Network Effects, Origin Stories, and the Evolution of Tech

“The rules of the game are different in tech,” argues — and has long argued, despite his views not being accepted at first — W. Brian Arthur, technologist-turned-economist who first truly described the phenomenon of “positive feedbacks” in the economy or “increasing returns” (vs. diminishing returns) in the new world of business… a.k.a. network effects. A longtime observer of Silicon Valley and the tech industry, he’s seen how a few early entrepreneurs first got it, fewer investors embrace it, entire companies be built around it, and still yet others miss it… even today.

If an inferior product/technology/way of doing things can sometimes “lock in” the market, does that make network effects more about luck, or strategy? It’s not really locked in though, since over and over again the next big thing comes along. So what does that mean for companies and industries that want to make the new technology shift? And where does competitive advantage even come from when everyone has access to the same building blocks (open source, APIs, etc.) of innovation? Because Arthur — former Stanford professor, visiting researcher at PARC, and external professor at Santa Fe Institute who is also known as one of the fathers of complexity theory in economics — has written about the nature of technology and how it evolves, observing that new technology doesn’t come out of nowhere, but instead, is the result of “combinatorial” innovation. Does this then mean there’s no such thing as a dramatic breakthrough?!

In this hour-long episode of the a16z Podcast, we (Sonal Chokshi with Marc Andreessen) explore many of these questions with Arthur. His answers take us from “the halls of production” to the “casino of technology”; from the “prehistory” to the history of tech; from the invisible underground autonomy economy to the “internet of conversations”; from externally available information to externalized intelligence; and finally, from Silicon Valley to Singapore to China to India and back to Silicon Valley again. Who’s going to win; what are the chances of winning? We don’t know, because it’s a very different game… Do you still want to play?

Show Notes

How the concept of network effects was born [1:05], its initial reception, and eventual adoption [9:53]

Discussion of diminishing returns [18:09] and the role of timing or luck [22:16]

How new technologies are created from existing elements [25:12]

The impact of computer networks [38:44], and the societal impact of machine intelligence [46:42]

Globalization and economies around the world [53:52]

Transcript

Sonal: Hi, everyone. Welcome to the “a16z Podcast.” I’m Sonal. So, today we have a special episode. We talk a lot about network effects as one of the most important dynamics, especially in software-based businesses. You can see much of ours and others’ thinking on the topic at a16z.com/networkeffects. But today, our special guest is W. Brian Arthur. He’s widely credited for first describing network effects, and beyond that, has had a long and very influential career in economics, especially as applied to the tech industry. So I asked Marc Andreessen to co-host and add a little color commentary. But first, more about Brian.

Brian was formerly a professor of economics at Stanford, is a visiting researcher at PARC, formerly Xerox PARC, and is also an external professor at the Santa Fe Institute — because besides his foundational work in network effects, he’s also considered one of the fathers of complexity theory — has written books on the nature of technology and how it evolves, and has also written a number of pieces on AI and the autonomy economy, all of which we’ll touch upon in today’s episode. We also cover a lot of neat history in between, and we end on the topic of innovation clusters around the world, including Silicon Valley. But first, we begin briefly with where Brian’s ideas came from.

The concept of network effects

You’re a really influential economist who’s — and I sometimes make fun of economics.

Prof. Arthur: Feel free.

Sonal: I know. But, you know, your work has really actually driven so much, or described so much, of what actually happens in technology, and there seems to be a gap often between the worlds of economics and technology, and you’re really at the heart of that. So why don’t we start with some of your most seminal work, starting with your famous classic paper around increasing returns and positive feedbacks.

Prof. Arthur: Sure.

Sonal: Like, if you were to just sort of distill and summarize some of the key concepts and how it contributed to the tech industry.

