ASTRO TELLER: About five years ago,
right as Google X was being birthed,
I sat down with Larry Page.
And I was trying to work with him on how we were going
to talk about what Google X is.
And I was having a hard time getting something concise out
of him.
So I just started throwing things at him to respond to.
So I said, are we a research center?
He said, "no."
I'm glad to hear that.
Are we an incubator?
"Sort of, not really."
Are we just another business unit for Google?
Is that what we're going to be?
"No."
The original vision statement that Kennedy gave to the nation
in 1961, that we were going to put a man on the moon
and return him safely by the end of the decade,
was the original moonshot proposal,
at least in the moonshot sense.
So I was delighted when I-- after 10 of these things,
I threw out to Larry, are we taking moonshots?
And he said, "yes, that's what we're doing."
That made me really happy.
So from that afternoon on, what I've
been telling the people at Google X
is that we're trying to build a moonshot factory.
What I mean by that is that we're trying to take moonshots.
That word is to remind us that we're
trying to work on things that are very hard, that aspire
to make the world 10 times better in some way
than it currently is, not 10% better,
to remind us about the risks that we're taking
and the long-term nature of the work that we have ahead of us
when we try to do these things.
The word factory is meant to remind us
that even though we are doing these risky long term things,
that we want to pursue doing them with an eye
to actually having the impact that we aspire to,
that we're building products and services for the real world.
Fast forward five years, I'm tickled, I confess,
to see that the word moonshot has
made its way, fairly heavily now, into the popular lexicon.
I understand though-- I haven't seen the show myself--
that the TV show, "Silicon Valley," the Google in it
is called Hooli.
And they've now started their own Google X-like organization,
which they call XYZ, instead.
And it's taking moonshots also.
And I've personally been upgraded
from captain of moonshots to head daydreamer in the TV show,
apparently.
The fact that it's out there is important.
And part of the reason that I think that it's important
is that there's this bizarre-- it's understandable,
but it's this frustrating game of "Not It"
that we all play with ourselves.
So the small companies say, I can't take moonshots.
That's for big companies to do because it costs a lot of money
to take moonshots.
The big companies say, well, we aren't
going to take moonshots because that
means taking a lot of risk.
That's not really our game.
That's what the small companies should do because they
have nothing to lose.
The governments say, well, you know like 50 years ago,
we were taking moonshots.
But that's not really our thing anymore.
We have to work on popular, immediate problems.
We don't have any money.
Like, that just can't be us, sorry.
Academics love talking about moonshots.
They like writing the papers.
They actually produce some of the underlying science that,
later, can turn into a moonshot, but they're not
the system builders who are going to build
the moonshot themselves.
Everyone thinks it's someone else's job.
But we're not going to fix the biggest problems in the world
if everyone thinks it's someone else's job.
The truth is, we can all work on moonshots.
Working on things that aspire to be 10 times better, rather
than 10% better, is a mindset.
That's what it is.
It's got nothing to do specifically
with the risk, or the money, or the time frame.
It's a mindset about what we're working towards.
And counterintuitive as it is, if you
work on things that aspire to be that much better,
it not only isn't harder, sometimes
it's literally easier because, when
you aspire to make the world that much better,
you have to start over.
And when you've acknowledged to yourself as a team
that you're going to start over, you
know that what's going to happen next
can't be built on what people have done before.
You have to, in a meaningful sense,
come at it from a new perspective.
And that often, not always, but often unlocks
possibilities that make the impossible seem possible.
So this is our blueprint for how we
take moonshots, for what a moonshot should
be in our minds.
The first thing is that there has
to be a huge problem in the world that we want to resolve,
that we want to have go away or mitigate
in some meaningful way.
So for example, 1.2 million people
die every year in car accidents.
More than a trillion dollars is wasted every year
with people sitting in traffic.
That is a legitimately world scale
problem it would be awesome if we could make go away.
Number two, there has to be a radical proposal for how
to make that problem go away.
If it's something that people have tried over and over before
in the past, the idea that we or you or anyone else
by just trying harder, or staying up later at night
is not really a good outcome.
It's not very likely to work.
So cars that drive themselves all the way from point A
to point B-- I think that's like the poster
child for a radical sounding proposal
to make that kind of problem go away.
