Transcript for Eric Schmidt: Google

I grew up in the 1960s as a boy where every boy wanted to be an astronaut and part of the space program. So like everyone else of my age, we would go out to the cow pasture behind my house, which was literally a cow pasture, and we would shoot model rockets off. And that I think is the beginning. And of course, generally today, it would be video games and all the amazing things that you can do online with computers.

I think what happens with people like myself is that you're a math enabled pretty early and all of a sudden you discover that you can use that to discover new insights. The great scientists will all tell a story, the men and women who are fantastic today, that somewhere when they were in high school or in college, they discovered that they could discover something themselves. and that sense of building something of having an impact that you own drives knowledge acquisition and learning. In my case, he was programming and the notion that I could build things that had not existed, that I had built, that I 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 will be driving me. And I'd be trying and learning and making mistakes and so forth in the ways that it works. the repository that represents, that GitHub represents and that open-source libraries represent is an enormous bank of knowledge of all of the people who are doing that. And one of the lessons that I learned at Google was that the world is a very big place and there's an awful lot of smart people. And an awful lot of them are underutilized. So here's an opportunity. For example, building parts of program, building new ideas to contribute to the greater of society.

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

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

And there's nothing wrong with starting off in the high end, as long as you have a plan to get to the middle class. There's nothing wrong with starting with a specialized market in order to learn and to build and to fund things. So you start a luxury market to build a general purpose market. But if you define yourself as only a narrow market, someone else can come along with a general purpose market that can push you to the corner, can restrict the scale of operation, can force you to be a lesser impact than you might be. So it's very important to think in terms of broad businesses and broad impact, even if you start in a little corner somewhere.

I wish I could say otherwise, but I simply found the technology platforms so exciting, that's what I was focused on. I think the majority of the people that I've worked with, and there are a few exceptions, Steve Jobs being an example, really saw this as a great technological play. I think relatively few of the technical people understood the scale of its impact. So I used NCP, which is a predecessor to TCPIP, It just made sense to connect things. We didn't think of it in terms of the internet and then companies and then Facebook and then Twitter and then politics and so forth. We never did that build. We didn't have that vision. And I think most people, it's a rare person who can see compounding at scale. Most people can see, if you ask people to predict the future, they'll say, they'll give you an answer of six to nine months or 12 months. Because that's about as far as people can imagine. But there's an old saying, which actually was attributed to a professor at MIT a long time ago, that we overestimate what can be done in one year. And we underestimate what can be done in a decade. And there's a great deal of evidence that these core platforms at hardware and software take a decade. So think about self-driving cars. Self-driving cars were thought about in the 90s. There were projects around them. The first DARPA challenge was roughly 2004. So that's roughly 15 years ago. And today we have self-driving cars operating in a city in Arizona. So 15 years and we still have a ways to go before they're more generally available.

The way to say it is that almost everybody has a one-year plan. Almost no one has a proper five-year plan. And the key thing to having a five-year plan is to having a model for what's going to happen under the underlying platforms. So here's an example. Compute more laws. We know it, the thing that powered improvements in CPUs has largely halted in its traditional shrinking mechanisms, because the costs have just gotten so high and it's getting harder and harder. But there's plenty of algorithmic improvements and specialized hardware improvements. So you need to understand the nature of those improvements and where they'll go in order to understand how it will change the platform. In the area of network connectivity, what are the gains that are going to be possible in wireless? It looks like there's an enormous expansion of wireless connectivity at many different bands. And that we will primarily, historically, have always thought that we were primarily going to be using fiber. But now it looks like we're going to be using fiber plus very powerful, high bandwidth sort of short distance connectivity to bridge the last mile. That's an amazing achievement. If you know that, then you're going to build your systems differently. By the way, those networks have different latency properties, right? Because they're more symmetric, the algorithms feel faster for that reason.

Well, and again, one of the great things about humanity is that we produce dreamers. We literally have people who have a vision and a dream. They are, if you will, disagreeable in the sense that they disagree with what the sort of zeitgeist is. They say, there is another way. They have a belief. They have a vision. If you look at science, science is always marked by such people who went against some conventional wisdom, collected the knowledge at the time and assembled it in a way that produced a powerful platform.

