SHINN: So welcome.
Welcome to the Council's new series on emerging
technologies.
Today's topic is Driverless Automobiles: Silicon
Valley Dream or Next Big Thing?
And we have some terrific, and particularly
well-informed expert guests to have this conversation
with us today: Jennifer Healey, who is a research
scientist at Intel Corporation; Erik, who's...
BRYNJOLFSSON: Are you going to say my last
name?
Brynjolfsson.
SHINN: ... Brynjolfsson, who is director of
the Digital Business Center, Center for Digital
Research at MIT, and a professor as well;
and Chunka Mui, who is the managing director
of the Devil's Advocacy Group.
And I should add that all three of our guests
are technically very savvy, with a good engineering
background, and all share roots at MIT, at
one point or another, in their career.
My name's Jim Shinn.
I teach at Princeton's faculty of Engineering
of Applied Sciences, which someone described
last week as a feeder school for Google, Goldman
and MIT's faculty of graduate computer science.
So I'm really glad to be here.
This is on the record, as you know, though
it'd be good to keep all of our listening
devices and cell phones off.
We welcome the members that are here, as well
as those who are participating in the meeting
by the video feed.
I'm told the format for today is divided into
two halves; first, a conversation with our
guests, and then following that, the second
half we'll have a to-and-fro of questions
and comments with the audience with the members
here, as well as those who are participating
by video link.
This, by the way, is a really cool series.
It's a wonderful reflection of the fact that
the Council has realized that emerging technologies,
like the driverless automobile, really are
having a transformational impact on the global
economy, and on U.S. foreign policy.
In fact, if you thumbed through your copy
hot off the presses of the March-April issue
of Foreign Affairs, it's called, "Next Tech,"
and five of the eight lead articles in this
issue are in fact about emerging technologies.
So the first question, I think, would be for
Jennifer.
What -- what do you think the key enabling
technologies of the driverless automobile
are?
What underlying innovations, or what combinations
of innovations are in the platform?
HEALEY: Sure.
Well, what's making the driverless car possible
right now is a technology called "LIDAR."
And it is basically a laser scanner, and it
basically can map depth.
It can actually do a 3D picture of the room,
and using a 3D LIDAR camera, the car can know
where all of you are, and the depth.
And using that, plus some computer-vision
technology, but mainly 3D LIDAR, can navigate
and not hit anyone.
SHINN: Before turning to Erik, we could illustrate
this with a short clip not intended as a Google
ad.
And Erik actually has had the experience of
riding in one of these vehicles, which if
you could share with us, I think would 
be terrific.
(VIDEO CLIP SHOWN)
SHINN: So with very high confidence, all of
you in the room will have this experience
sooner or later, your first ride in a driverless
automobile.
But Erik's already had the benefit.
What was it like?
BRYNJOLFSSON: It was remarkable.
I have to say, in a way, it was humbling,
because it caught me off guard how rapidly
this technology caught on.
About 10 years ago, 2004, 2005, I was teaching
a class at MIT, and one of the topics of discussion
was what machines could do, and what they
can't do.
And I gave an example of something machines
can't do, was driving a car.
I said, you know, it's structured tasks, good
for computers.
But there's too much visual and other information
that comes in.
No set rules of the road, especially in Boston,
about what needs to be done.
And in fact, I wasn't alone.
Frank Levy and Dick Murnane wrote a wonderful
book called "The New Division of Labor," where
they gave driving cars, an example, that couldn't
be done.
So in the summer of 2012, when I got to ride
in a driverless car, I was like, you know,
surprised at how effective it was.
We rode down out of Mountain View up -- down
Route 101 up to San Francisco and back again.
And I kind of had three kind -- three reactions
successively.
The first reaction, honestly, was a little
bit of fear.
We were driving down through the road, and
Route 101, middle of the afternoon, you'd
think it would be pretty clear.
But the reality was that the car in front
of us came to a complete dead stop; not just
slowed down, but just stopped right in the
middle of the highway.
And I couldn't help just sort of clutching
my seat a little bit, wondering if the Google
car was going to notice that.
And fortunately, it did.
And what was a little bit reassuring is it
has -- they were nice enough to have a little
laptop in the passenger seat that displayed
the LIDAR and everything else that it was
seeing.
Looked kind of like a video game.
So you could see out the windshield, you could
see the car in front of us.
And then you could also see little outlines
of what it was seeing, you know, drawn there,
not just the car, it also, actually, interestingly,
a little side note, it had these little boxes
in the back right of each car that were in
red, kind of blinking.
I asked, "What are those?"
And they said, "Well, those are the other
cars' blind spots."
Not Google's blind spots, but its understanding
of the blind spots that the other cars -- and
the Google car doesn't want to just avoid
hitting the other cars.
It also wants to avoid driving in other people's
blind spots.
So it had a bunch of these little rules of
thumb, but also didn't regulate speed by one
car ahead, but it'll look two, or three, or
four cars ahead, depending on which one it
thought was the right limit.
So it had 101 little rules that maybe you
were taught in driver's ed, but I don't know
how many people actually followed those kinds
of rules.
But they were programmed in.
After about five or 10 minutes, I got to be
pretty comfortable with it.
My next phase was exuberance.
And I was kind of thrilled.
And Andy McAfee, the co-author of my book,
"The Second Machine Age," and I were just
waving -- literally waving out the window
at the people as we drove by, "Hey, look at
us."
SHINN: Both hands out the window.
BRYNJOLFSSON: Both hands, exactly.
We were sitting in the back seat, just to
be -- you know, they had an engineer sitting
in the front seat.
And then, you know, that lasted for maybe
another five or 10 minutes.
And after awhile that got kind of boring.
And then we drove all the way up and all the
way back, and the traffic was -- never went
more than 55 miles per hour, even though the
other cars were whizzing by us at times.
And most of the ride, I think my main reaction
was kind of boredom.
You know, it was like, "OK, I get this."
And the car drives very, very smoothly, kind
of like, you know, your grandmother might,
very carefully.
Of course, it follows all the rules.
And after a while, I was like, "OK, I get
this."
It was kind of like you might react to, I
don't know, watching a dishwasher run, or
something like that.
I think that's a microcosm, some of the ways
that a lot of society will react to it.
You know, first fear and exhilaration, but
after a while, it will come to accept this
technology the way we have so many other technologies.
You know, elevators used to have humans in
them to make sure people felt comfortable
riding in them.
SHINN: If I could ask, which part of the system
in that vehicle was, in your view, and the
views of our other panelists, what was the
real breakthrough?
What was the real improvement in function,
or in cost of function that led to you being
relatively relaxed going down 101?