Prof. Arthur: Sure. To go back a little bit, I’ve been interested in technology for a long time. I was trained as an engineer, and then mathematician and operations researcher — basically algorithmic theory. So, my basis is actually technology, and then I added as a layer on top of that — I fell into the wrong company and became an economist. And I arrived in Stanford in 1982. At that time, Silicon Valley was blossoming. We said in ’82, it was all about electronics, then it was about computation, then the web, then the cloud. Now it’s about AI. So, Silicon Valley keeps morphing and changing. I was enormously taken, just by the sheer energy of the place in the early ’80s, and on through 26 years or so since. It keeps recreating itself. It’s like looking into, I don’t know, it’s like looking into some cauldron of everything bubbling and changing all the time. And it became very clear to me that there was a phenomenon going on in technology that you didn’t see so much in the rest of the economy.

Sonal: Right. The phenomenon of network effects, which I should clarify in your papers is also named positive feedbacks, and increasing returns.

Prof. Arthur: Yeah. In standard economics, if you get very large in the market, everybody runs into some sort of diminishing returns, and markets tend to balance. The market’s fairly well-shared. That was the theory when I came along, but it didn’t seem to me that tech worked that way. Go back to about 1982, ’84. At the time, we had VHS and we had Beta. Those were the basic operating systems for video recorders, and one of them happened to be better — Betamax was better — and I started to wonder why VHS dominated the market.

Sonal: I’ve always wondered this, actually.

Prof. Arthur: And then I realized that a host of small events early on had pushed VHS into a slight lead, and if you were going down to your local movie rental store — again, this is…

Sonal: Back when Blockbuster existed.

Prof. Arthur: …Blockbuster, you would tend to see more VHS movies. That meant you’d get a VHS recorder, and that meant that they would stock more VHS.

Sonal: Aren’t those complements, in economic terms?

Prof. Arthur: Oh, yeah. The two were kind of interacting. The more VHS is out there, the more I buy VHS. So I began to realize that I was seeing this in market after market. There weren’t diminishing returns. If VHS got ahead, it would get further advantage. The whole thing was quite unstable, and small events tilted you towards Beta or VHS. My analogy was, this was like bowling a ball perfectly down the middle of an infinitely long bowling alley. It could stay quite long in the middle, but if it started to drift to one side, it would go further, and then it would fall into the gutter at the side, and that side would lock in the market, so to speak.

Sonal: And by the way, you borrowed — I think I remember you telling me that you borrowed the lock-in jargon from the military, like locking in on a target.

Prof. Arthur: Yeah, the — lock-in wasn’t used heavily at the time. I’m sure there are other people who used the phrase, but with fighter jet radar, when you’re going at very high speed and you’re pursuing an enemy or something, or maybe a radar station itself on the ground, you lock into the target. It’s not just that you find the target, but you want to lock on to that target, and then you can release your weapons, and the weapons will stay locked into that particular…

Sonal: Right. I remember this from “Top Gun.” I mean, that was also very popular.

Prof. Arthur: So I borrowed “lock in,” and since, that’s become very popular. We’re locked into this, we’re locked into that. Basically, meaning that small chance events have landed you into something you can’t get out of. So what I realized through quite a few phenomena that have become famous — since this was all very embryonic in my mind — that the sort of firms I was looking at, if one of them got ahead out of half a dozen, it could get further ahead. You couldn’t predict which one would get ahead. It would start to get enough advantage that it could dominate the market and get still further ahead. It would lock in. It would have so much cost advantage — or now we’d say it’s so much user base — that it would be hard to dislodge. Microsoft got ahead with certain contracts very early in the game. They locked in a lot of the personal software in the 1980s.

Similarly, other systems came along since. There were search engines like AltaVista, as well as Google and others. Google gets ahead and began to dominate that market, and now has it pretty well locked in. You could say similar things for social media. So it was a general phenomenon, that anything that got ahead — because you wanted to be with the majority of people — could get further ahead. We now call it network effects. Companies like that set up a network of users, you want to be with the dominant network because your friends are with that, or…

Sonal: It’s more valuable the more you use it — are in it.

Prof. Arthur: Yeah. Or you know more about it, you hear more about it, or you understand it better. Five generations ago, none of our ancestors spoke English, but we’re all speaking English now.

