk about artificial intelligence, the conversation defaults to products like chat, GPT, and Claude, But artificial intelligence has been a core part of driver lest cars going back two decades. In the twenty ten's, neural net advances meant that you can now begin to feed a computer system large amounts of data and watch as its perception, prediction, and decision making abilities improved. Here's Sebastian Thront. 00:53:53 Speaker 11: Their technology of massive data training was with us from the get go, but has become more and more and more and more important. The surprise for all of us has been that size matters. When you put a million documents into an AI, it's fine, one hundred million is fine, And when you put one hundred billion documents into ANI, it is umbiliately smart. And then a thing shocked everybody, myself into. 00:54:20 Speaker 2: The Google brand team. The deep learning people started working with the driverless car team to use training data to help the computer driver learn things like how to better predict when another car was about to suddenly switch lanes, how to more reliably spot pedestrians. Over the years, as a car drove more miles, as the team gathered more data, plugged that data into their AI systems, and tweaked those systems. The engineers say the robot driver kept improving as they tested the car in new weather conditions, they discovered problems that required hardware fixes. For instance, in Phoenix, Weimo had to design miniature wipers for their cars. Led our sensors to deal with the dust storms and heavy rains. In twenty twenty, Weaimo finally debuts to the public in Arizona. In the years after, it'll roll out to ten more American cities. A funny consequence of Weymo's long development cycle is that the public's attitude towards Silicon Valley has just really changed in that time. There's more suspicion towards Google than there was back in two thousand and nine when the project first started, And
[1:03:51] Many people look at the Waimo
so now many people look at the Waimo driver with a raised eyebrow with a question immediately on their lips. Chapter five, Are you a good driver? 00:55:29 Speaker 4: All right? Autonomous vehicles can now get you around Atlanta yesterday. Driving through Austin is here, except it comes without. 00:55:36 Speaker 10: Drive Light hailing app is now taking passengers in Miami. 00:55:40 Speaker 2: A fleet of white electric Jaguars covered in forty different sensors, cameras, radar, lidar. It's an expensive car, as much as one hundred and fifty thousand dollars by some estimates. In the news stories, you see the inside where the human driver would normally sit. There's an empty seat you're not allowed in with a steering wheel in front of it. It turns itself. 00:56:01 Speaker 10: Cars without drivers are here. 00:56:03 Speaker 3: Yeah, it sounds like something out of the Jetsons. 00:56:05 Speaker 4: But get ready because you may look over at the car next to you and see it rolling down the street. 00:56:11 Speaker 2: The TV newscasters always use the same g whiz tone. They can never resist the Jetson's reference. In every city, the influencers hop into record testimonials for their daily serving of clout. 00:56:21 Speaker 14: So in today's video, I'm about to take my first ever driverless car. 00:56:25 Speaker 8: It's with an app called Weimo. 00:56:26 Speaker 3: Weimo is basically driverless car uber where it's like ride service. You call it going wherever you need it to go, but there's no driver. 00:56:36 Speaker 2: You guys, this is creepy. 00:56:37 Speaker 4: It's like I'm being driven around by a ghost person. It's a little terrifying. 00:56:41 Speaker 2: It is definitely Romo taxis pull hilariously badly. According to JD Power, a data analytics firm, among people who've not ridden in one consumer confidence is at twenty percent, but among people who have taken a ride Denver shoots up to seventy six percent. It's the thing that capture this story. But when I sad and won a couple of years ago. I just found it persuasive as an experience. 00:57:06 Speaker 1: You know what, I'm not as nervous as I thought I was gonna be. 00:57:09 Speaker 9: This is actually quite relaxing. 00:57:11 Speaker 2: Nice gradual turn, felt very safe. 00:57:13 Speaker 18: You know, it was kind of freaky at first, but now it's pretty chill smooth. 00:57:17 Speaker 4: Right though it wasn't driving fast, it wasn't jerking. 00:57:20 Speaker 18: It's driving like you always hope your Uber driver would. 00:57:22 Speaker 4: So I guess that's one of the big sells. 00:57:24 Speaker 2: Chris Arms and that methodical team leader had left Google years ago, but he told me about his experience as a civilian consumer trying away mom out in the world. 00:57:33 Speaker 8: My universal experience has been and you can tell me if this was your experience. The first couple of minutes in the vehicle, it's huh, that's crazy. I dished nobody behind the wheel swinging with sharks. And then a few minutes in and it's like, okay, you know, it's just just gonna drive. 00:57:53 Speaker 4: Is that all it does? 00:57:54 Speaker 8: And then you know, ten minutes and people are looking at their phone. 