BRYNJOLFSSON: So they worked on self-driving
cars at MIT as well.
They didn't succeed as well as Sebastian Thrun
and his team.
And my MIT colleagues tend to go, "Well, that's
because Sebastian kind of cheated."
And the way he cheated was, which I think
was the breakthrough, was that instead of
trying to make this car completely autonomous
and figure out on the fly what was going on,
they have a map in advance of everything,
not just the roads, but every stop sign, every
light pole, every stoplight, everything that's
there on the road.
So as it drives by, it doesn't have to figure
out, you know, "What is that?
Is that a stop sign?
Is that a light pole?"
It already knows all those things.
They're all annotated.
All it has to do is match them.
And as long as what it's seeing is sort of
roughly close to what it's expecting, it's
comfortable.
Now, if there's a snowstorm, or if it's very
foggy, or if there are other situations -- or
for that matter, if there's construction,
and somebody moves around some of the light
poles, then the Google car says, "Oh, I don't
feel comfortable," and it switches over, it
says, "You, human, you navigate this."
So that -- by doing that, it had an enormous
amount of data --it's really a big data problem
-- about the world, and was able to navigate
much more effectively than what most people
are trying to do, which was figure out the
car, how it could run on its own.
SHINN: That is really interesting.
So Chunka, you've written a lot about -- about
inflection points, right, how – as has Eric
– what sort of combination of events and
technology together produce some really transformational
technology that's fielded at scale.
What do you think the next step is going to
be that'll make driverless automobiles a reality,
so that many of the people in this room could
drive them, rather than an engineering exercise
on 101?
MUI: Well, I think there are still technology
problems to be dealt with, but those are not
going drive the inflection points.
What's going to drive the inflection point,
I believe, is the audacity of a region -- whether
that's a state, or a city, or a country -- to
create the environment for large-scale testing,
and then capital and technology, to walk into
that opportunity.
Because I think the benefits -- you know,
a million people get killed every year driving
cars.
And 50 million people get injured driving
cars.
The benefits, if you could make self-driving
cars to reduce accidents, are so huge, that
once the demonstration example happens, that
shows that the technology works, all the barriers
will drop away.
So that's the inflection point.
But it still requires technology.
It requires capital, which is easy.
And it requires policy, from a standpoint
of creating the environment for testing.
SHINN: Jennifer, do you see a technical inflection
point before this sort of next fielded trial,
or do you think all the pieces are there together?
HEALEY: Well, I think there's going to have
to be a cost inflection point.
And I know we have a slight disagreement about
that.
I wanted to say these maps that Google has
are 3D LIDAR maps.
So it's 3D maps.
You're just doing it -- and they use GPS.
So you overlay it, and then you get a very,
very good view.
But, as you mentioned, snow, right, snow will
change the way the map looks, because to a
laser, snow is as solid as concrete.
A leaf is as solid as concrete.
So it actually -- it won't -- it won't get
that match.
So if you have a leaf, or if a paper bag blows
in front of your LIDAR, it'll look like a
wall just suddenly appeared.
And I'm sure the Google car doesn't stop short
if it's going -- like they say, oh, it could
be running at 160 miles an hour, except if
a paper bag flies in front of your face, and
you have to stop at 160 miles an hour.
MUI: What it does is -- what it does, since
it's watching three cars ahead, it knows that
nobody else stopped for that paper bag.
So it can sort of assume that.
HEALEY: And that's going to get to my controversial
technology point, which is this idea that
you could have V to infrastructure, or V-to-V
communication.
SHINN: "V" being...
HEALEY: Oh, I'm sorry.
Vehicle, sorry.
I'm so deep in the world -- so, yes.
So we just rattle these acronyms off.
So vehicle-to-vehicle communication, or vehicle-to-infrastructure
communication.
And although LIDAR is a fabulous technology,
it can't see ahead forever.
It has limitations.
But information from forever-ahead can be
transmitted back to the car, you know, if
it went through the cloud, if it went through
roadside infrastructure, if it -- through
an intelligent gateway.
Or if a cars actually were all on the same
frequency, they could talk to each other and
say, "Watch out for the paper bag," or -- you
know.
Or you could do communication with the car
ahead of you and sort of do platooning, and
you wouldn't -- you would take that as evidence
versus, you know, the paper-bag blockade.
So it's intelligent sensor fusion between
different modes.
SHINN: How important do you think this kind
of network effect, automating not just one
vehicle, but multiple vehicles in the traffic
pattern, so they can talk to each other?
How important do you think that is in the
inflection point?
HEALEY: See, I think it's pretty important,
because I think it's going to allow more coordinated
action between both autonomous vehicles, semi-autonomous
vehicles, and human-driven vehicles, so that
you'll have information about -- people could
communicate information about their plans.
And the autonomous vehicles, aside from knowing
people's blind spots, might be able to know
their intentions, too.
MUI: And some people believe it's irrelevant.
Because if you require a vehicle to vehicle
communication, you'll never get there, right,
because if you need to have all the cars talking
to each other in order to make this work,
the cars will never all talk to one another.
So if you get to a point where you have no
benefits until they all talk to one another
-- so I'm not arguing that you might not need
it.
But what I'm saying is if you need it, we'll
never see this stuff.
BRYNJOLFSSON: Well, there's a tradeoff between
need and benefit from it.
So what I see is that it's not a black or
white thing that, you know, on such-and-such
day, 2022, we're going to have autonomous
cars.
It's going to be a much more gradual adoption.
That's one of the things I got to appreciate
from riding it, and talking to someone like
my colleagues, who were much more into the
technology.
John Leonard at MIT has come up with this
five-phase -- five levels of autonomy going
from no autonomy at all, to complete self-driving
autonomy.
And he would put Google at level 4, which
is pretty high up there, which is it could
drive on a highway with no intervention.
But, as Jen was saying, even in a situation,
you know, it starts snowing, or a bunch of
leaves blow by, or something, it'll say, "Wait
a minute.
There's something wrong here.
I don't understand."
And it'll turn control over to the human.
So you still want to have a human there.
And then there's the debate about how much
time you need to have for warning before you
do that switchover.
The highest level, though, is complete autonomy,
where you maybe don't have a human at all.
You say -- you tell the car, "Go pick up a
pizza for me," or, "Bring my child to soccer
practice."
Jen was suggesting she'd like to have that
for her kids.
Without an adult human driving there, that
I think is much, much harder.
It's not like a little bit harder.
It becomes a real leap when you go from, say,
95 or 99 percent autonomous, that last 1 percent
is very, very difficult.
So we're going to have, I think, an adoption
of a lot of things.
In fact, those of you who watched the Super
Bowl, you probably saw the Hyundai commercial,
where there was a car that had automatic braking.
