Are Human Drivers Finally Obsolete?

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PJ, how have you been?

I've been good, how have you been?

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

I love it.

Thank you.

Do you recognize that voice?

It is PJ vote host of the podcast search engine and friend of Freconomics Radio.

You may remember hearing him back in 2024 when we published a search engine episode called

the fascinatingly mundane secrets of the world's most exclusive nightclub about Bergheim 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've touched on many times over the years at Freconomics Radio,

but PJ decided to go deep.

The other day, I had a chance to ask him how he got interested in this.

There's a whole lesson in this, but I had gotten, and this is not the night since you're going to expect me to say,

too into bench pressing.

And that's not where I thought you were going.

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

I see.

I like some minor complications. I was not moving easily. I was in a lot of pain.

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 people were not talking about that more.

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, and why?

So in the first part, there's this guy Sebastian Thrun.

He's so good. He's a Sternborn roboticist, AI expert, who lost a friend as a teenager,

took 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 and at least plausible.

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

Vivid is a very blatant.

They're strongly opinionated. They sometimes commit gaps.

When you ask them about the gaps, 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 were 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 to have to do a lot of soon,

which is way competing, not totally reconcilable interests, and really take them seriously.

And that was us trying to do it for this one thing.

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

50 years?

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

No, I have no illusions about my driving skills. I'm not that good. I'm... I have a temper. I am distracted.

I rode in an autonomous test vehicle at Carnegie Mellon University that 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 you're putting the pedal to the metal for autonomous.

I hope it gives people a little context for this. All the questions people have is the safe. What is it going to do?

We've answered it as much as we can.

Today on Frekenomics Radio, we turn the mic over to PJ and our friends at the Sir Tension Podcast for the first of a two-part series on driverless cars.

Listeners, start your engines.

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, I don't have a identical. It's Monday morning.

It's Monday morning, and it's very early. Predon, you wake up to this really hard wrapping 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-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 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 and 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 work day.

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 ask the humans I met the same question. Are you a good driver?

Are you, do you consider yourself a good driver?

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

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?

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 like reading all the baby books, a good project to work on is just be calmer in the car.

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.

Drivenless 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.

Robotaxies, like Waymo, are operating in 10 American cities, providing millions of rides to Americans.

In China, the road is happening even more widely, there are in 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.

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 lamp lighters.

People have been thinking about a self-driving car for...

It's just 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 a human-driven car, which is said...

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.

When automobiles first arrived, these powerful and non-sension 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 failures, 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 horse-less 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 return 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 may 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 one in a hundred.

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.

So that thought is there early and like 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 from 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.

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.

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 2. DARPA's Million Dollar Prize.

DARPA's current goal is to develop autonomous military vehicles, machines that can operate on their own without drivers.

DARPA's always been intrigued with...

This is from a documentary called The Million Dollar Challenge.

Honestly, less a dock, 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,

thoroughly internet and the predator drone.

In 2002 DARPA decided to pursue the driverless car in a very unusual way.

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.

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

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.

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 .com boom,

DARPA is 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.

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.

You could have six wheels that could be a truck.

It could be a motorcycle.

It could be a tricycle.

It just couldn't attack other vehicles.

That was ruled out early on.

Was that a concern that people would just like sort of battle bot the thing?

Sometimes vehicle would have like a little shredder that would take out somebody else's.

Someone asked in the first Q&A at this, they said, can we attack other vehicles?

They said no.

And it's funny you bring up battle bots because a lot of teams who entered this had battle bots 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.

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 on to lead development

for the billion dollar companies creating today's drive for those 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 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 are already present.

The first engineer I want you to pay attention to is a man named Chris Irmson.

And way back in 2002, how did you end up being part of the DARPA ground challenge?

It sounded like fun.

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 Otacama desert, training for its future deployment on the surface of Mars.

My PhD advisor came down and was really excited about this DARPA ground challenge thing

and the idea that you have a robot running 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.

So Chris would join Carnegie Mellon's red team and help build a car called sandstorm.

A bright red Humvee with a top-lopped off, a plethora of futuristic sensors mounted to it.

Like Scanners at 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?

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.

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 Levindowski.

Can you tell me about Anthony Levindowski?

Anthony Levindowski.

We're at a begin.

So, Anthony is like an entrepreneur.

He's a really charming guy.

He's six foot six.

He's gangly. He has 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.

My name is Anthony Levindowski.

I was a grad student at Berkeley.

Instead of continuing to finish my PhD, I decided it was much better to do the grand challenge.

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 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 is only self-driving motorcycle.

It was named Ghost Rider, a stubby little thing covered in stickers,

but then antenna on the back and cameras on the front.

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 a triple acceleration to put you back up to the right.

And you're monitoring that in real time and making small adjustments and you stay balanced.

