vtt/episode_008_small.vtt

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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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 I am.

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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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 my name is Lex.

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 So thank you.

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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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 born.

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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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 would go?

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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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 did.

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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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 That's right.

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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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 everyone has.

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 And by the way, people have lots of problems, information, medicine, health, education,

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 and so forth.

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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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 take a decade.

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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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 five years.

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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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 in wireless?

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 It looks like there's an enormous expansion of wireless connectivity at many different

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 bands, right?

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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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 possible.

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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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 a while.

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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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 bets.

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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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 on and on and on.

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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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 those bets.

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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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 We call 20% time.

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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 10% on other.

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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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 Right.

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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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 obvious.

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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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 I go on and on.

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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

19:37.920 --> 19:39.480
 50 years out?

19:39.480 --> 19:42.000
 Has it ever been useful or productive?

19:42.000 --> 19:49.040
 In our industry, there are essentially no examples of 50 year predictions that have been correct.

19:49.040 --> 19:54.320
 Let's review AI, which was largely invented here at MIT and a couple of other universities

19:54.320 --> 19:57.840
 in 1956, 1957, 1958.

19:57.840 --> 20:01.800
 The original claims were a decade or two.

20:01.800 --> 20:08.040
 When I was a PhD student, I studied AI a bit and it entered during my looking at it a period

20:08.040 --> 20:13.880
 which is known as AI winter, which went on for about 30 years, which is a whole generation

20:13.880 --> 20:18.800
 of scientists and a whole group of people who didn't make a lot of progress because the

20:18.800 --> 20:22.160
 algorithms had not improved and the computers had not improved.

20:22.160 --> 20:26.160
 It took some brilliant mathematicians, starting with a fellow named Jeff Hinton at Toronto

20:26.160 --> 20:33.120
 in Montreal, who basically invented this deep learning model which empowers us today.

20:33.120 --> 20:40.400
 The seminal work there was 20 years ago and in the last 10 years, it's become popularized.

20:40.400 --> 20:43.520
 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.

20:50.240 --> 20:51.240
 Who knows?

20:51.240 --> 20:56.680
 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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 will live longer.

21:29.320 --> 21:32.080
 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.

21:40.760 --> 21:46.160
 You mentioned interesting statistic on some very large percentage, 60, 70% of people may

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 live in cities.

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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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 and safe.

22:22.800 --> 22:28.240
 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.

22:30.180 --> 22:37.080
 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?

22:49.600 --> 22:53.880
 One of the things that I learned as a young executive is that there's no single formula

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 for leadership.

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 They try to teach one, but that's not how it really works.

23:00.080 --> 23:04.360
 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.

23:06.800 --> 23:09.080
 They just know and they move fast.

23:09.080 --> 23:13.400
 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.

23:18.760 --> 23:24.120
 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.

23:29.040 --> 23:31.440
 There's no single formula to success.

23:31.440 --> 23:35.320
 There is one thing that unifies all of the people that you named, which is very high

23:35.320 --> 23:41.240
 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

23:46.360 --> 23:50.160
 make the right decisions all the time, but they were on top of it.

23:50.160 --> 23:54.600
 The other thing that's interesting about all those people is they all started young.

23:54.600 --> 23:58.560
 Think about Steve Jobs starting Apple roughly at 18 or 19.

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 Think about Bill Gates starting at roughly 2021.

24:01.840 --> 24:07.040
 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.

24:19.920 --> 24:24.480
 It's incredible how much experience they had compared to the rest of us who were busy getting

24:24.480 --> 24:25.480
 our PhDs.

24:25.480 --> 24:26.480
 Yes, exactly.

24:26.480 --> 24:32.760
 We should celebrate these people because they've just had more life experience and that helps

24:32.760 --> 24:34.520
 inform the judgment.

24:34.520 --> 24:41.360
 At the end of the day, when you're at the top of these organizations, all the easy questions

24:41.360 --> 24:43.680
 have been dealt with.

24:43.680 --> 24:45.840
 How should we design the buildings?

24:45.840 --> 24:48.400
 Where should we put the colors on our product?

24:48.400 --> 24:51.440
 What should the box look like?

24:51.440 --> 24:55.520
 The problems, that's why it's so interesting to be in these rooms, the problems that they

24:55.520 --> 25:00.200
 face in terms of the way they operate, the way they deal with their employees, their

25:00.200 --> 25:04.160
 customers, their innovation are profoundly challenging.

25:04.160 --> 25:09.360
 Each of the companies is demonstrably different culturally.