If it saw something go in front of you, the
car would apply the brakes.
Chunka was telling me earlier that a large
percentage of the people in car accidents
today, you go back and look at the records,
and they didn't fully apply the brakes, for
whatever reason.
So there's opportunities to creep in with
technologies like that.
Super-duper cruise control, you could call
it, when you go down the highway.
Maybe self-parking systems that get increasingly
sophisticated, not just in front of the -- park
the car -- the space to parallel park.
But maybe you are like a valet.
You go to the restaurant, and then you push
the button, and the car goes off into a little
predefined parking lot that doesn't have other
humans in it.
And the car can slowly park itself.
Those kinds of intermediate steps I could
see happening much more quickly.
SHINN: That's a good segue, actually, to a
point that Chunka has researched quite a bit,
has written about, which is who wins and who
loses?
There are more of a few people in the audience
who -- members who are interested in the effect
on industries, if not on individual firms,
of this kind of a transformational technology.
In terms of broad swaths, where are the gains,
and where are the losses?
MUI: Well, the first place to look is, if
this the stuff works, we spend about $450
billion a year in this country on businesses
that are dependent upon collisions.
$100 billion on auto repair, parts, things
of that sort.
We spend $200 billion a year on auto insurance
premiums.
That's a direct -- that's directly correlated
to how much it costs to fix the cars with
accidents.
So if you reduce accidents by 25 percent,
we're talking hundreds of billions of dollars
just in the U.S.
So the immediate losers are the people who
depend on accidents.
Now, the winners, of course, are the people
who are no longer getting into accidents.
And I think that the winners outweigh the
losers.
But in the long term -- in our book, we talk
a lot about the economic consequences of this
technology.
And one of the things we point to is that
we spend about $2.5 trillion a year in this
country on auto-related businesses, the revenues
of auto-related businesses.
And the business model of transportation changes
with driverless cars.
So I'll give you one example: $600 billion
a year flow through auto dealers, auto dealerships.
If you have driverless cars that are shared,
a significant percentage that won't be bought
by individuals, they'll be bought by the companies
that do the -- that manage the car sharing.
They don't buy through auto dealers.
So it's not that all the money goes away.
But there's a tremendous amount of money that
shifts.
And then, you know, the way you finance, the
way you service, the way you rent cars, all
that stuff changes.
SHINN: If, by the way, you didn't have a chance
to write all those numbers down, this is a
shameless plug for his book.
The first case study actually has some interesting
calculations upon the winners and the losers.
HEALEY: I just wanted to make a comment, it's
just an important thing to call out.
There's a very serious difference between
autonomous cars and driverless cars.
And what we're talking about is what Erik
talked about, which is that last 5 percent.
And I am not even comfortable with someone
who has 90 percent blind being -- driving
that Google car.
Because when it says, "OK, now you take over..."
"I'm blind, and there's a problem."
He can't.
He can't, unless he has some sort of drive-by
wire telemetry that someone could -- he could
do a call center.
You know, could there be some sort of -- you
need that backup.
Who's that backup?
What's that backup?
That's the difference between autonomous and
driverless.
SHINN: Which is one of the most difficult
policy issues, the first of a long string
of policy issues that you have to deal with,
not just come to an inflection point, but
actually to deal with the broader consequences
to the economy.
What do you think they are?
I mean, you've all written and talked about
the kind of public policy that would both
accelerate or enable this kind of inflection
point.
And then there's any number of them which
would inhibit it, both here and in the sort
of global -- global arena.
MUI: Well, you just have to scratch the surface
and look at all the business interest that
would be harmed by this technology working,
to rip off, you know, a whole range of issues.
So we have a whole bunch of issues in liability,
to jobs, to insurance, to how do you regulate?
You know, I was at a session in Florida recently
where they're trying to create the environment
for good testing of these issues.
But one of the regulators was telling me that
she was still in the process of trying to
figure out how they would do a driver's license
test for people who came in with cars that
had rearview cameras.
Do they also make them able to drive without
the rearview cameras?
How they deal with -- so there's 1,000 little
issues that, if we allow to, could get in
the way of these things.
And I think the important thing that we'll
have to remember is that it's just not economics.
There are a lot of lives and limbs involved
here.
So there's a lot of motivation for setting
aside these issues.
SHINN: What's the most important public policy
problem that would have to be dealt with in
order to enable this, even if it's a -- were
a gradual slope rather than sort of a sudden
step function?
BRYNJOLFSSON: I would agree with Chunka, that
a lot of the technology issues are racing
way ahead of where the public policy is.
And to pick up -- to answer your question,
probably the most important one that comes
to mind for me is liability.
One of the concerns that Google and other
companies have, is that they have very deep
pockets.
And even though I agree, I think, with both
my panelists here, that there would likely
be far fewer deaths -- there are about 30,000
highway deaths, 34,000 highway deaths in the
United States now, about 10 million accidents
of various types.
It's likely that that would be dramatically
lower, but it wouldn't be zero, OK.
And so the first time that one of these autonomous
cars runs over, you know, a four-year-old
boy or something, and worse yet, does it in
a way that no human would have made that same
mistake, it makes a different kind of mistake.
So, you know, if you were 90 percent of 99
percent safer, you're still having thousands
of potential deaths, and suing a deep-pocketed
company could, you know, totally change the
economics of it.
So that would be something that we have to
think about.
I'm actually -- I can see a scenario, though,
where the insurance companies come around
on the other side of it.
I see that some of their revenue should be
threatened.
But it wouldn't be very far-fetched for me
to imagine that sometime in the future, the
company would charge higher premiums to people
who didn't have some of these autonomous safety
features in place in their cars, or even say,
"Look, we're not going to insure you unless
you have, you know, this automatic braking,
and all these other features that are now
state-of-the-art," because that would lead
to a lot lower claims.
HEALEY: I'm just going to hardly agree.
I think the liability is the one thing the
public policy could really make a difference
here.
I mean, the current proper use of the Google
car, and other autonomous cars, is that the
driver's supposed to have their hands hovering
at 10 and two, and be ever-vigilant, is all
the driver's fault.
It's the same fault model that we have, even
at 95 percent autonomous.
You switch to driverless, there's now no human,
right.
So now the car has liability.
What if it's a shared car?
What if it's like an autonomous, you know,
little bus?
Now, who's liable when that thing makes...
So the question is, the public has to say,
"Do we want the current number of deaths?"
or, "Do we want half that number of deaths?"
even if half that number of deaths was caused
by, you know, some, you know, terminator-like
machine, right.
So what can we do to limit the liability?
Can you put a price on human life?
I mean, human life's being lost.
