Are you a good driver?

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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, pre-dawn.

You wake up to this really hard wrapping at your window.

That's the knocker upper, here to get you up for work.

Over 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, will 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.

In these driverless cars, they aren't the future.

They're actually already here.

But it's funny, if you just don't have into a living 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, who's ending promises to totally reshape the place

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 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 that in a horse-drawn carriage, the horse is not just going to run off a cliff

if you let go of the reins.

You lose sentience in your vehicle.

When automobiles first arrived, these powerful and non-essential 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, disassembled 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 stoplates

or turn signals.

Many of those deaths were children.

It took decades for society to mostly learn to live with cars.

The rest of the story is just the world you grew up in.

We invented laws, licenses, drivers, ed.

We learned to better design roads.

We invented the highway, the seatbelt, the airbag.

All those things 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.

All horses 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,

the early 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 Teather, 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 try from 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 Teather announces that the prize for whoever can win its grand challenge

will be $1 million.

The rules were very open.

There were little rules.

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.

It 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.

Oh, was that a concern that people would just like, sort of, battle bot the thing?

Your own time as vehicle would have a little shredder that would take out somebody else's?

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

And 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.

Some values when it came to things like the acceptability of risking human life.

Abstract differences that would become very concrete later on, to the point where people

would be charged with federal crimes.

That's the future.

But listening to this part of the story, what I listen for is how much of it can you detect

already, how much of the differences are already present.

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

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'd 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 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?

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.

The answer is...

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 Levantowski.

Can you tell me about Anthony Levantowski?

Anthony Levantowski.

We're going to 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-own.

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 Levantowski.

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.

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 a 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 the 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 bounce.

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, doesn't inexplicable you 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 Throne.

Sebastian Throne, he was at the first grand challenge.

He didn't bring a team, he wasn't participating.

Derby 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 Throne.

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 entrances 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 bigger wheel,

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, they 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 with a computer,

that is not a problem of bigger tires.

That's actually a video software problem.

Sebastian Throne 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.

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 of returned.

Chris Armson, back with the Carnegie Mellon team,

decided 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 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.

Thrunn is a computer scientist.

And Thrunn 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 sandwiched between Carnegie Mellon's two behemoths.

Highlander leads the pack.

Followed by Stanley and Sandstorm.

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 into it 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 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 Theron 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 it all the way through the desert.

And all three of them did be unthinkable.

It's such a fantastic success for this community.

I think we all win.

I 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'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.

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Have you ever felt like you were living just a B or B plus life? It's so dangerous to live that

more dangerous than a B minus or a C plus life because when you're living a B or B plus life,

you don't change it. You think it's good enough? Is it? I'm Susie Welch. I host a podcast called

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all in the process of becoming ourselves. Listen to Becoming You, wherever you get your podcasts.

Welcome back to the show.

Chapter four. Something actually useful for the world.

The race in the desert had been designed as a spectacle. Something flashy,

to dry out America's smartest 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 and 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

going to meeting with a 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 he use 20% of the 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 PhD advisor toward search engines instead. Now as a spectator at DARPA's second grand

challenge, you can 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 pay the self-driving car. They can drive anywhere in the world. And my immediate reaction

was, no, taking the technology we paid 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. And 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 expert the

past, not the future. And if you ask an expert about innovation, something crazy new,

there's at least like the 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

would 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

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 Sergey and I 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 soundways.

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 Prius 27.

This is Don Bernat. He'd been a researcher working on autonomous submarines.

He lost a friend in a car accident, separately gotten a bad accident himself,

and decided 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 and behavior decision-making team,

and my responsibility was to work on the nudging behavior.

Nudging would a big truck pass as 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 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

that happened 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 Irmson takes one of the Prius's out on the central expressway, speed limit 45 miles per hour.

There are human 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.

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 person 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 the 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 be incredibly uncomfortable. Like

incredibly uncomfortable. You would feel like you're at Mario Kart. 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? All these little problems to solve.

But there was one gift, which is that the team at this point had an overarching goal uniting them.

