LUCY SANDERS: Okay, here comes the next plenary. I was told that yesterday when I Opened the summit, I actually said something like, “And at this summit we’re going to get age on the stage.” [audience laughing] And now, I debated this. I happened to be with Jane Margolis, she says I said it, I said I didn’t. And so then I asked our CFO, Katie Ertz, and she said, “Oh yeah, you really did say it.” [laughing] So I think, why? There’s already age on the stage, right? I mean, I qualify for social security now, that’s pretty aged. But on a serious note, when we were starting to plan the agenda for this year, we do that almost a year out. We really wanted to take on this notion that technical innovation is for the under 40 crowd. There’s this myth, or not a myth but a perception, a very real perception I think, that technical innovation comes from an individual, a young individual, probably mostly young male individuals. I’d say it’s pervasive pretty much everywhere, but certainly in Silicon Valley there’s this myth, or perception, that that’s where innovation comes from. But what’s the evidence of that? We want to take that on. We want to understand if it’s true or not true, because it would have a lot to say about the work we do in our classrooms and in our corporations and in our startups. So our next plenary speaker is going to help us explore that topic. Like I said, it’s the first time we’ve ever had age on the stage, so we’re really looking forward to hearing him. Benjamin Jones is a Professor of Sociology at Northwestern University, and he is also leads one of their entrepreneurship and innovation efforts at the Kellogg School. He is a former Rhodes Scholar, although I might argue how could you ever be a former Rhodes Scholar? He is a Rhodes Scholar. His work has been quoted extensively in newspapers like the Wall Street Journal, and in journals like Science, and we’re really looking forward to what he has to say about age and innovation in the 21st century. So welcome to the stage Ben Jones. [audience clapping] [upbeat music]
BEN JONES: Well it’s a great pleasure to join you today, and have a chance to talk about these subjects. I titled my talk, Creativity in the 21st Century, and I want to address that broadly, but I’m going to start and spend quite a bit of time focused on the question of age, that Lucy alluded to. The question of possible agism, if people believe that the young are the drivers of technology and the drivers of science. I want to see if that’s actually true. I need a clicker. Is this a clicker? There we go, okay. So to start, let’s look back in history a bit, because the idea that young people drive scientific and technological breakthroughs, is very, very long-standing. Okay, I’ll come to Silicon Valley in a moment, but you can go back to the early 20th century, and take someone as notable and thoughtful and brilliant as Albert Einstein. What was his view? He said, “A person who has not made his great contribution “to science before the age of 30, “will never do so.” Einstein, in 1905, at the age of 26, did several things. A miracle year, for which he would ultimately win the Nobel Prize. So he was quite precocious. Dirac, that’s Paul Dirac, a contemporary of Einstein, who also did his Nobel Prize winning work at age 26, he went even further, he made a poem. He made a poem out of this idea. His poem is, “Age is, of course, a fever chill “that every physicist must fear. “He’s better dead than living still “when once he’s past his 30th year.” okay, now let’s fast forward today. There is, you can find endless quotes along these lines from various power players, and great innovators and achievers. There’s Mark Zuckerberg who said, for example, “Young people are just smarter.” Okay. And then on the right I have Paul Graham, who is the founder of Y Combinator, who has said the following, “The cutoff in investors’ head is 32.” He’s being slightly more generous, you get an extra two years. “After 32, they start to be a little skeptical.” okay, so that’s sort of the venture capital perspective. Now, before we get to whether this is actually true, why do people tend to have this view? Because I think we all know it’s fairly common, fairly persistent. And people who have this view, if they articulate their reasoning, will tend to think about, you tend to hear three different types of stories. One story is that young people are just better at deductive reasoning. So people who are more seasoned may have more wisdom, may be better at more inductive reasoning, but if it’s just sort of pure mathematical deductive horsepower, somehow people think young people may have an advantage. The second thing is that young people might have more energy. Now this is a claim sort of about health, but it’s also a claim about being unencumbered by other responsibilities like a family, children, those kinds of things. Maybe. And then the third, and I think perhaps the most common in the minds of those who really believe you need to bet on young people, is that somehow young people are better at more transformative thinking. That as we age, we become beholden to certain sets of ideas, ways of thinking, paradigms of thought, that make it harder for us to break out and have a truly radical new idea. Now young people who haven’t been exposed for so long, necessarily to those traditions or are exposed to the latest in technology first and foremost, may be more able to do something transformative. Have I lost my slides? There they go. Khosla, another venture capitalist, now he’s being a bit more generous. He says, “People over 45 basically die “in terms of new ideas.” [audience laughing] Now he, I think, is thinking mostly about this third point, that somehow the transformative thinking is the provenance of the young and the very young. Okay, so, we have agism. We have this belief by very prominent people. But what do we know about it? Can we do better than just a few quotes? Are young people really more capable, or is this actually not true? And the answer, is that it’s not true. So. [audience clapping] let’s look at some data, which includes Einstein and Dirac, for example. This is data that I collected in a paper that I wrote about 10 years ago. It collects Nobel Prize-winning research, all the Nobel Prize-winning research, about 550 different prize winners, and we go through biographies and we determine the age at which they did they thing for which they would ultimately win the Nobel Prize. That’s the dark blue line. The dashed red line is a different data set. That goes through Technological Almanacs. These are histories of science and technology, and would include things like the light bulb, the telephone, the first spreadsheet, the MRI machine, and it goes year by year and says oh the great innovation this year was X, and then you can go and look up who invented it and how old they were. So the first thing to note, of course, is these two samples have almost exactly overlapping distributions of age. That’s like the second thing to note, because the first thing to not for our purposes today, is that it’s not about being below age 30. In fact, that’s pretty