Kind: captions
Language: en
the following is a conversation with
eric schmidt
he was the ceo of google for 10 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 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 um and
of course generationally
today it would be video games and all
the amazing things that you can do
online
uh with computers there's a
transformative inspiring
aspect of science and math that maybe
rockets would bring
wooden stone in individuals you've
mentioned yesterday
the 8th grade math is where the journey
through mathematical universe diverges
for many people
it's this fork in the roadway
there's a professor of math of berkeley
edward franco
he uh i'm not sure if you're familiar
with him i i am he has
written this amazing book i recommend to
everybody called love and math
two of my favorite uh words
he says that if if painting was taught
like math then the students would be
asked to paint a fence
which is his analogy of essentially how
math is taught
and you see 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 you're
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
the notion that i could build things
that had not existed
that i had built right that 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 tr making mistakes and so
forth in the ways
that it works the repository that
represent 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 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 uh but more
seriously
in that time in the 70s as an engineer
personal computers are being born
do you think you'd be able to predict
the 80s 90s and the odds
of where computers would go i'm sure i
could not and would not have gotten it
right
um 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 the since then
and i have learned the benefit of scale
i always look for things which are going
to scale to platforms
right so mobile phones android all those
things
there are the world is a numerous 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 uh you're a big fan of the
middle class
uh because there's so many of them
there's so many of them by definition
so any product any any thing
that has a huge impact improves their
lives is is a great business decision
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 uh
dream to create the gods
is there a feeling when you wrote that
program that you were creating another
entity
um 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 tcp
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 you know
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 6 to 9 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 right so think about
self-driving cars self-driving cars were
thought about in the 90s there were
projects around them the first darpa
duran 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 uh
into the future he's spoken about the
importance of uh
thinking five years ahead and having a
plan for those five years
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 mechanisms
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
uh sort of short distance connectivity
to
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 uh 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 uh in predicting the next five years
i'd like to talk about the impossible
and the possible
well and again and again one of the
great things about humanity
is that we produce dreamers right 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 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 uh 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
make the impossible real make it happen
and
slow you know that's how programmers
often think and slowing things down
and uh saying well this is the rational
this is the possible the pragmatic the
uh
the dreamer versus the pragmatist so so
it's helpful to have
a model which including 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 in bets are in areas of
fundamental importance to
to the world whether it's artificial
intelligence
medical technology self-driving cars uh
connectivity through balloons
uh on and on and on and there's more
coming and more coming so one way you
can 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 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 bottom's 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 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 it
seems seems to be um that
sergey again 15 years ago
came up with a concept called 10 10 of
the budget
should be on things that are unrelated
it was called 70 2010
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 percent 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 in education
and just making information more
accessible right
in the ai community there is a kind of
debate
uh so there's this shroud of uncertainty
as we face this new world with
artificial intelligence in it
and there is some people uh like elon
musk
you've disagreed and at least on the
degree of emphasis he places on the
existential threat of ai
so i've spoken with stuart russell max
tegmark who shared elon musk's view
and yoshio banjo stephen 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 uh
of course so what do you think is the
healthiest way for the ai community to
and and really for the general public
to think about ai and the concern
of the technology being mismanaged
uh in some in some kind of way so the
source of education for the general
public has been robot killer movies
right and terminator etc
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 in their
great movies
were the killer robots to to start we
would find a way to to stop them
right so i'm i'm not concerned about
that
and much of this has to do with the time
frame 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
time frame in the next
five to ten years we're not facing those
questions
what we're facing in the next five to
ten years is how do we spread this
disruptive
technology as broadly as possible to
gain the maximum benefit
of it the primary benefits should be in
healthcare and in education
healthcare because it's obvious we're
all the same even though we don't 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 get you know 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 right and
those translate for
decades and decades and we'll all
benefit from them
there are people who are working on ai
safety which is the issue that you're
describing
and 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
right so it helps guide the research but
the killer robots are not arriving this
year and they're not even being built
and and on that line of thinking you
said the time scale
in in in this topic or other topics have
you found a useful
uh on the business side or the
intellectual side to think beyond
five ten 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
um let's review ai right 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
and 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 science
scientists and a whole group of people
who didn't make a lot of progress
because the algorithms had not improved
and the computers did not approved
it took some brilliant mathematicians
starting with a fellow named jeff hinton
at toronto and montreal who basically
invented this deep learning model which
powers us today
those the seminal work there was 20
years ago
and in the last 10 years it's become
popularized
so 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 is limited you know
in the ways we all know
and that the kind of platforms that are
going to get built
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
that i've discussed with you and of
course people will live longer life
extension
is continuing a pace a baby born today
has a reasonable chance of living to a
hundred
right which is pretty exciting well past
the 21st century so we better take care
of them
and you mentioned an interesting
statistic on some very large
percentage 60 70 percent 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 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 pachai
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 right 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 and the other thing that's
interesting about all those people is
they all started young
so think about steve jobs started
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
a 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 are busy getting our
phds yes
exactly so we we should celebrate these
people because
they've just had more life experience
right 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 right
how should we design the buildings where
should we put the colors on our product
what should the box look like right
the problems that's why it's so
interesting to be in these rooms the
problems that they face right 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
right 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 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
alta vista infoseek ask jeeves 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 uh in 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
assistant every step i've taken has been
towards that goal
now i'm a research scientist in human
centered 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 98
uh this simple startup 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 the 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 centered 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 yes right
how do children learn starting at age
10 and others 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 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 tells the story he and his
co-founder 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-centered ai or something else
that's the next great startup and the
psychology of that moment
so when sergey and larry talk about it
and listen to a few interviews it's very
nonchalant well here's
here's the very fascinating web data
and uh 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
and i could say 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
right yeah and then they went and they
found a house and they had
google world headquarters of five people
right to start the company and they
raised a hundred thousand dollars from
andy bechtelsheim
who was the sun founder to do this and
dave cherton 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 in larry and sergey's case
it was pagerank which was a brilliant
idea one of the most cited papers in
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 as 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
well 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 it's 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 uh excitement about
powerful and wealthy leaders of one
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 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
you