back to indexEric Schmidt: Google | Lex Fridman Podcast #8
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The following is a conversation with Eric Schmidt.
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He was the CEO of Google for ten years and a chairman for six more, guiding the company
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through an incredible period of growth and a series of world changing innovations.
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He is one of the most impactful leaders in the era of the internet and the powerful voice
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for the promise of technology in our society.
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It was truly an honor to speak with him as part of the MIT course on artificial general
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intelligence and the artificial intelligence podcast.
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And now here's my conversation with Eric Schmidt.
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What was the first moment when you fell in love with technology?
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I grew up in the 1960s as a boy where every boy wanted to be an astronaut and part of
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the space program.
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So like everyone else of my age, we would go out to the cow pasture behind my house,
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which was literally a cow pasture, and we would shoot model rockets off.
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And that I think is the beginning.
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And of course, generationally, today, it would be video games and all the amazing things
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that you can do online with computers.
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There's a transformative, inspiring aspect of science and math that maybe rockets would
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bring, would instill in individuals.
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You've mentioned yesterday that eighth grade math is where the journey through Mathematical
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University diverges from many people.
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It's this fork in the roadway.
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There's a professor of math at Berkeley, Edward Franco.
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I'm not sure if you're familiar with him.
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He has written this amazing book.
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I recommend to everybody called Love and Math, two of my favorite words.
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He says that if painting was taught like math, then students would be asked to paint a fence,
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which is his analogy of essentially how math is taught, and you never get a chance to discover
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the beauty of the art of painting or the beauty of the art of math.
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So how, when, and where did you discover that beauty?
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I think what happens with people like myself is that your math enabled pretty early, and
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all of a sudden you discover that you can use that to discover new insights.
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The great scientists will all tell a story, the men and women who are fantastic today,
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that somewhere when they were in high school or in college, they discovered that they could
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discover something themselves, and that sense of building something, of having an impact
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that you own drives knowledge, acquisition, and learning.
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In my case, it was programming, and the notion that I could build things that had not existed
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that I had built, that it had my name on it, and this was before open source, but you could
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think of it as open source contributions.
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So today, if I were a 16 or 17 year old boy, I'm sure that I would aspire as a computer
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scientist to make a contribution like the open source heroes of the world today.
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That would be what would be driving me, and I'd be trying and learning and making mistakes,
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and so forth, in the ways that it works.
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The repository that GitHub represents and that open source libraries represent is an
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enormous bank of knowledge of all of the people who are doing that, and one of the lessons
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that I learned at Google was that the world is a very big place, and there's an awful
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lot of smart people, and an awful lot of them are underutilized.
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So here's an opportunity, for example, building parts of programs, building new ideas to contribute
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to the greater of society.
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So in that moment in the 70s, the inspiring moment where there was nothing, and then you
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created something through programming, that magical moment.
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So in 1975, I think you've created a program called Lex, which I especially like because
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Thank you for creating a brand that established a reputation that's long lasting reliable
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and has a big impact on the world and still used today.
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So thank you for that.
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But more seriously, in that time, in the 70s, as an engineer, personal computers were being
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Do you think you'd be able to predict the 80s, 90s, and the aughts of where computers
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I'm sure I could not and would not have gotten it right.
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I was the beneficiary of the great work of many, many people who saw it clearer than I
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With Lex, I worked with a fellow named Michael Lesk, who was my supervisor, and he essentially
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helped me architect and deliver a system that's still in use today.
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After that, I worked at Xerox Palo Alto Research Center, where the Alto was invented, and the
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Alto is the predecessor of the modern personal computer, or Macintosh, and so forth.
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And the altos were very rare, and I had to drive an hour from Berkeley to go use them,
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but I made a point of skipping classes and doing whatever it took to have access to this
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extraordinary achievement.
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I knew that they were consequential.
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What I did not understand was scaling.
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I did not understand what would happen when you had 100 million as opposed to 100.
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And so since then, and I have learned the benefit of scale, I always look for things
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which are going to scale to platforms, so mobile phones, Android, all those things.