rare. Actually, ages peaked for Nobel Prize-winning research or great technological insights, more in the late 30s, towards 40, historically. In fact, if you look at this closely, it’s a little hard to read from a distance, but there’s more people over age 50 doing this thing than there are below age 30. Now that’s just looking at Nobel Prize winners. You might look at other studies. This question of when people peak, older, younger, has fascinated people in the sciences, people who study the sciences, for a very long time. I think partly because it tells us a lot about innovation and creativity, but also people introspect a bit. They want to know, are my best years ahead of me, behind me, et cetera. So I can go back and the first study I know, I wrote a review article on this a couple years ago, I found a study from Beard, 1874. He collected 19th century scientists and people excelling in the creative arts, and looked at how old they were when they did their great thing, and they were between the ages of 35 and 40. If you go down this list, you’re gonna see that time and again, people who study when people peak, the answer is middle-age, not young. But wait, I mean that’s all historical, right? That happened a long time ago, because this is today and look at what’s happening in Silicon Valley, et cetera. It MUST surely must be the case that, while that might have been true once, people have gotten younger, right? They’re younger now. So that’s all irrelevant. What do we know? This is the same data I showed you about Nobel Prize winners and great technological innovators. But now I’ve divided it up into three different periods. And the answer is not that they’re getting younger. It’s not even that people are staying the same age. People are actually getting older. The great breakthroughs are coming from older and older people. It’s the reverse. So the blue line is great inventions in technology or Nobel Prize-winning research that was done before 1935. The one farthest to the left, shifted to the youngest ages. If you look at the next line, the red-dashed line, those are those insights and achievements between 1935 and 1965. And if you go farthest to the right, you see that green-dashed line, that’s after 1965. So actually the peak age moved from early 30s, to 40 or early 40s over the course of the 20th century. You can see it’s sort of a fattening tail on the left there, on your right. My left, your right going further out in ages. Many people in their 50s, et cetera, and particularly many, many fewer people in their 20s. Now, in part, this may make it seem like it’s never been better to be 55 and innovating, which is probably true. I should be a little careful though, because there’s something that’s gone on demographically, of course, which is that people have gotten older, so we have more people who are over 50. If the population is entirely over the age of 50, guess what. All of the great achievements will come from people who are over 50. And if everyone’s really young, like they were in the 19th century, people will be younger. So you have to think carefully about this data, and control for demographics. If you do that, and you sort of parse out, I’ll show you in the next slide what happens. What you’re gonna be left with is, it’s less that people who are older have gotten more innovative, they’ve gotten neither more nor less innovative. They’re about as innovative as they always were. What’s happened though, is that people who are younger, are just much, much less innovative, below the age of 30 than they used to be. So I’m looking also there at the great breakthroughs of the 20th century in technology and science, which there are not very many. But here on the left, I’m looking at the age at first patent, so this would be much more ordinary, run-of-the-mill inventions. You look at patents held at the US Patent and Trademark Office and look at how old people are at first patent. And that’s been going up. In fact it’s going up at the same rate as it’s going up for Nobel Prize winners in terms of their age at achievement. On the right I’m going back to that Nobel Prize winning and great technology data, and I’m taking, using some statistics, out the demographic issue, the fact that people are getting older on average. And this is what you’re left with. What you’re left with is that, people in their 20s at the left, the blue line on the right, that’s 1900. That’s your innovation potential, that’s when you’re gonna peak. When’s my, you can do it at lots of ages, that’s another message, but you know you had a highest probability around age 30, early 30s. So Einstein actually back in 1905, smart guy, he wasn’t entirely wrong in his time. If you go to the red line, that’s 2000, now your chance of innovating at Einstein’s age, like at 26 or even below 30, it can happen. It still does happen, but it’s just very unlikely compared to what it used to be, and it’s very unlikely compared to say, someone who is 40, very unlikely compared to someone who is 50, even people who are gonna be 60, are even more likely to have a big insight than someone who’s 25, in terms of science and technology. So in that sense, the relative potential over the lifecycle, really is no longer favoring young people. It never really did, but it’s certainly not today, and it’s favoring older people all the more. With those facts, we can sort of reject, if you will this common view that it’s about the very young, that those are the great drivers of ideas. It may be true that young people have lots of energy, it may be true that they even are pretty good at transformative ideas and they may be pretty good at deductive reasoning, but that, it’s being overwhelmed by something else. Because that’s not what’s going on when you actually get to the data. What’s going on is older people have the net advantage of whatever forces are at work in creativity, and increasingly so. So the next question is, why? Why is this happening? And what I want to do now is I want to give you a real simple theory for understanding why the age pattern has shifted. This same theory will also allow us to think about other aspects of creativity today, and where the best ideas are gonna come from to get more into the who, the when in life, and the how of great insight and great advances. So I’ll keep coming back to age as we go, but I’m gonna actually step back as well and try to give you a bigger picture of what we know about creativity and how it has changed in some fundamental ways today. To begin to have a theory, I’m gonna go back even further in time. I’ll go to Isaac Newton who once said, famously, “If I have seen further it is by standing “on ye shoulders of giants.” Meaning, knowledge accumulates over time, and the reason I am able to make a much greater insight than you did 100 years ago, is because I have the benefit of what you’ve done in the last 100 years. Knowledge keeps accumulating, there’s more and more knowledge today, and so I have the advantage of that in my creativity today because I can use it in making the next step. But the other side of this, is that if you have to climb up on the shoulders