25:09.360 --> 25:11.800
 They are not, in fact, cut of the same.

25:11.800 --> 25:16.680
 They behave differently based on input, their internal cultures are different, their compensation

25:16.680 --> 25:24.920
 schemes are different, their values are different, so there's proof that diversity works.

25:24.920 --> 25:33.440
 So when faced with a tough decision, in need of advice, it's been said that the best thing

25:33.440 --> 25:39.840
 one can do is to find the best person in the world who can give that advice and find a

25:39.840 --> 25:44.880
 way to be in a room with them, one on one and ask.

25:44.880 --> 25:51.920
 So here we are, and let me ask in a long winded way, I wrote this down, in 1998 there were

25:51.920 --> 26:01.960
 many good search engines, Lycos, Excite, Altavista, Infoseek, AskJeeves maybe, Yahoo even.

26:01.960 --> 26:07.040
 So Google stepped in and disrupted everything, they disrupted the nature of search, the nature

26:07.040 --> 26:12.040
 of our access to information, the way we discover new knowledge.

26:12.040 --> 26:19.120
 So now it's 2018, actually 20 years later, there are many good personal AI assistants,

26:19.120 --> 26:22.360
 including of course the best from Google.

26:22.360 --> 26:28.720
 So you've spoken in medical and education, the impact of such an AI assistant could bring.

26:28.720 --> 26:34.920
 So we arrive at this question, so it's a personal one for me, but I hope my situation represents

26:34.920 --> 26:41.200
 that of many other, as we said, dreamers and the crazy engineers.

26:41.200 --> 26:46.680
 So my whole life, I've dreamed of creating such an AI assistant.

26:46.680 --> 26:50.800
 Every step I've taken has been towards that goal, now I'm a research scientist in Human

26:50.800 --> 26:58.920
 Senate AI here at MIT, so the next step for me as I sit here, facing my passion, is to

26:58.920 --> 27:04.880
 do what Larry and Sergey did in 1998, this simple start up.

27:04.880 --> 27:10.640
 And so here's my simple question, given the low odds of success, the timing and luck required,

27:10.640 --> 27:14.280
 the countless other factors that can't be controlled or predicted, which is all the

27:14.280 --> 27:16.560
 things that Larry and Sergey faced.

27:16.560 --> 27:23.080
 Is there some calculation, some strategy to follow in this step, or do you simply follow

27:23.080 --> 27:26.560
 the passion just because there's no other choice?

27:26.560 --> 27:32.880
 I think the people who are in universities are always trying to study the extraordinarily

27:32.880 --> 27:37.360
 chaotic nature of innovation and entrepreneurship.

27:37.360 --> 27:42.880
 My answer is that they didn't have that conversation, they just did it.

27:42.880 --> 27:48.840
 They sensed a moment when, in the case of Google, there was all of this data that needed

27:48.840 --> 27:53.940
 to be organized and they had a better algorithm, they had invented a better way.

27:53.940 --> 28:01.040
 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

28:12.320 --> 28:15.200
 graduate students to work on new approaches.

28:15.200 --> 28:19.120
 Here at MIT, there are people who are looking at learning from the standpoint of looking

28:19.120 --> 28:23.840
 at child learning, how do children learn starting at each one?

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 And the work is fantastic.

28:25.560 --> 28:30.120
 Those approaches are different from the approach that most people are taking.

28:30.120 --> 28:33.980
 Perhaps that's a bet that you should make, or perhaps there's another one.

28:33.980 --> 28:40.200
 But at the end of the day, the successful entrepreneurs are not as crazy as they sound.

28:40.200 --> 28:43.200
 They see an opportunity based on what's happened.

28:43.200 --> 28:45.400
 Let's use Uber as an example.

28:45.400 --> 28:49.840
 As Travis sells the story, he and his cofounder were sitting in Paris and they had this idea

28:49.840 --> 28:52.160
 because they couldn't get a cab.

28:52.160 --> 28:56.800
 And they said, we have smartphones and the rest is history.

28:56.800 --> 29:04.040
 So what's the equivalent of that Travis Eiffel Tower, where is a cab moment that you could,

29:04.040 --> 29:08.800
 as an entrepreneur, take advantage of, whether it's in Human Senate AI or something else?

29:08.800 --> 29:11.480
 That's the next great start up.

29:11.480 --> 29:13.760
 And the psychology of that moment.

29:13.760 --> 29:20.120
 So when Sergey and Larry talk about, and listen to a few interviews, it's very nonchalant.

