Eric Schmidt: Google | Lex Fridman Podcast #8

1. 0:00 – 15:00

   The following is a…

   1. LFLex Fridman0:00

      The following is a conversation with Eric Schmidt. He was the CEO of Google for 10 years and the chairman for six more, guiding the company through an incredible period of growth and a series of world-changing innovations. He is one of the most impactful leaders in the era of the internet and a powerful voice for the promise of technology in our society. It was truly an honor to speak with him as part of the MIT course on Artificial General Intelligence and the Artificial Intelligence Podcast. And now, here's my conversation with Eric Schmidt. What was the first moment when you fell in love with technology?

   2. ESEric Schmidt0:40

      Um, I, I grew up in the 1960s as a boy where every boy wanted to be an astronaut and part of the space program. So like everyone else of my age, we would go out to the cow pasture behind my house, which was literally a cow pasture-

   3. LFLex Fridman0:55

      Mm-hmm.

   4. ESEric Schmidt0:56

      ... and we would shoot model rockets off.

   5. LFLex Fridman0:58

      (laughs)

   6. ESEric Schmidt0:58

      And that, I think, is the beginning. Um, and of course, generationally, today, it would be video games and all the amazing things that you can do online, uh, with computers.

   7. LFLex Fridman1:09

      There's a transformative inspiring aspect of science and math that maybe rockets would bring, would instill in individuals. You've mentioned yesterday that 8th grade math is where the journey through mathematical universe diverges for many people. It's this, uh, fork in the roadway. There's a professor of math at Berkeley, Edward Franco. He, uh... I'm not sure if you're familiar with him.

   8. ESEric Schmidt1:32

      I, I am.

   9. LFLex Fridman1:33

      He has, uh, written this amazing book I recommend to everybody called Love and Math, two of my favorite, uh, words. (laughs) Uh, h- he says that, uh, if, if painting was taught like math, then, uh, students would be asked to paint a fence, which is his analogy of essentially how math is taught, and so you never get a chance to discover the beauty of the art of painting or the beauty of the art of math. So h- how, when, and where did you discover that beauty?

   10. ESEric Schmidt2:05

       I, I think what happens with people like myself is that you're math-enabled pretty early.

   11. LFLex Fridman2:10

       Mm-hmm.

   12. ESEric Schmidt2:11

       And all of a sudden you discover that you can use that to discover new insights. The great scientists will all tell a story, the men and women who are fantastic today, that somewhere when they were in high school or in college they discovered that they could discover something themselves.

   13. LFLex Fridman2:27

       Mm-hmm.

   14. ESEric Schmidt2:27

       And that sense of building something, of having an impact that you own, drives knowledge acquisition and learning. In my case it was programming and th- the notion that I could build things that had not existed that I had built, right? That had my name on it. And this was before open source, but you could think of it as open-source contributions. So today, if I were a 16 or 17-year-old boy, I'm sure that I would aspire as a computer scientist to make a contribution like the open-source heroes of the world today. That would be what would be driving me, and I'd be trying and learning and tr- making mistakes and so forth in the ways that it works. The repository that represent... that GitHub represents and that open-source libraries represent is an enormous bank of knowledge of all of the people who are doing that. And one of the lessons that I learned at Google was that the world is a very big place and there's an awful lot of smart people.

   15. LFLex Fridman3:23

       Hmm.

   16. ESEric Schmidt3:23

       And an awful lot of them are underutilized. So here's an opportunity, for example, building parts of programs, building new ideas to contribute to the greater of society.

   17. LFLex Fridman3:36

       So in that moment in the '70s, the, the inspiring moment where there was nothing and then you created something through programming, that magical moment. Uh, so in 1975, I think, you've created a program called Lex, which I especially like because my name is Lex. So thank you. Thank you for creating a brand that established a reputation that's long-lasting, reliable, and has a big impact on the world and still used today. So thank you for that. Uh, but more seriously, i- i- in that time, in the '70s, as an engineer, personal computers were being born. Do you think you'd be able to predict the '80s, '90s and the aughts of where computers would go?

   18. ESEric Schmidt4:19

       I'm sure I could not and would not have gotten it right.

   19. LFLex Fridman4:22

       (laughs)

   20. ESEric Schmidt4:22

       Um, I was the beneficiary of the m- great work of many, many people who saw it clearer than I did. Um, with Lex, I worked with a fellow named Michael Lesk-

   21. LFLex Fridman4:32

       Mm-hmm.