of the giants who came before in order to facilitate your capacity to make the big next step, as knowledge accumulates over time, what happens if the height of the giant keeps rising. It’s like a longer climb up their backs. We can begin to think about why it might be harder at age 22, 25 to make the big discovery, ’cause you’re probably still climbing up the backs and you’re learning to get there. So I’ll come more to that in a second. I should note, it’s sort of amusing if, Newton a very brilliant guy, not very nice. Very not nice, in fact. Not a kind soul, apparently. He actually wrote this in a letter to Hook, who was another physicist at the time, and although he meant it sort of substantively, he was also digging at Hook because Hook was known for being very, very short. So what Newton is really saying is, yes, yes, I may have built on your ideas, you giant. He’s trying to insult Hook in his in Newton’s not very nice way. But the broader point is that back at that time, say in the 17th century, we didn’t have a lot of codified scientific knowledge. The Enlightenment hadn’t been going on for too long. Scientific progress was happening. A lot of tinkering was going on in technology. Progress was happening, but it wasn’t like there was a massive amount of received knowledge about the frontier of how you do things that was very well understood. So John Harvard, that’s a statue of John Harvard, John Harvard got naming rights to Harvard University. Why? Because he left them a bequest. His bequest was a small amount of money, incidental for the most part. What he really left them was his library. His massive 320 volume library encapsulating all this great codified knowledge, which is so substantial and so useful for teaching these students. Now of course, we’ve accumulated a lot of information since then, we know a lot more about how the universe works on many, many dimensions, such that if you take the Web of Science, which is the world’s largest repository of scientific articles, going back to 1945 to the present, there are 25 million journal articles, new pieces of knowledge in the Web of Science currently being published and collected at a rate of over a million per year. Separately, there are hundreds of thousands of patents coming every year. There’s just a massive accumulation of knowledge, a massive flow, kind of a fire hose of flow of new advances coming all the time. So, if you will, the climb up the shoulders of the giants was pretty low once upon a time, but now there’s just this vast edifice of knowledge in physics, astronomy, biology, technology, material science, you know how to etch very fine scale transistors onto certain materials with certain kinds of lithographic equipment. I mean there’s just a really, really, really, really deep profound set of knowledge that goes into those tasks. So a simple theory that we can call the Burden of Knowledge, says that if knowledge accumulates over time, inevitably, and that’s a good thing in a way because we’re making progress and we’re getting a lot of socioeconomic advances out of that, hopefully. But then, training must kind of have to shift, right? Because every generation is born as children with empty minds, but we’re successively born into new worlds which have so much more knowledge and technical knowledge in them, what are we gonna do to fill up our empty minds to get towards that knowledge frontier where we ourselves can make the next generation of contributions. One natural response is that you can extend your training. There’s more to know in a field, there’s just more foundational knowledge, theories, facts, et cetera, so it’s gonna take me longer to learn that before I can actually innovate myself. So going back to why we might see fewer and fewer innovations by people in their 20s, systematically over time, that can simply be because these people are actually still learning as opposed to innovating for the most part. So that’s kind of coming back to the age point, and why young people increasingly maybe don’t have the advantage in technology and science and practice, even if some people think they do, and fund them for it. I’ll come back to that. The other element though, is of course, it’s not just that I might extend my training. I could extend my training to a million years probably at this point, reading one or two articles a day, I still wouldn’t read all 25 million articles in the Web of Science. You can’t possibly just extend your training out of this problem. You have to become a lot narrower. You’re not allowed to know all of biology anymore. You might know something, like a lot about viruses, or about protein-folding, but you don’t know all of biology. This is a fundamental narrowing effect that is leading, I think, to large organizational changes in how we innovate successfully and how we should think about organizing ourselves to innovate successfully. I’ll talk a bit more about both of these things, but we have longer training on the one hand, narrower expertise on the other. All from the same premise that there’s more and more to know. So return to age for a second, this is similar data. I’ve taken three Nobel Prize-winning fields, chemistry, medicine, physics. The blue line, this is over the 20th century, the blue line is the average age at which people are producing the great insights in that field. So start with chemistry on the left, you see the blue line is just kind of going up. As I showed you before, the average age is increasing overall. The orange line underneath the blue line, is the age at PhD. For the laureates who are going to win the Nobel Prize, so you can see that the age at which they got their PhD is going up quite a lot. It used to be in the beginning of the 20th century that sort of the capstone degree in electrical engineering was a Bachelor’s Degree. Then you could get a Master’s Degree. Later you could get a PhD, now you can get a Post Doc, and you can go deeper and deeper and deeper, for some. Medicine you see a similar pattern. Physics is on the right. On average, you’re gonna see aging at great achievement, and you’re gonna see aging at the ultimate degree that these people receive, which is kind of prima facie evidence that people are spending more time in their 20s, say, learning, getting to the frontier, as opposed to innovating. I’ll spend another minute, though, on physics, because if you look to the far right of this, it’s a little more interesting. It’s actually going down, the blue line. The age is actually dropping into around 1927 or 30 and then it goes up. And the PhD age is actually going down in the beginning of the 20th century and then it’s going up. So what’s going on in physics? In a way, it’s this exception that proves the rule, perhaps, it’s this natural experiment, because what’s going on in early-20th century physics is a revolution of thought. It’s the quantum mechanics revolution, where by the end of the 19th century, there are a very small number of empirical results that don’t make any sense within classical physics. So physicists had begun to realize that classical physics was not going to explain what was really going