Are Human Drivers Finally Obsolete?

Summary

Freakonomics Radio: "Are Human Drivers Finally Obsolete?"

Air Date: March 20, 2026
Host: Stephen J. Dubner
Co-host/Featured Guest: PJ Vogt, Host of "Search Engine"

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Episode Overview

This special episode of Freakonomics Radio, hosted by Stephen J. Dubner and centered on PJ Vogt’s deep-dive series from the "Search Engine" podcast, explores the history, science, business, and future of driverless cars. The narrative charts the evolution from early failed attempts and utopian dreams to cutting-edge AI-driven vehicles now carrying passengers across American cities. The episode blends personal stories, pivotal moments in the autonomous vehicle (AV) field, and philosophical questions about safety, labor, and technology’s place in society.

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Key Discussion Points & Insights

1. Setting the Stage: The End of Human Driving?

• Opening Analogy: PJ Vogt invites listeners to imagine the world of 200 years ago, sharing the fate of professions lost to automation—knocker uppers, lamplighters, and wonders aloud whether "driver" is next on history's chopping block.
• Theme: How technology transforms mundane jobs into automated functions, and the social disruptions that follow (e.g., dishwasher, printer, computer).

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2. Why Do We Want Driverless Cars? (06:00–13:59)

• Historical Motivation:
  • Early cars were seen as dangerous and job-destroying; "red flag" laws required people to precede vehicles on foot.
  • Fight to regulate versus embrace new, disruptive technologies.
  • Analogy to today's AV debate: lessons learned from unregulated car adoption and fatalities.
  • Safety is the core pitch of driverless technology: automation to reduce the high rate of car accident deaths (~1 in 100 Americans die in car accidents).

Notable Quote:

"These driverless cars, they aren't the future. They're actually already here."
— Host/Narrator (09:02)

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3. The Birth of the Autonomous Vehicle Revolution: DARPA Grand Challenge (14:36–29:27)

The Contest

• DARPA’s Incentive: The Pentagon launched the Grand Challenge in the early 2000s, offering a $1 million prize for a robot car that could navigate the Nevada desert unaided.
• Key Characters:
  • Chris Urmson: Represented the methodical, academic approach at Carnegie Mellon.
  • Anthony Levandowski: Enterprising and boundary-pushing, entered with the first self-driving motorcycle.
  • Sebastian Thrun: AI-focused, recognized that the key issue was not hardware, but building software to "replace the human."
• First Race (2004): A total failure—every vehicle crashed or broke down quickly. Yet this loss galvanized the field and seeded the teams for the future.

Notable Quotes:

"The 2004 Grand Challenge is an utter hysterical disaster."
— Alex Davies (22:37)

"The challenge is really to take the person out of the driver's seat and replace it by a computer. That is not a problem of bigger tires. That's actually really a software problem."
— Sebastian Thrun (24:44)

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4. Cracking the Code: The AI Breakthrough & “Larry 1K” (32:37–47:43)

• Google’s Project Chauffeur Formation:
  • Larry Page (Google co-founder) recognized self-driving as a major opportunity. Recruited Thrun and others to begin work in stealth.
  • Larry 1K: Page and Sergey Brin laid out ten challenging California routes (~1,000 miles) that had to be navigated without human intervention.
  • Use of machine learning: “Stanley” (Thrun’s car) became the foundation for using self-trained AI in driving.

Notable Moment:

“The specificity of the mission meant they never had to squabble about why they were there.”
— Host/Narrator (45:41)

Engineering Insights:

• Comfort Algorithms: Don Burnett describes how encoding "nudging" (easing away from trucks) and rider comfort metrics were surprisingly complex, since context matters (e.g., high acceleration in a cul-de-sac feels wild, but on an on-ramp feels normal).
• Rapid Progress: The Google team achieved the Larry 1K in about a year, twice as fast as expected, inspiring a big celebration—but then faced the question: What next?

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5. Innovation, Division, & Mutiny (47:58–59:13)

• Internal Strains at Google/Waymo:
  • Debates over strategy: build an "assistive" feature or hold out for "full replacement" (robotaxis).
  • Growing urge among some for faster action and real-world application.
  • Team splits into risk-tolerant (move fast) vs. cautious (safety first) factions, particularly Chris Urmson vs. Anthony Levandowski.
  • Uber’s Entry: Uber aggressively lures away top robotics talent and eventually buys out Levandowski’s startup, setting off a major lawsuit over alleged theft of trade secrets.
  • Accidents Shape Policy: Uber’s laxer standards led to a fatality; contrasts with cautious Waymo approach.

Notable Quote:

"Anecdote was going to demolish data if they weren't extremely careful."
— Host/Narrator (54:33)

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6. AI Power & Real-World Performance (61:01–69:08)

• AI Scaling:
  • Waymo’s success tied to deep learning: more data means better, safer driving (per Thrun: “When you put 100 billion documents into an AI, it is unbelievably smart.”)
• Deployment:
  • By 2020, Waymo debuts in Arizona, expands to 10 US cities.
  • Early rider experiences are first uncanny, then quickly become mundane—dubbed the “elevator moment.”

Notable Quotes:

"The first time, it feels like the first time you’re in an airplane and by the third time it feels like you’re in an elevator."
— PJ Vogt (02:20)

"My universal experience has been… The first couple minutes in the vehicle, it’s, ‘huh, that's crazy.’ …10 minutes in, people are looking at their phone."
— Chris Urmson (65:45)

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7. Safety Data: Are Robots Better Than Humans? (66:10–70:40)

• Crash Data:
  • Waymo is ~80% safer for airbag-triggering crashes and 90% safer for injury crashes compared to human drivers.
  • Counterpoint: Large numbers are still needed for statistical confidence on fatalities.
  • High-profile incidents: child hit in Santa Monica (2026) with quick accident response and federal investigation; edge cases and “gotchas” (e.g., robot confusion at dead stoplights or school buses) remain in the public eye.

Notable Quotes:

"For every 100 million miles humans drive, we cause a little over one fatal crash. The Waymo driver has driven 200 million miles without causing a fatal crash. But statistically speaking, that could still be a fluke."
— Host/Narrator (67:55)

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8. The Clash Over Jobs & the Future of Driving (71:24–74:26)

• Booming Competition:
  • Amazon Zoox, Uber (partnerships), and many former Waymo engineers launch new AV efforts (trucks and taxis).
• Labor Pushback:
  • 4.8 million Americans drive for a living; unions are organizing, especially in blue cities, to resist what is seen as a tech grab at working-class jobs.
  • Unlike lopsided fights against lamplighters, drivers may be a powerful, organized bloc.