The strobe light is on the command from the tower is to move.

Ladies and gentlemen, sandstorm!

The race happens on a Saturday in March of 2004.

Autonomous vehicle traversing the desert with the goal of keeping our young military personnel out of harm's way.

Who ya?

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

The 2004 Grand Challenge is an utter historical disaster.

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.

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.

Even sandstorm got stuck on a berm.

Chris Irmson just standing there, unable to help his robot.

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.

And so there's this flumes of black smoke before they killed it.

For the roboticists, this was obviously very disappointing.

Chris Irmson 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.

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, and he thinks, I can do better than this.

I looked at the very first iteration of this grand challenge, where it didn't participate, I was a spectator.

This, of course, is Sebastian Thrun.

He grew up in West Germany, moved to the U.S., taught at Carnegie Mellon before moving to Stanford.

Watching that day, he saw this fundamental error, he believed all the interns had made.

I saw that all the teams treated this like a hardware problem.

They looked at this and say, we have to build a roller, bigger wheels, and bigger chassis, and so on.

And I looked at this and said, wait a minute, the challenge really is to build a self-driving car

that can drive for the desert.

I can get a rental car that can do it just fine, provide there's a person inside,

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 of bigger tires, that's actually a video software problem.

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.

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

In October of 2005, 43 teams have brought their vehicles to compete in a unique event,

a race driven not by testosterone, but computer code.

Chapter 3.

Machine.

Learning.

The race course is a circular maze that zigzags for 132 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 Lyskow.

Familiar faces have returned.

Chris Irmson, 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 entrance.

Compared to Sandstorm, the bulked up Hummer,

the car looks immediately.

A blue SUV, donated by Volkswagen.

A baby faced the run smiles next to his soccer mom looking vehicle.

The vehicle's name is Stanley.

So Stanley is nothing else but Stanford.

But it also gives the vehicle the personality.

We think of the vehicle more and more as an intelligent decision maker.

Throne is a computer scientist.

The run 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.

The robot Stanley was able to train itself as it ran.

And the way it worked is his eyes looked way ahead

and it could see stuff way at distance.

When it drives over the stuff, he could tell it wasn't a good place to drive or not

because it could measure how slippery or how bumpy the road was.

And then he could then retroactively train and say,

there's 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.

And so it was able to detect patterns

and generalize from what it had learned?

Yeah, absolutely.

And it did this like 30 times a second.

I mean, just like a person.

The race kicks off with Stanley Sandwich

between Carnegie Mellon's two behemoths.

Highlander leads the pack,

followed by Stanley and Sanskrit.

What happens in the second race?

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

Nearly every entering 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, once we passed the front running team,

we kind of saw the vehicle descend in two of us,

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 a little feature that made us believe,

wow, there's something happening that's magical.

And this dust cloud then all of a sudden turned blue-ish

because the car was blue and came closer.

And then it came first to the finish line.

It was unbelievable magical.

At the end of the dock,

over some criminally corny piano music,

Sebastian Thrawn 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.

There was just amazing to see this community of people.

That community succeeded today.

Behind me, there are three robots that made all the way to the desert

and all three of them did the unthinkable.

It's such a fantastic success for this community.

I think we all win.

A made for TV, Coombayah 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'd been observing from the stands that day,

disguised in a hat and sunglasses,

who'd watch the challenge while his mind spun.

That's after a short break.

I'm Stephen Dubner and you are listening to a special episode

of the podcast Search Engine here on Frekenomics Radio.

We will be right back.

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Hey there, it's Stephen Dubner.

Today we are running an episode from the Search Engine podcast

with host PJ Vote.

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 robot assists.

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 in sunglasses in disguise.

He found Sebastian Throne and button-hold 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.

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 the robot.

And he sent me a message and said,

I'm going to show you the robot I've built.

And in a spur of craziness,

I send the message back saying,

Larry, I'm so glad

that Google lets you use 20% of your time

to use 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.

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 Ph.D. 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 Thron,

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,

this project showed furb again?

Like, Larry Page says to you,

I have a mission, like, how does this happen?

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.

They can drive anywhere in the world.

And my immediate reaction was, no,

taking the technology we built for this empty desert

and putting 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.

Both of us got increasingly more frustrated.

Like, God damn it, it can't be done.

And eventually came and said,

look, Sebastian, okay, I get it.

You don't want to, 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 in 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, I usually explore 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.

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 Armson was running

most things day to day.

Anthony Levin-Dowski,

the flashy motorcycle guy,

would work on hardware.

Dimitri 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.

And so, Google builds this very quiet,

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.

So Larry and Sergenai said together,

and the two of them carved out a thousand

total miles of road surface in California.

They open up Google Maps,

and they just click around,

and they look for 10 separate

100 mile routes

that are really tricky.