on at the very small level. And then they had no idea what to do. So basically it devalued knowledge of classical physics. Anyone, here’s four empirical puzzles. Everyone have at it. You didn’t need to spend a lot of time learning classical physics to have a good chance at potentially making those breakthroughs, and you did see a lot of young people making breakthroughs in this 1920, 1900-1927 period in physics. Once they locked down on the quantum mechanics paradigm, and it works, then you see age start to increase again, all the way through the end of the 20th century ’cause they haven’t had a similar revolution of thought devaluing existing knowledge and opening the doors to everyone else. Actually Werner Heisenberg is a very interesting example of this. Heisenberg, of the Heisenberg Uncertainty Principle, which he comes up with when he’s 25, [laughs] he, in his PhD exams a few years before, almost fails his PhD exams because he’s getting an oral examination and one his examiners said, “He knows nothing about classical electromagnetism. “I’m not sure I can pass this guy. “I mean he’s really smart, but he doesn’t know anything about physics.” And you know, three years, four years later, Heisenberg is proving that wasn’t especially salient. He’s able to make huge achievements without that knowledge, because it wasn’t actually relevant to the kinds of breakthroughs that were about to come. So it can be, there can be periods in a revolution, for example, where you can imagine younger people getting access and being able to make those contributions, but for the most part that’s the exception. Now coming back to specialization. I want to return to Einstein, partly because I said he was wrong before, and it’s rare that he’s wrong. [laughs] He said something else, about which I think he’s exactly right. This is the idea that we have to become narrower. Einstein once said in 1932 that that is in fact inevitable. He said, “knowledge has become vastly more profound “in every department of science. “But the assimilative power of the human intellect “is and remains strictly limited.” There’s only so much you could possibly know in here. “Hence,” he says, “it was inevitable that the activity “of the individual investigator should be confined “to a smaller and smaller section.” He saw that narrowing of expertise as an inevitable byproduct of the progress of science and technology. So then what do you do? And here I want to extend things a little bit to think about one of the key insights about how to do creativity, how to do science, how to do technology today. And that’s about teams, because to put it in Francis Bacon’s words, going back even further, he said, “Men begin to know their strength “when instead of great numbers doing all the same things, “one shall take charge of one thing “and another of another.” We each specialize, there’s a division of labor, and by coming together we can, of course, aggregate lots of expertise and then do frontier-level work. So the other fact, the other key fact is not only that people are getting older, and young people are kind of dropping off the map, relatively speaking in great breakthroughs with time. The other thing that’s going on is it’s all coming in teams these days. It’s no longer the individual. The individual is too narrow, so if you want to do something big, you’re gonna have to assemble a group of people with the requisite skills and knowledge. That is interesting because it’s no longer just brilliance on your own staring out the window. You need to have some team-building skills. You’ve got to work well with others to some extent. You’ve gotta think about all those softer skills that might be essential. This is just a graph showing the average team size on every journal article. There’s tens of millions of journal articles on science and engineering. The average number of authors on social science papers, like what I do, the average number of inventors listed on a patent, that’s the blue line in the middle. It’s all going up. That’s grouping everything. I could look at any individual field, invention, or science. It’s going up, it’s just going up and going up. Concrete example would be airplanes. The first airplane, the Wright brothers. Two guys, considered leading aeronauts of their time, produce a functioning airplane. On the right I have a 787, that’s 30 different disciplines just to design and produce the jet engines. Just the jet engines. There’s a lot going on in a jet engine, when you think about it. A lot of different kinds of engineering have to come together to make us fly, really does. Very simple example but a clear one, that you know the advances are going to be coming through very large teams being able to do important things today. One last piece of evidence there, you just look at where the big insights are coming from, I’m alluding to this but let me just try to be evidence-based today ’cause we know what happens if people aren’t evidence-based, they say all sorts of crazy things. About age, for example. If you look at home runs in papers or patents, so a home run is often measured, for convenience sake, as an article or patent that receives a lot of citations. It’s influential to ongoing work. It’s highly cited. Here’s the probability that you get more than a hundred citations if you write a paper or if you invent a patent. If you look in science and engineering papers, it’s hard to write a paper that is that impactful. These are rare. But team authors have over four times the likelihood today of writing such a paper than a solo author. Same goes in social sciences. In patenting it’s not quite as dramatic, but it’s still a huge difference. Teams of inventors are 65% more likely to produce a home run, than a solo inventor. And this is the way it is now. This is the 21st century. This is the beginning of the 21st century, but actually what’s interesting here is that if I went back in time this wasn’t true in a lot of fields. If I go back to the 50s, there were many fields where as a solo person I had a higher probability of hitting a home run than if I went to bat as a team. So this has been a transition. Today, almost all fields, like 98% of all fields, there’s a team advantage over the solo. Again, a way to think about that, whether you take my Wright brothers to 787 example, or you just want to think about people getting narrow, they need to get other teammates around to tackle effectively, big problems. One little coda, ’cause I want to think a little bit about, and open us up to thinking about marketplace innovation. Not just scientific breakthroughs, and not just a patentable invention, but something that is gonna, an organizational-based innovation in a marketplace, perhaps entrepreneurship, perhaps innovation in a corporate setting. Because I’ve been talking a lot about the weight of, and accumulation of scientific and technical knowledge. But there’s another kind, and that’s that. That’s that piece. There’s another kind of knowledge, of course, that matters a lot if I want to get something into a market effectively. And that’s what we can call market knowledge. Knowledge about