And then, the third one is there has
to be some reason to believe some breakthrough
technology, some aha from science or engineering,
which makes us believe that, even if it's not guaranteed
to work, we have a decent shot at learning through the process
and maybe, just maybe, getting there.
In the case of self-driving cars,
that was the DARPA Grand Challenge
work that originally happened and some advances
in smart software and smart sensors.
So each project that fits into this mold then
has to describe not just that it fits these things,
but that, in principle at least, it
could produce in the long run a Google scale business
or Google scale value to the company in order for us
to help it move forward.
Our goal is to have each of these things
create a ton of value for the world,
but then also create back to Google
a fair or equitable return on its investment
for taking these big risks.
And five years in, I'm very happy to say
that we've started to make real progress in this space
through the graduations that we've done.
Some of them play out in different ways.
So for example, the massive neural network project
that we originally built at Google X,
we graduated back into the main part of Google,
called knowledge, which is what you might think of as search.
And in that part of Google, it now
is servicing over 50 products and services helping
all these different parts of Google turn signals
into symbols more effectively, which is helping
Google to be successful.
And certainly, that's not all our credit
because they've done a lot since they left.
But we helped to get that going.
And that is a good example of the sort of thing
that we're shooting for.
In a very different way, the smart contact lens
work that we built, it wasn't going
to probably work out optimally for us,
not only to do the original work on that project,
but to take it all the way to the market ourselves.
So we developed a partnership with Alcon, the eye care
division of Novartis, and now we are headed towards the market
through this still very complex process
of trying to make contact lenses be
able to sense the glucose in your eye to help diabetics
manage their diabetes better.
But that is another example where
value can be released through this work, in this case,
through a partner.
Another of the critical operating principles
that we have at Google X is throwing ourselves out
into the world to get contact with the real world
as fast as possible.
It's not sometimes a natural thing to do,
but it is an absolute critical thing
to do, especially when you're taking on particularly
big, hard projects.
You can't possibly know at beginning the right thing
to do, but you can have a process where
you discover faster, rather than slower, that you're
on the wrong track.
That, you can do.
So we go through these processes for things
like the self-driving cars, for our flying wind turbines,
for Project Loon, for contact lens work that we do,
and for others.
We go through this process where we force ourselves
to seek out this contact.
And sometimes, this turns out to be us dragging our balloons up
to South Dakota to expose the balloons to Arctic winds.
Sometimes, it's asking a really specific tiny question,
like how long will this glucose sensor
the size of a piece of glitter actually
be able to sense glucose while sitting in this tear fluid.
The question is how and how fast can you
discover that what you're working on
is the wrong thing to be working on.
And the secret is it's discouraging
to hear these things.
We all avoid going out into the world,
throwing ourselves at the world to discover these things.
But no matter how discouraging it
is now, if you put more time into doing it,
you will unconsciously avoid even more doing it tomorrow,
or a week from now, or a month from now.
And that's why doing it as fast as you can
is actually the easiest time and the most efficient time
to discover that you're on the wrong path.
And that's why it is sort of central to how Google X works
on solving these problems.
I want to emphasize-- what this basically
means is we don't know.
And I would go so far so to say, nobody really
knows the right way to build any of these projects.
If you listen to the media stories,
you get this nice, tight arc where the entrepreneurs that
make it were destined to make it,
and the ones that didn't work were losers who were never
going to make it anyway.
And it completely misses the point,
that feeling in the pit of your stomach
where you know where you want to get to,
but you really don't know how to get there.
I have those feelings all the time.
Every single one of our project leads at Google X
has those feelings.
You're not alone.
That's just the truth of the world
that we have those feelings all the time.
All we can do is take our best guess about what
we should be building.
And then, don't wait.
Get quickly out into the world to discover
how wrong you are, which parts are salvageable,
and which parts you need to go back
to the drawing board about.
That's the only way to race forward.
So I'm going to tell you about Project
Wing as an example of this.
Project Wing had some kind of bumpy months--
some very bumpy months in late 2013, early 2014.
So the goal for Project Wing is self-flying vehicles
for delivery.
That huge problem with the world that we aspire to,
we believe that there are still a significant amount
of friction in how physical things are moved around
in the physical world.