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There are many, many people in the world.
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People really have needs.
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They really will use these platforms, and you can build big businesses on top of them.
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So it's interesting.
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So when you see a piece of technology, now you think, what will this technology look
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like when it's in the hands of a billion people?
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So an example would be that the market is so competitive now that if you can't figure
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out a way for something to have a million users or a billion users, it probably is not
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going to be successful because something else will become the general platform, and your
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idea will become a lost idea or a specialized service with relatively few users.
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So it's a path to generality.
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It's a path to general platform use.
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It's a path to broad applicability.
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Now, there are plenty of good businesses that are tiny, so luxury goods, for example.
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But if you want to have an impact at scale, you have to look for things which are of
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common value, common pricing, common distribution, and solve common problems, the problems that
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And by the way, people have lots of problems, information, medicine, health, education,
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Work on those problems.
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Like you said, you're a big fan of the middle class because there's so many of them by definition.
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So any product, any thing that has a huge impact, it improves their lives is a great
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business decision and it's just good for society.
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And there's nothing wrong with starting off in the high end as long as you have a plan
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to get to the middle class.
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There's nothing wrong with starting with a specialized market in order to learn and to
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build and to fund things.
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So you start a luxury market to build a general purpose market.
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But if you define yourself as only a narrow market, someone else can come along with a
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general purpose market that can push you to the corner, can restrict the scale of operation,
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can force you to be a lesser impact than you might be.
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So it's very important to think in terms of broad businesses and broad impact, even if
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you start in a little corner somewhere.
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So as you look to the 70s, but also in the decades to come, and you saw computers, did
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you see them as tools or was there a little element of another entity?
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I remember a quote saying AI began with our dream to create the gods.
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Is there a feeling when you wrote that program that you were creating another entity, giving
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life to something?
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I wish I could say otherwise, but I simply found the technology platforms so exciting.
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That's what I was focused on.
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I think the majority of the people that I've worked with, and there are a few exceptions,
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Steve Jobs being an example, really saw this as a great technological play.
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I think relatively few of the technical people understood the scale of its impact.
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So I used NCP, which is a predecessor to TCPIP.
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It just made sense to connect things.
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We didn't think of it in terms of the internet, and then companies, and then Facebook, and
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then Twitter, and then politics, and so forth.
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We never did that build.
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We didn't have that vision.
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And I think most people, it's a rare person who can see compounding at scale.
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Most people can see, if you ask people to predict the future, they'll say, they'll give
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you an answer of six to nine months or 12 months.
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Because that's about as far as people can imagine.
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But there's an old saying, which actually was attributed to a professor at MIT a long
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time ago, that we overestimate what can be done in one year, and we underestimate what
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can be done in a decade.
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And there's a great deal of evidence that these core platforms at hardware and software
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So think about self driving cars.
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Self driving cars were thought about in the 90s.
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Over projects around them, the first DARPA Durand Challenge was roughly 2004.
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So that's roughly 15 years ago.
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And today we have self driving cars operating in a city in Arizona, right, so 15 years.
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And we still have a ways to go before they're more generally available.
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So you've spoken about the importance.
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You just talked about predicting into the future.
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You've spoken about the importance of thinking five years ahead and having a plan for those
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And the way to say it is that almost everybody has a one year plan.
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Almost no one has a proper five year plan.
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And the key thing to having a five year plan is having a model for what's going to happen
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under the underlying platforms.
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So here's an example.
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Computer Moore's Law, as we know it, the thing that powered improvements in CPUs has largely
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halted in its traditional shrinking mechanism, because the costs have just gotten so high.
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It's getting harder and harder.
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But there's plenty of algorithmic improvements and specialized hardware improvements.
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So you need to understand the nature of those improvements and where they'll go in order
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to understand how it will change the platform.
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In the area of network connectivity, what are the gains that are going to be possible
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It looks like there's an enormous expansion of wireless connectivity at many different
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And that we will primarily, historically, I've always thought that we were primarily
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going to be using fiber, but now it looks like we're going to be using fiber plus very
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powerful high bandwidth sort of short distance connectivity to bridge the last mile, right?