The DARPA challenge to 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. They start knocking out routes.

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

Down Route 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 Lewandowski 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 XYZ. Then we got back to the

office. We regrouped. We went back out, I think, at 11 p.m. and by 1 a.m. we had completed the route.

They'd 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 complete it.

And one by one, they pick off the Larry 1K routes. They think this is going to take them about

two years when they start out. 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 Armson. I think we had a big party

up at Sebastian's house in Los Alamos Hills. It was pretty spectacular. 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 done this safely. They 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.

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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 this. What was the actual

product they were developing here? Here's the fashion thread. 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 two billion dollars at the time. I remember this. Maybe we should

have a nine-side. 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? The Thrawn would eventually come around to this version of self-driving.

Specifically, he'd come around to the idea of self-driving robot taxis.

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 time. Imagine a city without parked cars where every car is being

utilized called its 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

cities. The idea that robot 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. 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 budding heads all the time. Chris and Anthony, meaning Chris Armson, official head of the

project versus Anthony Lewandowski, who I still think of as the motorcycle guy.

The main difference in their approach is how quickly they want to move. Anthony is very

okay with risk. 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 necessarily logging data. He's just

enjoying his self-driving car and taking it all over the place. Chris comes from an academic

background. He's that Canadian, very nice, very careful, very risk averse.

When I asked Chris Armson about all this, his memory was slightly different. In his memory,

team Anthony was pretty much just Anthony. And Anthony, he said, was a move fast and break things

kind of guy. Move fast and break things. A motto famously coined by Mark Zuckerberg,

it defines a way of developing technology which once might have felt cute and revolutionary,

but which today, at least to me, feels pretty irresponsible.

Chris didn't think that philosophy was an option for their team. Even if their cars were

statistically safer than human drivers, he knew that the first news story about a self-driving car

in a fatal accident was going to be a huge deal. Anicto 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 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

and 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 onto 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.

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

sues 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 charged 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 Lewandowski. Now, he's accused of using his personal laptop and downloading more

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 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 death of a woman named Elaine Hertzberg.

Uber is sitting the brakes 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 the 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 Vasquez 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. 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 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 Wayne 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 Wayne Hurtzberg's death, Uber halted its testing program.

Uber has temporarily suspended its driverless fleet nationwide as the NTSB,

police, Uber, and the National Highway Traffic Safety Administration investigate.

We reached out to Uber for comment. A spokesperson said that the fatal collision was indeed a tragedy,

which had a significant impact on Uber and the entire industry.

There would be other competitors who would shut down after similar accidents.

There would also be Tesla, which by 2020 was publicly marketing a product

that company called full self-driving, but which absolutely was not.

Meanwhile, Waymo had slowly continued to develop its tech.

Their 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, GPT, and quad. But artificial intelligence has been a core part of

driverless cars going back two decades. In the 2010s, neural net advances meant that you can now

begin to feed a computer system large amounts of data and watch as its perception, prediction,

and decision-making abilities improved. Here's Sebastian Throne.

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.

When you put a hundred billion documents into an AI, it is unbelievably smart.

And that I think shocked everybody. My seven tutors.

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, it discovered problems

that required hardware fixes. For instance, in Phoenix, Waymo had to design miniature wipers for

their cars' lidar 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'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 5. Are you a good driver?

A fleet of white electric jaguars covered in 40 different sensors, cameras, radar,

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 empty seat, you're not allowed in,

with a steering wheel in front of it, vestigial, it turns itself. Cars without drivers are here.

Yes, 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 newscasters always use 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 ride service, 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.

Robo Taxi's pole hilariously badly. According to JD 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.

You know what? I'm not as nervous as I thought I was going to be. This is actually quite relaxing.

Nice gradual 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 drive-past, it went jerking.

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

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

Chris Irmson, the methodical team leader, had left Google years ago,

but he told me about his experience as a civilian 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.

I, there's nobody behind the wheel.

Ooh, it's flowing with sharks.

And then a few minutes in, it's like, okay, you know, it's just 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 released 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 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 ask him how much our picture of the Waymo safety data has been evolving.