Notable Quotes:

"These drivers are represented by unions, backed by politicians. And in cities across America, blue cities, they're organizing. So far, they're winning."
— Host/Narrator (74:16)

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9. Memorable and Philosophical Moments

• On the Comfortable Inevitability of Tech:

"I was trying to describe to somebody recently... the first time, it feels like the first time you’re in an airplane and by the third time it feels like you’re in an elevator."
— PJ Vogt (02:20)

• On Expertise & the Future:

"Experts are usually experts of the past, not the future. And if you ask an expert about innovation, something crazy new, they’re the least likely person to say, yes, it can be done."
— Sebastian Thrun (36:53)

• On the Social Question of the Human Driver:

"Are you a good driver? Do you consider yourself a good driver?"
— PJ Vogt (08:17)

• On Risk Appetite in Tech:

"Move fast and break things... which today, at least to me, feels pretty irresponsible."
— Host/Narrator (54:33)

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Key Timestamps for Segments

• 01:13–04:49: Why obsess over saving lives through automation? Personal stories, the lure and danger of the human driver.
• 06:00–10:19: The persistent “driver” job and early reactions to vehicles.
• 14:36–29:37: DARPA races; birth of modern AVs; key personalities.
• 32:37–47:43: Google’s secret project, Larry 1K, and milestone progress.
• 53:01–59:13: Internal Google/Waymo splits and the Uber-Levandowski controversy.
• 61:01–70:40: Public deployment, safety statistics, notable incidents.
• 71:24–74:26: AV industry expansion and organized labor resistance.

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Conclusion & Next Episode Tease

The episode ends with the AV revolution at an inflection point. Waymo and its competitors have delivered on the technical promise, but the future will be shaped as much by public perception, labor resistance, and regulatory decisions as by AI and data. The battle over who controls the streets—and whether “driver” becomes as extinct as lamplighter—is just heating up.

Next Week:
The struggle between drivers, their unions, and the encroaching automation is set for a dramatic showdown in Part Two, focusing on the human side of driving, political fights, and social costs.

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For Listeners Who Missed It:
This episode not only covers how driverless cars work and how we got here but delves into the messy, fascinating, and unresolved battle between techno-optimism and labor. It’s a smart, balanced, and often funny look at a transformation many never thought would arrive—now hiding in plain sight at your local intersection.

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Notable Final Quote:

"Is it possible that you were really stoned on painkillers in that first Waymo ride?"
— Stephen Dubner (76:08)
"I mean, I wasn't stoned on painkillers, and I don't think I was stoned at all. I think I really had a sense of normal technological awe."
— PJ Vogt (76:08)

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End of summary for "Are Human Drivers Finally Obsolete?" (Freakonomics Radio, March 2026)

Transcript

[Stephen Dubner]

Freakonomics Radio is sponsored by Cigna Healthcare. For many men, mental health challenges aren't recognized until they've already taken a toll. Work pressure, financial stress, changing relationships, and traditional expectations around masculinity can quietly wear men down, often without clear warning signs. In season three of the Visibility Gap, Dr. Guy Winch and his guests explore how these pressures show up, how to spot them earlier, and how men can access meaningful support. Listen to the of the Visibility Gap, a podcast presented by Cigna Healthcare.

[Host/Narrator (Freakonomics Radio)]

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[Alex Davies]

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[Host/Narrator (Freakonomics Radio)]

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[Stephen Dubner]

Pj, how have you been?

[PJ Vogt]

I've been good. How have you been?

[Stephen Dubner]

Yeah, I'm a little better now having listened to your series.

[Host/Narrator (Freakonomics Radio)]

I love it.

[PJ Vogt]

Oh, thank you.

[Stephen Dubner]

Do you recognize that voice? It is PJ Vogt, host of the podcast Search Engine and friend of Freakonomics Radio. You may remember hearing him back in 2024 when we published a search engine episode called the Fascinatingly Mundane Secret of the World's Most Exclusive Nightclub about Berghain in Berlin. That was a great story. And not too long ago, PJ came to us with another one. It's a two part series on driverless cars. This is a topic that we have touched on many times over the years at Freakonomics Radio. But PJ decided to go deep. The other day I had a chance to ask him how he got interested in this.

[PJ Vogt]

There's a whole lesson in this. But I'd gotten and this is not the next sentence you're going to expect me to say, too into bench pressing.

[Stephen Dubner]

Yeah, that's not where I thought you were going.

[PJ Vogt]

And I injured myself. I had a hernia and then I had to have a hernia repair.

[Stephen Dubner]

I see.

[PJ Vogt]

So there were like some minor complications. I was not moving easily.

[Host/Narrator (Freakonomics Radio)]

I was in a lot of pain.

[PJ Vogt]

So I had kind of limited mobility and I was visiting a friend in San Francisco and I took a Waymo and it was such an experience of the future that immediately becomes normal. First the idea that I would press a button on my phone, a car would come out of nowhere, driven by nobody. I would get in, watch the steering wheel turn itself. I was trying to describe to somebody recently, I was like, the first time, it feels like the first time you're in an airplane and by the third time it feels like you're in an elevator. It was a moment where I thought, oh, a lot's about to change. And it was confusing to me that

[Host/Narrator (Freakonomics Radio)]

people were not talking about that more.

[Stephen Dubner]

What should we expect to hear in the series? There are two parts. The first is really about the car and then the second is really about the driver. Tell me who you think are some of the most compelling characters in more

[Host/Narrator (Freakonomics Radio)]

why so in the first part there's

[PJ Vogt]

this guy, Sebastian Thrun, he's so good. He's this German born roboticist AI expert who lost a friend as a teenager to a car accident. And he really thinks that his invention is not just going to make money for a tech company or be more convenient. He wants to reshape the modern world as it exists. And it's just the story of him and his team beginning to figure that out and having ideas that sounded crazy 20 years ago and with every year towards the present have sounded more sane

[Host/Narrator (Freakonomics Radio)]

and at least plausible.

[PJ Vogt]

And then in the second part, I find the Boston politicians to be very vivid talkers, very opinionated people.

[Stephen Dubner]

Vivid is a very polite word.

[PJ Vogt]

They're strongly opinionated. They sometimes commit gaffes. When you ask them about the gaffes, they are totally like, yep, I screwed that one up. The thing that I most enjoyed about this story, which is what I'm always looking for, is that particularly in the second half, every time I spoke to someone, as they were talking, I thought everything they're saying makes sense. I totally get it. I would be nodding my head vigorously for the most part and then I would go talk to the next person who saw things completely differently and it would just spin my head the other way and I would think, well, this makes sense too. And it was about trying to really do what I think we're all going

[Host/Narrator (Freakonomics Radio)]

to have to do a lot of

[PJ Vogt]

soon, which is way competing, not totally reconcilable interests, and really take them seriously. And that was us trying to do

[Host/Narrator (Freakonomics Radio)]

it for this one thing.

[Stephen Dubner]

I'll be honest with you, I've been anti human driver for about 50 years now.

[Host/Narrator (Freakonomics Radio)]

50 years?

[Stephen Dubner]

Oh yeah. I mean, have you ever seen a human drive a car, including yourself? We're not that good.

[PJ Vogt]

No.