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That's an amazing achievement.
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If you know that, then you're going to build your systems differently.
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By the way, those networks have different latency properties, right?
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Because they're more symmetric, the algorithms feel faster for that reason.
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And so when you think about whether it's a fiber or just technologies in general.
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So there's this barber, wooden poem or quote that I really like.
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It's from the champions of the impossible rather than the slaves of the possible that
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evolution draws its creative force.
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So in predicting the next five years, I'd like to talk about the impossible and the
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Well, and again, one of the great things about humanity is that we produce dreamers, right?
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We literally have people who have a vision and a dream.
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They are, if you will, disagreeable in the sense that they disagree with the, they disagree
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with what the sort of zeitgeist is.
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They say there is another way.
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They have a belief.
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They have a vision.
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If you look at science, science is always marked by such people who, who went against
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some conventional wisdom, collected the knowledge at the time and assembled it in a way that
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produced a powerful platform.
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And you've been amazingly honest about in an inspiring way about things you've been wrong
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about predicting and you've obviously been right about a lot of things.
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But in this kind of tension, how do you balance as a company in predicting the next five years,
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the impossible, planning for the impossible.
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So listening to those crazy dreamers, letting them do, letting them run away and make the
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impossible real, make it happen and slow, you know, that's how programmers often think
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and slowing things down and saying, well, this is the rational, this is the possible,
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the pragmatic, the, the dreamer versus the pragmatist.
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So it's helpful to have a model which encourages a predictable revenue stream as well as the
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ability to do new things.
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So in Google's case, we're big enough and well enough managed and so forth that we have
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a pretty good sense of what our revenue will be for the next year or two, at least for
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And so we have enough cash generation that we can make bets.
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And indeed, Google has become alphabet so the corporation is organized around these
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And these bets are in areas of fundamental importance to, to the world, whether it's
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digital intelligence, medical technology, self driving cars, connectivity through balloons,
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And there's more coming and more coming.
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So one way you could express this is that the current business is successful enough
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that we have the luxury of making bets.
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And another one that you could say is that we have the, the wisdom of being able to see
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that a corporate structure needs to be created to enhance the likelihood of the success of
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So we essentially turned ourselves into a conglomerate of bets.
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And then this underlying corporation Google, which is itself innovative.
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So in order to pull this off, you have to have a bunch of belief systems.
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And one of them is that you have to have bottoms up and tops down the bottoms up.
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And the idea is that people can spend 20% of the time on whatever they want.
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And the top down is that our founders in particular have a keen eye on technology and they're
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reviewing things constantly.
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So an example would be they'll, they'll hear about an idea or I'll hear about something
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and it sounds interesting.
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Let's go visit them and then let's begin to assemble the pieces to see if that's possible.
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And if you do this long enough, you get pretty good at predicting what's likely to work.
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So that's, that's a beautiful balance that struck.
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Is this something that applies at all scale?
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So seems seems to be that the Sergei, again, 15 years ago, came up with a concept that
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called 10% of the budget should be on things that are unrelated.
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It was called 70, 20, 10, 70% of our time on core business, 20% on adjacent business
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And he proved mathematically, of course, he's a brilliant mathematician, that you needed
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that 10% right to make the sum of the growth work and it turns out he was right.
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So getting into the world of artificial intelligence, you've, you've talked quite extensively and
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effectively to the impact in the near term, the positive impact of artificial intelligence,
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whether it's machine, especially machine learning in medical applications and education
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and just making information more accessible, right in the AI community, there is a kind
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of debate, so there's this shroud of uncertainty as we face this new world with artificial
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intelligence in it.
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And there is some people like Elon Musk, you've disagreed on at least on the degree of emphasis
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he places on the existential threat of AI.
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So I've spoken with Stuart Russell, Max Tagmark, who share Elon Musk's view, and Yoshio Benjio,
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Steven Pinker, who do not.