But now it's being kind of lost by accident.
MUI: We put a price on human life all the
time.
It's called "insurance."
So I think it's -- liability is a real issue,
but it's solvable.
And part of it is whether or not we're willing
to innovate.
So, you know, how an insurance company treats
it -- some insurance companies will say, "Not
in my lifetime."
Other insurance companies will say, "This
is an opportunity," you know, "to take advantage
of this technology."
But what if -- you know, what if we had a
trial of 20,000 massively-shared cars in Ann
Arbor, and Google says, "You know what?
I'll buy a $5 billion bond."
You know, you can solve these kind of issues
if you're -- if you're creative about...
SHINN: Who do you think will solve them first?
I mean, given how litigious the U.S. is, not
just in this particular innovation, but others,
what are the odds are that the first place
where this will really scale up will be someplace
like Singapore?
BRYNJOLFSSON: What's funny, you should mention
Singapore, because we were just talking about
that before.
Daniela Rus, who's the head of the Computer
Science and A.I. lab at MIT is doing a lot
of work on driverless cars.
And she has been coordinating with the governor
of Massachusetts.
Now, you might think that they'd be coordinating
someplace in Massachusetts, from the Massachusetts
Institute of Technology, working with the
governor of Massachusetts.
No.
They were working in Singapore.
And they have set up a part of Singapore where
they have autonomous cars, not just on the
highway, but actually in sort of an urban
environment.
And a lot of it is exactly what you say, Jim,
that there's a difference in liability.
They don't have the same kind of type of democracy
that we have I guess you could say.
And so they're able to push forward with some
ideas that would be a lot harder to do in
the United States, or even in Massachusetts.
MUI: I'll give you a more startling example.
If you think about it, what automotive company
in this world is most wedded to the idea of
safety, and depends upon it from a brand value,
is Volvo.
Who owns Volvo?
A Chinese company.
What happens if Volvo develops this technology?
It would be a tremendous economic benefit
to the owners of this technology.
Where are the biggest markets for automotive
-- for cars these days?
China.
So I think there's a tremendous geopolitical
set of issues here as well, because we're
talking about the biggest innovation since
the Model T. And the question is, who's going
to enable it?
SHINN: To the degree that you can -- you have
control over designing both the vehicle itself,
the kind of sensors that are embedded in all
the other vehicles, the urban transportation
infrastructure, and then the hard part, the
legal -- the legal and political institutions
that surround all of that, not limited just
to liability, you think that may be one of
the more important factors in where this starts?
MUI: Absolutely.
SHINN: And I guess probably how it propagates?
BRYNJOLFSSON: You mentioned a lot of interdependent
parts.
And I think the real key -- and I think Chunka
mentioned this earlier -- is that you don't
want to wait to have all the other pieces
in place before it works.
So the successful governments, companies,
systems, are the ones that figure out how
to scale it up piece by piece.
Can you implement a part of it, you know,
a self-parking system just for parking lots?
Can you get some benefit when 5 percent, or
20 percent of the cars are able to talk to
each other, but not 100 percent?
Can you get some benefit from having the car
be autonomous on the highway, but still need
humans in the city?
And by having those incremental stepping stones,
you're much more likely to get adoption than
if you say, "Well, we're going to wait until
we have the whole package in place."
HEALEY: We were talking about this earlier.
And the LIDAR is still a very expensive technology.
But with just stereo camera, something like
2D LIDAR, which is a fraction of the cost,
you can do the autonomous parking, you can
do platooning.
So if the government was to designate an HOV
lane as now the autonomous lane, doing just
front-back platooning is a much simpler technical
problem, which is much less risk than trying
to drive in the streets of New York.
So if we created those things, we could have
partial adoption, which creates markets, which
reduces cost when you get in -- what you were
calling, Chunka, I think the virtual spiral
of adoption.
And I think that...
BRYNJOLFSSON: Just to give you a quick mini
tutorial, we've thrown out the word LIDAR.
I don't think anybody defined it.
So LIDAR is basically light-based radar.
And those systems cost on the order of $80,000.
If you see the Google car, if you saw that
little thing on the top there, it's spinning
around, and it's detecting everything very
rapidly with this light-based radar.
They also, I think, have actual radar -- at
least the car I rode in, they're changing
it, which looks a little bit further ahead.
And there are a bunch of other technologies
all together.
That whole package of technology I'm told
-- don't quote me on this.
I guess we're on the record.
But my guess is -- my guess is...
SHINN: You could be quoted.
BRYNJOLFSSON: I was told that it was -- cost
about $150,000 for the whole set of electronics.
Now, that number is falling very rapidly,
because when you do things at scale, it can
be done much more quickly.
One of the most dramatic examples of that
was what was done to solve the SLAM problem,
which up until 2008 -- there was an article
in a journal saying this was a very difficult
problem.
SLAM is the problem of Simultaneous Localization
and Mapping.
All of us can do it intuitively.
Like when I say "go," everyone point to a
door.
Ready, go.
See, we all did that pretty easily.
A robot, a machine, would have incredible
difficulty doing that.
Up until just a couple of years ago when John
Leonard, the computer scientist I mentioned
earlier, basically solved the SLAM problem
for a room about this size.
And the way he did it was not with the really
super-expensive technology.
The way he did it was by adopting -- adapting
a Microsoft Connect device -- you know, those
things in the Xboxes that cost a couple hundred
dollars -- which was able to scan a room,
and figure out where the features were.
And with a little bit of extra coding, he
was able to solve that problem.
So the cost of these technologies, and the
breakthroughs that are happening so much more
rapidly, have really -- I mean, they've impressed
me.
And I'm trying to keep up with my optimistic
expectations every time I hear about one of
these breakthroughs, both on the technology
side, but also on the cost side.
HEALEY: It's also easier if you have map of
the room, and it says, "door."
BRYNJOLFSSON: And that's the other way.
You could cheat, right.
Exactly.
And that's legit, too.
Because what you want is a working problem.
It's not just a theoretical thing.
You want to have something that works practically.
MUI: I think the takeaway from the tutorial
is essentially, ignore all the people who
throw up cost as an issue.
Because we know what the curve is.
HEALEY: And I think cost would be an issue
to the initial adoption.
MUI: Yes, yes, yes.
But we're talking -- I mean, we're in the
business of developing the technology that
has trillions of dollars of benefits.
You know, and we know the cost curve of every
information-based technology, what it looks
like.
So by the time we figure out the technology
issues, the regulatory issues, the legal issues,
all that stuff that will allow this stuff
to be used, it's a non-factor.
I mean, of course, it's a factor.
But here, let me give -- let me give you a
data point.