And if you look, historically, at the Pony Express
and the introduction of the post office,
at mail order catalogs-- which are kind of weirdly
the precursor to ecommerce on the internet-- If you look
at boats, planes, trains, every time people have introduced
a way to more efficiently move physical things around
in the world, it has utterly and positively changed the world.
There's lots of friction left, let's remove some more of it.
But that's too hard to work on, to get your arms around
at the beginning.
It's just too much.
So we had to pick something to start with.
There was a process of brainstorming.
And the thing that we got excited about at the beginning
was to deliver defibrillators to people.
So if somebody is having a heart attack-- you've probably
seen like an ER show, they say, clear.
And they hit their chest.
And the guy wakes up again, hopefully.
So there is in this building, I guarantee you,
like five AEDSes, Automatic External Defibrillators.
I bet you not a single person in this room knows where it is.
And if somebody fell down on the floor right now,
I doubt you anyone would find it in the next 5 to 10 minutes,
sadly.
What if defibrillators could come
find you-- at least get to the front of the building,
maybe even get all the way to the person who needed it.
So this was our vision.
It's small.
And it's something where time matters.
It's about eight to nine minutes from when someone
has a heart attack to when they actually
get help, today on average.
If we could shave four minutes off of that time,
we would save 20,000 lives a year just in the US.
So the team got really excited about this.
That's a mission.
That's a great beachhead mission for this bigger goal.
So we started two things in parallel.
The engineers, all psyched up because they
were going to save all these lives,
started building a first version, or a vehicle,
which by the way-- this turns out
not to be the right vehicle.
Story for another day.
But that was another failure mode that we had.
And we're now doing a very different vehicle.
That was a painful moment for the engineers.
But while they were building the hardware, the software,
the sensors for these vehicles, we also immediately
had a real world team that went out to ask,
can we kill this project right now, or at least
that direction for the project, by finding out that there was
something wrong with the plan.
We went out and we talked to people,
actually had them, on dummies, use
these automatic external defibrillators.
We went and we talked to the emergency response people
in communities.
We literally sat and watched them take phone calls
and talk to the person who ran the 911 center in various towns
and said, well, could we plug into what you're doing.
And we quickly found out the whole plan
wasn't going to work.
That's good though.
I promise you, this was enormously depressing
for the team, but it was only going to be depressing for them
in a much bigger way if we'd let them
go for another year and a half til they were all done,
and then, discovered that it was the wrong thing to do.
That would have been much more expensive, much more painful.
It turned out that it wasn't the right thing
to do because number one, the defibrillators
are quite hard to use.
In principle, they're not, but most people
don't apply them really well.
So if you get someone something they
don't know how to use really quickly,
you actually haven't made the world
as better as you were hoping.
And the other one is that 911 is just not set up
to let us start flying to where the call came
from the moment they get the call.
In fact, you would be surprised how much they don't even
understand where the call comes from.
It's a somewhat antiquated system.
So it just wasn't a good plan, and this was really depressing
for the team.
The team took a big step back.
Initially, we were trying to figure out what
a better beachhead would be.
And we came to what I think was the right answer.
This is about 9, 10 months ago where we said,
you know what, we're not going to pick a beachhead.
Instead, let's just throw ourselves into the world.
And we went to Australia and we started doing deliveries.
And we discovered things in that process.
We discovered a ton of things in that process.
But among other things, we discovered
things people wanted delivered that we hadn't even imagined.
Cattle ranchers were saying to us,
do you know what would be awesome is vaccines.
We're out in the field.
The vaccines have to be stored in a refrigerator,
so we never have them with us.
They have a really poor shelf life,
so we often don't even have them in our barns.
But you want it like right there, that's when you need it.
If something could just come in the middle of the field
and hand me the vaccine, that would be amazing for the cows
that they're taking care of.
We would never have figured that out sitting
in a conference room on a whiteboard, I promise you.
So Project Loon.
The goal for Project Loon is to beam internet
down to the four billion people in the world who
do not currently have a good connection
to the digital world.
Four billion people.
It's sort of hard to imagine something more worth working on
than that.
And most of these people, surprisingly by the way,
already have the device.
More than half of them already have a device.
It's actually the absence of the connection to the digital world
which is stymieing their ability to participate in modern life
and to get access to better health outcomes, literacy,
the ability to vote, lots of other things.