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And so there's a, there's a, there's a lot of very smart people who are thinking about
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this stuff, disagreeing, which is really healthy, of course.
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So what do you think is the healthiest way for the AI community to, and really for the
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general public to think about AI and the concern of the technology being mismanaged
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in some, in some kind of way.
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So the source of education for the general public has been robot killer movies.
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And Terminator, et cetera, and the one thing I can assure you we're not building are those
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kinds of solutions.
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Furthermore, if they were to show up, someone would notice and unplug them, right?
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So as exciting as those movies are, and they're great movies, where the killer robots to start,
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we would find a way to stop them, right?
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So I'm not concerned about that.
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And much of this has to do with the timeframe of conversation.
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So you can imagine a situation a hundred years from now, when the human brain is fully understood,
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and the next generation and next generation of brilliant MIT scientists have figured
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all this out, we're going to have a large number of ethics questions, right, around science
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and thinking and robots and computers and so forth and so on.
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So it depends on the question of the timeframe.
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In the next five to 10 years, we're not facing those questions.
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What we're facing in the next five to 10 years is how do we spread this disruptive technology
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as broadly as possible to gain the maximum benefit of it?
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The primary benefit should be in healthcare and in education, healthcare because it's
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We're all the same, even though we somehow believe we're not.
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As a medical matter, the fact that we have big data about our health will save lives,
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allow us to deal with skin cancer and other cancers, ophthalmological problems.
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There's people working on psychological diseases and so forth using these techniques.
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The promise of AI in medicine is extraordinary.
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There are many, many companies and startups and funds and solutions and we will all live
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much better for that.
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The same argument in education.
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Can you imagine that for each generation of child and even adult, you have a tutor educator
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that's AI based, that's not a human but is properly trained, that helps you get smarter,
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helps you address your language difficulties or your math difficulties or what have you.
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Why don't we focus on those two?
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The gains societally of making humans smarter and healthier are enormous.
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Those translate for decades and decades and will all benefit from them.
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There are people who are working on AI safety, which is the issue that you're describing.
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There are conversations in the community that should there be such problems, what should
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the rules be like?
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Google, for example, has announced its policies with respect to AI safety, which I certainly
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support and I think most everybody would support and they make sense.
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It helps guide the research but the killer robots are not arriving this year and they're
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not even being built.
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On that line of thinking, you said the timescale, in this topic or other topics, have you found
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a useful, on the business side or the intellectual side, to think beyond 5, 10 years, to think
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Has it ever been useful or productive?
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In our industry, there are essentially no examples of 50 year predictions that have been correct.
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Let's review AI, which was largely invented here at MIT and a couple of other universities
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in 1956, 1957, 1958.
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The original claims were a decade or two.
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When I was a PhD student, I studied AI a bit and it entered during my looking at it a period
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which is known as AI winter, which went on for about 30 years, which is a whole generation
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of scientists and a whole group of people who didn't make a lot of progress because the
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algorithms had not improved and the computers had not improved.
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It took some brilliant mathematicians, starting with a fellow named Jeff Hinton at Toronto
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in Montreal, who basically invented this deep learning model which empowers us today.
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The seminal work there was 20 years ago and in the last 10 years, it's become popularized.
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Think about the time frames for that level of discovery.
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It's very hard to predict.
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Many people think that we'll be flying around in the equivalent of flying cars.
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My own view, if I want to go out on a limb, is to say that we know a couple of things
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about 50 years from now.
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We know that there'll be more people alive.
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We know that we'll have to have platforms that are more sustainable because the earth
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is limited in the ways we all know and that the kind of platforms that are going to get
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billed will be consistent with the principles that I've described.
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They will be much more empowering of individuals, they'll be much more sensitive to the ecology
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because they have to be, they just have to be.
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I also think that humans are going to be a great deal smarter and I think they're going
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to be a lot smarter because of the tools that I've discussed with you and of course people
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Life extension is continuing apace.
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A baby born today has a reasonable chance of living to 100, which is pretty exciting.