[Host/Narrator (Freakonomics Radio)]

I have no illusions about my driving skills.

[PJ Vogt]

I'm not that good. I have a temper, I am distracted.

[Stephen Dubner]

I rode in an autonomous test vehicle. At Carnegie Mellon University. They had a test track in Pittsburgh on an old steel mill property. And after this one 20 minute or whatever ride, I said, give me the autonomous vehicles. It's so plainly better than I am as a driver, certainly. So I'm eager for it. And I appreciate your putting the pedal to the metal for autonomous.

[Host/Narrator (Freakonomics Radio)]

I hope it gives people a little context for this. All the questions people have, is it safe? What is it going to do? Like we've answered as much as we can.

[Stephen Dubner]

Today on Freakonomics Radio, we turn the mic over to PJ and our friends at the Search Engine podcast for the first of a two part series on driverless cars. Listeners, start your engines.

[Host/Narrator (Freakonomics Radio)]

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 200 years ago and unfortunately, in our hypothetical, it's Monday morning. It's Monday morning and it's very early, pre dawn. You wake up to this really hard rapping at your window. That's the knocker upper here to get you up for work. We're in the 1800s, 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 knocker upper for work? Nobody knows. But this is a job. A job that'll actually exist for another century. Outside, the gas streetlamps 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 hasn't 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 at his light. You yourself are a driver. Professional driver 200 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 streetlight. 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 ask the humans I met the same question. Are you a good driver? Are you? Do you consider yourself a good driver?

[Alex Davies]

I do. Within limits. I think I'm a good driver because I understand the limitations of my driving.

[Host/Narrator (Freakonomics Radio)]

This is Alex Davies. He wrote an excellent book called 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?

[Alex Davies]

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.

[Host/Narrator (Freakonomics Radio)]

Congratulations.

[Alex Davies]

Thank you. And I thought that along with like reading all the baby books, a good project to work on is just be calmer in the car.

[Host/Narrator (Freakonomics Radio)]

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 driverless car, which is really a car driven by a computer. Driverless cars don't get drunk, tired or distracted. They never text or feel road rage. And these driverless 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 Waymo are operating in 10American cities, providing millions of rides to Americans. In China, the rollout is happening even more widely than twice as many cities. But here, if you live in a place like San Francisco or Austin today, a driverless 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 promises to totally reshape the places we live.

[PJ Vogt]

And today we're going to tell you

[Host/Narrator (Freakonomics Radio)]

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.

[Alex Davies]

People have been thinking about a self driving car for just about as long as there's been A human driven car.

[Host/Narrator (Freakonomics Radio)]

Why?

[Alex Davies]

There's this funny thing you lose when you move from the horse to a 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 the sentience in your vehicle.

[Host/Narrator (Freakonomics Radio)]

When automobiles first arrived, these powerful and non sentient cars, there was actually a passionate fight to keep them off the streets. It was the 1800s 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 proposed requiring horseless carriage drivers who encountered livestock to stop, disassemble their car and hide the parts behind the bushes. The governor vetoed it. But the thing 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 1900s, deaths accumulated in a Detroit without driver's licenses, stoplights 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, driver's ed. We learned to better design roads. We invented the highway, the seatbelt, the airbag. All those things made driving less deadly. Although the smartphone reversed some of that progress nationally. Today, deaths from cars are about as common in America as deaths from guns or opioids. About 1 in 100. It'll probably happen to someone you know in your life, maybe several someones. 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.

[Alex Davies]

So that thought is there early and early visions of it 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, yeah, 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.

[Host/Narrator (Freakonomics Radio)]

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. 9, 8, 7, 6, 5, 4, 3, 2, 1, 0. Recognition. Liftoff. Deep within the Department of Defense, there's a little known military agency that has created some of the most innovative technology of the 20th century. This is the story of DARPA. Chapter two. DARPA's million dollar prize. DARPA's current goal is to develop autonomous military vehicles. Machines that can operate on their own without drivers.

[Alex Davies]

DARPA's always been intrigued with.

[Host/Narrator (Freakonomics Radio)]

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 M16, the early Internet and the Predator drone. In 2002, DARPA decided to pursue the driverless car in a very unusual way.

[Alex Davies]

The director of DARPA at the time, a guy named Tony Tether, who had been a door to door salesman in his youth, definitely has that flair and 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.

[Host/Narrator (Freakonomics Radio)]

Oh right. You would have to like literally gridlock a huge American city so people could put robot cars on it.

[Alex Davies]

Exactly. 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.

[Host/Narrator (Freakonomics Radio)]

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 dotcom Boom, DARPA's trying to inspire tech to build something Besides another website. DARPA's Tony Tether announces that the prize for whoever can win its grand challenge will be $1 million.

[Alex Davies]

The rules were 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, be a tricycle. It just couldn't attack other vehicles. That was ruled out early on.

[Host/Narrator (Freakonomics Radio)]

Oh, was that a concern that people would just like sort of battlebot, the thing your autonomous vehicle would have like a little shredder that would take out somebody else's.

[Alex Davies]

Someone asked in the first Q and A at this, like they said, can we attack other vehicles? They said no. And it's funny you bring up BattleBots because a lot of teams who entered this had BattleBots history.

[Stephen Dubner]

Interesting.

[Alex Davies]

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.

[Host/Narrator (Freakonomics Radio)]

I'm going to tell you what happened in this robot race in the desert, and 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 on to lead development for the billion dollar companies creating today's driverless cars. And these 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 are the differences already present? The first engineer I want you to pay attention to is a man named Chris Urmson. And way back in 2002, how did you end up being part of the DARPA Grand Challenge?

[Chris Urmson]

It sounded like fun.

[Host/Narrator (Freakonomics Radio)]

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 Atacama Desert. Training for its future deployment on the surface of Mars.

[Chris Urmson]

My PhD advisor came down and was really excited about this DARPA Grand Challenge thing. And the idea that you'd have a robot run across the desert at 50 miles an hour just sounded exciting, having spent the last couple of weeks walking behind a robot at very low speed.

[Host/Narrator (Freakonomics Radio)]

So Chris would join Carnegie Mellon's Red team and help build a car called Sandstorm. A bright red Humvee 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 Humvee at all?

[Chris Urmson]

How does a computer make the steering wheel turn? How does a computer change the pressure on the brake and the throttle? Those are the issues that we're fighting through right now.

[Host/Narrator (Freakonomics Radio)]

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 Levandowski. Can you tell me about Anthony Lewandowski?

[Alex Davies]

Anthony Levandowski. 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 out. 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.

[Anthony Levandowski]

My name is Anthony Lewandowski. I was a grad student at Berkeley. Instead of continuing on to finish my PhD, I decided it was much better to do the grand challenge.

[Host/Narrator (Freakonomics Radio)]

We asked Anthony for an interview, he didn't respond. But here he is in the footage from back then.