So when we started because the idea was
to do this from stratospherically
based balloons-- so this is 60,000 to 90,000 feet,
much up above where the weather is and up above were
airplanes fly.
We were going to put thousands of these balloons, which
you can think of like flying cell towers.
And they would talk to each other.
And they would beam internet down
to cell phones, or laptops, or whatever on the ground.
We knew we had a lot to learn because we had not
spent-- truthfully, almost nobody
has spent any meaningful time hovering up in that area.
But we misestimated how much we had to learn.
So one of the things that was a basic part of our plan--
it's called the stratosphere because the winds go
at different speeds and different directions
quite close to each other.
It is stratified.
And this is a big deal for us, a good deal for us,
because we want the balloons to go up and down just
a little bit because it doesn't take much energy to cause
them to go up and down.
And then they can catch winds going in different directions
and at different speeds.
And so we can teach the balloons.
We have taught the balloons to sail on those winds.
What we did not fully anticipate,
once we got up there, was, if you have winds going like this
and winds going like that, and your balloon is
the size of a house, when it crosses that boundary,
it gets whipped around.
And the violence of that process,
every time we went through these layers
was something we really hadn't properly
designed into the balloon.
And there was a major redesign process
to make sure that it was ruggedized
to survive those particular kinds of things.
Even after that, we were flying balloons
and our goal was to get them to stay up for 100 days.
Our modeling of how this could turn into a Google scale
business told us that, on average, these things
needed to last 100 days in order for this
to be a viable business.
So at first, they would go up, they
would explode after five hours, and come down.
So we could learn pretty quickly that we
were on the wrong track.
It was kind of a mess though because, when it came down,
you didn't necessarily knew why it blew up.
It just sort of pops.
These things are full of helium.
But after a while, once we solved those problems,
our success became an even bigger problem
because they would leak really slowly.
We would wait a week.
We would wait three weeks.
Then it would start leaking.
It would take several more weeks for it to come down in a way
where we could get it to come down
where we could capture it, and then go send someone to like
Southern Chile to pick it up.
This was an incredibly inefficient way
to find out that we weren't doing the right thing.
We needed to fail faster.
We were in the real world.
It was giving us these signals, but we
needed to accelerate the real world in some way.
So we created, in this particular case,
something called a leak squad.
And their goal was to create, detect, and then fix
the leaks that we were having.
And they studied a lot of other things
in the world where people care about very thin things that
don't leak.
And it's important that they not leak.
Doritos bags, sausage casings, condoms.
There's a lot of work that's going on in this area.
All three of those are important not to leak
for somewhat different reasons.
So imagine something the size of a house rubbing soap
over it, and then, filling it so full of helium
that somewhere the leak would begin.
And then looking around it for little bubbles.
Or having a wand, about this big,
which is a helium detector.
And kind of like a metal detector,
it would go eee if it could smell the helium.
And then waving this thing around this thing
the size of a house while you've got
it overpressurized with helium, trying
to get it to leak so we could find the leak.
Then we could redesign how the balloon was structured.
Or taking it, as I mentioned before, to South Dakota.
There's like this weird polar vortex where Santa Claus
weather had come down.
It was like minus 40, minus 50 degrees below zero
for a couple weeks there.
And that was good because that's actually
the temperature it is most of the time up
in the stratosphere.
So most of the stuff did not work.
But we did find several really important things.
One of my favorite is that in the manufacturing process,
the balloons are so big that we have to stand on them in order
to make them.
And so someone brought up the question,
I wonder if them walking on the balloons
is what's causing some of the weeks.
And so we did a test.
Everything's an experiment.
Failing is fine as long as you set everything up
as an experiment with a hypothesis.
So the hypothesis here was are their feet making little holes?
So the control group was them standing on the balloons
the way they were, which is wearing their normal socks.
And then we got some really fluffy socks.
But we need to control the experiment.
So we actually had a line dance, like a can can line dance.
And we had all these manufacturing guys
on the balloon doing the line dance in their normal socks.
And then we got another balloon.
And we had them all put on their big fluffy socks
and do the same line dance.
And in fact, it turned out that the balloon
with the fluffy socks had fewer leaks in it.