Absolutely everything,

like the Bay Bridge,

and Lake Tahoe,

and Heavy One Tool,

Los Angeles,

and Market Street,

and even Crooked Lombard Street.

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 car.

To get off to a running start,

the team licenses the code

from Sanford'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,

spending 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.

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.

This is Don Bernat.

He'd been a researcher

working on autonomous submarines.

He also friend in a car accident.

He separately got in a bad accident himself,

and decided that 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 the motion-planning

a behavior decision-making team,

and my responsibility was

to work on the nudging behavior.

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.

I'm 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.

A team of academic roboticists,

some of whom 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 Ampitheater.

In 2009, 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 plain sight.

A Prius,

driving itself around the Ampitheater 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,

those close to their offices.

Spring 2009,

the team tries actual,

real road driving for the first time.

Chris Irmson takes one of the Prius'

out on the central expressway,

speed limit,

45 miles per hour.

There are humans driving here.

And immediately,

outside the confines

of the empty parking lot

and the empty airplane runway,

here's what's clear.

They had a real problem.

The car was swerving wildly.

It was,

we've been 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.

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.

In the other seat was their partner,

watching the monitor

displaying a graphical interface,

designed by Dimitri 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 percent team

is manning the cars,

logging errors,

going back to the office

to troubleshoot,

and then updating the code.

I asked Don Bernad 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?

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

a 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.

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 wanted 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.

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 two 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.

You would feel like you

were at Mario Kart.

Yes, it would feel

Mario Kart.

And remember,

this is a four.

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?

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 ten 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.

They start knocking

out routes.

Each one of the routes was

unique and distinct

and different and had

its own challenges.

Downroot 1, Silicon Valley

to Carm Out.

The bridges run.

We had to go across all

of the bridges in the Bay

area, starting in mountain

view, finishing crossing

the Golden Gate Bridge.

It's Chris Hermsson

in the car. It's Anthony

Levantowski in the car.

I was in the car with

Dimitri, Chris, and Anthony.

It was the four of us

in the Prius.

They were figuring out

the technology much faster

than they thought they

could.

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.

It was really a proof

of concept exercise.

Can you even make this

happen 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 extra

and then we got back

to the office.

We regrouped.

We went back out, I think,

at like 11 p.m.

and by 1 a.m.

we had completed the route.

They buy a bottle of

Corbell champagne.

They all write their

names on it.

Corbell 1399 a bottle.

The champagne they have

at Trader Joe's.

They had one for every

route they completed.

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 fall of 2010,

they're done.

Here's Chris Hermeson.

And I think we had a big

party up at Sebastian's

house in Los Alta

Hills, so, you know,

it was pretty spectacular,

right?

They throw each other

in the pool,

they celebrate,

and then they're not

entirely sure what to do

next.

It was kind of,

okay, 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'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

that the team was

moving too slowly,

would actually take

matters into his own

hands in a particularly

extreme way.

After the break,

mutiny.

Hey there,

Steven Dubner.

That, again,

is PJ Vote,

and you're listening to

an episode of the

Search Engine

podcast here on

Freakinomics Radio.

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Welcome back to the

show.

As early as 2010,

Google's driverless

car project had

a 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 the

Bastion Throne.

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

an hindsight.

But joking

aside here,

there was a debate

whether this is more

of an assistive

technology or a

disruptive replacement

technology.

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?

Throne would eventually

come around to this

version of self-driving.

Specifically, he

came around to the idea

of self-driving

robo-taxings.

A taxi service

tap system is way

more capital-efficient

than ownership.

An owned car is

being used

about 4% of the time,

in park 96% of the time.

Imagine a city

without park cars,

where every car is

being utilized

called 50% of the time.

Which means we have

only 10% of the

number of cars needed

that we need today

when we own cars.

That's going to happen

there's no

absolute question.

What Sebastian

is describing here

so matter of factually

is a fairly

radical reimagination

of American city.

The idea that

robo-taxies

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

never expected.

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.

And within the team,

you get Team Chris

and Team Anthony.

And they start

butting heads all the time.

Chris and Anthony,

meaning Chris

Irmson,

official head of the project,

versus Anthony Levantowski,

who I still think of as

the motorcycle guy.

The main difference

in their approaches,

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.

When I asked Chris Irmson

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.

Kristen 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

and a fatal accident

was going to be a huge deal.

Anicto was going to demolish data

if they weren't extremely careful.

By all accounts,

Anthony Levin-Dowski

felt differently.

But he actually wasn't the only one.

Here's Don Bernat.

There were some people on the team,

very famously,

including myself,

that started to get the itch

kind of towards a 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.

But competition would soon arrive

in the form of Uber.

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.

In 2013,

then CEO of Uber,

Travis Kaloneck,

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 be 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 Bernat.