[PJ Vogt]

Anthony did not have the engineering experience

[Host/Narrator (Freakonomics Radio)]

or resources of a team like Carnegie Mellon's Red team. So he 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

[PJ Vogt]

with an antenna on the back and

[Host/Narrator (Freakonomics Radio)]

cameras on the front.

[Anthony Levandowski]

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 fall to the left, you steer left, that makes you turn the left and then you get centripetal acceleration that puts you back up to the right. And you're monitoring that in real time and making small adjustments, and you stay balanced.

[Alex Davies]

The strobe light is on. The command from the tower is to move. Ladies and gentlemen, Sandstorm.

[Host/Narrator (Freakonomics Radio)]

The race happens on a Saturday in March of 2004.

[Alex Davies]

Autonomous vehicle traversing the desert with the goal of keeping our young military personnel

[Chris Urmson]

out of harm's way.

[Host/Narrator (Freakonomics Radio)]

Booyah. What happens the first time they try to do this competition?

[Alex Davies]

The 2004 Grand Challenge is an utter hysterical disaster.

[Host/Narrator (Freakonomics Radio)]

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.

[Alex Davies]

Anthony, good effort. And then every vehicle after it fails miserably, like one vehicle drives up onto a berm, 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.

[Host/Narrator (Freakonomics Radio)]

Even Sandstorm got stuck on a berm. Chris Urmson just standing there, unable to help his robot.

[Chris Urmson]

Poor thing was trying to get going, but its wheels were just spinning on the gravel and, and tried so hard that it actually melted the rubber of the tires. And so there's these plumes of black smoke before they killed it.

[Host/Narrator (Freakonomics Radio)]

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 26 miles. What this contest had done, though, was it had flushed all these inventors out, it had jump started 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.

[Alex Davies]

Sebastian Thrun, he was at the first Grand Challenge. He didn't bring a team. He wasn't participating. Gabriel 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 and he thinks, I can do better than this.

[Sebastian Thrun]

I looked at the very first iteration of this Grand Challenge where I didn't participate. I was a spectator.

[Host/Narrator (Freakonomics Radio)]

This, of course, is Sebastian Thren. He grew up in West Germany, moved to the us, Taught at Carnegie Mellon before moving to Stanford. Watching that day, he saw this fundamental error he believed all the entrants had made.

[Sebastian Thrun]

I saw that all the teams treated this like a hardware problem. They looked at this and said, we have to build a road with bigger wheels and bigger chassis and so on. And I looked at this and said, well, wait a Minute. The challenge really is to build a self driving car that can drive through the desert. I can get a rental car that can do it just fine, provided there's a person inside. And the challenge is really to take the person out of the driver's seat and replace it by a computer. That is not a problem of bigger tires. That's actually really a software problem.

[Host/Narrator (Freakonomics Radio)]

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?

[Sebastian Thrun]

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 Darbuck invented something that would save a million lives a year?

[Host/Narrator (Freakonomics Radio)]

In October of 2005, 43 teams have

[Alex Davies]

brought their vehicles to compete in a unique event. A race driven not by testosterone, but computer code.

[Host/Narrator (Freakonomics Radio)]

Chapter three, Machine Learning. The race course is a circular maze

[Alex Davies]

that zigzags for 132 miles.

[Host/Narrator (Freakonomics Radio)]

18 months later, for the second Grand Challenge, DARPA doubled the bounty $2 million. 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 Armson back with the Carnegie Mellon team, this time 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 measly. A blue SUV donated by Volkswagen. A baby faced Thrun smiles next to his soccer mom looking vehicle.

[Sebastian Thrun]

The vehicle's name is Stanley. So Stanley is nothing else but Stanford, but it also gives the vehicle a personality. We think of the vehicle more and more as an intelligent decision maker.

[Alex Davies]

Thrun is a computer scientist and Thrun really brought more artificial intelligence, which at the time we're talking 2005 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.

[Sebastian Thrun]

The robot Stanley was able to train itself as it went and the way it worked is its eyes looked way ahead and it could see stuff way at distance. When it drives over the stuff, it could tell was it a good place to drive or not, because it could measure how slippery or how bumpy the road was. And then you could then retroactively train and say, this green stuff over there, it's something good to drive on. AKA grass. And this brownish stuff, AKA mud, is not so good to drive.

[Host/Narrator (Freakonomics Radio)]

And so it was able to detect patterns and generalize from what it had learned?

[Sebastian Thrun]

Yeah, absolutely. And it did this like 30 times a second. I mean, just like a person.

[Host/Narrator (Freakonomics Radio)]

The race kicks off with Stanley sandwiched between Carnegie Mellon's two behemoths.

[Alex Davies]

Highlander leads the pack, followed by Stanley and Sandstorm.

[Host/Narrator (Freakonomics Radio)]

What happens in the second race?

[Alex Davies]

The second race is as successful as the first race is disastrous.

[Host/Narrator (Freakonomics Radio)]

Nearly every entrant 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?

[Sebastian Thrun]

Well, 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, very treachery mountain pass. And we saw at a distance, a dust cloud. We saw a helicopter. We saw little features that made us believe, wow, there's something happening that's magical. And this dust cloud then all of a sudden turned bluish because the car was blue and came closer. And then it came first to the finish line. It was unbelievably magical.

[Host/Narrator (Freakonomics Radio)]

At the end of the dock, over some criminally corny piano music, Sebastian Thrun 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.

[Sebastian Thrun]

It was just amazing to see this community of people. That community succeeded today. Behind me, there are three robots that made it all the way through the desert. And all three, three of them did the unthinkable. It's such a fantastic success for this community. I think we all win.

[Host/Narrator (Freakonomics Radio)]

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 in a hat and sunglasses, who'd watched the challenge while his mind spun that's after a short break.

[Stephen Dubner]

I'm Stephen Dubner and you are listening to a special episode of the podcast Search Engine here on Freakonomics Radio. We will be right back. Freakonomics Radio is sponsored by Mint Mobile. Are you one of those people who actually likes their money? Unfortunately, traditional big wireless carriers like your money too. So if you're tired of spending hundreds on crazy high wireless bills, bogus fees and free perks that cost you more in the long run, then a premium wireless plan from mint mobile for 15 bucks a month might be right for you. Stop overpaying for wireless just because that's how it's always been. Mint exists purely to fix that. Bring your own phone and number, activate with ESIM in minutes and start saving immediately. No long term contracts, no hassle. All plans come with high speed data and unlimited talk and text delivered on the nation's largest 5G network. If you like your money, Mint Mobile is for you. Shop [e-mail] freak that's mintmobile.com freak Upfront payment of $45 for 3 month 5 gigabyte plan required equivalent to $15 per month. New customer offer for first 3 months only. Then full price plan options available, taxes and fees extra. See Mint Mobile for details. Freakonomics Radio is sponsored by LinkedIn ads. Ever invested in something that didn't live up to the hype? Marketers know that feeling. They optimize for the numbers that look great, like impressions. But then they don't see revenue. LinkedIn has a word for bullspend. Instead you can get the highest roas of major ad networks with LinkedIn. Cut the bullspend. Advertise on LinkedIn, spend $250 and get a $250 credit. Go to LinkedIn.com freakonomics Terms apply. You ready?