So it's helpful to have a model which encourages a predictable revenue stream as well as the ability to do new things. So in Google's case, we're big enough and well enough managed and so forth that we have a pretty good sense of what our revenue will be for the next year or two, at least for a while. And so we have enough cash generation that we can make bets. And indeed, Google has become alphabet. So the corporation is organized around these bets. And these bets are in areas of fundamental importance to the world, whether it's artificial intelligence, medical technology, self-driving cars, connectivity through balloons, on and on and on. And there's more coming and more coming. So one way you can stress this is that the current business is successful enough that we have a luxury of making bets. And another one that you could say is that we have the wisdom of being able to see that a corporate structure needs to be created to enhance the likelihood of the success of this bets. So we essentially turned ourselves into a conglomerate of bets and then this underlying corporation, Google, which is itself innovative. So in order to pull this off, you have to have a bunch of belief systems. And one of them is that you have to have bottoms up and tops down. The bottoms up we call 20% time and the idea is that people can spend 20% of the time and whatever they want. And the top down is that our founders in particular have a keen eye on technology and they're reviewing things constantly. So an example would be they'll hear about an idea or all hear about something and it sounds interesting, let's go visit them. And then let's begin to assemble the pieces to see if that's possible. And if you do this long enough, you get pretty good at predicting what's likely to work.

So in terms seems to be that Sergey again 15 years ago came up with a concept called 10 10% of the budget should be on things that are unrelated. It was called 70 2010 70% of our time on core business. 20% on adjacent business and 10% on other. And he proved mathematically, of course, he's a brilliant mathematician, that you needed that 10%. To make the sum of the growth work, any turns that he was right.

So the source of education for the general public has been robot killer movies, right, and Terminator, et cetera, and the one thing I can assure you were not building are those kinds of solutions. Furthermore, if they were to show up, someone would notice and unplug them. So as exciting as those movies are and their great movies were the killer robots to start, we would find a way to stop them. I'm not concerned about that. Much of this has to do with the time frame of conversation. So you can imagine a situation 100 years from now when the human brain is fully understood. And the next generation, the next generation of brilliant MIT scientists have figured all this out, we're going to have a large number of ethics questions around science and thinking and robots and computers and so forth and so on. So it depends on the question of the time frame. In the next five to ten years, we're not facing those questions. What we're facing in the next five to ten years is how do we spread this disruptive technology as broadly as possible to gain the maximum benefit of it? The primary benefit should be in health care and in education. health care 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 that our health will save lives allow us to get you know deal with skin cancer and other cancers opt them logical problems there's people working on psychological diseases and so forth using these techniques I go on and on the promise of AI in medicine is extraordinary There are many, many companies and startups and funds and solutions and we will all live much better for that. The same argument in education. Can you imagine that if for each generation of child and even adult, you have a tutor educator that's AI-based, that's not a human but is properly trained, that helps you get smarter, helps you address your language difficulties, or your math difficulties, or have you, why don't we focus on those two? The gains societally of making human smarter and healthier are enormous, right? And those translate for decades and decades and will all benefit from them. There are people who are working on AI safety, which is the issue that you're describing, and there are conversations in the community that should there be such problems, what should the rules be like? Google, for example, has announced its policies with respect to AI safety, which I certainly support, and I think most everybody would support, and they make sense, right? So it helps guide the research, but the killer robots are not arriving this year, and they're not even being built.

There are essentially no examples of 50 year predictions that have been correct. Let's review AI right AI which was largely invented here at MIT in a couple of other universities in the 1956-1957-1958. The original claims were a decade or two. And when I was a PhD student, I studied AI bit and it entered during my looking at it, a period which is known as AI Winter, which went on for about 30 years. which is a whole generation of science, scientists and a whole group of people who didn't make a lot of progress because the algorithms had not improved and the computers had not approved. It took some brilliant mathematicians starting with a fellow named Jeff Hinton at Toronto Montreal. who basically invented this deep learning model, which empowers us today, those, the seminal work there was 20 years ago. And in the last 10 years, it's become popularized. So think about the time frames for that level of discovery. It's very hard to predict. Many people think that we'll be flying around in the equivalent of flying cars, who knows. My own view, If I want to go out on a limb, it's to say that we know a couple of things about 50 years from now. We know that there'll be more people alive. We know that we'll have to have platforms that are more sustainable because the Earth is limited in the ways we all know and that the kind of platforms that are going to get build will be consistent with the principles that I've described. They will be much more empowering of individuals. They'll be much more sensitive to the ecology because they have to be. They just have to be. I also think that humans are going to be a great deal smarter. And I think they're going to be a lot smarter because of the tools that I've discussed with you and of course people will live longer. Life extension is continuing a pace, a baby born today, has a reasonable chance of living to 100, which is pretty exciting. It's well past the 21st century, so we better take care of them.