For all the companies out there -- I know
some of you cover them -- car companies who
say this stuff is really way out there, Google
has spent thus far about what it costs to
develop a new bumper on this research.
That's -- I mean, that's the scale of the
investment so far.
And we're talking about a massive, massive,
you know, pot of potential value to be gained.
So, you know, the technology issues -- need
to deal with cost issues, need to be dealt
with.
But that's -- I mean, we can see that curve.
We can see that curve.
SHINN: That's true.
Gordon Moore was working at Intel when I first
worked in Silicon Valley back -- a long time
ago.
Back in the '80s.
And the Moore Law continues to drive the cost
down of these.
HEALEY: Well, Chunka and I actually disagree,
so this is our one little point of disagreement.
As a -- working for a technology company,
I know that if there was clearly one answer
going forward, and it was the only way forward,
and there was clearly all that money that
you mentioned, we would be going there in
an extremely rapid fashion.
But the fact is that they're like, "Well,
is it going to be 3D LIDAR, or (inaudible)
the 2D LIDAR and the stereo cameras?
Maybe the M-to-M communication."
So do we put all the money into reducing the
cost of 3D LIDAR, or do we put all the money
into sensor fusion?
And because right now we're at sort of a period
of flux, where it looks like there's many
different solutions, and policy's going to
come in here, too, if there's -- you know,
if they're like, "Oh, well, we're going to
demand that everyone have," you know, "front-back,"
you know, "platooning capabilities."
There's more than one option.
MUI: But the great thing about that -- the
great thing about that is there are clearly,
radically different paths being pursued to
this goal.
HEALEY: Yes.
MUI: Radically different paths.
And competition is good for innovation, right.
So different companies are making different
technology bets.
But from a systems standpoint, it's driving
innovation forward.
SHINN: Yes.
I mean, the takeaway here is that there's
going to be a very interesting mutual interaction
between technical innovation and public policy,
for not just enabling the inflection point,
but almost certainly thereafter.
With an eye on the clock, we've come to the
second half of the program where we would
-- we would encourage comments, questions,
from members here, and out in cyber land.
I would ask you, please, to raise your hand
-- I'll point in the direction -- to use the
mike.
And if you would, please, speak clearly and
distinctly, your name and your affiliation
for the benefit of the people that actually
aren't in this room, that are listening.
QUESTION: Craig Drill, Craig Drill Capital.
Would you please tell the story of the development
of LIDAR, and who owns it today?
HEALEY: I don't know the story.
I know that Velodyne owns the technology.
I know that it's a laser-range-finder that
goes around, around, around in a circle.
And that, you know, basically, it's a bunch
of 2D LIDARs that are doing a sweep everywhere.
So I actually don't know how this technology
came to be.
I know it's based on what laser-range-finders
are.
MUI: I think the interesting thing about LIDAR
-- I didn't jump in on this earlier -- is
that all the hardware here are essentially
accessible to everybody.
There's no I.P. around the hardware.
I mean, somebody has to sell it.
But mostly, you know, all the various researchers
can get access to it.
And I think the real distinction is the software.
BRYNJOLFSSON: Yes.
So I don't think that it's controlled by any
one person.
There's different people who have different
algorithms.
The other thing, just to underscore what Chunka
was saying earlier, I've talked to some of
the people, and I do think that those costs
could come down dramatically if we got the
volumes up, I mean, like down into the sub-1,000
category, or even lower than that.
Somebody -- one of the people was telling
me that they thought it would be comparable
to a digital camera, ultimately, if we could
get those kinds of volumes.
And again, it's not the only technology.
QUESTION: This is probably one of the coolest
conversations we've had at CFR.
My name is Binta Brown, and I'm a senior fellow
at the Harvard Kennedy School this year.
So I have two questions.
The first is, the point at which the LIDAR
stops working -- snow, bad conditions -- is
probably the point at which we would rather
human beings not drive, either.
And so -- in some cases, right.
I mean, it sounds like very, very heavy fog,
really bad snow, it's probably pretty unsafe
for human beings.
And so is there a point at which we say if
a computer can't handle this condition, it
may also be true that a human being can't
handle this condition, from a policy perspective?
That's the first...
SHINN: We'll do one question at a time.
How about that?
HEALEY: Kind of have a strong answer to that
one, which is the computer and the human fail
differently.
Like when we were talking about doing the
3D LIDAR registration map, the problem is
if the map you have is on a clear day, the
road looks this big.
If someone's -- it's not snowing.
But the snowplows have now changed the contour
of the road.
Now you can't get a match, even if it's perfectly
safe to drive.
So the computer's like, "I have no idea."
But the person would be fine.
BRYNJOLFSSON: Yes.
But your original point, I think there's a
lot of truth to in terms of the visual.
So when I was driving the Google car, I asked
them exactly that question, "What are the
boundaries of this?"
And the engineer said, "To a first approximation,
if you can't see something, then it can't
see something."
If it's foggy, if there's really heavy rainstorm,
or if there's other obstructions, then it's
going to have trouble as well.
So there unfortunately is a fair amount of
overlap in terms of what we can see and what
it can see.
It's not like it can magically can see around,
or vice versa.
Now, there are things like the snowplows and
other things that we might have the common
sense to work around, but I think a lot of
the disagreement between Jen and Chunka is
really -- has to do with the time frame.
In the shorter time frame, I tend to be much
-- agree much more with Jen's saying.
But in the longer time frame, I think a lot
of these issues will start working themselves
out, and the costs are going to come down,
and we'll find other ways.
You could imagine having maps under multiple
different conditions, and interpolating them,
for instance.
You know, it would cost two or 10 times as
much, but ultimately, those kinds of numbers
won't be -- won't be determinative.
HEALEY: If could just add something about
models.
But I just wanted to bring up the flip problem.
If the driver is distracted, or inebriated,
or if the driver is incapable of driving,
potentially, you might want the A.I. to take
over.
So you might want to think about doing an
intelligent hand-off between who's in better
shape.
SHINN: Did you have a second part of that
question, or do you want to...
QUESTION: It just goes to the point of transformative
technology, right?
So this is great from a safety perspective,
potentially.
It's great from a cost perspective, potentially.
But it doesn't necessarily -- unless you have
vehicle-to-vehicle communication, it doesn't
necessarily improve the flow of movement in
a major metropolitan areas.
So -- or does it?
And if it does, if you could speak a little
bit to how it will better facilitate the movement
of people, and get rid of these horrific jams
so many of us get ourselves caught up in.
SHINN: Jennifer -- could I make another plug?
She gave a great TED talk on this, which I
highly recommend.
HEALEY: This was assuming the V-to-V communication.
In V-to-V communication, one of the things
is the sensors are actually better than people.