00:57:58 Speaker 2: People tend to feel safe in these but are they actually so we know that the Weimo driver has now driven over two hundred million real world miles, and they release safety data so far for the first one hundred and twenty seven million miles. Weymo's fairly transparent. They release their crash and safety data unredacted to the public. By contrast, Tesla redacts the details of its crashes. The company says they are confidential business information. In Weymo's case, I've looked at the data, I've looked at how the company interprets it, how skeptical independent researchers interpret it. I wanted to walk through it with an autonomous vehicle reporter I trust. His name is Timothy Beeley, author of the newsletter Understanding AI. I asked him how much our picture of the Weymos safety data has been evolving. 00:58:45 Speaker 19: So it's been pretty consistent the last couple of years. They are scaling up, and so all the numbers get bigger, like the total number of miles get bigger, the number of crashes get bigger, but the light crashes per mile have not changed a ton, Weimos says, and I think this is correct, that it's roughly eighty brass safer in terms of crashes are severe enough to turn down an airbag. Crashes severe enough to cause an injury, and also crashes involving vulnerable road users like pedestrians or bicyclists. 00:59:16 Speaker 2: So eighty percent fewer air bag crashes than human drivers, and actually ninety percent fewer crashes that cause a serious injury. Some independent experts have small quibbles with the methodology, but broadly they find Waymos's data credible. Timothy pointed out, there's one very important thing we don't know, the fatal crash comparison. For every one hundred million miles humans drive, we cause a little over one fatal crash. The Waimo driver has driven two hundred million miles without causing a fatal crash, but statistically speaking, that could still be a fluke. Some academics have suggested we need about three hundred million miles to have statistical confidence in the hundreds of millions of miles the Waymo driver has traveled. It was involved in two fatal crashes which it did not appear to cause. Here are the details of those crashes. In one, a speeding human driver rear ended a line of vehicles at a stoplight. There's an empty Weimo in the line of struck cars. In another crash, a Weimo is yielding for a pedestrian. It was rear ended by a motorcycle. The motorcycle driver was then struck by a second car. That's everything when Timothy bee Lee looks at the entire safety picture, the results we have so far from this big experiment Weimo is conducting on American roads, what he sees is mainly promising. 01:00:37 Speaker 19: So far it's been better than human drivers, and so far, I think the case for allowing them they continue. 01:00:41 Speaker 4: The experiment is very strong. 01:00:44 Speaker 2: Which doesn't mean we shouldn't scrutinize this Weimo experiment as it continues. I find myself paying a lot of attention to Weimo crashes, which isn't hard. They make headlines. The most harrowing one recently was this January. 01:00:57 Speaker 6: A child at near to Elementary school in Santa Monica is Weymo. 01:01:01 Speaker 18: A child ran across the street from behind a double part car and a Weimo hit the kid. 01:01:05 Speaker 13: Santa Monica police say the child, a ten year old girl, was not hurt. 01:01:09 Speaker 2: The company issued a statement. Weimo said its driver had breaked hard, reducing speed from seventeen to under six miles per hour, a faster reaction, they claimed than a human driver would have been capable of what happened next at the accident scene. Actually answers a question i'd had, what does a WEIMO do after a car crash. Since there's no human driver to help, WEIMO employs what they call human fleet response agents, human beings who can't remotely drive the cars, but who the car can ask questions to if it gets confused. In Santa Monica, the WEIMO called one of those humans, the human called nine one one. And this is the strangest part of Weymo's statement. Apparently the car then waited at the scene of the accident until the police dismissed it. That's what we know so far. But there's two federal agencies investigating this crash, and so we'll have a full report in the future. One problem that's not really captured in the safety data that I've seen is what i'd call troubling edge cases. You see them in videos on social media. A WAIMO gets stuck at a dead stop light or blocks an emergency vehicle, or an example, Timothy gave waymo's were driving past stopped school buses in Austin. 01:02:16 Speaker 19: I think it's reasonable to say this is like a clear cut rule that the vehicle should follow this role. These educads are still very rare, and so if it's a one to ten million thing, I think it's not that big a deal as long as they are making progress, which for most of these I think they are. 01:02:29 Speaker 2: Timothy pointed to one area where Waymo's not been as transparent as he'd like, those human response agents, some of which are based here some of the Philippines. There's questions about what specifically they do and about how this will all work as way most scales up. We asked Waymo for comment on everything you heard in this episode, especially the recent safety incidents. A spokesperson said that the data to date indicates that the Weimo driver is already making roads safer in the places where they operate, and says that Weymo can used to work with policymakers and regulators to improve its technology. That's the safety picture so far, which to me, after many months of looking at this and talking to experts, looks pretty good. As Weimo continues its rollout, other companies are quickly following behind. 