The following is a conversation with Eric Schmidt.
He was the CEO of Google for ten years and a chairman for six more, guiding the company
through an incredible period of growth and a series of world changing innovations.
He is one of the most impactful leaders in the era of the internet and the powerful voice
for the promise of technology in our society.
It was truly an honor to speak with him as part of the MIT course on artificial general
intelligence and the artificial intelligence podcast.
And now here's my conversation with Eric Schmidt.
What was the first moment when you fell in love with technology?
I grew up in the 1960s as a boy where every boy wanted to be an astronaut and part of
the space program.
So like everyone else of my age, we would go out to the cow pasture behind my house,
which was literally a cow pasture, and we would shoot model rockets off.
And that I think is the beginning.
And of course, generationally, today, it would be video games and all the amazing things
that you can do online with computers.
There's a transformative, inspiring aspect of science and math that maybe rockets would
bring, would instill in individuals.
You've mentioned yesterday that eighth grade math is where the journey through Mathematical
University diverges from many people.
It's this fork in the roadway.
There's a professor of math at Berkeley, Edward Franco.
I'm not sure if you're familiar with him.
I am.
He has written this amazing book.
I recommend to everybody called Love and Math, two of my favorite words.
He says that if painting was taught like math, then students would be asked to paint a fence,
which is his analogy of essentially how math is taught, and you never get a chance to discover
the beauty of the art of painting or the beauty of the art of math.
So how, when, and where did you discover that beauty?
I think what happens with people like myself is that your math enabled pretty early, and
all of a sudden you discover that you can use that to discover new insights.
The great scientists will all tell a story, the men and women who are fantastic today,
that somewhere when they were in high school or in college, they discovered that they could
discover something themselves, and that sense of building something, of having an impact
that you own drives knowledge, acquisition, and learning.
In my case, it was programming, and the notion that I could build things that had not existed
that I had built, that it had my name on it, and this was before open source, but you could
think of it as open source contributions.
So today, if I were a 16 or 17 year old boy, I'm sure that I would aspire as a computer
scientist to make a contribution like the open source heroes of the world today.
That would be what would be driving me, and I'd be trying and learning and making mistakes,
and so forth, in the ways that it works.
The repository that GitHub represents and that open source libraries represent is an
enormous bank of knowledge of all of the people who are doing that, and one of the lessons
that I learned at Google was that the world is a very big place, and there's an awful
lot of smart people, and an awful lot of them are underutilized.
So here's an opportunity, for example, building parts of programs, building new ideas to contribute
to the greater of society.
So in that moment in the 70s, the inspiring moment where there was nothing, and then you
created something through programming, that magical moment.
So in 1975, I think you've created a program called Lex, which I especially like because
my name is Lex.
So thank you.
Thank you for creating a brand that established a reputation that's long lasting reliable
and has a big impact on the world and still used today.
So thank you for that.
But more seriously, in that time, in the 70s, as an engineer, personal computers were being
born.
Do you think you'd be able to predict the 80s, 90s, and the aughts of where computers
would go?
I'm sure I could not and would not have gotten it right.
I was the beneficiary of the great work of many, many people who saw it clearer than I
did.
With Lex, I worked with a fellow named Michael Lesk, who was my supervisor, and he essentially
helped me architect and deliver a system that's still in use today.
After that, I worked at Xerox Palo Alto Research Center, where the Alto was invented, and the
Alto is the predecessor of the modern personal computer, or Macintosh, and so forth.
And the altos were very rare, and I had to drive an hour from Berkeley to go use them,
but I made a point of skipping classes and doing whatever it took to have access to this
extraordinary achievement.
I knew that they were consequential.
What I did not understand was scaling.
I did not understand what would happen when you had 100 million as opposed to 100.
And so since then, and I have learned the benefit of scale, I always look for things
which are going to scale to platforms, so mobile phones, Android, all those things.
There are many, many people in the world.