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It's well past the 21st century, so we better take care of them.
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You mentioned interesting statistic on some very large percentage, 60, 70% of people may
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Today more than half the world lives in cities and one of the great stories of humanity in
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the last 20 years has been the rural to urban migration.
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This has occurred in the United States, it's occurred in Europe, it's occurring in Asia
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and it's occurring in Africa.
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When people move to cities, the cities get more crowded, but believe it or not their health
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gets better, their productivity gets better, their IQ and educational capabilities improve,
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so it's good news that people are moving to cities, but we have to make them livable
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So you, first of all, you are, but you've also worked with some of the greatest leaders
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in the history of tech.
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What insights do you draw from the difference in leadership styles of yourself, Steve Jobs,
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Elon Musk, Larry Page, now the new CEO, Sandra Pichai and others from the, I would say, calm
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sages to the mad geniuses?
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One of the things that I learned as a young executive is that there's no single formula
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They try to teach one, but that's not how it really works.
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There are people who just understand what they need to do and they need to do it quickly.
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Those people are often entrepreneurs.
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They just know and they move fast.
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There are other people who are systems thinkers and planners, that's more who I am, somewhat
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more conservative, more thorough in execution, a little bit more risk averse.
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There's also people who are sort of slightly insane, right, in the sense that they are
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emphatic and charismatic and they feel it and they drive it and so forth.
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There's no single formula to success.
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There is one thing that unifies all of the people that you named, which is very high
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intelligence, at the end of the day, the thing that characterizes all of them is that they
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saw the world quicker, faster, they processed information faster, they didn't necessarily
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make the right decisions all the time, but they were on top of it.
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The other thing that's interesting about all those people is they all started young.
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Think about Steve Jobs starting Apple roughly at 18 or 19.
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Think about Bill Gates starting at roughly 2021.
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Think about by the time they were 30, Mark Zuckerberg, a more good example at 1920.
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By the time they were 30, they had 10 years, at 30 years old, they had 10 years of experience
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of dealing with people and products and shipments and the press and business and so forth.
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It's incredible how much experience they had compared to the rest of us who were busy getting
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We should celebrate these people because they've just had more life experience and that helps
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inform the judgment.
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At the end of the day, when you're at the top of these organizations, all the easy questions
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have been dealt with.
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How should we design the buildings?
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Where should we put the colors on our product?
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What should the box look like?
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The problems, that's why it's so interesting to be in these rooms, the problems that they
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face in terms of the way they operate, the way they deal with their employees, their
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customers, their innovation are profoundly challenging.
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Each of the companies is demonstrably different culturally.
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They are not, in fact, cut of the same.
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They behave differently based on input, their internal cultures are different, their compensation
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schemes are different, their values are different, so there's proof that diversity works.
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So when faced with a tough decision, in need of advice, it's been said that the best thing
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one can do is to find the best person in the world who can give that advice and find a
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way to be in a room with them, one on one and ask.
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So here we are, and let me ask in a long winded way, I wrote this down, in 1998 there were
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many good search engines, Lycos, Excite, Altavista, Infoseek, AskJeeves maybe, Yahoo even.
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So Google stepped in and disrupted everything, they disrupted the nature of search, the nature
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of our access to information, the way we discover new knowledge.
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So now it's 2018, actually 20 years later, there are many good personal AI assistants,
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including of course the best from Google.
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So you've spoken in medical and education, the impact of such an AI assistant could bring.
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So we arrive at this question, so it's a personal one for me, but I hope my situation represents
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that of many other, as we said, dreamers and the crazy engineers.
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So my whole life, I've dreamed of creating such an AI assistant.
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Every step I've taken has been towards that goal, now I'm a research scientist in Human
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Senate AI here at MIT, so the next step for me as I sit here, facing my passion, is to
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do what Larry and Sergey did in 1998, this simple start up.
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And so here's my simple question, given the low odds of success, the timing and luck required,
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the countless other factors that can't be controlled or predicted, which is all the
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things that Larry and Sergey faced.