And you lose a lot in a car jam, because you're
stopping and starting.
And the problem is, you have to, with your
eyes, sense that car stopping.
You have to apply your brakes.
You have to leave a safe stopping distance
that people can react to.
When the sensors -- when we trust them, they
can react much faster.
They can start leaving a much lower, safe
stopping distance.
And theoretically, if they all coordinated
-- this would be with the V-to-V communication,
even if it's only front-back V-to-V communication,
nothing fancy.
They could make a coordinated action to go
forward.
BRYNJOLFSSON: Just to put some numbers on
that, when a highway it totally packed and
full, traffic jam, about 90 percent of the
pavement is not being used, because on average,
if you measure it, there's about four to five
car lengths that people leave between them.
The lanes are defined to be twice as wide
as a car.
So most of the pavement isn't being used.
With a good autonomous system, especially
if you allow for the V-to-V, but even without
it, you could probably double that, or more,
in terms of the efficiency of the use of just
the existing infrastructure, let alone a change
in infrastructure.
QUESTION: (OFF-MIKE)
MUI: One last point on that is that most accidents
happen in congested traffic.
SHINN: Gentleman over here.
I had an engineering student at Princeton
last year who went to work for Google.
And she had spent all of her undergraduate
research working on efficient engines.
She said, you know, "Forget about designing
a more efficient, combustion engine.
The greatest savings in gasoline consumption
is from just this."
QUESTION: That's a great connection to the
question I wanted to ask.
I'm David Schatsky.
I'm with Deloitte.
And when you hear about driverless or autonomous
vehicles, you tend to hear a long series of
potential benefits.
And we've heard half a dozen of them here
from saving lives, to saving fuel, to reducing
congestion.
Google has said it would cut down on real
estate expenses for parking lots.
The question is which do you see as the most
compelling benefit?
And if I can -- an appendix, if it's saving
lives, how much do we really know about this
benefit, considering that the circumstances
under which lives are lost on the road may
not be amenable to autonomous driving alternatives?
MUI: I'll take a first crack at that.
And I think this is where the beauty of the
story comes in.
As we're seeing, most accidents happen in
congested traffic, highway-congested traffic,
oftentimes.
We don't need fully autonomous cars to make
that problem a lot better, right.
So you start reducing congestion, you start
reducing accidents, you start dealing with
the ripple effect of that congestion.
And suddenly, you're also reducing cost, right.
So suddenly, you're gaining cost, because
-- by that accident reduction.
So in talking to some folks, some actuaries
at big auto insurance companies, there are
models that estimate that if you just had
a 15 to 20 percent adoption of these collision-avoidance
technologies -- not driverless, but just the
collision-avoidance technologies -- that would
have material impact on the number of accidents,
to the point where rates would have to drop
precipitously.
And that happens in the short term, not the
long term.
SHINN: Question way in the back, then we'll
come up here.
QUESTION: My name's April Powers (ph) from
CCG (ph) Therapeutics.
Quick question on sort of applying this to
three dimensions.
When -- what's -- where are we in terms of
having this for aircraft, or for other vehicles?
Because it seems like it should be.
BRYNJOLFSSON: I think we're further along
by and large.
We all know there are a lot of auto pilots
that pilots put on, and sometimes they forget
to turn off, and fly past their destinations
if they don't program them correctly.
And there's auto-land systems, there's auto-take-off
systems.
So there is -- you know.
And more than half of the aircraft that the
Air Force buys today are pilotless, OK.
So we're pretty far along in terms of some
of those technologies.
I don't think it'll be that -- it'll probably
be before we have a lot of fully-autonomous
cars on the highway that we'll see UPS or
FedEx flying airplanes and jets that are autonomous
from point to point.
MUI: It is true that every aircraft flying
above us now is -- they are transmitting to
each other in realtime their GPS coordinates
and their velocity.
And they all have a collision-avoidance system
as well.
So I think to your point, it's already up
there, just not down here.
BRYNJOLFSSON: Exactly.
HEALEY: They have the V-to-I collision-avoidance
system, vehicle's infrastructure coordinates,
because there's far fewer things to hit.
I mean, that's the beauty of the 3D, that
you're not surrounded by other planes.
How close are you ever to another plane?
SHINN: Sir?
QUESTION: So I think you referred to this...
SHINN: Could you identify...
QUESTION: I'm Bob Mallard (ph).
So I think you probably all agree that this
is going to happen.
It's really just a question of when.
MUI: We disagree on what "this is," but...
QUESTION: "This is," meaning driverless, autonomous,
whichever the definition is.
MUI: No, I think we agree that something big
is going to happen, but we disagree on what
will happen.
For example, I think Jen is very far from
believing that we're going to have 100 percent
driverless...
QUESTION: When you come back to earth 50 years
from now, or 100 years, or 1,000 years, you
would all agree that automobiles will be an
autonomous or driverless...
BRYNJOLFSSON: Auto, automobile.
HEALEY: Real automobile.
QUESTION: So I take it there's another layer
of complexity that needs to be invented, which
I think you talked about in your book, which
some sort of A.I. which overlays the mapping
functions, I guess.
But if you agree that this is going to happen,
how quickly do you think that technology will
-- will develop, the artificial intelligence
part?
HEALEY: I think this is our primary disagreement,
is the timeline, I think -- I believe that
things are going to happen incrementally.
I don't think policy changes that rapidly,
unless something, you know, really dramatic
-- there's a really huge demand for it.
So I think it's going to be slow adoption.
I think it's going to be gradual adoption.
I think we're going to see advanced driver
assist, advanced driver assist.
You're going to see self-parking.
Then we may see autonomous driving lanes.
We may see zones of cities where there are
these like autonomous golf carts that are
free, that just take people around, and there's
no cars anywhere.
On the other hand...
MUI: Well, there are a bunch of different
pronouncements.
Serge Brin from Google has said that he wants
it in five years.
I think he said that last year, so it's four
years now.
The chairman -- the chairman of the Chinese
holding company that owns Volvo recently said
that he believes that 2020 -- by 2020, fully-autonomous
cars.
The prediction that I love the best, because
there's some intrinsic connection to me about
it is Chris -- Chris Urmson, who leads the
Google car project at Google, had said that
he would want the car to be available for
purchase by the time his son is old enough
for a driver's license.
My son is nine.
His son is 10.
I like his prediction.
BRYNJOLFSSON: I think that's a little on the
ambitious -- I do think it's important to
make this distinction between the autonomous
cars where there's still the potential to
hand off to a human, versus one that tries
to be truly 100 percent without the human.
That last bit I think is going to be much
harder.
But I've been wrong before, so I want to be
careful about that.