People really have needs.
They really will use these platforms, and you can build big businesses on top of them.
So it's interesting.
So when you see a piece of technology, now you think, what will this technology look
like when it's in the hands of a billion people?
That's right.
So an example would be that the market is so competitive now that if you can't figure
out a way for something to have a million users or a billion users, it probably is not
going to be successful because something else will become the general platform, and your
idea will become a lost idea or a specialized service with relatively few users.
So it's a path to generality.
It's a path to general platform use.
It's a path to broad applicability.
Now, there are plenty of good businesses that are tiny, so luxury goods, for example.
But if you want to have an impact at scale, you have to look for things which are of
common value, common pricing, common distribution, and solve common problems, the problems that
everyone has.
And by the way, people have lots of problems, information, medicine, health, education,
and so forth.
Work on those problems.
Like you said, you're a big fan of the middle class because there's so many of them by definition.
So any product, any thing that has a huge impact, it improves their lives is a great
business decision and it's just good for society.
And there's nothing wrong with starting off in the high end as long as you have a plan
to get to the middle class.
There's nothing wrong with starting with a specialized market in order to learn and to
build and to fund things.
So you start a luxury market to build a general purpose market.
But if you define yourself as only a narrow market, someone else can come along with a
general purpose market that can push you to the corner, can restrict the scale of operation,
can force you to be a lesser impact than you might be.
So it's very important to think in terms of broad businesses and broad impact, even if
you start in a little corner somewhere.
So as you look to the 70s, but also in the decades to come, and you saw computers, did
you see them as tools or was there a little element of another entity?
I remember a quote saying AI began with our dream to create the gods.
Is there a feeling when you wrote that program that you were creating another entity, giving
life to something?
I wish I could say otherwise, but I simply found the technology platforms so exciting.
That's what I was focused on.
I think the majority of the people that I've worked with, and there are a few exceptions,
Steve Jobs being an example, really saw this as a great technological play.
I think relatively few of the technical people understood the scale of its impact.
So I used NCP, which is a predecessor to TCPIP.
It just made sense to connect things.
We didn't think of it in terms of the internet, and then companies, and then Facebook, and
then Twitter, and then politics, and so forth.
We never did that build.
We didn't have that vision.
And I think most people, it's a rare person who can see compounding at scale.
Most people can see, if you ask people to predict the future, they'll say, they'll give
you an answer of six to nine months or 12 months.
Because that's about as far as people can imagine.
But there's an old saying, which actually was attributed to a professor at MIT a long
time ago, that we overestimate what can be done in one year, and we underestimate what
can be done in a decade.
And there's a great deal of evidence that these core platforms at hardware and software
take a decade.
So think about self driving cars.
Self driving cars were thought about in the 90s.
Over projects around them, the first DARPA Durand Challenge was roughly 2004.
So that's roughly 15 years ago.
And today we have self driving cars operating in a city in Arizona, right, so 15 years.
And we still have a ways to go before they're more generally available.
So you've spoken about the importance.
You just talked about predicting into the future.
You've spoken about the importance of thinking five years ahead and having a plan for those
five years.
And the way to say it is that almost everybody has a one year plan.
Almost no one has a proper five year plan.
And the key thing to having a five year plan is having a model for what's going to happen
under the underlying platforms.
So here's an example.
Computer Moore's Law, as we know it, the thing that powered improvements in CPUs has largely
halted in its traditional shrinking mechanism, because the costs have just gotten so high.
It's getting harder and harder.
But there's plenty of algorithmic improvements and specialized hardware improvements.
So you need to understand the nature of those improvements and where they'll go in order
to understand how it will change the platform.
In the area of network connectivity, what are the gains that are going to be possible
in wireless?
It looks like there's an enormous expansion of wireless connectivity at many different
bands, right?
And that we will primarily, historically, I've always thought that we were primarily
going to be using fiber, but now it looks like we're going to be using fiber plus very
powerful high bandwidth sort of short distance connectivity to bridge the last mile, right?