   22. ESEric Schmidt4:32

       ... who was my supervisor, and he essentially helped me architect and deliver a system that's still in use today. After that, I worked at Xerox Palo Alto Research Center where the Alto was invented, and the Alto is the predecessor of the modern personal computer or Macintosh and so forth. And the Altos were very rare, and I had to drive an hour from Berkeley to go use them. But I made a point of skipping classes and doing whatever it took to have access to this extraordinary achievement. I knew that they were consequential. What I d- did not understand was scaling. I did not understand what would happen when you had 100 million as opposed to 100. And so the... Since then, and I have learned the benefit of scale, I always look for things which are going to scale to platforms, right? So mobile phones, Android, all those things. There are... The world is a numerous... There, there are many, many people in the world, people really have needs, they really will use these platforms and you can build big businesses on top of them.

   23. LFLex Fridman5:32

       So it's interesting, so when you see a piece of technology now you think, what will this technology look like when it's in the hands of a billion people?

   24. ESEric Schmidt5:39

       That's right. So, so an example would be that, um, the market is so competitive now that if you can't figure out a way for something to have a million users or a billion users, it probably is not gonna be successful because something else will become the general platform and your idea will become, uh, a, a lost idea or a specialized service with relatively few users. So it's a path to generality, it's a path to general platform use, it's a path to broad applicability. Now there are plenty of good businesses that are tiny, so luxury goods for example, but if you want to have an impact-... at scale, you have to look for things which are of common value, common pricing, common distribution, and solve common problems. They're problems that everyone has. And by the way, people have lots of problems. Information, medicine, health, education, and so forth. Work on those problems.

   25. LFLex Fridman6:32

       Mm-hmm. Like you said, uh, you're a big fan of the middle class, uh, so-

   26. ESEric Schmidt6:36

       'Cause there's so many of them-

   27. LFLex Fridman6:37

       ... there's so many of them, so-

   28. ESEric Schmidt6:38

       ... by definition.

   29. LFLex Fridman6:39

       (laughs) So any product, any- anything that has a huge impact that improves their lives is- is a- is a great business decision and it's just good for society.

   30. ESEric Schmidt6:48

       And there's nothing wrong with starting off in the high end as long as you have a plan to get to the middle class. There's nothing wrong with starting with a specialized market in order to learn and to build and to fund things. So you start with, you know, a luxury market to build a general-purpose market. But if you define yourself as only a narrow market, someone else can come along with a general-purpose market that can push you to the corner, can restrict the scale of operation, can force you to be-
2. 15:00 – 30:00

   Mm-hmm. …

   1. ESEric Schmidt15:00

   2. LFLex Fridman15:00

      Mm-hmm.

   3. ESEric Schmidt15:01

      70% of our time on core business, 20% on adjacent business, and 10% on other.

   4. LFLex Fridman15:06

      Mm-hmm.

   5. ESEric Schmidt15:06

      And he proved mathematically, of course, he's a brilliant mathematician, that you needed that 10%, right, to make the sum of the growth work, and it turns out he was right.

   6. LFLex Fridman15:18

      So getting into the world of artificial intelligence, you've, you've talked quite extensively and effectively to the impact in the near term, the positive impact of artificial intelligence, uh, whether it's machine, especially machine learning in, uh, medical applications, in education, and just making information more accessible, right? In the AI community, there is a kind of debate, uh, so there's this shroud of uncertainty as we face this new world with artificial intelligence in it, and there are some people, uh, like Elon Musk, you've, uh, disagreed on, uh, at least on the degree of emphasis he places on the existential threat of AI. So I've spoken with Stuart Russell, Max Tegmark, who share Elon Musk's view, and Yoshua Bengio, Steven Pinker, who do not. And so there's a, there's a, there's a lot of very smart people who are thinking about this stuff, disagreeing, which is really healthy, uh, of course. So what do you think is the healthiest way for the AI community to, and, and really for the general public, to think about AI and the concern of the technology being mismanaged, uh, in some, in some kind of way?

   7. ESEric Schmidt16:33

      So the source of education for the general public has been, uh, robot killer movies.

   8. LFLex Fridman16:37

      Right. Terminator.

   9. ESEric Schmidt16:40

      And, uh, Terminator, et cetera, and the one thing I can assure you we're not building are those kinds of solutions.

   10. LFLex Fridman16:46

       (laughs)

   11. ESEric Schmidt16:46

       Furthermore, if they were to show up, someone would notice and unplug them, right?

   12. LFLex Fridman16:50

       Right. Yeah.

   13. ESEric Schmidt16:51

       So as exciting as those movies are, and they're great movies, were the killer robots to, to start, we would find a way to, to stop them, right?

   14. LFLex Fridman16:59

       Mm-hmm. Yeah.

   15. ESEric Schmidt17:00

       So I'm, I'm not concerned about that. Um, and much of this has to do with the timeframe of conversation.

   16. LFLex Fridman17:07

       Right.