Morgan Stanley put out a report just last
week that predicted 2022 for the fully-autonomous
car.
I would be surprised if it was for like city
traffic in New York or Boston, at that level.
But I have no problem for highways, for parking
lots, for other situations where you have
a more controlled environment.
MUI: Maybe a way to think about it is that
I believe that by the time Chris's son is
16, that we'll have a car available that we'll
look at from today's standpoint and say that
is a radically different animal than what
we have.
SHINN: Gentleman at the table right back there.
QUESTION: I'm Paul Sacks from Multinational
Strategies.
We know that in voice communication, bandwidth
availability has become a constraint to the
development of the industry.
And I guess my question is, do you see that
as a constraint to this process?
Will there be enough bandwidth for driverless
cars?
MUI: Depends on when that -- Jen is right
or I'm right.
HEALEY: Yes, if Chunka's right, we won't need
any bandwidth.
It'll be fine.
All the sensors are self-contained.
And that's a question of using the bandwidth
intelligently.
And it might mean making, you know, a lot
of intelligent gateways, if you want to do
drive-by wire along the road.
And again, it has to do with the adoption
and cost issue.
If you think that 3D LIDAR is just going to
come down in cost, there's no reason not to
use that.
You're not going to be as dependent on bandwidth.
If you want to try to get away with just front-back
collision and stereo cameras, and try to offer
autonomous driving for $1,000 in a package,
yet the government wants to provide drive-by
wire, and there's like autonomous safe routes
to get around with these discount autonomous
cars, then you're going to have bandwidth,
which is going to have to be addressed by,
you know, repeater stations, investments in
infrastructure.
BRYNJOLFSSON: Or -- to Jim's earlier point,
this is a place where government could make
a big difference, because there's an enormous
amount of bandwidth being allocated to television
stations.
A lot of it -- and even the space between
television stations that could be freed up.
And a fraction of that bandwidth would go
a long way towards making these technologies
a lot easier.
SHINN: Sir?
Nancy, is there a way to take questions from
members in cyberspace?
Not yet?
QUESTION: I'm Steven Berkenfeld from Barclays.
I'm going to ask the question I always ask
about these sort of things.
We talked a lot about the benefits.
But one of the negatives I see is the impact
on employment and jobs.
Even in a limited application where you need
a standby driver, I see over-the-road trucks
being able to be operated 24/7, instead of
having to stop for 10 hours, even if you can
eliminate 80 percent of the accidents in a
limited way, there's a lot of people who repair
cars.
So how does this factor into your consideration,
this technology?
And then maybe no different than all the other
technologies we're developing, but I think
it's part of the discussion, especially on
something which, by its nature, are local
jobs.
It's a little bit different than the globalization
and outsourcing that we talk about with other
technology.
MUI: I think that this is different.
And the reason it's different is that we're
not just talking about cost savings.
So we don't -- I think that it may come down
to us choosing between lives and jobs; not
dollars and jobs, but lives and jobs.
Lives, the million people killed a year, you
know, and jobs.
And I think we have to choose the lives.
BRYNJOLFSSON: So, Steve, you've thought a
lot about these issues.
And I'm glad you brought up -- because it
is -- it is something that I think will also
be a factor.
There are on the order of 4 million people
who drive trucks professionally, maybe another
half million who are taxi drivers, chauffeurs,
you count some kinds of delivery, and people
who do it part-time.
So there are a lot of jobs that potentially
could be affected.
I would say there also may be some new jobs
created, but probably not in the same scale.
I think there'll be new business models created,
and there may be certainly new technologies
that will be implemented and developed.
But the net effect probably would be that
that category of jobs there would be fewer,
less demand for it, and some of them would
change.
I could imagine some of those long-haul truckers,
you know, there might be -- the truck goes
most of the way, and there's sort of like
a docking, you know, a tugboat comes in and
drives it the last bit.
Or maybe the person is sleeping in the truck
and doing other work, you know, clerical work
and other stuff, and then occasionally is
available for, you know, if there's construction,
or something unexpected happens.
It would be a very different kind of a job.
But like, with a lot of these technologies,
there's going to be a big shift in the demand
for the kind of people.
There'd be more demand for people doing software
and creative solutions, for these autonomous
vehicles, the V-to-V, the V-to-A infrastructure,
everything else, and less demand for people
who are doing relatively routine kinds of
tasks.
And that's something that will have effects
on inequality, as well as jobs.
HEALEY: Yes, I agree, it's going to be a job
shift.
I mean, we don't have the Bell telephone switch
operators.
We don't have typists.
We don't have elevator drivers now.
I mean, you know, jobs are going to go away.
Hopefully, you know, accident -- personal
injury law -- lawyers will go away.
So those jobs will change.
People will lose jobs.
I agree.
I mean, you know, but it's, I think, you know,
it's a better technology.
Hopefully, people will find better things
to do with their time.
MUI: There are hundreds of thousands of people
in this country that work in auto-insurance
call centers.
What are you going to do?
Say, "We're not going to do this stuff because
we have to keep their jobs"?
SHINN: We were joking among ourselves how
terrified we were, the prospect of all those
unemployed, personal liability lawyers.
HEALEY: What were they going to do with their
time?
MUI: Using their creativity in other ways.
SHINN: In the far back?
QUESTION: Hi.
Jessica Harris from NPR.
I'm just wondering if there've been any -- if
there's research done on the safety of having
the LIDAR, and the radar, and the GPS, and
all this concentrated in your car.
I mean, I'm somebody who walks around with
her iPod -- with her phone not like in my
front pocket, because I worry about my ovaries.
So I'm just wondering if there have been -- I
shouldn't have shared that.
But anyway, I'm wondering if there has been
much attention to, you know, the safety of
all this?
And again, I don't know the underlying technology
enough to -- it's probably all pervasive anyway,
already.
HEALEY: What I know about LIDAR is it's light.
So it's a lot safer than radio waves.
I think there's probably not been enough studies
on the dangers of radio waves.
But this is a different technology.
It's way above your car, and it's pointing
outward.
So I think it's both far removed from you,
and it's less potentially harmful technology
than cell phone technology.
SHINN: Sir, by the pillar here.
QUESTION: I'm curious to know...
SHINN: Identify your name and affiliation,
please.
QUESTION: Earl Karr (ph), thank you.
I'd be curious to know, could you see any
scenarios in which this technology could not
be adopted in the future?
What could potentially derail your scenarios
from this actually coming to fruition?
MUI: Oh, I think there are 1,000 ways where
this could get derailed.
I mean, from one standpoint, you look at -- earlier
question -- you look at all the entrenched
business interests for whom this is a very
bad day.