It is literally at that level that we've
had to push the real world towards us
so we can fail quickly, so we can discover, so we can fix it.
I really believe Loon has made unbelievable progress.
It hasn't even been four years yet,
and they've moved from this crazy sounding science project
to a viable venture.
Our balloons now stay up, by the way, for more than six months
at a time.
And we can sail them around the world
to within 500 yards of where we want them to go, routinely.
That they've made that progress is
because they've been so hungry to fail,
because they've been so hungry to get into the real world
and to try things.
Maybe more than any other project that we have,
the self-driving cars have to get into the real world
in order to do their learning.
It's just the way it's wired.
And of course, when we started, we
couldn't possibly know the 10,000 things
that we would need to do right in order for a car
to drive itself really well because pretty good most
of the time is not good enough when you
get into a self-driving car.
But we couldn't know what that list was.
We weren't going to sit on some white board
and write down the 10,000 weird situations that would happen.
So you get out into the real world.
And the truth is that the making of that list of those 10,000
things is half of all of what's hard about doing something
like this, like making a self-driving car is figuring
out what the real world wants to tell you.
So 2 and a 1/2 years ago-- this was in the fall of 2012,
we were done.
We were just done, pretty much.
We had this great commute helper.
We had it on highways.
It was executing beautifully.
We were so done that we gave a bunch of our Lexus vehicles,
like you can see in this picture,
to a bunch of Googlers who did not work at Google X.
And we said take this home with you.
Use it for your commute.
You just drive to the freeway, you push a button,
it'll take care of the rest.
Driving on the freeway is actually not that hard.
You pretty much stay in your lane.
You change lanes occasionally.
Don't hit the guy in front of you.
Occasionally, some bad driver makes
things a little interesting.
But basically, we had it covered.
So we gave it to these people.
And we made them swear that they were
going to pay 100% attention because when we have our safety
drivers in the car, they're like hovering like this
over the steering wheel.
The car drives for eight hours at a time,
and they never touch the steering wheel,
but they just have to be like this the whole time,
so we were telling these Googlers,
you have to do the same thing.
We're going to put cameras in the car.
This is for your commute, but you got to pay attention.
Yeah, oh yeah, totally.
We're going to pay so much attention you can't believe it.
The cars, thankfully, performed flawlessly.
The people did not.
People don't even pay attention to driving
when they're driving.
They're like makeup, and the texting, and the burrito.
It's horrible.
And that's when they're actually supposed to be driving.
So imagine what they do when they think the car's mostly
got it covered, and like once in a blue moon,
I'm going to need to take over.
It was not pretty.
It was sufficiently not pretty that we stopped doing it.
And we said, OK, this is not going to work.
Humans cannot be a backup system for the computer.
Our success was a failure when you factored in human nature.
And so we knew that if there was going to be a self-driving car,
it was going to have to be something that
could go all the way from point A
to point B by itself with no help from the person.
This was a major existential moment for this team.
They thought they were done.
And now all of a sudden, they weren't just not done,
it wasn't just like a half step back.
They were like, is this even possible?
This is 2 and a 1/2 years ago.
The first thing was there was like this frantic brainstorming
about how we could force people to always pay attention
to the road because then we'd be like fixed, like training
wheels.
And it quickly became clear that was not the right way
to solve the problem.
So after quite a bit of emotional process and soul
searching, the team did what I now believe
is exactly the right thing.
They said, OK, if humans are not a reliable backup system
for the computer, let's build a car that
doesn't have a steering wheel.
Then, we won't be tempted to use that as a crutch
when we hit these parts of the process
where we don't know how to handle.
We won't just pump that and be like, oh, we'll
people handle that one.
So we did.
We've been doing that for 2 and a 1/2 years.
And we just announced recently that these cars
will start being on city streets this summer.
Super exciting.
[APPLAUSE]
At first, they're going to have this temporary steering
wheel in it.
And the safety driver will just be sitting there.
But that's the right, safe thing to do so that we can make sure
that these cars are doing what our bigger cars have been
practicing for a long time now, which is to always do
the right thing on these city streets, which are much more
complicated than being on the highway,
going all the way from point A to point B.
There's no way we could have learned this sort of thing
without spending a ton of time out in the real world.
It's also a priceless opportunity for us
to start being in these city streets
in a more widespread way.