That's an amazing achievement.
If you know that, then you're going to build your systems differently.
By the way, those networks have different latency properties, right?
Because they're more symmetric, the algorithms feel faster for that reason.
And so when you think about whether it's a fiber or just technologies in general.
So there's this barber, wooden poem or quote that I really like.
It's from the champions of the impossible rather than the slaves of the possible that
evolution draws its creative force.
So in predicting the next five years, I'd like to talk about the impossible and the
possible.
Well, and again, one of the great things about humanity is that we produce dreamers, right?
We literally have people who have a vision and a dream.
They are, if you will, disagreeable in the sense that they disagree with the, they disagree
with what the sort of zeitgeist is.
They say there is another way.
They have a belief.
They have a vision.
If you look at science, science is always marked by such people who, who went against
some conventional wisdom, collected the knowledge at the time and assembled it in a way that
produced a powerful platform.
And you've been amazingly honest about in an inspiring way about things you've been wrong
about predicting and you've obviously been right about a lot of things.
But in this kind of tension, how do you balance as a company in predicting the next five years,
the impossible, planning for the impossible.
So listening to those crazy dreamers, letting them do, letting them run away and make the
impossible real, make it happen and slow, you know, that's how programmers often think
and slowing things down and saying, well, this is the rational, this is the possible,
the pragmatic, the, the dreamer versus the pragmatist.
So it's helpful to have a model which encourages a predictable revenue stream as well as the
ability to do new things.
So in Google's case, we're big enough and well enough managed and so forth that we have
a pretty good sense of what our revenue will be for the next year or two, at least for
a while.
And so we have enough cash generation that we can make bets.
And indeed, Google has become alphabet so the corporation is organized around these
bets.
And these bets are in areas of fundamental importance to, to the world, whether it's
digital intelligence, medical technology, self driving cars, connectivity through balloons,
on and on and on.
And there's more coming and more coming.
So one way you could express this is that the current business is successful enough
that we have the luxury of making bets.
And another one that you could say is that we have the, the wisdom of being able to see
that a corporate structure needs to be created to enhance the likelihood of the success of
those bets.
So we essentially turned ourselves into a conglomerate of bets.
And then this underlying corporation Google, which is itself innovative.
So in order to pull this off, you have to have a bunch of belief systems.
And one of them is that you have to have bottoms up and tops down the bottoms up.
We call 20% time.
And the idea is that people can spend 20% of the time on whatever they want.
And the top down is that our founders in particular have a keen eye on technology and they're
reviewing things constantly.
So an example would be they'll, they'll hear about an idea or I'll hear about something
and it sounds interesting.
Let's go visit them and then let's begin to assemble the pieces to see if that's possible.
And if you do this long enough, you get pretty good at predicting what's likely to work.
So that's, that's a beautiful balance that struck.
Is this something that applies at all scale?
So seems seems to be that the Sergei, again, 15 years ago, came up with a concept that
called 10% of the budget should be on things that are unrelated.
It was called 70, 20, 10, 70% of our time on core business, 20% on adjacent business
and 10% on other.
And he proved mathematically, of course, he's a brilliant mathematician, that you needed
that 10% right to make the sum of the growth work and it turns out he was right.
So getting into the world of artificial intelligence, you've, you've talked quite extensively and
effectively to the impact in the near term, the positive impact of artificial intelligence,
whether it's machine, especially machine learning in medical applications and education
and just making information more accessible, right in the AI community, there is a kind
of debate, so there's this shroud of uncertainty as we face this new world with artificial
intelligence in it.
And there is some people like Elon Musk, you've disagreed on at least on the degree of emphasis
he places on the existential threat of AI.
So I've spoken with Stuart Russell, Max Tagmark, who share Elon Musk's view, and Yoshio Benjio,
Steven Pinker, who do not.