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Is there some calculation, some strategy to follow in this step, or do you simply follow
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the passion just because there's no other choice?
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I think the people who are in universities are always trying to study the extraordinarily
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chaotic nature of innovation and entrepreneurship.
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My answer is that they didn't have that conversation, they just did it.
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They sensed a moment when, in the case of Google, there was all of this data that needed
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to be organized and they had a better algorithm, they had invented a better way.
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So today with Human Senate AI, which is your area of research, there must be new approaches.
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It's such a big field, there must be new approaches, different from what we and others are doing.
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There must be startups to fund, there must be research projects to try, there must be
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graduate students to work on new approaches.
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Here at MIT, there are people who are looking at learning from the standpoint of looking
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at child learning, how do children learn starting at each one?
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And the work is fantastic.
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Those approaches are different from the approach that most people are taking.
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Perhaps that's a bet that you should make, or perhaps there's another one.
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But at the end of the day, the successful entrepreneurs are not as crazy as they sound.
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They see an opportunity based on what's happened.
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Let's use Uber as an example.
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As Travis sells the story, he and his cofounder were sitting in Paris and they had this idea
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because they couldn't get a cab.
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And they said, we have smartphones and the rest is history.
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So what's the equivalent of that Travis Eiffel Tower, where is a cab moment that you could,
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as an entrepreneur, take advantage of, whether it's in Human Senate AI or something else?
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That's the next great start up.
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And the psychology of that moment.
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So when Sergey and Larry talk about, and listen to a few interviews, it's very nonchalant.
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Well, here's the very fascinating web data.
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And here's an algorithm we have for, you know, we just kind of want to play around with that
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And it seems like that's a really nice way to organize this data.
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Well, I should say what happened, remember, is that they were graduate students at Stanford
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and they thought this is interesting, so they built a search engine and they kept it in
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And they had to get power from the room next door because they were using too much power
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So they ran an extension cord over.
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And then they went and they found a house and they had Google World headquarters of
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five people to start the company.
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And they raised $100,000 from Andy Bechtelstein, who was the sun founder to do this, and Dave
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Chariton and a few others.
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The point is their beginnings were very simple, but they were based on a powerful insight.
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That is a replicable model for any startup.
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It has to be a powerful insight.
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The beginnings are simple.
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And there has to be an innovation.
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In Larry and Sergey's case, it was PageRank, which was a brilliant idea, one of the most
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cited papers in the world today.
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What's the next one?
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So you're one of, if I may say, richest people in the world, and yet it seems that money
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is simply a side effect of your passions and not an inherent goal.
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But it's a, you're a fascinating person to ask.
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So much of our society at the individual level and at the company level and its nations is
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driven by the desire for wealth.
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What do you think about this drive?
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And what have you learned about, if I may romanticize the notion, the meaning of life,
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having achieved success on so many dimensions?
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There have been many studies of human happiness and above some threshold, which is typically
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relatively low for this conversation, there's no difference in happiness about money.
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The happiness is correlated with meaning and purpose, a sense of family, a sense of impact.
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So if you organize your life, assuming you have enough to get around and have a nice
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home and so forth, you'll be far happier if you figure out what you care about and work
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It's often being in service to others.
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It's a great deal of evidence that people are happiest when they're serving others
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and not themselves.
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This goes directly against the sort of press induced excitement about powerful and wealthy
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leaders of one kind and indeed, these are consequential people.
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But if you are in a situation where you've been very fortunate as I have, you also have
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to take that as a responsibility and you have to basically work both to educate others and
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give them that opportunity, but also use that wealth to advance human society.
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In my case, I'm particularly interested in using the tools of artificial intelligence
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and machine learning to make society better.
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I've mentioned education.
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I've mentioned inequality and middle class and things like this, all of which are a passion
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It doesn't matter what you do.
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It matters that you believe in it, that it's important to you and that your life will be
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far more satisfying if you spend your life doing that.
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I think there's no better place to end than a discussion of the meaning of life.
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Eric, thank you so much.
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Thank you very much.