customers. Knowledge about what consumers want, what their tastes are, what’s a problem they have that I could solve, whether it’s literally an individual or it’s another company who has a problem I can solve, and it’s a business-to-business kind of innovation. People often think of Steve Jobs as a great example of someone who was very good at the market knowledge piece. Understanding what consumers wanted even if they couldn’t articulate it themselves. When you’re thinking about business success here, we have to often, in tech, tech business success, we have to bridge both technical knowledge, which is gonna have the specialization and age issues, with market knowledge, that could also be a team. And we see that a lot, of course. The market knowledge isn’t just understanding customers, it’s also understanding how to run a business, operations, marketing, doing the accounting right, not breaking the law, et cetera. Regulations, et cetera. So that could be Mark Zuckerberg pairing ultimately with Sheryl Sandberg, or it could be the Google founders pairing with Eric Schmidt. They can often be bringing in someone who has certain kinds of market skills and knowledge that can make the whole thing go. Typically, you can’t just have technical knowledge or just market knowledge in tech innovation, for the market you’ve got to have both pieces functioning together. A few implications. Coming back to age. If we bet on young people, what’s gonna happen? There is this common belief that young people have more valuable ideas. Now I’ve tried to show you that that’s not correct. I haven’t shown you entrepreneurship data, but it’s not correct in entrepreneurship data as well. But it’s believed, and because it’s believed, a lot of VC, a lot of venture capital, maybe other resources target young people. Which isn’t to say young people don’t have good ideas. Young people have some great ideas. But when you do that, what are you going to get? You’re going to affect the rate and the direction of innovation. That’s going to be a consequence. As Peter Thiel has said, “We wanted flying cars, “but instead we got 140 characters.” Now, that’s nothing to say negative about Twitter, per se, because Facebook, Twitter, these are really useful things in many spheres. It’s not that we don’t honestly want those things, but are we not getting, why aren’t we getting the flying cars, too? Might be how I would put it. And if we are not betting on people with deep technical knowledge, we’re not gonna necessarily get the things that require deeper technological knowledge like biotech or clean energy or a lot of things that aren’t so easy to do when you’re very young. So if I were to give you a descriptive classification of the kinds of innovations we tend to get when you bet one way or another, younger people are gonna do really, really cool things, typically in lower tech. Tech, but lower tech side of tech. So computer programming is a skill lots of people can master at a relatively young age, and do successfully well enough to do things. And younger people are often gonna have ideas that are about things that they understand and markets that they would like to buy in themselves. So there’s the consumer markets, often it’s consumer focus, it’s often young consumer focus, it’s something that I would call, it’s not just B2C, meaning Business to Consumer, it’s sort of B2P, Business to Peer, or even B2M, Business to Myself. Things I would myself wish I could solve. But we don’t. That’s actually useful, because if you know it’s a problem you’re trying to solve, you’re probably right that it’s a problem people are trying to solve, and so you’re gonna dedicate your energies towards a problem people really have. That’s Waldron on the left, he’s one of the co-founders of Zynga, so he’s your quintessential– he was 19, he dropped out of college. Programming mobile computer games using his programming skill. On the right, further along, is one of IBM’s increasingly famous, top inventors, that’s Seacat DeLuca who’s got hundreds of patents. But again, it’s mobile. It’s sort of computer software mobile focused. And then I’ll put up on the other side, and again this is not a value judgment against these things, just sort of noting the kinds of things you’re gonna get one way or another. If you go to the middle-aged plus and older people, you’re gonna open up more doors toward deeper technical knowledge. People who are truly at the very frontier of harder areas, and these are gonna be a bit more Business to Business because they probably existed in industry, and they kind of know the problems other businesses are trying to solve. So I give two examples there. With the mustache, that’s an older picture of Herbert Boyer, the founder of Genentech, so he actually himself with a colleague, developed a lot of the recombinant DNA technology in a lab in an academic setting in his 30s, and then he starts Genentech when he’s 40, that company will eventually be acquired by Roche 30 years later for something like $45 billion. That’s an entrepreneurship coming out of really, really deep frontier-level expertise in biomedicine. On the far right, I put David Duffield, who was the founder of PeopleSoft and now Workday, which had an IPO for like $12 billion, I think. Duffield started PeopleSoft I think in his mid to late 40s, started Workday in his 60s. These are companies that do Business to Business, CRM, SCM, ERP, kind of database management software. It’s the stuff that you would only know is important if you work in large corporations. Because he’s done that, he’s someone who can see these opportunities based on his life experience, and provide very valuable innovations. The thing is, if you’re thinking about the implications for the direction of innovation, I want to give you a flavor of that here. If you exclusively bet, or bet too much on younger people, you’re gonna get a lot more of the 140 characters, than other things. So you might want to rebalance. It’s also the case that a lot of the things that are gonna drive economic growth or drive medical advances, energy advances, drive productivity advances within existing companies, are often maybe gonna come from a somewhat older class of innovators because of their expertise and the kinds of problems they’re familiar with. Speaking of economic growth, there’s another fact, it is that we live in a challenging environment for economic growth. I don’t mean right now. We’ve obviously been in a challenging environment recently. But if you ask a growth economist and go back over a long period of time, 100 years, 150 years, growth is actually really pretty steady in the United States. It goes up and down, but for the most part it’s remarkably steady. Economic growth, growth in people’s standards of living, ultimately socioeconomic prosperity per person, is driven by increases in productivity. And increases in productivity are driven by innovation. Building on science, building on technology. That’s what’s going on, but it turns out that the number of research dollars, R&D dollars, or R&D workers, people we throw at innovation has gone up much, much faster than the growth we’ve