01:03:12 Speaker 3: Amazon's new driverless taxi is launching in Las Vegas this summer, and it's expected to arrive. 01:03:18 Speaker 2: And now there's other robo taxi companies like Amazon, Zookes. Uber is back in the mix, not making technology, but partnering with these robo taxi companies. We Ride recently struck partnership with Uber to bring its avs to Abu Dhabi, another sign of it. And many of those early WEIMO engineers are now CEOs of autonomous companies themselves. Dmitri Dolgov is actually co CEO Weimo, but other team members run driverless trucking companies. 01:03:43 Speaker 18: Got Don Burnette, founder and CEO of kodiak Ai. Don, thank you so much for joining us. 01:03:47 Speaker 12: It's good to see you again. 01:03:48 Speaker 2: Don Burnett is head of kodiak Ai, which has its technology deployed in driverless trucks in the premium basin. 01:03:54 Speaker 18: Please welcome CEO of Aurora, Chris Ermthin. 01:03:59 Speaker 4: A big round of a plot. 01:04:00 Speaker 2: Chris Armsen now heads Aurora, which currently has semi trucks on Texas highways. And my personal favorite plot development which just emerged this week. 01:04:08 Speaker 18: I just broke on the information that Uber founder Travis Kalanik is starting a new self driving car company with financial backing from Uber and in partnership with Anthony Lewandowski. 01:04:21 Speaker 17: Now, for those who've been they. 01:04:22 Speaker 2: Say there's no second acts in American lives. Somehow, both of these men seem to be on their fourth. The big picture, though, is that everywhere in America today that you see a driver, taxi, truck, food delivery, there are several companies working on the robot version trying their best to make driver as a job start to go the way of the knocker Upper of the Lamplighter. Those knocker Ruppers, by the way, they disappeared quietly. The Lamplighters did not. Writer Carl Benedict Frey tells the story of the Lamplighters Union, how their strikes plunged New York City briefly into darkness to the light of lovers and thieves. In Vervier, Belgium, the Lamplighters strikes turned violent, ending in an attack on the local police headquarters. The army was brought in. The lamp Layers lost their fight, in part just because they were so outnumbered. But the drivers today fighting to save their livelihoods are a significantly bigger force. 01:05:19 Speaker 8: Please stand up, everybody that's ride share union members are someone who drives the vehicle. 01:05:27 Speaker 4: Stand up. 01:05:29 Speaker 2: Four point eight million Americans drive for a living. It's one of the most common jobs we have, and these workers do not plan to surrender to the California tech companies. They're doing this because they stand to make an unfathomable amount of money if they eliminate driving jobs for working class of people. 01:05:46 Speaker 4: I understand they this a business, they this capitalism, but not in my city at the expense of our jobs. 01:05:55 Speaker 2: These drivers are represented by unions backed by politicians and in cities across America blue cities. They're organizing. So far they're winning. Humans drive the city, lot machines, labor drives this city, keep the workers in the workforce. 01:06:10 Speaker 4: If it works in another city, great, have fun, not here, not Boston. 01:06:14 Speaker 9: Thank you. 01:06:20 Speaker 2: Next week the Fight to save a Job, to save the human Driver. 01:06:25 Speaker 5: Don't miss this one. 01:06:41 Speaker 2: Thank you for listening to our episode. I just want to say, making deeply reported stories like this one is only possible because for our listeners, particularly our premium subscribers who pay to support the show. We are releasing our full interview with Sebastian Throne, who used to lead Google X their secret Special Projects Lab. Totally fascinating conversation with the kind of person who just sort of lives in the future and has a million strange ideas about it. We are releasing that for our incognito mode members only. It'll be in your feed. If you would like to know the future, sign up at search Engine Dot Show and again. Your membership specifically enables projects like this one, so thank you. Search Engine is a presentation of Odyssey. It is created by me PJ Vote and Truthy Pinaminini. Garrett Graham is our senior producer. Emily Malterre is our associate producer. Theme, original composition and mixing by armand Bazarian. Our production intern is Piper Dumont. This episode was fact checked by Mary Mathis. Our executive producer is Lea Reese Dennis. Thanks to the rest of the team at Odyssey, Rob Mirandy Craig Cox, Eric Donnelly, Colin Gaynor, Mark Curran, just Fina Francis, Kurt Courtney, and Hillary Scheff. Thanks for listening. We'll see you next week with the second part of this story. 01:07:54 Speaker 1: That was part one of this two part story on one of the most transformative technologies of today, driverless car. If you want to hear how much more complicated the story gets as the technology rolls into American cities you can find search Engine wherever you get your podcasts.