[Host/Narrator (Freakonomics Radio)]

It is time. Go.

[Stephen Dubner]

I'm gonna take that as a yes.

[Movie Reviewer (Project Hail Mary segment)]

Project Hail Mary is the number one movie in the universe.

[Host/Narrator (Freakonomics Radio)]

So what we do now?

[Sebastian Thrun]

We party.

[Movie Reviewer (Project Hail Mary segment)]

It's exhilarating and awe inspiring. The perfect film.

[Host/Narrator (Freakonomics Radio)]

Amaze, amaze, amaze.

[Movie Reviewer (Project Hail Mary segment)]

Ryan Gosling gives a performance that goes downhill.

[Alex Davies]

How do you know when the hug is done?

[Host/Narrator (Freakonomics Radio)]

Would you just get in here?

[Movie Reviewer (Project Hail Mary segment)]

This is one for the ages. Project hail Mary Ricky PG13 may be inappropriate for children under 13 now playing only in theaters.

[Stephen Dubner]

Hey there, it's Stephen Dubner. Today we are running an episode from the search engine podcast with host PJ Vogt.

[Host/Narrator (Freakonomics Radio)]

Chapter 4. Something actually useful for the world. The race in the desert had been designed as a spectacle. Something flashy to draw out America's smartest roboticists. But it had drawn another person who'd come for his own reasons. Google's Larry Page arrived at the DARPA Grand Challenge in a baseball hat and sunglasses. A disguise. He found Sebastian Thrun and buttonholed him, asking him a million highly specific questions about things like the wavelength his LiDAR system used. But this meeting in the desert, this was not actually their first introduction.

[Sebastian Thrun]

Well, the first time I met Larry was a bit earlier. He had built a small little robot that acted as a telepresence for meetings, and he was trying to drive it around the Google offices instead of himself going to meeting with a robot. And he sent me a message and said, I want to show you the robot I've built. And in a spur of like, craziness, I sent a message back saying, larry, I'm so glad that Google lets you use 20% of your time. Do 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 robot, I fixed it next 24 hours, and he was very happy.

[Host/Narrator (Freakonomics Radio)]

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 toward 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 Thrun, along with fellow DARPA 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 does Project Chauffeur begin? Like Larry Page says to you, I have a mission. Like, how does this happen?

[Sebastian Thrun]

This is an embarrassing moment for me. It's about two years later, 2009, where I sit in my cubicle and Larry Page comes by and says, sebastian, I think you should build a self driving car that can drive anywhere in the world. My immediate reaction was no. Taking the technology we built 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 him the same answer. And both of us got increasingly more frustrated, like, God damn it, it can't be done. And eventually he came and said, look, Sebastian, okay, I get it, you can't do it. I want to explain to Eric Schmidt, the CEO at the time, and Sergey Brin, my co founder, 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 driving 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 are usually experts. The past, not the future. And if you ask an expert about innovation, something crazy new, they're the least likely person to say, yes, it can be done.

[Host/Narrator (Freakonomics Radio)]

So this is where the Google self driving car project begins in 2009. It's led by Sebastian, joined by others from the DARPA challenges. The methodical Chris Urmson was running most things day to day. Anthony Lewandowski, the flashy motorcycle guy, would work on hardware. Dmitry Dolgov, another DARPA veteran, would be responsible for planning and optimization. It was a secret project. They'd report directly to Larry Page, a small enough team that there'd be no bureaucracy, few emails, fewer meetings, just 11 engineers who writer Alex Davies says represented some of the best young talent in the country.

[Alex Davies]

And so Google builds this very quiet team and it says to them, build us a self driving car. And because that goal is super nebulous, they give them two challenges. They say, safely log 100,000 miles on public roads. But they also give them a challenge called the Larry 1K.

[Sebastian Thrun]

So Larry and Sergey and I sat together and the two of them carved out a thousand total miles of road surface in California.

[Alex Davies]

They open up Google Maps and they just click around and they look for 10 separate 100 mile routes that are really tricky.

[Sebastian Thrun]

Absolutely everything like the Bay Bridge and Lake TAO and Highway 1 to Los Angeles and Market street and even crooked Lombard Street.

[Alex Davies]

And they say to the team, you have to drive each of these 100 mile routes without one human takeover of the system, without one failure of the

[Host/Narrator (Freakonomics Radio)]

car to get off to a running start. The team licenses the code from Stanford's 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, a LiDAR system spinning 360 degrees on top. LiDAR like radar, but it shoots lasers instead of sound waves. At first, the team gives each Prius a cool name like Knight rider.

[Don Burnett]

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 27.

[Host/Narrator (Freakonomics Radio)]

This is Don Burnett. He'd been a researcher working on autonomous submarines. He lost a friend in a car accident separately, got in 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. In its early days, I was on

[Don Burnett]

the motion planning and behavior decision making team, and my responsibility was to work on the nudging behavior.

[Host/Narrator (Freakonomics Radio)]

Nudging. When 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.

[Don Burnett]

You're trying to encode the behavior that you would use as a driver under kind of partially good perception. And it's a really tricky problem.

[Host/Narrator (Freakonomics Radio)]

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 e. There's this big concert venue near Google's offices called the shoreline amphitheater. In 2009, you could have seen Sheryl 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 plain 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 sensors could really recognize another car, that the computer in the car was abiding by their orders. These were the baby steps they'd happen in this parking lot and at an empty airplane Runway that was close to their offices. Spring 2009. The team tries actual real road driving for the first time. Chris Urmson takes one of the Priuses out on the Central Expressway. Speed limit 45 mph. 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.

[Chris Urmson]

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.

[Host/Narrator (Freakonomics Radio)]

One more problem to fix. Listening to this 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 sat behind the steering wheel ready to take over. In the other seat was their partner, watching the monitor displaying a graphical interface designed by Dmitry 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 manning the cars, logging errors, going back to the office to troubleshoot, and then updating the code. I asked Don Burnett 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?

[Don Burnett]

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. And 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. So you're going to slow down, but how much do you slow down? Right. And it turns out that's contextual.

[Host/Narrator (Freakonomics Radio)]

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 that lateral acceleration is 2 meters per second squared. But the surprising thing is that number only applies on the on ramp.

[Don Burnett]

If I put you at a cul de sac in a neighborhood, and you were going to do a U turn at the end of the cul de sac, even though the speed is significantly slower, if you did 2 meters per second squared of lateral acceleration around a cul de sac, you would tell your driver they were crazy. It would feel incredibly uncomfortable. Like, incredibly uncomfortable.

[Host/Narrator (Freakonomics Radio)]

You would feel like you were in Mario Kart.