And so there's a, there's a, there's a lot of very smart people who are thinking about
this stuff, disagreeing, which is really healthy, of course.
So what do you think is the healthiest way for the AI community to, and really for the
general public to think about AI and the concern of the technology being mismanaged
in some, in some kind of way.
So the source of education for the general public has been robot killer movies.
Right.
And Terminator, et cetera, and the one thing I can assure you we're not building are those
kinds of solutions.
Furthermore, if they were to show up, someone would notice and unplug them, right?
So as exciting as those movies are, and they're great movies, where the killer robots to start,
we would find a way to stop them, right?
So I'm not concerned about that.
And much of this has to do with the timeframe of conversation.
So you can imagine a situation a hundred years from now, when the human brain is fully understood,
and the next generation and next generation of brilliant MIT scientists have figured
all this out, we're going to have a large number of ethics questions, right, around science
and thinking and robots and computers and so forth and so on.
So it depends on the question of the timeframe.
In the next five to 10 years, we're not facing those questions.
What we're facing in the next five to 10 years is how do we spread this disruptive technology
as broadly as possible to gain the maximum benefit of it?
The primary benefit should be in healthcare and in education, healthcare because it's
obvious.
We're all the same, even though we somehow believe we're not.
As a medical matter, the fact that we have big data about our health will save lives,
allow us to deal with skin cancer and other cancers, ophthalmological problems.
There's people working on psychological diseases and so forth using these techniques.
I go on and on.
The promise of AI in medicine is extraordinary.
There are many, many companies and startups and funds and solutions and we will all live
much better for that.
The same argument in education.
Can you imagine that for each generation of child and even adult, you have a tutor educator
that's AI based, that's not a human but is properly trained, that helps you get smarter,
helps you address your language difficulties or your math difficulties or what have you.
Why don't we focus on those two?
The gains societally of making humans smarter and healthier are enormous.
Those translate for decades and decades and will all benefit from them.
There are people who are working on AI safety, which is the issue that you're describing.
There are conversations in the community that should there be such problems, what should
the rules be like?
Google, for example, has announced its policies with respect to AI safety, which I certainly
support and I think most everybody would support and they make sense.
It helps guide the research but the killer robots are not arriving this year and they're
not even being built.
On that line of thinking, you said the timescale, in this topic or other topics, have you found
a useful, on the business side or the intellectual side, to think beyond 5, 10 years, to think
50 years out?
Has it ever been useful or productive?
In our industry, there are essentially no examples of 50 year predictions that have been correct.
Let's review AI, which was largely invented here at MIT and a couple of other universities
in 1956, 1957, 1958.
The original claims were a decade or two.
When I was a PhD student, I studied AI a bit and it entered during my looking at it a period
which is known as AI winter, which went on for about 30 years, which is a whole generation
of scientists and a whole group of people who didn't make a lot of progress because the
algorithms had not improved and the computers had not improved.
It took some brilliant mathematicians, starting with a fellow named Jeff Hinton at Toronto
in Montreal, who basically invented this deep learning model which empowers us today.
The seminal work there was 20 years ago and in the last 10 years, it's become popularized.
Think about the time frames for that level of discovery.
It's very hard to predict.
Many people think that we'll be flying around in the equivalent of flying cars.
Who knows?
My own view, if I want to go out on a limb, is to say that we know a couple of things
about 50 years from now.
We know that there'll be more people alive.
We know that we'll have to have platforms that are more sustainable because the earth
is limited in the ways we all know and that the kind of platforms that are going to get
billed will be consistent with the principles that I've described.
They will be much more empowering of individuals, they'll be much more sensitive to the ecology
because they have to be, they just have to be.
I also think that humans are going to be a great deal smarter and I think they're going
to be a lot smarter because of the tools that I've discussed with you and of course people
will live longer.
Life extension is continuing apace.
A baby born today has a reasonable chance of living to 100, which is pretty exciting.