Today more than half the world lives in cities. And one of the great stories of humanity in the last 20 years has been the rural to urban migration. This is occurred in the United States. It's occurred in Europe. It's occurring in Asia and it's occurring in Africa. When people move to cities, the cities get more crowded, but believe it or not, their health gets better, their productivity gets better, their IQ and educational capabilities improve, so it's good news that people are moving to cities that we have to make them livable and safe.

One of the things that I learned as a young executive is that there is no single formula for leadership. They try to teach one, but that's not how it really works. There are people who just understand what they need to do and they need to do it quickly. Those people are often entrepreneurs. They just know and they move fast. There are other people who are systems thinkers and planners that's more who I am, somewhat more conservative, more thorough in execution, a little bit more risk ofers. There's also people who are sort of slightly insane, in the sense that they are emphatic and charismatic and they feel it and they drive it and so forth. There's no single formula to success. There is one thing that unifies all of the people that you named, which is very high intelligence. At the end of the day, The thing that characterizes all of them is that they saw the world quicker faster, they processed information faster, they didn't necessarily make the right decisions all the time, but they were on top of it. And the other thing that's interesting about all those people is they all started young. So think about Steve Jobs starting Apple roughly at 18 or 19. Think about Bill Gates starting at roughly 2021. Think about by the time they were 30, Mark Zuckerberg, a good example at 1920. By the time they were 30, they had 10 years. A 30 years old, they had 10 years of experience of dealing with people and products and shipments and the press and business and so forth. It's incredible how much experience they had compared to the rest of us who were busy getting our PhDs. Yes, exactly. So we should celebrate these people because they've just had more life experience. Right. And that helps inform the judgment at the end of the day. When you're at the top of these organizations, all the easy questions have been dealt with, right? How should we design the buildings? Where should we put the colors on our product? What should the box look like? Right? The problems that's why it's so interesting to be in these rooms. problems that they face, right? In terms of the way they operate, the way they deal with their employees, their customers, their innovation are profoundly challenging. Each of the companies is demonstrably different culturally, right? They are not in fact cut of the same. They behave differently based on input, their internal cultures are different, their compensation schemes are different, their values are different. So there's proof that diversity works.

I think the people who are in universities are always trying to study the extraordinarily chaotic nature of innovation and entrepreneurship. My answer is that they didn't have that conversation, they just did it. They sensed a moment when, in the case of Google, there was all of the data that needed to be organized and they had a better algorithm. They had invented a better way. So today, with human centered AI, which is your area of research, there must be new approaches. It's such a big field. There must be new approaches. different from what we and others are doing. There must be startups to fund. There must be research projects to try. There must be graduate students to work on new approaches. Here at MIT, there are people who are looking at learning from the standpoint of looking at child learning. Yes. Right. How do children learn starting at each other and the work is fantastic. Those approaches are different from the approach that most people are taking. perhaps that's a bet that you should make, or perhaps there's another one. But at the end of the day, the successful entrepreneurs are not as crazy as they sound. They see an opportunity based on what's happened. Let's use Uber as an example. As Travis sells the story, he and his co-founder were sitting in Paris and they had this idea because they couldn't get a cab. And they said, we have smartphones. and the rest of history. So what's the equivalent of that Travis Eiffel Tower where is a cab moment that you could as an entrepreneur take advantage of whether it's in humans in a day eye or something else? That's the next great startup.

I should say what happened to remember is that they were graduate students at Stanford and they thought there was this interesting so they built a search engine and they kept it in their room. And they had to get power from the room next door because they were using too much power in their room so they ran an extension cord over. And then they went and they found a house and they had Google World Headquarters of five people, right, to start the company and they raised $100,000 from Andy Bechtoschai, who was the son founder to do this, and Dave Charterton and a few others. The point is, their beginnings were very simple, but they were based on a powerful insight. That is a replicable model for any startup. It has to be a powerful insight that beginnings are simple, and there has to be an innovation in, in, in, Larry and Sergey's case. It was Page rank, which was a brilliant idea. One of the most cited papers in the world today. What's the next one?

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