[Don Burnett]

Yes, it would feel Mario Kart. 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 0.75. It's almost three times less than you would be willing to tolerate as you accelerate onto a highway. And so there were things like that where you couldn't just say, humans have specific physical restrictions, right? From a forces perspective, the context matters. And when the context matters, now, all of a sudden, anything is game. So things like that is where I spent my time as a researcher, trying to figure out, okay, how are we going to make this comfortable for passengers?

[Host/Narrator (Freakonomics Radio)]

All these little problems to solve. But there was one gift, which was that the team at this point had an overarching goal uniting them. The DARPA challenge had told them drive across this patch of desert. The Larry 1K challenge told them drive these 10 routes without human intervention. The specificity of the mission meant they never had to squabble about why they were there. By 2010, just a year in, the team was really on a roll.

[Alex Davies]

They start knocking out routes.

[Don Burnett]

Each one of the routes was unique and distinct and different and had its own challenges.

[Alex Davies]

Down Route 1, Silicon Valley to Carmel, the bridges run.

[Don Burnett]

We had to go across all of the bridges in the Bay Area, starting in Mountain View, finishing crossing the Golden Gate Bridge.

[Alex Davies]

It's Chris Urmson in the car. It's Anthony Lewandowski in the car.

[Don Burnett]

I was in the car with Dimitri, Chris and Anthony. It was the four of us in the Prius.

[Alex Davies]

They're figuring out the technology much faster than they Thought they could.

[Host/Narrator (Freakonomics Radio)]

The Larry 1k 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 on to the next one.

[Chris Urmson]

It was really a proof of concept exercise. Can you even make this happen?

[Alex Davies]

Once when they fail a route, they know what the car can't handle. So they go back and say, you have to be better at doing xyz.

[Don Burnett]

And then we got back to the office, we regrouped, we went back out, I think at like 11pm and by 1am we had completed the route.

[Alex Davies]

They buy a bottle of Corbel champagne. They all write their names on it.

[Host/Narrator (Freakonomics Radio)]

Corbel, $13.99 a bottle. The champagne they have at Trader Joe's. They had one for every route they completed.

[Alex Davies]

And one by one, they pick off the Larry 1K 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 by

[Host/Narrator (Freakonomics Radio)]

fall of 2010, they're done. Here's Chris Urmson.

[Chris Urmson]

And I think we had a big party up at Sebastian's house in Los Altos Hills. So, you know, it was pretty spectacular, right?

[Alex Davies]

They throw each other in the pool, they celebrate, and then they're not entirely sure what to do next.

[Chris Urmson]

It was kind of okay.

[Host/Narrator (Freakonomics Radio)]

And now 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'd done this safely. They'd 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.

[Stephen Dubner]

Hey there, Stephen Dubner. That again is PJ Vogt, and you are listening to an episode of the search engine podcast here on Freakonomics Radio. We will be right back. Freakonomics Radio is sponsored by Amazon Business. How can you free your team from time consuming office tasks? Amazon Business empowers leaders to not only streamline purchasing, but better support their teams. Smart business buying tools enable buyers to find and purchase items fast so they can focus on strategy and growth. It's time to free up your teams and focus on your future. Learn more about the technology insights and Support [e-mail]. Freakonomics Radio is sponsored by Dell. Dell PCs with Intel inside are built for the moments you plan and the ones you don't. They're for the all night study sessions. The times you're deep in your flow and can't be interrupted by an auto update. That's why Dell builds tech that adapts to you. Built with long lasting batteries so you're not scrambling for an outlet. And built in intelligence that makes updates around your schedule, not in the middle of it. Find technology built for the way you [e-mail] PCs built for you. You ready?

[Host/Narrator (Freakonomics Radio)]

It is time. Go.

[Stephen Dubner]

I'm gonna take that as a yes.

[Movie Reviewer (Project Hail Mary segment)]

Project Hail Mary is the number one one movie in the universe.

[Host/Narrator (Freakonomics Radio)]

So what we do now?

[Sebastian Thrun]

We party.

[Movie Reviewer (Project Hail Mary segment)]

It's exhilarating and awe inspiring. The perfect film.

[Host/Narrator (Freakonomics Radio)]

Amaze, amaze, amaze.

[Movie Reviewer (Project Hail Mary segment)]

Ryan Gosling gives a performance that goes down in history.

[Alex Davies]

How do you know when the hug is done?

[Host/Narrator (Freakonomics Radio)]

Would you just get in here?

[Movie Reviewer (Project Hail Mary segment)]

This is one for the ages. Project hail Mary Ricky PG13 may be inappropriate for children under 13 now playing only in theaters.

[Host/Narrator (Freakonomics Radio)]

Welcome back to the show. As early as 2010, Google's driverless car project had developed some very impressive self driving technology. But what they were struggling to decide was what was the actual product they were developing here. Here's Sebastian Thrun.

[Sebastian Thrun]

We had a lot of debates inside Google what the right business model was. At some point we actually had a big debate we should just buy Tesla. And Tesla was worth $2 billion at the time. I remember this. Maybe we should have in hindsight. But joking aside here there was a debate whether this is more of an assistive technology or a disruptive replacement technology.

[Host/Narrator (Freakonomics Radio)]

Basically, should they follow the route that Tesla ultimately would 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? Theron would eventually come around to this version of self driving. Specifically he'd come around to the idea of self driving Robo taxis.

[Sebastian Thrun]

A taxi service type system is way more capital efficient than ownership. An owned car is being used about 4% of the time and it's parked 96% of the time. Imagine a city without parked cars where every car is being utilized, call it 50% of the time, which means we have like only 10% number of cars needed that we need today when we all own cars. That's going to happen.

[Host/Narrator (Freakonomics Radio)]

There's no absolute question what Sebastian is describing here so matter of factly is a fairly radical reimagination of American cities. The idea that robo taxis 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 far fetched 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 Burnett 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.

[Don Burnett]

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.

[Alex Davies]

And within the team. You get team Chris and team Anthony and they start butting heads all the time.

[Host/Narrator (Freakonomics Radio)]

Chris and Anthony meaning Chris Urmson, official head of the project, versus Anthony Lewandowski, who I still think of as the motorcycle guy.

[Alex Davies]

The main difference in their approach is how quickly they want to move. Anthony is very okay with risk. We'll say he gets one of these cars and he's driving it back. And he lives in Berkeley, works in Palo Alto. He's just using this car like on the Bay Bridge every day, probably outside the bounds of what the team actually wanted. And he's not like necessarily logging data, he's just enjoying his self driving car, taking it all over the place. Chris comes from an academic background. He's that Canadian, very nice, very careful, very risk averse.

[Host/Narrator (Freakonomics Radio)]

When I asked Chris Urmson 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 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.

[Don Burnett]

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? How are they going to use it? Where are they going to use it? 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.

[Host/Narrator (Freakonomics Radio)]

But competition would soon arrive in the form of Uber.

[Don Burnett]

This was the oh shit moment for me. 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.