Sonal: English is a network effect? I’ve never thought about that.

Prof. Arthur: We speak English because we wanna be understood by everybody else.

Sonal: Right. You’re right. I never even thought about it.

Prof. Arthur: And if small events had gone otherwise in the 1700s, it might’ve been French. Or, if you were betting in the 1500s, it could have been Latin or whatever.

Sonal: So, how was it received, when you first put out this paper arguing against diminishing returns in tech — more towards positive feedbacks, increasing returns?

Prof. Arthur: Well, I wrote a paper on this in 1983, sent it to four leading economics journals. Not all at the same time, one after another. I finally got it published 6 years later in 1989.

Sonal: So they didn’t really accept it?

Prof. Arthur: They did not like it. I kept getting reviews saying, “We can’t find fault in this, but this isn’t economics.” And in the meantime, the idea was out there, but there was no citation because no journal dared to publish this. There’s a good reason. In those days, what I was saying is that the economy could lock in to technologies — or to products, or even to ways of doing things — that might be inferior, because that came up, maybe, early on by chance and got locked in. And during the Cold War, in the mid-’80s, this was not popular. I gave the talk in Moscow in 1984. I was saying, in a capitalist economy, you can lock in to an inferior product. Hands went up, you know, <in a Russian accent> “Professor, we want to point out that in Soviet Union, such a thing not possible because with socialist planning, we do not make such mistakes.”

Marc: The central planners will dictate the correct outcome.

Prof. Arthur: Yeah. I came back to Stanford, got a Ph.D student. I said, “Figure this out. I don’t believe it.” He did. He wrote a beautiful paper. Robin Cowan is his name. And he showed that even with the best of planning, you can’t foresee what’s gonna happen…

Sonal: That’s fascinating.

Prof. Arthur: …and of course, you can lock in to the wrong thing. Economists hated this. The whole idea was, everybody’s free to choose, and that lands you in the right solution. And I thought, “Is that correct? I’m free to choose. Do we always choose the best spouses? Social statistics might suggest otherwise.” But what it made for was a very different game in Silicon Valley.

Early reception and current views

Sonal: So, speaking of it being a different game. You know, we have a lot of entrepreneurs that listen to our podcast. How does it change the game? Because people always use the phrase “game-changing” very freely.

Prof. Arthur: Well, first of all, entrepreneurs in Silicon Valley are really smart, and they didn’t exactly get all these ideas from me. I’m not being modest, I’m just being realistic. When I brought out this theory, it kind of corroborated their intuition. So, what I’d say is this. If you are thinking in standard terms — go back to brewing beer, or a company like General Foods — if you want to make profits, you’re thinking of getting production up and running properly, getting your costs down, making sure everything’s terribly efficient. The game was different in tech. The whole game was to try to, early on, grab as much advantage as you could. And I remember that I wrote a paper on this — the “Harvard Business Review,” in 1996. And as that paper got circulated very widely in Silicon Valley, I remember hearing one story that Sun Microsystems had developed Java, and naturally, that cost a huge amount of money. So the guys with the green shades…

Sonal: The accountants?

Prof. Arthur: …were saying, naturally enough, we should charge a huge amount of money for anybody who buys this. And the other people, who had read this theory, said, “No, no, no, no, no. Give it away, give it away, give it away for free.” And there was a tremendous hullabaloo over this. And finally, somebody took my article and just slammed it in…

Sonal: Fighting with papers, I love it.

Prof. Arthur: …Scott McNealy’s desk, and it was game over. He got the point immediately that what you do in an increasing returns market is you try to build up your user base. Now, that’s become completely intuitive since. There was a time it wasn’t standard, that the accountants were saying, “We need to amortize all the R&D money, and we need to get that outlay back as fast as we can, so we’ll charge arms and legs. Later, we can drop the cost.”

Marc: Right. It requires deferral of gratification, right? It requires long-term thinking.

Prof. Arthur: That’s right.

Marc: It requires, in other words, not only strategic thinking, but also long-term thinking.