gotten out of it. Which means that the individual’s contribution per capita of an R&D worker in the modern American economy, for example, is much smaller today than it used to be. We get to remit smaller and smaller. Two reasons for that are along the lines that I’ve told you. At least in part, it’s age. It’s that we sort of cut out the 20s and people’s lifecycle of innovation and made it hard to innovate when you’re young, but also we’re just much narrower. So it’s much harder for one individual to do an economy-wide, productivity enhancing idea. But in light of that, we have to be smart about how we invest. We have to get rid of behavioral biases, we have to invest in places in a balanced way, lots of people have good ideas, that meets the reality of where big ideas are coming from. I think there is a lag between perceptions, for example on age, and where we should be investing in a more balanced way to get the kinds of growth and productivity that we need. We have to fight against the fact that it’s getting harder. We’re gonna fight against, and then we’re gonna win that. We’re gonna win that battle by being smart about how we make those investments. Where do we allocate our time and our resources, and to whom. So I want to close briefly since I’ve set all this up, with a few other lessons about creativity from studying scientific breakthroughs and technological breakthroughs. The last couple of years we’ve learned quite a bit about how you can effectively interface with prior knowledge to push forward the envelope. This will also give us more nuanced insight I think on age, and teams as well. It’s not just that there’s some mass of prior knowledge. There’s a huge mass of prior knowledge. We’ll never know all of it. Even a team will only know a part of it. But how do we interface with that huge mass of knowledge in some sort of textured way to make sense of it? And to make valuable gains? The first thing to think about is that in creativity, what we’re really typically doing is new combinations of existing things. We take existing stuff that exists, some of it only recently exists, and we recombine it in some new way that is new to the world again and has some value that people hadn’t discovered or used before. Mendel, he’s discoverer of genetic inheritance, he’s literally combining peas in order to learn how inheritance works. A better example would be say, Thomas Edison, among his innovations, inventions is the light bulb, which he called originally the electric candle, because he was taking the idea of light in your room, which is a candle, a very long-standing old idea, and now mixing it with something called electricity, which is a relatively new idea and saying hey, I can make a new way of producing light, and it’s called the electric candle, which we now call the light bulb. It’s a combination of existing ideas in a new way. Kary Mullis, whose polymerase chain reaction is really one of the essential technologies of biomedical revolution, is really combining a better and better understanding of DNA replication with a particular enzyme, that was discovered by others in Yellowstone National Park in a geyser. It was a particular enzyme that can survive at very high heat, which turns out to be very important to be able to replicate DNA which is of course essential to all the forensics and all of the gene sequencing and the genetic based understanding of disease that has come with that technology so we want to think about new combinations, but what combinations? Even with a hundred ideas, I could combine them in almost infinity different ways. How do I know what to combine? What are fruitful combinations? How do I do that? What we’ve learned the last few years, studying this by taking very large data sets is that there are remarkably powerful patterns that reveal what combinations seem to be especially productive. The first insight is that, of course at some level, you have to be new, so you want kind of a novel twist. You want to be combining two things in a way no one has combined before, but actually the vast majority of what you’re doing with a new idea is being hyper, hyper-conventional. And I’ll say more, that might sound like a contradiction. I’ll say more about that in a second. The second idea is that you might think it’s all about the latest insights, [stammers] you’re right in the frontier, right in the froth of the latest stuff, and I gotta take those new things and combine those. And you do want to do that, but actually if you just do that and don’t also bring in older ideas, ideas that have stood the test of time, you don’t tend to do very well, either. So let me just show you that, very briefly, and then I’ll conclude and take questions. The point here is that if you do these things right, you’re gonna double or more your chances of a very high-impact idea. The first thing I said is you want to be both novel and conventional. How do we measure that? A patent, or a paper, a lot of other things, they cite prior work. They sort of pull together prior ideas. What you can do, is you can say all right, take two ideas that a given patent has pulled together by citing those two prior ideas, and we can ask are those two ideas common? Are they often appearing together in lot of other creative work? Or are they really rare, like no one every combines those. Then you can ask, all right which patents do really well? Is it they do really well when they combine conventional things that have been combined a lot before, or do they do really well when they combine new things? And the answer is, they have to do both. So the green bar, which is where you get you bang for your buck here, and not very many patents do this. This is the minority of patents will succeed at this. They are characterized by most of the combinations they are making are conventional. And they’re not just conventional in the sense that other people make them, they’re hyper-conventional, they’re like the ones people make all the time. They’re right deep in the mainstream of field, and how people think about things and what goes together. They’re actually hyper-conventional, but just as they’re moving towards hyper-conventionality, they reach back and they have a little bit of spice. They have a little bit of novelty, which is almost never seen. When you can mix being actually extra-conventional and yet reach back for a little bit of spice, that’s when you see the ideas that are really high impact. The red bar is like everything’s novel. You can over spice your food. You can’t just have tons of spices, [stammers] it tastes bad. And you can’t be totally bland. It can’t all be conventional, but it needs to be based in conventionality and then reaching for novelty. The second one is about new versus old. So this is looking at everything papers reference. By the way, the data in these slides is tens of millions of papers or patents. The vertical axis here is measuring when I site prior work, how old is that work? Is it a year old? Is it 15 years old? The green, which I’m pointing to and calling it the hotspot, are papers or patents that are going to be really high impact, two, three, four