I think if the Lexus vehicles that we had really,
eventually were to become the first fully self-driving cars
on the streets, it could provoke some anxiety in people.
And one of the things that we're hoping
is that this car, because it's smaller, it's cuter,
because it's only going to go 25 miles an hour,
because we've actually built a foam front end
and flexible windshield in here-- I hope we never
have to use it.
But we get to experiment with these things,
and on a bunch of different fronts,
make sure as we're learning that we're keeping the world super,
super safe.
So I'm excited to see this next step for that project.
Now in order for this to really be done-- that list of 10,000
things-- we've become-- the self-driving car team has
become part of its own problem.
We are so good at driving now on city streets.
We drive 10,000 miles a week in Mountain View.
And nothing happens because the car just does the right thing
all the time.
It is incredibly boring for the safety drivers.
It's not very helpful for the engineers
either because they aren't learning much if the cars just
like, do ta do ta do.
And they're like, OK, give it a new address.
Do ta do ta do.
We need more negative examples.
The teams at Google X are hungry for these negative examples.
So we've created a team on the self-driving car
team whose full time job is to find creative ways to create
negative examples under safe conditions for the cars.
Now one of the ways we can do this
is just by going to new cities.
And we are starting to do this.
So for example, coming to San Francisco,
there are interesting things like different whether.
It's more cloudy here.
Maybe you get a little bit more rain, probably not these days.
We're definitely not going to get snow.
But as we move from city to city,
we can rack up some new experiences.
San Francisco-- one of the interesting things
it'll give us is hills.
It's not that the cars are going to struggle up the hill.
They can go up the hills.
But it's a reasonable question.
How will the sensors on our cars have a hard time, or not,
when we're tilted like this up a steep San Francisco hill.
So that's a good thing for us to learn.
But that's still not learning fast enough.
So this team has been trying to find more and more ways
to challenge the cars.
And the ways in which we have to find
these rare, weird conditions fall into three
different categories Situation number one is people
drive illegally.
Situation number two is we just have
to react suddenly because people weren't paying attention.
And then there's just weird stuff.
The mail trucks are jumping out in front of us.
That's the canonic version of this.
And we have tried thousands of examples
to simulate that jumping out in front of us
just to try to stress our cars on our test track.
We throw beach balls at it.
We've gone to the Halloween store
and gotten big birds to swing by the windshield of the car.
We've had people hide in canvas bags
and pop out in the middle of the road
just to see what the car would do.
On take your children to work day, we parked the cars,
but had the sensors running just so the kids would play around
them, so we could get the computer
used to watching kids move so we could learn how they move.
And the reason that we could do the following
is because we did all that.
A few months ago, we were coming down a suburban side street.
The car turned a corner and came automatically to a stop.
There was a woman, an elderly woman
in an electric wheelchair, holding a broom,
in the middle of the road, trying to shoo a duck out
of the middle of the road.
And they were going around in little circles
in the middle of the road.
I'm sure this was shocking to the car
as well as well as to the safety drivers.
But eventually, she got the duck out of the road,
she moved off the street, and the car continued autonomously.
That it stopped autonomously and started autonomously again,
and there was no problem, is thanks to these field tests
that we're doing.
Getting out and getting into contact with the real world
is the most valuable thing you can do for your project.
All of our audacious yearly and quarterly goals
have this characteristic of what are
we going to build, how are we going to test it
in the real world, and how are those tests designed to reveal
as fast as possible the unanticipated design
flaws with what we're building.
I really believe that the progress
of Google X has made over the last five years
comes from our excitement and throwing ourselves
at the world, of pursuing these projects
with the maniacal focus of a startup,
and then the being hungry to be wrong,
being hungry to make mistakes, to get
these negative experiences and then harness them
to do it better the next time.
I know it sounds like a weird thing to be excited about,
but I think it's our special sauce.
I've always wanted Google X not just to take its own moonshots,
but to encourage other people to think this way,
to take on some of these problems, too.
So even if you're not working on a self-driving car,
I hope you can take something away from this approach
and set yourself up for creative, productive, mistake
filled contact with the real world.
Thank you.
[APPLAUSE]
I'm happy to take a few questions if you like.
There are microphones here.
AUDIENCE: Is that working?