Prof. Arthur: Long-term thinking.

Marc: You have to project forward to what the economics will be when you win.

Prof. Arthur: Yeah. And, again, I think that that makes [a] very different atmosphere in tech. Tech is not about making profits. It’s about positioning yourself in markets, and trying to build up user base, or network advantages — trying to build on those positive feedbacks. Think of amazon.com. For years and years, they kept reinvesting and kept betting on the positive feedbacks, and eventually, they dominated that whole market. Now they can make huge profits and keep expanding. But it gives you a very different way of thinking. I called the standard way of doing things, “The Halls of Production” — you know, these big factories — but it seemed to me that what was happening in tech was not the halls of production. I called that “The Casino of Technology.”

As if you had this huge marquee. There are many tables, with different games going on, you know — oh, yeah, we’re doing a game on face recognition over here, or whatever. And people come up to the table, just as search engines say, “Okay, who’s gonna be here?” “We don’t know. The technology hasn’t really started.” “What’s the technology gonna be like?” “I have no idea, Monsieur.” “How much will I have to put upfront?” “Well, you know, you could join the game, Monsieur for maybe 1 billion.” “What are my chances of winning?” “I have no idea. Perhaps if there are 10 players, your chances might be 1 in 10. Do you still want to play?” So it was a very different game.

And I don’t want to make it sound like too much luck, because the particular entrepreneurs who — kind of, knowing that their technology was right — and they had a sort of instinctive idea of positioning the technology, and building that user base early. Rather than saying, “We want to get profits out of this.” The game keeps changing, but my point is that the basic game in tech is not the same as the basic game in standard production. And every once in a while, you see somebody taken from the standard production side of the economy — some CEO — brought into a tech firm, and they don’t quite get it. The classic case was Apple. The classic case was Sculley.

Sonal: That famous quote, “Do you want to sell sugar water for the rest of your life?” For him to be enticed away from a beverage company to work at Apple?

Prof. Arthur: Yeah. And CEOs are very smart indeed, but it’s not just a matter of intelligence. It’s a different way of thinking. And it’s so familiar to us now — this new way of thinking in the valley, in Silicon Valley — that we take it for granted that we always thought that way. But we didn’t.

Marc: Do you think that — I don’t know if you have a view on this or not. Do you think financial markets understand this, to the degree that they should, even after all this time?

Prof. Arthur: No. I’ll give you two instances. Warren Buffet very, very famously said, “I don’t dabble in high-tech.” He says, “I don’t touch that, simply because I don’t understand it.” A friend of mine, Bill Miller of Legg Mason — I’ve known him for 20 or more years through the Santa Fe Institute. Bill read this stuff, got it, understood it, and did extremely well. So the best answer I can give to that is, it’s not general knowledge among investors fully yet. Certainly wasn’t 20 years ago. But there’s an increasing number of people who get that the rules of the game are different in tech from in standard business.

Marc: One of the smartest hedge fund managers I know — he says there’s still, in financial markets — is what he calls the New York-Palo Alto arbitrage, right? And basically, he said his strategy is [to] spend half his time in New York, and understand what all those assumptions are — which basically are the drivers — New York is the driver of asset prices. That’s where most of the really smart investors are, at least in the U.S. And then he says, basically, come to Palo Alto, figure out all the ways they’re wrong, and then place the contrary bet. And the theory, I think, that you’re laying out underneath that is, basically — you might say that the New York mindset stereotypically might be the “halls of production” mindset, even still.

Prof. Arthur: Yes, that’s right. Yeah.

Marc: Right.

Prof. Arthur: It certainly is that way in Europe. I’m always amazed, and slightly appalled, that people think of technology in Europe as something that’s done by very big companies, and it’s pretty good technology. But they don’t get that this is a game of positioning, of building a user base. And it’s well understood in California. It gets less well-understood on the East Coast, and then not very well understood elsewhere.