29:20.120 --> 29:25.280
 Well, here's the very fascinating web data.

29:25.280 --> 29:29.080
 And here's an algorithm we have for, you know, we just kind of want to play around with that

29:29.080 --> 29:30.080
 data.

29:30.080 --> 29:32.520
 And it seems like that's a really nice way to organize this data.

29:32.520 --> 29:38.000
 Well, I should say what happened, remember, is that they were graduate students at Stanford

29:38.000 --> 29:41.320
 and they thought this is interesting, so they built a search engine and they kept it in

29:41.320 --> 29:43.400
 their room.

29:43.400 --> 29:47.520
 And they had to get power from the room next door because they were using too much power

29:47.520 --> 29:48.520
 in the room.

29:48.520 --> 29:51.640
 So they ran an extension cord over.

29:51.640 --> 29:55.360
 And then they went and they found a house and they had Google World headquarters of

29:55.360 --> 29:57.600
 five people to start the company.

29:57.600 --> 30:02.560
 And they raised $100,000 from Andy Bechtelstein, who was the sun founder to do this, and Dave

30:02.560 --> 30:04.520
 Chariton and a few others.

30:04.520 --> 30:11.960
 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.

30:14.320 --> 30:16.520
 It has to be a powerful insight.

30:16.520 --> 30:17.680
 The beginnings are simple.

30:17.680 --> 30:19.960
 And there has to be an innovation.

30:19.960 --> 30:24.280
 In Larry and Sergey's case, it was PageRank, which was a brilliant idea, one of the most

30:24.280 --> 30:26.880
 cited papers in the world today.

30:26.880 --> 30:29.880
 What's the next one?

30:29.880 --> 30:37.280
 So you're one of, if I may say, richest people in the world, and yet it seems that money

30:37.280 --> 30:43.200
 is simply a side effect of your passions and not an inherent goal.

30:43.200 --> 30:48.360
 But it's a, you're a fascinating person to ask.

30:48.360 --> 30:55.080
 So much of our society at the individual level and at the company level and its nations is

30:55.080 --> 30:58.920
 driven by the desire for wealth.

30:58.920 --> 31:01.280
 What do you think about this drive?

31:01.280 --> 31:07.000
 And what have you learned about, if I may romanticize the notion, the meaning of life,

31:07.000 --> 31:10.520
 having achieved success on so many dimensions?

31:10.520 --> 31:16.960
 There have been many studies of human happiness and above some threshold, which is typically

31:16.960 --> 31:23.600
 relatively low for this conversation, there's no difference in happiness about money.

31:23.600 --> 31:30.120
 The happiness is correlated with meaning and purpose, a sense of family, a sense of impact.

31:30.120 --> 31:34.440
 So if you organize your life, assuming you have enough to get around and have a nice

31:34.440 --> 31:40.400
 home and so forth, you'll be far happier if you figure out what you care about and work

31:40.400 --> 31:41.800
 on that.

31:41.800 --> 31:44.120
 It's often being in service to others.

31:44.120 --> 31:47.840
 It's a great deal of evidence that people are happiest when they're serving others

31:47.840 --> 31:49.640
 and not themselves.

31:49.640 --> 31:57.480
 This goes directly against the sort of press induced excitement about powerful and wealthy

31:57.480 --> 32:01.840
 leaders of one kind and indeed, these are consequential people.

32:01.840 --> 32:06.720
 But if you are in a situation where you've been very fortunate as I have, you also have

32:06.720 --> 32:12.160
 to take that as a responsibility and you have to basically work both to educate others and

32:12.160 --> 32:16.760
 give them that opportunity, but also use that wealth to advance human society.

32:16.760 --> 32:20.440
 In my case, I'm particularly interested in using the tools of artificial intelligence

32:20.440 --> 32:22.800
 and machine learning to make society better.

32:22.800 --> 32:24.000
 I've mentioned education.

32:24.000 --> 32:29.040
 I've mentioned inequality and middle class and things like this, all of which are a passion

32:29.040 --> 32:30.160
 of mine.

32:30.160 --> 32:31.920
 It doesn't matter what you do.

32:31.920 --> 32:36.560
 It matters that you believe in it, that it's important to you and that your life will be

32:36.560 --> 32:40.480
 far more satisfying if you spend your life doing that.

32:40.480 --> 32:45.320
 I think there's no better place to end than a discussion of the meaning of life.

32:45.320 --> 32:46.320
 Eric, thank you so much.

32:46.320 --> 32:47.320
 Thank you very much.

32:47.320 --> 33:16.320
 Thank you.