[Host/Narrator (Freakonomics Radio)]

In 2013, then CEO of Uber, Travis Kalanick, 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 Mellon'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 2016 would leave Google, quitting just before he could be fired for recruiting team members away, including Don Burnett. Anthony would then start his own autonomous vehicle company. Uber would soon buy that company for almost $700 million, 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 asset seemed to be its people?

[Alex Davies]

This is where Google goes into its computer security logs and realizes that not long before he left, Anthony Lewandowski downloaded something like 14,000 technical files onto his computer and moved them onto an external disk.

[Host/Narrator (Freakonomics Radio)]

Obviously you can't do that. I mean, I'm assuming obviously you can't do that.

[Alex Davies]

No, you definitely cannot do. And this is the kind of thing that maybe if he had stayed there, this is the kind of thing Anthony would have done. And he would have been like, oh, it's just so I could have access to it somewhere else. And 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 Waymo. At this point, Google's program sues Uber and puts Anthony at the center of an enormous legal battle between these tech giants,

[News Reporter]

secrets and subterfuge in Silicon Valley, a former Google engineer has been charged with stealing files from Alphabets self driving car project and taking them to Uber.

[Host/Narrator (Freakonomics Radio)]

Specifically, it involves a former lead engineer

[Alex Davies]

of Google's self driving car unit, Anthony Levandowski. Now he's accused of using his personal laptop and downloading more than 14.

[Host/Narrator (Freakonomics Radio)]

In 2016, Google had just spun its driverless car unit into a new entity, Waymo. Waymo sued Uber. Uber had to settle to the tune of $245 million. 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 2018 leads to the death of a woman named Elaine Hertzberg. Uber is hitting the brakes on its self driving cars after one of them hit and killed a woman in Arizona.

[News Reporter]

The vehicle was an autonomous mode, but it did have a safety driver on board. But 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.

[Host/Narrator (Freakonomics Radio)]

The way this story was reported, nearly everyone blamed the safety driver. She was on her phone, she was streaming an episode of the Voice.

[News Reporter]

Tempe investigators saying had Vasquez been paying attention to the road, she could have stopped the car 42ft before impact. The NTSB slamming Uber.

[Host/Narrator (Freakonomics Radio)]

There was some important additional context, which is that Uber's robot driver was also just much worse than Waymo's. A statistic I found jaw dropping. At this point, Waymo safety drivers were having to take over from the car once every 5,600 miles. Uber's safety drivers that year had to intervene more than once every 13 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 Elaine Hertzberg's life, the robot spent an indefensible 5.6 seconds trying and failing to guess the shape in the road. There was a human body pushing a bike. Over those 5.6 seconds, the robot kept reclassifying her. Was she 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.

[News Reporter]

Uber has temporarily suspended its driverless fleet nationwide. As the ntsb, police, Uber and the National Highway Traffic Safety Administration investigators, we

[Host/Narrator (Freakonomics Radio)]

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 would be other competitors who would shut down after similar accidents. There would also be Tesla, which by 2020 was publicly marketing a product the company called full self driving, but which absolutely was not. Meanwhile, Waymo had slowly continued to develop its tech. Their robo taxis would be ready for riders by 2020. The team had gotten an unexpected boost from a technology that was at the time, very little understood. In 2026, when most people talk about artificial intelligence, the conversation defaults to products like ChatGPT and Claude. But artificial intelligence has been a core part of driverless cars going back two decades. In the 2000s, neural net advances meant that you could 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 Thrun.

[Sebastian Thrun]

That 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, 100 million is fine. But when you put 100 billion documents into an AI, it is unbelievably smart. And that, I think shocked everybody, myself included.

[Host/Narrator (Freakonomics Radio)]

The Google Brain 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 the 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, Waymo had to design miniature wipers for their car's LIDAR sensors to deal with the dust storms and heavy rains. In 2020, Waymo finally debuts to the public in Arizona. In the years after, it'll roll out to 10 more American cities. A funny consequence of Waymo'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 2009. When the project first started. And so now many people look at the Waymo driver with a raised eyebrow, with a question immediately on their lips. Chapter five. Are you a good driver?

[Stephen Dubner]

All right. Autonomous vehicles can now get you around Atlanta.

[Timothy B. Lee]

Yesterday morning.

[Stephen Dubner]

The future of driving through Austin is

[Host/Narrator (Freakonomics Radio)]

here, except it comes without a driver. App is now taking passengers in Miami. A fleet of white electric jaguars covered in 40 different sensors, cameras, radar, lidar. It's an expensive car, as much as $150,000 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. Vestigial, it turns itself. Cars without drivers are here.

[News Reporter]

Yeah, sounds like something out of the Jetsons. But get ready, because you may look over at the car next to you and see it rolling down the street.

[Host/Narrator (Freakonomics Radio)]

The TV newscasters always use the same gee whiz tone. They can never resist a Jetsons reference. In every city, the influencers hop in to record testimonials, meals for their daily serving of clout.

[Don Burnett]

So in today's video, I'm about to take my first ever driverless car.

[Host/Narrator (Freakonomics Radio)]

It's with an app called Waymo.

[Alex Davies]

Waymo is basically driverless car Uber, where it's like ride service. You call it, go wherever you need

[Host/Narrator (Freakonomics Radio)]

it to go, but there's no driver.

[Alex Davies]

You guys, this is creepy.

[Host/Narrator (Freakonomics Radio)]

It's like I'm being driven around by a ghost person.

[Alex Davies]

It's a little terrifying.

[Host/Narrator (Freakonomics Radio)]

It is definitely Robo taxis pull hilariously badly. According to J.D. power, a data analytics firm, among people who've not ridden in one, consumer confidence is at 20%. But among people who have taken a ride, the number shoots up to 76%. It's a thing I didn't capture in this story, but when I sat in one a couple years ago, I just found it persuasive as an experience.

[Anthony Levandowski]

You know what?

[Host/Narrator (Freakonomics Radio)]

I'm not as nervous as I thought I was going to be.

[Don Burnett]

This is actually quite relaxing.

[Host/Narrator (Freakonomics Radio)]

Nice gradual turn.

[Don Burnett]

Felt very safe.

[News Reporter]

You know, it was kind of freaky at first, but now it's pretty chill.

[Alex Davies]

It's a smooth ride, though.

[Host/Narrator (Freakonomics Radio)]

It wasn't driving fast. It wasn't jerking.

[News Reporter]

It's driving like you always hope your Uber driver would.

[Alex Davies]

So I guess that's one of the big sells.

[Host/Narrator (Freakonomics Radio)]

Chris Urmson, the methodical team leader, had left Google years ago, but he told me about his experience as a civilian consumer trying a Waymo out in the world.

[Chris Urmson]

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. There's nobody behind the wheel. Ooh, swim with sharks. And then a few minutes in, it's like, okay, you know, is this just going to drive? Is that all it does? And then, you know, 10 minutes in, people are looking at their phone.