Marc: So, question. Another very smart guy, Peter Thiel, takes it a step further. He asserts that in the long run, every kind of industry — every kind of product — either becomes a monopoly or a commodity. In other words, in the long run, the margins either go to infinity, or they’re 100% — or they go to zero. And it’s just a question of time, and if you don’t have increasing returns, you’re on a long-term downward slide to commodity. <Yep.> And he asserts that the things we view as intermediate cases — businesses today that are, like, 20% margins — are fated to decline to zero over time. Is his view, do you think, too extreme, or would you support even a view, kind of, that stark?

Prof. Arthur: I like the idea. I think he’s basically on target, but there are perennially commodity industries — I’m thinking of airlines — where the margins are pretty low. They’re usually lower than 10%, but still these persist, and quite often, governments intervene. Yeah, I have a lot of sympathy for Peter Thiel’s view. I think that in the long, long run, things do tend to get dominated by only one or two players, even in the standard businesses. And the reason that’s not completely and utterly true all the time is that there are new products getting launched all the time in standard product space. And that keeps us in this more standard economic setup.

Diminishing returns and luck

Sonal: When you describe the work on increasing returns, <Yeah.> you also mention the flip side of this, sort of, effect of increasing returns — which is sometimes you might get to the point where the network can go back to a point where it goes to diminishing returns.

Prof. Arthur: Yes.

Sonal: For example, if there’s too many listings in a marketplace, or something. Do you have any thoughts on that, or any new takeaways around that? Because if the network is more valuable as more people use it, why would there be a diminishing return at a certain point, if it gets too big? Like, is there an ideal size?

Prof. Arthur: No, I don’t think so. I think it depends very much on the network itself. Some networks can eventually become commoditized. And so, if it’s a commodity, anybody can, sort of, come in and offer the same thing. But a much more common pattern, and the pattern that I would expect, is that there is a network. Go back to 1984. Microsoft moves in, other companies move in, Microsoft dominates. But eventually what happens in [an] increasing returns market is that the next invention comes along. And some other company [that] is offering web services, or something, comes to dominate. So you can dominate for a while in one large niche in the digital economy, but then the next set of technology comes in, and new players come in with that. Google recognizes this, and Google’s trying to stay ahead of them by being in on the new technologies.

Sonal: Well, it reminds me [of] when they tried to do, like, social networking when Facebook came along, and now they’re, sort of, just decided to become an AI-first company. So it kind of brings that full circle.

Prof. Arthur: Yes, that’s right. But companies don’t always make that transition from one technology to the next very well. Apple’s been very lucky, where they’ve invented some of the technologies, and then they’re able to surf on that new board, so to speak. But the overall thing is that lock-ins tend to last for a certain amount of time, and then they become obsolete, and some new game comes along.

Sonal: Right. Or they become ubiquitous, utility-like — and the new game still comes along. Because I would argue that Google’s always gonna be around for search. <Oh yeah, sure.> Because they’ve, sort of, dominated that market, but they may become, like, utility in that application — if not in something new.

Prof. Arthur: That’s right. And then the advertisers may drift off to something hotter.

Sonal: You mentioned earlier that you don’t think it’s luck, and this discussion makes it almost sound like it’s an accident that there’s a winner-take-all effect. But is there some way of knowing early on — the entrepreneur who maps out the future, who knows the ecosystem — how do we sort of know that these are the ones that will figure out how to tip the market in their favor? What are some of the indicators — it’s not an accident. Like, they’re pulling levers strategically.

Prof. Arthur: Yeah. Let me give you an analogy. Shows how hard this is to predict. I remember sitting — in 1991, I was invited to the Senate building to brief Al Gore, who was a senator then. It was an afternoon, and was quite hot. And they’re all sitting there, everybody was a little bit sleepy. And then Gore says, “Can you give me an example I can latch on to?” And I said, “Yeah, presidential primaries.” <laughter> And they got it immediately. The phenomenon I’m talking about, you know — if something gets ahead, it tends to get further ahead. It’s true in presidential primaries, that if some candidate pulls ahead, they get more financial backing, they can be more visible. The more visible they are, the more likely it looks that they might win the presidency.