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,

it was only months old,

which raised a mystery.

Why would Uber pay so much

for a company whose only assets

seem to be its people?

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 on to an external disk.

Obviously you can't do that.

I mean, I'm assuming, obviously,

you can't do that.

No, you definitely cannot do that.

Hey.

And this is the kind of thing

that maybe if you 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

it 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,

Sue's Uber,

and puts Anthony

at the center of

an enormous legal battle

between these tech giants.

Secrets and subterfuge

in Silicon Valley,

a former Google engineer,

has been challenged

with stealing files

from Alphabet's

self-driving car project

and taking them to Uber.

Specifically,

it involves a former lead engineer

of Google's self-driving car,

unit Anthony Levandowski.

Now, he's accused

of using his personal laptop

and downloading more than 14 thousand files

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 Levandowski

put 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 deathful woman

named Elaine Hertzberg.

Uber is sitting the breaks

on its self-driving cars,

after one of them

hit and killed a woman

in Arizona.

The vehicle was in 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.

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.

Tempe investigator

saying had Vask

has been paying attention

to the road.

She could have stopped

the car 42 feet

before impact.

The NTSB slamming Uber

There were some important

additional context,

which was that Uber's

robot driver was

also just much worse

than Waymo's.

A statistic I found

jaw-dropping,

was that Uber's

robot driver was

also just much worse

than Waymo's.

At this point,

Waymo's 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 cruise.

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 Lane Hurtzberg's

life, the robot

spent an indefensible

5.6 seconds trying

and failing to guess

the shape in the road

that was a human body

pushing a bike.

Over those 5.6 seconds,

the robot kept reclassifying

her, which had

unknown object,

a vehicle,

a bicycle.

During that time spent

wondering, the car

did not slow down.

Soon after

Lane Hurtzberg's death,

Uber halted its

testing program.

Uber has temporarily

suspended its driver

list fleet nationwide

as the NTSB,

police,

Uber,

and the National Highway

Traffic Safety

Administration

investigate.

We reached out to Uber

for comment.

As 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.

There were robot 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 chat,

GVT, and quad.

But artificial intelligence

has been a core part

of driver list cars

going back two decades.

In the 2010s,

neural net advances,

meant that you could now

begin to feed a

computer system

larger amounts of data,

and watch as its

perception,

prediction,

and decision-making abilities

improved.

Here's Sebastian Thron.

That technology

of master data training

was with us

from the get-go,

but has become 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.

A hundred million is fine.

Of any

put a hundred billion

documents into an

AI, it is

unbelievably smart.

And that

I think shocked

everybody in my

seven tutors.

The Google Brain

team, the deep learning

people, started working

with the driver list

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

lines.

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

the 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 cars

lead our sensors

to deal with the dust storms

and heavy rains.

In 2020, Waymo finally

debused to the public

in Arizona.

In the years after,

it will roll out

to ten more American

cities.

A funny consequence

of Waymo's

long development cycle

is that the public's

attitude toward

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 5.

Are you a good driver?

All right,

autonomous vehicles can

now get you around

Atlanta.

Yesterday, you drove

driving through Austin

is here.

Except, it comes without

a driver.

The light healing 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 set

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.

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.

The TV newscaster

always used the same

G-Wiz tone.

They can never

resist the Jetsons

reference.

In every city,

the influencers

happen to record testimonials

for their daily serving

of cloud.

So in today's video,

I'm about to take my

first ever driverless car.

It's with an app called

Waymo.

Waymo is basically

driverless car

Uber,

where it's like

a driverless car.

You call it,

go wherever you

need it to go,

but there's no driver.

You guys, this is

creepy.

It's like I'm being driven

around by a ghost person.

It's a little terrifying.

It is definitely

a driverless car.

RoboTaxi's poll

hilariously badly.

According to JD Power,

a data analytics firm

among people who've

not written 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.

You know what?

I'm not as nervous

as I thought I was going

to be.

This is actually quite

relaxing.

I scheduled turn.

It felt very safe.

You know, it was kind

of freaky at first,

but now it's pretty chill.

It's smooth right

though.

It went driving fast.

It went jerking.

It's driving like you

always hope your Uber

driver would.

So I guess that's one

of the big sellings

in the methodical

team leader had left

Google years ago.

But he told me about

a million consumer,

trying to weigh

him out in the world.

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.

It's falling with

sharks.

And then a few minutes

in, it's like,

okay, you know,

it's just going to drive.

Is that all it does?

And then, you know,

10 minutes and people

are looking at their

phone.

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 really

safety data so far

for the first 127 million

miles.

Waymo'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 Waymo's case,

I've looked at the data.

I've looked at how

the company's

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

Bealey, author of the

newsletter, Understanding

AI.

I asked him how much

our picture of the

Waymo safety data has

been evolving.

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,