[Host/Narrator (Freakonomics Radio)]

People tend to feel safe in these cars, but are they actually. So we know that the Waymo driver has now driven over 200 million real world miles and they've released safety data so far for the first 127 million miles. Waymo's fairly transparent. They released 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 Waymo'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 B. Lee, author of the newsletter Understanding AI. I asked him how much our picture of the Waymo safety data has been evolving.

[Timothy B. Lee]

So it's been pretty consistent the last couple 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 crashes per mile have not changed a ton. Waymo says, and I think this is correct, that it's roughly 80% safer in terms of crashes that are severe enough to trigger an airbag, crashes severe enough to cause an injury, and also crashes involving vulnerable road users like pedestrians or bicyclists.

[Host/Narrator (Freakonomics Radio)]

So 80% fewer airbag crashes than human drivers and actually 90% fewer crashes that cause a serious injury. Some independent experts have small quibbles with the methodology, but broadly they find Waymo's data credible. Timothy pointed out there's one very important thing we don't know. The fatal crash comparison. For every 100 million miles humans drive, we cause a little over one fatal crash. The Waymo driver has driven 200 million miles without causing a fatal crash. But statistically speaking, that could still be a fluke. Some academics have suggested we'd need about 300 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 Waymo in the line of struck cars. In another crash, a Waymo was 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 B. Lee looks at the entire safety picture, the results we have so far from this big experiment Waymo is conducting on American roads, what he sees is mainly promising.

[Timothy B. Lee]

So far it's been better than human drivers and so far I think the case for allowing them to continue the experiment is very strong.

[Host/Narrator (Freakonomics Radio)]

Which doesn't mean we shouldn't scrutinize this Waymo experiment as it continues. I find myself paying a lot of attention to Waymo crashes, which isn't hard. They make headlines. The most harrowing one recently was this January.

[Alex Davies]

A child near an elementary school in Santa Monica is struck by a Waymo.

[Host/Narrator (Freakonomics Radio)]

A child ran across the street from behind a double parked car and a Waymo hit the kid.

[Stephen Dubner]

Santa Monica police say the child, a

[Host/Narrator (Freakonomics Radio)]

10 year old girl, was not hurt. The company issued a statement. Waymo said its driver had braked hard, reducing speed from 17 to under 6 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 Waymo do after a car crash? Since there's no human driver to help, Waymo 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 Waymo called one of those humans, the human called 911 is, and this is the strangest part of Waymo'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 Waymo gets stuck at a dead stoplight or blocks an emergency vehicle or an example Timothy gave Waymos were driving past stopped school buses in Austin.

[Timothy B. Lee]

I think it's reasonable to say this is like a clear cut rule that the vehicle should follow this rule. These edge cases are still very rare and so if it's a 1 in 10 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.

[Host/Narrator (Freakonomics Radio)]

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 in the Philippines, there's questions about what specifically they do and about how this will all work as Waymo 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 Waymo driver is already making roads safer in the places where they operate and says that Waymo continues 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 Waymo continues its rollout, other companies are quickly falling behind.

[News Reporter]

Amazon's new driverless taxi is launching in Las Vegas this summer and it's expected to arrive in la.

[Host/Narrator (Freakonomics Radio)]

There's other robo taxi companies like Amazon, Zoox, Uber is back in the mix, not making technology but partnering with these robo taxi companies.

[News Reporter]

We Ride recently struck a partnership with Uber to bring its AVS to Abu Dhabi. Another sign that and many of those

[Host/Narrator (Freakonomics Radio)]

early Waymo engineers are now CEOs of autonomous companies themselves. Dmitry Dolgov is actually co CEO at Waymo, but other team members run driverless trucking companies.

[News Reporter]

Got Don Burnett, Founder and CEO of Kodiak AI. Don, thank you so much for joining us. It's good to see you again.

[Host/Narrator (Freakonomics Radio)]

Don Burnett is head of Kodiak AI which has its technology deployed in driverless trucks in the Permian Basin.

[News Reporter]

Please welcome CEO of Aurora, Chris Urmson.

[Host/Narrator (Freakonomics Radio)]

A big round of applause. Chris Urmson now heads Aurora, which currently has semi trucks on Texas highways. And my personal favorite plot development which just emerged this week, I just broke

[News Reporter]

on the information that Uber founder Travis Kalanick is starting a new self driving car company with financial backing from Uber and in partnership with Anthony Levandowski. Now for those who have been they

[Host/Narrator (Freakonomics Radio)]

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 uppers 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 delight 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 Lamplighters 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.

[Alex Davies]

Please stand up. Everybody. That's rideshare, union members or someone who drives a vehicle, stand up.

[Host/Narrator (Freakonomics Radio)]

4.8 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

[Stephen Dubner]

they eliminate driving jobs for working class people.

[Alex Davies]

I understand it is business, it is capitalism, but not in my city at

[Host/Narrator (Freakonomics Radio)]

the expense of our jobs. These drivers are represented by unions backed by politicians. And in cities across America, blue cities, they're organizing. So far they're winning.

[Alex Davies]

Humans drive this city, not machines. Labor drives this city.

[Chris Urmson]

Keep the workers in the workforce.

[Alex Davies]

If it works in another city, great. Have fun.

[Host/Narrator (Freakonomics Radio)]

Not here. Not Boston.

[Alex Davies]

Thank you.

[Host/Narrator (Freakonomics Radio)]

Next week, the fight to save a job to save the human driver. Don't miss this one.

[Stephen Dubner]

Many thanks to PJVote and the entire search engine team for this story. You will hear part two right here on Freakonomics Radio very soon. Until then, take care of yourself. And if you can, someone else too. Freakonomics Radio is produced by Renbud Radio. You can find our entire archive on any podcast app. It's [e-mail] where we publish transcripts and show notes for search engine. This episode was produced by Emily Maltaire. The show was created by P.J. vogt and Shruti Pinamaneni. Garrett Graham is their senior producer. Leah Rhys Dennis is their executive producer. Fact checking was done by Mary Mathis and sound design and original composition by Armin Bazarian. Their production intern is Piper Dumont. For Freakonomics Radio, this episode was produced by Dalvin Abraham and edited by Ellen Frankman. The Freakonomics Radio Network staff also includes Augusta Chapman, Eleanor Osborne, Elsa Hernandez, Gabriel Roth, Ilaria Montenicourt, Jasmine Klinger, Jeremy Johnston, Teo Jacobs, and Zach Lipinski. Our theme song is Mr. Fortune by the Hitchhikers and our composer is Luis Guerra. As always, thanks for listening. Is it possible that you were really stoned on painkillers in that first Waymo ride?

[PJ Vogt]

I mean, I wasn't stoned on painkillers, and I don't think I was stoned at all. I think I really had a sense of normal technological awe.

[News Reporter]

The Freakonomics Radio network.

[Don Burnett]

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[News Reporter]

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[Stephen Dubner]

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[Stephen Dubner]

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