times more impactful than typical papers or patents. The blue would be like the duds. The vast majority of papers are duds. How you get to the green? You get to the green by being really low, low down to the bottom of that graph, which means that on average, everything you’re citing, the vast majority of what you’re citing is very recent, so you really are looking for the latest things. This is the place young people might have some advantages, they’re right in the new stream of stuff. Oh I could take this, and I could take Instagram and I could write an app, and I could, right in the new stream. Get to the recent stuff. But what’s going on in the X-axis is it’s saying that actually you don’t want to just have recent stuff. If you just had recent stuff, you’d be way over to the right of that corner. You’d be a dud. If you’re all recent, you’re a dud. What you need to be is mostly recent, and yet still have a tail that reaches into older ideas, the ones that have stood the test of time the ones that remain. My final word, to close. I’ve tried to tell you, in the end a pretty big set of ideas about creativity. But really to put the age question in perspective, get around it, the key thing is that individuals have much less capacity to innovate, to comprehend, to combine frontier knowledge than they used to, especially at young ages, because in deep technological areas, they have to get to the frontier and that’s just an increasingly long climb. It’s hard. They’re also narrower. That’s people. If you think about the ideas, where do high-impact ideas come from, they’re mixing novelty and conventionality. They’re mixing the old and the new. When you think about the individual, you think about an older individual, it’s actually older individuals probably who have that understanding of conventionality. They’re the ones who could be grounded in the expertise at the frontier. They’re also the ones who can reach beyond just the latest things in the lab, and I see this all the time in my work, but still know about that, oh yeah there’s other ideas. 20, 30, 40 years ago, those are really essential to what we’re doing here today. This is where it really makes sense. This new thing, it’s really gonna make a difference when you think about that other thing we thought about a long time ago, let’s bring that back. Now we can make progress. So you can begin to see, and this is just being a bit more speculative here, you can certainly begin to see how teams of individuals who can mix expertise, and you can see how older ages plus mixed with younger ages on a team, can have serious advantages. As you’re going forward, you can think about these middle things, about what your trying to do with your own creativity. Don’t over spice the food, try to get expertise and go for the novel twist. You’re probably gonna do that with teams of experts, and you’re probably gonna do that by making sure it’s not just young people that you’ve got some wisdom around because left to their own devices it’s really not the very young who are driving breakthroughs as people in their middle ages and beyond. Thank you very much. I look forward to questions and comments. [audience clapping] I think there’s a mic coming.
AUDIENCE MEMBER: Thank you so much. I have a question about the audience that you’re finding for this work, because I think it’s really interesting. Obviously I’m on the other end of the spectrum. In the VC community, is this conversation happening? Do you find that people are looking at data and responding to it?
BEN JONES: By virtue of running innovation and entrepreneurship programs at my business school, I do interact with VCs fairly frequently, and I would say they’re very open and very interested in this information. So yes, because they want to make the right bets, and so they’re willing to change their mind in light of new evidence. I think that, I talked a lot about technology, talked a lot inventions, talked a lot about science. Of course the VCs most interested in entrepreneurs. I haven’t given you data on entrepreneurs. I’m working on a paper, using Census data, on entrepreneurs, but I can’t quite tell you what the results are because they’re not disclosed yet by various government procedures. I can only hint at them, but the hint would be that it doesn’t really change anything I told you today. [audience laughing]
AUDIENCE MEMBER: Yes, with venture capitalists and everything, the thing that’s not really mentioned here, is naivete. Is there this thing of young people are innovators just like a guise, where they’re also not really smart with money? Greylock Partners probably made a lot more money on Facebook than Mark Zuckerberg did. So it’s like, well, yes I’m gonna go after that person because we’re gonna take a majority stake of their valuation, and they don’t know any better. Then an older person has a solid idea, they’re like well let’s go find a young person with a similar solid idea and we’re gonna… I mean, that’s a very dark place to go with this, but I don’t know what your thoughts are on that.
BEN JONES: Interesting. It is, there are many, many, many stories, and we see it all the time. You’re the young entrepreneur, you don’t have any cash so you have to use external financing which means you dilute your control, and then eventually you are thrown off the boat and other people take over the company, or you got a very small share of the rest. That happens a lot, so I think one of the challenges for entrepreneurs, one of the dilemmas they face, is always whether to bootstrap or to rely on external financing for that reason. So in entrepreneurship, just to go a little further, again I talked a lot about technical knowledge, but in entrepreneurship, one of the advantages of being older, even beyond… Even in shallower fields, might be that you have more financial resources you’ve saved, so you can go forward without as much reliance on external finance and diluting yourself, for example. I wouldn’t hope to take such a cynical dark view of VCs myself, that they’re doing it strategically, but it is of course true that VCs are looking for a return. So what you said is certainly within the realm of possibility. Let me go to the right. Yeah, thanks.
AUDIENCE MEMBER: Hi, I’m an undergraduate, first and foremost, so I think also there’s an important perspective from a young person’s side, and it’s that, from what I’ve seen, many of my peers and fellow other aspiring entrepreneurs, et cetera, is that they almost feel as though they have sort of, they’re pressed for time. [stammers] they take the quote that Einstein said, about, oh innovations only come before age 30 and it almost becomes a mantra where it really drives them. They’re like I have to make this deadline of age 30. That’s actually something I’ve seen very, a really unhealthy mantra in Silicon Valley and other areas. So I just want to thank you for saying what you said, and I think that’s something especially important and also not only that should also, well that should really be shared with the younger community. And on that note, I guess, what advice would you have from your work on this for young people?