We already know that auto dealers have tremendous
amount of political -- local political power.
You look at auto dealers, and cab drivers,
and truckers, there are a lot of -- there
are a lot entrenched interests that could
delay this, delay it by, you know, throwing
out the regulatory kinds of issues.
So the politics, the regulation, is an issue.
The liability, you know, if we don't address
that is an issue.
There's still technology problems to be solved.
I mean, those could be issues.
But I think that's less of an issue, because
we can get incremental benefits for those,
from that standpoint.
So, I mean, there's a lot of -- there's a
lot of hurdles that have to be dealt with.
SHINN: The gentleman right there?
QUESTION: Phil Huyck from Encite, a micro
fuel cell company.
I can imagine a scenario where cars -- as
cars as we think of them simply disappear.
You're talking about autonomous transportation.
I suspect in 25 years, we'll all be getting
in our own pods.
But my question goes to every time there's
a major technological innovation, there's
also a vulnerability that comes with it.
Is there any chance that hackers could get
into this system, and you would have the mother
of all traffic jams?
MUI: Oh, for sure.
BRYNJOLFSSON: Oh, there's a certainty.
I don't think there's a chance.
I think that that's one of the new kinds of
risks that we would have, that we wouldn't
have previously, is that, you know, our cars
will become computers, increasingly already
are.
Already, people have hacked into parts of
automobile systems.
And so you're going to have to have layers
of security.
And I hate to say this, but probably there's
going to be holes in them from time to time.
And people will, either maliciously or by
accident, bad things will happen.
So I still think that when you weigh the costs
and the benefits, it's very uneven.
The benefits vastly outweigh the costs.
But I wouldn't say that there's zero cost
of those kinds of security things.
And so of course, not just cars, our entire
lives, everything.
We were talking earlier about the Internet
of things.
All of the objects that we'll be interacting
with increasingly are going to communicate
with each other electronically, and that means
potentially there's room for hackers to abuse
that.
HEALEY: I think there needs to be security
at all levels, you know, firmware, software,
communications -- it's difficult, because
with every security measure, you know, you
don't want any denial of service.
You don't want to -- you know, because of
some cyber encryption didn't work right, you
don't want to have to not be able to drive
your car.
So it's a very complex and important issue.
SHINN: How many hackable operating systems
are embedded in both microprocessor and the
microcontrollers in that Google car, do you
suppose?
HEALEY: Do we understand the question?
I'm sorry.
SHINN: How many -- how many individual operating
systems are embedded in that -- in that car
in its sensors?
HEALEY: There's a lot of different ways that
sensors work in cars.
I'm not exactly sure how the Google car works.
The best of my understanding, it has its own
after-market solution, which is the communication
from the LIDAR.
And that interfaces with the controls of the
vehicle.
I don't know what they're using for that.
I would assume that it would be one of the
Google-friendly platforms, something like
SHINN: I suspect so, yes.
HEALEY: Yes.
I'm not -- I don't know what they're using.
But I do know that currently there's a lot
of embedded controllers in your car that are
all independent, and they work in very old
languages to prevent this kind of thing.
I know that the new technology is very advanced.
But I don't know what their security is.
SHINN: Certainly, the vulnerabilities is connected
to the show-stopper question, which (inaudible)
is obviously related to the public-policy
question that seems to be on the mind of a
number of the members of the audience.
HEALEY: Just with regard to public -- this
is I think important when you're talking about
the technologies.
The LIDAR and the sensor fusion, that's one
level of software integration.
And then if you're just doing front-back detection,
that's a single sensor.
So there's a difference between doing fault
detection in a single sensor versus fault
detection in a system.
So different level of risk.
SHINN: Gentleman in a French blue shirt.
Is that right?
QUESTION: I'm Dennis Neal (ph) of the Fox
business.
And now I'm with a tiny media company.
The one thing I haven't heard discussed at
all is consumer resistance.
And Silicon Valley guys all the time are giving
us great new features because they can, instead
of because we wanted them.
And the car is one of our last domains -- I
don't own one, I don't drive one, but I know
people who do -- of our own privacy, our own
control, our own power.
When you're on that traffic jam for 45 minutes,
you're away from your spouse and your kids.
And you don't have to e-mail anybody.
And driverless cars will change all of that.
Are you guys underestimating that we consumers
are unwilling to give up control?
MUI: No.
SHINN: I think that's a rhetorical question.
MUI: I think -- I think you're only partially
right.
Because your description fits a segment of
the population.
So the key will be whether or not whatever
techniques, whatever products are advanced,
what level of adoption is required in order
for the benefits to be gained?
So I think what we need, is we need technology
that can be adopted incrementally, and doesn't
depend on 100 percent adoption.
And then you can keep your car.
Actually, you don't have one, but you dream
of having a car.
And then somebody else can choose to have
a driverless car, where they're chauffeured
around.
Now, I'm shocked that you don't believe that
being chauffeured around is more luxurious
than having...
QUESTION: (OFF-MIKE) I noticed that even the
blind man had to site at the wheel.
I think that we're even farther away, aren't
we, from you being able to sit in the back
seat and just trust it 100 percent?
I mean, I don't think that Jennifer is comfortable
with that.
HEALEY: I have a big problem with the 95 to
100 percent.
And I would want a different backup system.
If there's not a human driving, I would want
a different set of checks.
BRYNJOLFSSON: These are both really the same
point, which Chunka made, which is that it
is not an all-or-nothing.
It's not taking away your existing car.
It's a matter of how you can set up an incremental
adoption that some people will flock to.
Because there's people at the other end of
the curve as well, the early adopters that
will adopt almost anything.
And there's going to be people in the middle,
there's going to be people at the end.
And the real key is the people who develop
business models and technologies that are
scalable smoothly through that entire adoption
curve that don't require everybody to buy
in before anybody can buy in.
HEALEY: And I just wanted to make an analogy
between like the manual transmission and automatic
transmission.
I think there are people -- and I know them
-- out there who want the manual, because
they want that kind of control.
It's not really driving without that.
BRYNJOLFSSON: Exactly.
HEALEY: And I think there will be those people.
I just think that segment will shrink as the
technology proves its benefits.
MUI: There's an interesting aspect to this
question, is that I think a lot of those people
sit in car companies.
So I think there's actually the danger here
is that the folks in the car companies will
take that view of, "That's not a car.
I'm not going to build that," and they're
going to miss out because of that.
SHINN: One of the utterly inflexible rules
of the Council on Foreign Relations is that
we end meetings on time.
And we are at 2:00.
I would ask you to wait at least until you
step out onto 68th Street before you text
your tech trades from this meeting.
And I hope you'll also join me in thanking
our three remarkable guests.