So it's been pretty consistent the last couple of years.

They are scaling up, and so all the numbers get bigger,

like the total number of miles get bigger, the number of crashes get bigger,

but the like 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 very enough to trigger an airbag,

crashes severe enough to cause an injury,

and also crashes involving vulnerable road users,

like pedestrians or bicyclists.

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 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 this crash.

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.

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.

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.

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

A child ran across the street from behind a double-part car,

and a Waymo hit the kid.

Santa Monica, please say the child, a ten-year-old girl was not hurt.

The company issued a statement.

Waymo said its driver had break-tired, reducing speed from 17 to under six miles per hour.

A faster reaction they claimed than a human driver would have been capable of.

What happened next at the accident scene actually answered 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, 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 stop school buses in Austin.

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 one 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.

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 ask Waymo for comment on everything you heard in this episode,

especially the recent safety incidents. As spokesperson said that the data to date

indicates that the Waymo driver is already making road safer in the places where they operate,

and says that Waymo continues to work with policy makers 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 following behind. Amazon's new driverless taxi is launching in Las Vegas this summer,

and it's expected to arrive in LA. There's other rubber taxi companies like Amazon Zoox. Uber

is back in the mix, not making technology, but partnering with these rubber taxi companies.

We ride recently struck a partnership with Uber to bring its AVs to Abu Dhabi.

And many of those early Waymo engineers are now CEOs of autonomous companies themselves.

Dimitri Dolgov is actually co-CEO at Waymo, but other team members run driverless trucking

companies. Got Don Burnett, founder and CEO of Kodiak AI. Don, thank you so much for joining us.

It's good to see you again as well. Don Burnett is head of Kodiak AI, which has a technology

to put in driverless trucks in the Permian Basin. Please welcome CEO of Aurora,

Chris Irmson, a big round of applause. Chris Irmson 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 on the information that Uber Fowder Travis Kalanick is starting a new

self-driving car company, with financial backing from Uber and in partnership with Anthony Levindowski.

They 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 lamp

lighter. Those knocker uppers, by the way, they disappeared quietly. The lamp lighters did not.

Writer Carl Benedict Frey tells the story of the lamp lighters union, how their strikes

plunged New York City briefly into darkness, to the delight of lovers and thieves. In Verivier,

Belgium, the lamp lighters' strikes turned violent, ending in an attack on the local police

headquarters. The army was brought in. The lamp lighters 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. We stand up, everybody that's rideshare, union members, or someone

who drives a vehicle. Stand up. 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 attack companies.

They're doing this because they stand to make an unfathomable amount of money if they eliminate

driving jobs for working class people. I understand it is a business, it is capitalism,

but not in my city, at 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. Humans drive the city, not machines, labor drives this city, keep the workers

in the workforce. If it works in another city, great. Have fun. Not here, not Boston. Thank you.

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

Thank you for listening to our episode. I just want to say, making deeply reported stories

like this one is only possible because of our listeners, particularly our premium subscribers

who pay to support the show. We are releasing our full interview with Sebastian Thron,

who used to leave Google X, their secret special projects lab, totally fascinating conversation

with the kind of person who just sort of lives in the future and has a million strange ideas about

it. We are releasing that for our incognito mode members only, it'll be in your feed.

If you would like to know the future, sign up at searchengine.show, and again, your membership

specifically enables projects like this one. So thank you.

Search Engine is a presentation of Odyssey. It is created by me, PJ vote, and truthy pinnemanating.

Garrett Graham is our senior producer, Emily Molterra is our associate producer,

theme, original composition, and mixing by Armin Bazarian. Our production in turn is Piper Dumont.

This episode is fact-checked by Mary Mathis. Our executive producer is Leah Restennis,

thanks to the rest of the team at Odyssey. Rob Morandi, Craig Cox, Eric Donnelly,

Colin Gainer, Mark Hurrin, Josephina Frances, Kurt Courtney, and Elri Schuff.

Thanks for listening. We'll see you next week with the second part of this story.

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