So they get further ahead and more backers. You have to be quite a way into the game before it’s pretty clear. That’s the best I can do on that. Meaning, sometimes if there’s a very early tilt, like, within a few months, it’s pretty clear what’s gonna take over. But it can be very much like presidential primaries. It’s all the same mechanism. And predicting exactly who that’s going to be might look easy afterwards, but on the spot, it’s very difficult to do.

Marc: Well, this goes, actually, to the nature, I think, of how history is written, right? Which is, the way history gets written is, the victor is imputed all kinds of positive qualities. Like, genius, visionary, marvelous executer, right? And everybody knew, right? Everybody predicted. And then, of course, the people who don’t win, it’s like, “Oh, idiot, you know, losers, what were they thinking?”

Prof. Arthur: Yeah. Exactly.

Marc: We experience this in venture capital. It’s like, we basically get two kinds of press coverage. One is what a bunch of geniuses we were for backing the successful company, and what a bunch of morons we were for backing the failing company. And I keep pointing out we’re the same people. We don’t whip between genius and moron. We’re somewhere in the middle. But to your point, it’s the nature of the technology casino. The other thing I’ve observed on this point is — I don’t know if it’s cynical sense, or maybe a realistic sense — in a sense, the question of, like, what is the spark that causes one to jump ahead? In a sense, it kind of doesn’t matter.

Sonal: Oh, that’s actually, kind of, very sacrilegious.

Marc: Or even say, a less cynical way to put it might be — there might be 20 different ways somebody gets that initial jump. It might be they start two months earlier, it might be they raise a little more money, it might be they get a key distribution partnership. It kind of doesn’t matter exactly what it is as long as there is — as long as something actually happens. And so, there is a lot of idiosyncratic kind of history to these things.

Prof. Arthur: Yeah. And my shorthand term for all that is luck. Of course, there’s no such thing. It’s just all small events. Who sat beside whom in a airplane, and chatted up somebody or whatever.

Marc: Or whose mother happened to be on the board of United Way — the CEO of IBM, as one example.

Prof. Arthur: Yes. Yeah, yeah. Famous story. Yeah. Yeah.

Sonal: Oh, right. This is that infamous Bill Gates biography story — where, because his mom was on the board of United Way, she met the CEO of IBM, and then that meeting led to Microsoft and IBM striking a software deal that helped Microsoft in the early days.

Marc: Right. Exactly right. Right. The other interesting kind of situation that we run into a lot on this, when we try to figure this out is — it’s fairly often you’ll have a scenario where you’ll have 2 — you might have 20 in the field, but you’ll have 2 companies that you kind of think have the highest probability of winning. And one of them is a little bit further ahead, but has a somewhat less-skilled or experienced founder. And then you’ll have another company that maybe started a little bit later, that will be further behind for the moment, but has a much more experienced and qualified founder CEO. And if you’re going entirely based on current trends, you go with the less-experienced, less-knowledgeable founder. On the other hand, you often have somebody very sharp who’s like, “Oh, yeah, I know exactly what I’m doing. He doesn’t know what he’s doing. I can take him out.” And like, that’s a real — that’s a conundrum that we face every day. <crosstalk> And it really elevates this kind of question of, like, how important actually is skill?

Prof. Arthur: I mean, you’ve pretty much answered your own question, I think. Skill is extremely important, but it’s not tech skill. It’s not even skill in raising money. Those are kind of necessary, but not sufficient. What sort of skill is really, really important is strategic skill. It’s feel for how to build here, how to build up there. Basically, I often thought of this as surfing. You either get a wave or you don’t. If you get the wave, the whole momentum of that wave pulls you forward, and then you’ve got to maneuver and stay in the green water.

How new technology is created

Sonal: Oh, yeah. That was an analogy that Pete Pirolli used to use a lot at PARC for innovation, because he’s such an avid surfer. He would compare the two. And I remember reading an article years ago by JSB as well, that compared executives to surfers.