It's well past the 21st century, so we better take care of them.
You mentioned interesting statistic on some very large percentage, 60, 70% of people may
live in cities.
Today more than half the world lives in cities and one of the great stories of humanity in
the last 20 years has been the rural to urban migration.
This has occurred in the United States, it's occurred in Europe, it's occurring in Asia
and it's occurring in Africa.
When people move to cities, the cities get more crowded, but believe it or not their health
gets better, their productivity gets better, their IQ and educational capabilities improve,
so it's good news that people are moving to cities, but we have to make them livable
and safe.
So you, first of all, you are, but you've also worked with some of the greatest leaders
in the history of tech.
What insights do you draw from the difference in leadership styles of yourself, Steve Jobs,
Elon Musk, Larry Page, now the new CEO, Sandra Pichai and others from the, I would say, calm
sages to the mad geniuses?
One of the things that I learned as a young executive is that there's no single formula
for leadership.
They try to teach one, but that's not how it really works.
There are people who just understand what they need to do and they need to do it quickly.
Those people are often entrepreneurs.
They just know and they move fast.
There are other people who are systems thinkers and planners, that's more who I am, somewhat
more conservative, more thorough in execution, a little bit more risk averse.
There's also people who are sort of slightly insane, right, in the sense that they are
emphatic and charismatic and they feel it and they drive it and so forth.
There's no single formula to success.
There is one thing that unifies all of the people that you named, which is very high
intelligence, at the end of the day, the thing that characterizes all of them is that they
saw the world quicker, faster, they processed information faster, they didn't necessarily
make the right decisions all the time, but they were on top of it.
The other thing that's interesting about all those people is they all started young.
Think about Steve Jobs starting Apple roughly at 18 or 19.
Think about Bill Gates starting at roughly 2021.
Think about by the time they were 30, Mark Zuckerberg, a more good example at 1920.
By the time they were 30, they had 10 years, at 30 years old, they had 10 years of experience
of dealing with people and products and shipments and the press and business and so forth.
It's incredible how much experience they had compared to the rest of us who were busy getting
our PhDs.
Yes, exactly.
We should celebrate these people because they've just had more life experience and that helps
inform the judgment.
At the end of the day, when you're at the top of these organizations, all the easy questions
have been dealt with.
How should we design the buildings?
Where should we put the colors on our product?
What should the box look like?
The problems, that's why it's so interesting to be in these rooms, the problems that they
face in terms of the way they operate, the way they deal with their employees, their
customers, their innovation are profoundly challenging.
Each of the companies is demonstrably different culturally.
They are not, in fact, cut of the same.
They behave differently based on input, their internal cultures are different, their compensation
schemes are different, their values are different, so there's proof that diversity works.
So when faced with a tough decision, in need of advice, it's been said that the best thing
one can do is to find the best person in the world who can give that advice and find a
way to be in a room with them, one on one and ask.
So here we are, and let me ask in a long winded way, I wrote this down, in 1998 there were
many good search engines, Lycos, Excite, Altavista, Infoseek, AskJeeves maybe, Yahoo even.
So Google stepped in and disrupted everything, they disrupted the nature of search, the nature
of our access to information, the way we discover new knowledge.
So now it's 2018, actually 20 years later, there are many good personal AI assistants,
including of course the best from Google.
So you've spoken in medical and education, the impact of such an AI assistant could bring.
So we arrive at this question, so it's a personal one for me, but I hope my situation represents
that of many other, as we said, dreamers and the crazy engineers.
So my whole life, I've dreamed of creating such an AI assistant.
Every step I've taken has been towards that goal, now I'm a research scientist in Human
Senate AI here at MIT, so the next step for me as I sit here, facing my passion, is to
do what Larry and Sergey did in 1998, this simple start up.
And so here's my simple question, given the low odds of success, the timing and luck required,
the countless other factors that can't be controlled or predicted, which is all the
things that Larry and Sergey faced.