   17. ESEric Schmidt17:08

       So you can imagine a situation 100 years from now-

   18. LFLex Fridman17:13

       Mm-hmm.

   19. ESEric Schmidt17:13

       ... when the human brain is fully understood, and the next generation and next generation of brilliant MIT scientists have figured all this out-

   20. LFLex Fridman17:20

       Mm-hmm.

   21. ESEric Schmidt17:20

       ... we're gonna have a large number of ethics questions, right, around science and thinking and robots and computers and so forth and so on. So it depends on the question of the timeframe. In the next five to 10 years, we're not facing those questions. What we're facing in the next five to 10 years is how do we spread this disruptive technology as broadly as possible to gain the maximum benefit o- of it. The primary benefit should be in healthcare and in education.

   22. LFLex Fridman17:50

       Mm-hmm.

   23. ESEric Schmidt17:50

       Uh, healthcare because it's obvious. We're all the same even though we don't, we somehow believe we're not. As a medical matter, the fact that we have big data about our health will save lives, allow us to get, you know, deal with skin cancer and other cancers, ophthalmological problems. There's people working on psychological, um, diseases and so forth using these techniques. I can go on and on.

   24. LFLex Fridman18:11

       Mm-hmm.

   25. ESEric Schmidt18:11

       The promise of AI in medicine is extraordinary. Uh, there are many, many companies and startups and funds and solutions, and we will all live much better for that. The same argument in, uh, in, um, education.Can you imagine that for each generation of child and even adult, you have a tutor/educator that's AI based-

   26. LFLex Fridman18:33

       Mm-hmm.

   27. ESEric Schmidt18:33

       ... that's not a human but is properly trained, that helps you get smarter, helps you address your language difficulties or your math difficulties, or what have you? Why don't we focus on those two? The gains societally of making humans smarter and healthier are enormous, right? And those translate for decades and decades, and we'll all benefit from them. Um, there are people who are working on AI safety-

   28. LFLex Fridman18:56

       Mm-hmm.

   29. ESEric Schmidt18:56

       ... which is the issue that you're describing, and there are conversations in the community that should there be such problems, what should the rules be like?

   30. LFLex Fridman19:03

       Mm-hmm.
3. 30:00 – 33:14

   Mm-hmm. …

   1. ESEric Schmidt30:00

   2. LFLex Fridman30:00

      Mm-hmm.

   3. ESEric Schmidt30:00

      ... who was the Sun founder, to do this and Dave Cheriton and a few others. The point is their beginnings were very simple-

   4. LFLex Fridman30:07

      Mm-hmm.

   5. ESEric Schmidt30:08

      ... but they were based on a powerful insight. That is a replicable model for any startup. It has to be a powerful insight, the beginnings are simple, and there has to be an innovation. In, in, uh, Larry and Sergey's case, it was page rank, which was a brilliant idea, one of the most cited papers in, in the world today. What's the next one?

   6. LFLex Fridman30:29

      So you're one of, if I may say, richest people in the world. (laughs) And yet it seems that money is simply a side effect of your passions and not an inherent goal. But it's, uh-- you're a fascinating person to ask. So much of our society at the in-individual level and at the company level and as nations is driven by the desire for wealth. What do you think about this drive? And what have you learned about, if I may romanticize the notion, the meaning of life having achieved success on so many dimensions?

   7. ESEric Schmidt31:10

      Well, there have been many studies of, uh, human happiness. And above some threshold, which is typically relatively low for this conversation, there's no difference in happiness about money. It's-- the happiness is correlated with meaning and purpose, a sense of family, a sense of impact. So if you organize your life, assuming you have enough to get around and have a nice home and so forth, you'll be far happier if you figure out what you care about and work on that. It's often being in service to others. There's a great deal of evidence that people are happiest when they're serving others and not themselves. This goes directly against the sort of, uh, press-induced, uh, excitement about powerful and wealthy leaders, uh, of one kind-- and indeed these are consequential people. But if you are in a situation where you've been very fortunate, as I have, you also have to take that as a responsibility. And you have to basically work both to educate others and give them that opportunity but also use that wealth to advance human society. In my case, I'm particularly interested in using the tools of artificial intelligence and machine learning to make society better. I've mentioned education. I've mentioned, uh, inequality and middle class and things like this, all of which are a passion of mine. It doesn't matter what you do. It matters that you believe in it, that it's important to you, and that y-your life will be far more satisfying if you spend your life doing that.

   8. LFLex Fridman32:40

      I think there's no better place to end than a discussion of the meaning of life, Eric. Thank you so much.

   9. ESEric Schmidt32:46

      Okay. Well, thank you very much, Alex.

Episode duration: 33:07