But let’s actually now shift gears and talk about, like, you know, once you understand these concepts that we’ve been talking about so far. Once you have these building blocks, like, network effects and positive versus diminishing returns — that you can essentially manipulate to pull levers and get the outcome you want, maybe luck, maybe not. The bigger question is, are there macro forces at play here too? I don’t mean macroeconomic. I mean more around the nature of technology and how it evolves — which coincidentally is the name of the book you published in 2009. I have a copy from you on my shelf. Anyway, it surprised me that you once argued that tech evolution is not like evolution in the obvious sense. So, tell us about that.

Prof. Arthur: Well, yeah. Quite a while ago, about 15, 20 years ago, I got really interested in where technology comes from. And the idea around that we have is that there’s some genius in an attic or something.

Sonal: Usually a garage.

Prof. Arthur: Yeah, a garage. That’s right. Cooking up technology and coming up with inventions. What started to become clear to me as having looked in detail at some inventions — that technologies, in a way, come out of other technologies. If you take any individual technology, say, like, a computer in 1940s, it was made possible by having vacuum tubes. By having relay systems, by having very primitive memory systems, maybe mercury delay tubes. All of those things existed already. And so, it seemed to me that technologies evolved by people not so much discovering something new, or inventing, but by putting together different Lego blocks, so to speak, in a new way. Once something was put together — like, say, a radio circuit for transmitting radio waves — it could be thrown back in the Lego set. And occasionally then, some of the new combinations would get a name and be tossed back in. Things like gene sequencing were put together from existing molecular biology technologies, and then that becomes a component in yet other technologies…

Sonal: Right. I mean, CRISPR is a great example.

Prof. Arthur: CRISPR. Exactly.

Sonal: Now you have CRISPR, which itself is a gene-editing tool, which then creates so many other things.

Prof. Arthur: Yep. And that tool will be a component in future technologies. And I began to realize this wasn’t Darwinian. It wasn’t Darwin’s mechanism. It’s evolution, but it wasn’t that you vary radial circuits, or you vary air piston engines…

Sonal: Right. It’s not like a natural selection effect.

Prof. Arthur: Yeah. You can’t vary radial circuits and then suddenly get a computer out of that, or radar. You can’t vary air piston engines in 1930 and get a jet engine out of that. These things come along as completely new combinations, using new principles, and that keeps adding to your Lego set. And that starts to explain why there’s a controversy or a question, say, in the 1920s. Anthropologists were asking, why don’t you have trams and steam engines in the Trobriand Islands? And they began to say, “Well, it’s not because the islanders are stupid. It’s because they don’t have these building blocks to build it out of.” And that, in turn, has many implications. One of them is, if you get a region like Silicon Valley, with an enormous number of these building blocks — and more important, it has people who understand the craft to put all this together — not just the science, but what parameters — then it can very quickly keep coming up with new combinations.

Sonal: What’s the implication for adoption though, for industry?

Prof. Arthur: The implication is that if you have a new collection of technologies — let me just mention AI, artificial intelligence — those are all building blocks. Industry doesn’t adopt AI. AI is a slew of technologies. It’s a new Lego set. Industry is using its own technologies. And what really happens is that industries — the medical industry, the healthcare industry, the aircraft industry, the financial industry — they encounter this new Lego set of AI, and they pick and choose components to create their own new things.

Sonal: <inaudible> and recombine, right.

Marc: One of the interesting, sort of, aspects of that, I find, is as a consequence of what you’re describing. There, it seems to me, is a long pre-history of almost any “new technology,” right? A couple favorite examples I have of that — the French had optical telegraphy working, I think, 40 years before other people figured out electromechanical telegraphy. So, literally, tubes of glass underground in Paris with light pulses going through, and this is like the 1820s or 1830s.

Prof. Arthur: Really?

Sonal: I had no idea.

Marc: Super early. Another great example…

Prof. Arthur: With telescopes or something?