ASTRO TELLER: Yes.
AUDIENCE: There we go.
So looking at a product at a typically development
IT organization and balancing innovation
from product management.
So from a company who's typically
focused on building the next product,
pounding the sales team lines, building, building, building,
do you have any suggestions or recommendations of how
to actually put engineering culture first
on top of that innovation.
Is it just a matter of saying, OK, let's put you guys aside.
Let's insulate you.
Is there an insulation process in that,
inside of the heavily driven, product management,
sales driven company?
ASTRO TELLER: I'm not sure this is the answer you want.
It's a weird thing to say at a developers conference.
I don't think an engineering heavy approach is
any better than a sales heavy driven approach.
I think any company that is overweighted
on any of these things is missing the point.
When we look to fail in the real world,
we depend equally on finance, on product managers,
on designers and UX people, on engineers.
Anyone who can find a reason why we're on the wrong track
is going to be the super hero of the month for us.
And so I guess what I would say is
rather than trying to isolate engineering
from these pressures, what I would try to do
is evangelize to them that they're missing
half of their opportunities.
And that if they think about it as discovering
as fast as possible why what we're currently doing
isn't the right thing to do, that's the most efficient way
to make progress.
And cutting off some of the reasons why it
might be a bad reason.
If you know that it breaks the law of physics
and you're not allowed to say so, your company is broken
and it's wasting its money.
And surely like the sales people are like, yeah, that's true.
Then you can say, then, invite me to the meetings.
Yeah
AUDIENCE: I just had a question about the self-driving car.
I think there was a quote from Elon Musk or something.
He was saying sort of what you said, right?
Freeway speeds are easy.
0 to 35 range is easy.
But it's that 35, to say, 55 mile an hour
zone where you can't simply stop, right?
And a lot of crazy things can happen.
So I guess I'm just curious to hear
what the testing you do in that range is
and what your experiences are.
ASTRO TELLER: Look none of it's easy, to be fair.
The highways are very constrained situations.
There are some oddities that happen,
but a microscopic fraction of the oddities
that happen on city streets.
So that's partly about speed.
Part of the issue, which is some of what
Elon is getting at there is that the energy of the car, m v
squared.
When you double the velocity of the car, you get four times
the energy, four times the braking distance,
it does complicate things when you're going faster.
Our experience has been with that going slower
will help from a safety perspective.
That's part of why we're focused on 25 miles an hour
and under for the moment.
But what I would highlight is the really hard bits
of this are the duck.
When someone walks out into the street holding a stop sign,
like a crossing guard, that's a legal stop sign.
The car has to figure that out.
Figuring out the 10,000 things like that.
When a bike is coming the wrong way
down a bike lane, which should dominate in the computer's way
of approaching that problem?
The fact that they should be going that way, and bikes
tend to be going that way, or the physical reality
that its momentum is this way.
Those 10,000 things-- my experience--
are the really hard things.
Yeah.
AUDIENCE: How would you characterize
Google X's experience when bringing Google Glass
into the real world?
ASTRO TELLER: So I've said this before.
So first of all, Google Glass, I think,
is making really good progress.
It's graduated from Google X to Tony Fidel's part of Google.
And we will be hearing more in the future about that.
But I think we did something right and something wrong.
The thing that we did right, that I would absolutely
do again, is getting it out into the world, and in some ways,
even in the way we got it out into the world.
The Explorer Program was exactly the right thing to do,
and we owe a ton to Explorers.
Thank you, if you're one of them.
But we were trying to learn also about the social issues
around Glass.
And there were ways in which by trying to figure out
how people would think about Glass,
we ended up sending signals that it wasn't a prototype,
that it was a finished product.
Things like putting it on a runway.
I mean, even that was not crazy as an experiment
to see how people would think about it and respond to it.
But it did leave people with the feeling
that it was a finished product when
we weren't productizing it yet.
We were just trying to learn.
And I think we left people with some confusing messages there.
And that I wish we had done differently.
AUDIENCE: Thank you.
AUDIENCE: How do you think about financial resourcing
each of these products?
Almost by definition, a moonshot,
these could have infinite, or almost infinite,
financial resources.
So how do you decide which products
are going to get a dollar or which one's
going to get a billion dollars?