BEN JONES: Well, I think it really depends on what kind of innovations and entrepreneurship you want to do. So I mean, to your first point, I think people can relax a little bit. Entrepreneurship can often be your second career, or a third career. It’s not like you have to start off at age 22 or you can’t do it. In fact, a lot of people are gonna make their first foray into entrepreneurship much later and be quite successful at it, for a whole variety of reasons. Because they’re gonna, you know if you live in an industry long enough you’re gonna see real problems that are more niche problems, that not everyone gets to see, and so you might have some really interesting ideas. You’re gonna obviously gain a lot of experience in communications and running teams and dealing with finance. A lot of nuts and bolts of running organizations and businesses that you don’t have to learn on the fly, while you’re also trying to innovate in an entrepreneurial setting. So I think one should feel comfortable waiting, and if one wants to really make their mark in clean energy, there’s a different path, and you’ve got to do certain things that probably get a real lot out of your expertise, than if you wanted to, say, do something in certain other areas, which are equally valid but may not require certain other choices in terms of [stammers] getting from point A to point B I would say though that, entrepreneurship itself is an experience, and one learns a lot from it. I think people might not often succeed their best on their first attempt, so there can be learning by doing and trying it out a little bit. As a young person, learning if you have this taste for it, the psychology for it, and even if it fails learning from the failure can also be valuable in pursuit of that ultimate success, even if it comes later on. I wouldn’t want to discourage people who are entrepreneurially inclined from going for it. I think you can. And obviously there are very successful entrepreneurs, at young ages too. Whether they get lucky or not, they’re very talented, who can do that if that’s their dream. Yes.
AUDIENCE MEMBER: Thank you for your insights. I wanted to go back to one of the slides that you had where you talked about because of the additional knowledge, the burden of information, people are having to extend training and also to narrow their area of expertise. This seems to me to have a lot of ramifications to it. One instance being the implications on family life. And do you see any movement, either within the venture capital community or also even within the academic community to provide more support to sort of counter these trends?
BEN JONES: That’s a great question. It’s a multi-faceted question, because when there is something kind of inevitable in the nature of scientific progress, it’s kind of coming at us and can try to push back on it in various ways, but it’s coming at us and we have to be pretty creative. I think that, certainly, recognizing that it may take longer to get to the frontier, et cetera. We need to think about when people are peaking later do we have enough flexibility on family policy to allow people, because now we’re getting into child-rearing and sort of timing out child-rearing and how do we create flexibility to allow people to make choices that are pro-family and pro-innovation as well. I think a lot of universities, I don’t know the numbers, are moving towards systems where you get an extra year on your tenure clock, for example, if you have a child and those sort of things, which can help. I think though, there’s some really interesting questions about how we think about structuring earlier education in light of it being harder to get to the frontier. And really on both dimensions, because one is about can I learn, can I get to the frontier faster? Could I teach people more efficiently in K to 12? Another one is, if I’m going to be collaborating all the time, do I need to start baking in collaborative skills, like really early? [stammers] At the most appropriate times. Whatever that might be. These are hard questions, because of course K-12 pedagogy is tricky. I don’t know the answers. I mean, for example, I could say, oh I want to I want to teach algebra now in 6th grade or 5th grade. I’ve got to move it forward to get these kids further along. But how am I gonna do that? What am I squeezing out if I tried to do that? That might be a disaster. And then you can get into debates about how do I, you want creative time, you want artistic time, you want kids to have liberal arts kind of education as well to understand other kinds of values and things. So I think it’s a really tricky question, but it does bear thinking about what are the ways we train people, I think, to get them in a position for success in a world where this is happening.
AUDIENCE MEMBER: Check, check. Many of us are educators and so our product is not a widget, but it is trying to change how people think and how people learn. So I’m wondering from you, you touched on it right now from your previous answer, but I’m wondering from your perspective, is there a notion of time being different for how people think and how they learn, as opposed to in business where you want to bring it to market as fast as possible and get your funding for that. Does it make a difference how fast we try and grow, from your perspective?
BEN JONES: How fast we grow as individuals or as an economy?
AUDIENCE MEMBER: As the enterprise of changing computer science education, for instance.
BEN JONES: Oh. Just to reiterate my last answer for a second, and then I’ll go on. I just, I think we have to be, I think the natural response is to try to be more efficient. And I think, to be honest, American K-12 education could be a lot more efficient. [stammers] There’s a lot of things we would hope to do, especially in serving everybody in K-12. I live in Chicago, I live in a city where a a lot of kids, a high-proportion of kids aren’t gonna graduate from high school, et cetera that aren’t necessarily having the kinds of experiences that are gonna set them up for the kinds of careers we might hope that they might have. At least the opportunities we certainly hope that they would have. So I think there’s a lot we could do in K-12 education, to get students higher quality and greater learning earlier, and not have bad years where they kind of slip behind and never catch up. I think computer science is actually pretty interesting, in this regard. I think computer science is an area where it’s not obvious to me that there is a huge accumulation. I mean, certainly there is in the hardware of computer science, buildin/Users/clarissacoy/Desktop/Day 2-8 Plenary III.txt_Updated.txtg out, fighting Moore’s Laws a pretty deep duck type of endeavor, done by people with post Docs and PhDs, but in terms of programming, kids have proven they’re pretty good at programming. They like programming. They’re increasingly digitally oriented in their daily lives, in a way that gives them a lot of insight into kind of facility with computers and sort of as a user, insight into the kind of things you can do with computers, and that can sort of serve to juice their creativity in interesting ways. What I like about computer science in that regard, is you can teach it as a general skill that gives kids a lot of access to frontier and the capacity to innovate in certain ways, or be part of innovative teams on the computer side that isn’t going to require quite so much education in the long run. By which I mean graduate degrees, et cetera. So it might be kind of a nice lower-barrier entryway into tech for a lot of people. So I’m really excited about teaching it in high school. Not only because it might inspire them to do a lot of other things, but it’s actually a skill that they can really build on and use, hopefully quite efficiently. In the same sense, to be more explicit, that first course in physics in high school, isn’t gonna turn them into practicing physicist typically, but some good training in computer science, even at that young age, can really make them do pretty interesting things with computers and that’s an exciting opportunity I think we should seize, [stammers] you might disagree with me, but it strikes me as an opportunity for NCWIT and also I think more broadly for education in the country. [audience clapping]