Is there some calculation, some strategy to follow in this step, or do you simply follow
the passion just because there's no other choice?
I think the people who are in universities are always trying to study the extraordinarily
chaotic nature of innovation and entrepreneurship.
My answer is that they didn't have that conversation, they just did it.
They sensed a moment when, in the case of Google, there was all of this data that needed
to be organized and they had a better algorithm, they had invented a better way.
So today with Human Senate AI, which is your area of research, there must be new approaches.
It's such a big field, there must be new approaches, different from what we and others are doing.
There must be startups to fund, there must be research projects to try, there must be
graduate students to work on new approaches.
Here at MIT, there are people who are looking at learning from the standpoint of looking
at child learning, how do children learn starting at each one?
And the work is fantastic.
Those approaches are different from the approach that most people are taking.
Perhaps that's a bet that you should make, or perhaps there's another one.
But at the end of the day, the successful entrepreneurs are not as crazy as they sound.
They see an opportunity based on what's happened.
Let's use Uber as an example.
As Travis sells the story, he and his cofounder were sitting in Paris and they had this idea
because they couldn't get a cab.
And they said, we have smartphones and the rest is history.
So what's the equivalent of that Travis Eiffel Tower, where is a cab moment that you could,
as an entrepreneur, take advantage of, whether it's in Human Senate AI or something else?
That's the next great start up.
And the psychology of that moment.
So when Sergey and Larry talk about, and listen to a few interviews, it's very nonchalant.
Well, here's the very fascinating web data.
And here's an algorithm we have for, you know, we just kind of want to play around with that
data.
And it seems like that's a really nice way to organize this data.
Well, I should say what happened, remember, is that they were graduate students at Stanford
and they thought this is interesting, so they built a search engine and they kept it in
their room.
And they had to get power from the room next door because they were using too much power
in the room.
So they ran an extension cord over.
And then they went and they found a house and they had Google World headquarters of
five people to start the company.
And they raised $100,000 from Andy Bechtelstein, who was the sun founder to do this, and Dave
Chariton and a few others.
The point is their beginnings were very simple, but they were based on a powerful insight.
That is a replicable model for any startup.
It has to be a powerful insight.
The beginnings are simple.
And there has to be an innovation.
In Larry and Sergey's case, it was PageRank, which was a brilliant idea, one of the most
cited papers in the world today.
What's the next one?
So you're one of, if I may say, richest people in the world, and yet it seems that money
is simply a side effect of your passions and not an inherent goal.
But it's a, you're a fascinating person to ask.
So much of our society at the individual level and at the company level and its nations is
driven by the desire for wealth.
What do you think about this drive?
And what have you learned about, if I may romanticize the notion, the meaning of life,
having achieved success on so many dimensions?
There have been many studies of human happiness and above some threshold, which is typically
relatively low for this conversation, there's no difference in happiness about money.
The happiness is correlated with meaning and purpose, a sense of family, a sense of impact.
So if you organize your life, assuming you have enough to get around and have a nice
home and so forth, you'll be far happier if you figure out what you care about and work
on that.
It's often being in service to others.
It's a great deal of evidence that people are happiest when they're serving others
and not themselves.
This goes directly against the sort of press induced excitement about powerful and wealthy
leaders of one kind and indeed, these are consequential people.
But if you are in a situation where you've been very fortunate as I have, you also have
to take that as a responsibility and you have to basically work both to educate others and
give them that opportunity, but also use that wealth to advance human society.
In my case, I'm particularly interested in using the tools of artificial intelligence
and machine learning to make society better.
I've mentioned education.
I've mentioned inequality and middle class and things like this, all of which are a passion
of mine.
It doesn't matter what you do.
It matters that you believe in it, that it's important to you and that your life will be
far more satisfying if you spend your life doing that.
I think there's no better place to end than a discussion of the meaning of life.
Eric, thank you so much.
Thank you very much.
Thank you.