ASTRO TELLER: I'm not sure I agree
that you need near infinite resources for these things.
That's true only if you're too shy to say
for long periods of time that you're
working on the wrong thing.
It's amazing how in every project when you're
doing something dumb, when it's going in the wrong direction,
I guarantee you 20% or more of the team knows it within weeks.
You guys are engineers.
You know what I'm talking about, right?
You know, and no one will listen to you.
What if you lived in a culture where people are like, really.
Well, let's stop then.
It's unbelievable how much money you can save when you do that.
[APPLAUSE]
Thanks.
But just to answer the rest of your question,
it is also the case that as long as we start on a moonshot
and we believe that there's a good return on investment
over some very long period of time, where a reward to risk
ratio is good for Google, then as we go through the process,
if you're building this moonshot for us,
I'm going to have a dialogue with you about how much
resources have I given you and how much have
you do derisked the project.
And as long as the risks are coming
down faster than I'm giving you money,
our ROI is actually getting better.
And as long as that's true, I'm just
going to keep handing you the resources.
And if we go the other way, where
I'm giving you more resources and it's not
getting any less risky, then probably the resources
shouldn't keep coming.
I think I have time for one or two more.
AUDIENCE: Specifically about the self-driving cars,
could you talk about the 11 accidents
that the cars have been involved in over the past year
or so, both how they happened and what you've learned
from them going forward.
ASTRO TELLER: So there are certain things
that I'm not even allowed to get into.
There's legal issues around it.
I don't mean at Google.
I mean the DMV and things like that.
But what we've said publicly, and I encourage everyone here,
if you're interested, to go look at the blog
that we post that we put out about this is 7 of those 11,
we were parked.
I mean not parked on the side, we were stationary.
So the car wasn't driving.
The human wasn't driving either.
We were just rear ended.
I don't know what we could do about that.
That's just going to keep happening.
We should expect that to happen at roughly the rate it's
been happening to us because we can't not
have a back end to the car.
The other four, the person was driving.
Our safety drivers are pretty miraculous.
And they have a much better record than me or you guys do.
But I'm sure we could improve that in some ways.
We have yet to have the car be driving itself
when any of these have happened.
So I don't have more to report to you on that.
But I guess that's good news.
But we plan to continue to do these blog posts like we've
been doing them to help people understand what we're learning
and how we can improve.
All right.
Last question.
AUDIENCE: I had a question about you personally.
ASTRO TELLER: I'll get you too.
AUDIENCE: You do a lot of projects.
You manage a lot of projects.
You speak a lot.
And like I've always seen you at your 100%.
So what are some things that we can
do that you do to switch contexts between projects
while remaining efficient.
ASTRO TELLER: I'm not a maker.
I mean, I used to be a maker.
I have a PhD in computer science.
But to be fair, I'm good at context switching.
Makers, the people who are actually building the stuff,
do not context switch at the speed that I context switch.
So there's obviously a much longer thing.
Maybe I'll do a op-ed on it or something.
I think it's an interesting question.
But my short answer is hold yourself to the energy level
that you aspire to, but don't necessarily hold yourself
to the context switching level that I
do because that's not always the way to be the most productive.
AUDIENCE: I'm an engineer at Salesforce.
So I was wondering, I always had this attitude
where I go to the different departments.
I go to the strange people in the different departments
to get some code, to just tell me
how they did some things so I'm not writing this from scratch.
And I'm talking to the people from the boss,
like what they think.
I have these strange things.
And it's very beneficial.
I found this progressing faster.
I'm just curious from the people around me,
I didn't see a lot of such people.
And do you think from your experience, it comes naturally
or like you can teach or motivate someone
to do this because a lot of people are just shy.
ASTRO TELLER: I understand the question.
I believe everyone here is a moonshot thinker,
in your hearts.
I promise you, I would not be giving this talk this way
if I did not believe that.
I believe that most of us are not
in a context where we can be as open minded, as honest,
as dispassionate when appropriate,
as authentic as we want to be, as our natural selves would be.
And I think you need to ask your context for that opportunity.
And if it's really not going to give it to you, be humble,
try a few times.
And if it doesn't work, go find a new context.
You all deserve to be able to let the best
part of your selves out.
Thank you very much.
[APPLAUSE]