Marc: Yeah. Some sort of — I mean, they were, like, relay stations, but it was little flashes of light, like lanterns, through glass tubes. And so it was, sort of, fiber optics — 160, 180 years prematurely. My other favorite example is — MIT published a great book called “Tube” years ago. It was about the pre-history of television. And we think of television as being, like, 1930s, 1940s, Philo Farnsworth, all these guys. Well, it turns out the idea for television emerged immediately upon the idea for radio. And there was a Scottish inventor named John Logie Baird. And in the — I think 1910s — he invented mechanical television, because he couldn’t do the electric. He did mechanical.

And so he literally had spinning wooden blocks. So he had pixels. It was almost like a computer display, but made out of, literally, wooden blocks. And the pixels would basically spin — the wooden blocks would spin to form pictures. And one of the funniest scenes in the book is, he takes it to the board of governors of the BBC, in 1912 or something, and they’re like, “You are completely out of your mind.” And he’s just like, “Oh, just let me prove it. Let me prove it. Let me get some sets out there. I’ll prove that people want to do this.” And they finally gave him a programming block. They gave him access to the radio frequency — Thursday night, midnight for 15 minutes — and he was broadcasting for months…

Sonal: That’s amazing.

Marc: …you know, mechanical TV that nobody ever saw, right? And then 30 years later, right, people picked it up and actually made the version that works. So then I go through all this just to kind of say — so then what we can project forward is that all of the breakthrough technologies of the next 30, 40, 50 years — they already, in a sense, exist in some form. Is that…

Prof. Arthur: Yes, that’s right. Pretty much. To get a new technology, you need two things. You need to sort of have a principle — meaning a way of doing things. Early television worked on this idea that you could pick up pixels, or little snippets of light or darkness in the image you’re looking at, and then transmit those by radio — very high frequency — decode it at the other end, and reproduce on some screen or another. So, yeah, you need a principle and you need the components. There’s a famous example Stanford was involved in. In the very early 1900s, the U.S. Navy was very interested in telegraphy, or telegraph.

What they had at the time was spark radio. So, you could sort of, you know, <sound of electrical current> send these Morse code things across the whole spectrum. Anybody could pick it up. So they were looking for a perfect sine wave, as continuous-time radio — a continuous wave, not just a spark wave — at a single frequency. There was a company formed, Pacific Telegraph, sometime around 1906, 1907. They managed to get the guy who invented the triode vacuum tube…

Marc: Oh, de Forest?

Prof. Arthur: Yeah, Lee de Forest. Came out from Yale, kind of on the run from predators. De Forest and Federal Telegraph spent several years trying to get a perfect sine wave so that they could transmit radio waves on a single frequency offshore to naval vessels. They couldn’t really do it. And in 1912, AT&T put out a call for inventions. Their idea was to be able to telephone from New York to Chicago, but you needed to have some sort of repeating circuitry — needed to clean up the wave every 20 miles or so, and then retransmit it. It turned out that within about six months, three inventors — de Forest among them — came up with a triode vacuum tube early amplifier. That amplifier was fed back — that becomes an oscillator, kind of like a microphone shrieking. The oscillator gives you a perfect sine wave. You could modulate that and send that out as a radio message to ships offshore, or to anything.

And if I recall right — this is quite early in the game. These radio guys with headphones — they were always called sparks, the radio officers in ships — they were listening to Morse code one time, not very far from here, and suddenly, somebody transmitted music. They all, kind of, jumped — what the hell, you know? <laughter> They’re listening to dot-dot, you know? <Beethoven.> And suddenly there’s music coming out of their headphones, and it blew their minds that this was possible. It’s a bit of a long story, but the point is that individual inventions, like the triode vacuum tube, when put together in clever ways with other components, give you an oscillator, which is the basis of radio transmission. They give you radio receivers, etc., and that builds up the broadcasting industry, which, in turn — parts of that are used to give you television. And then in relay form — on or off switches — these things start to give you logic circuits, and in turn, that gives you early computers, etc.

Sonal: Which in turn… the semiconductor industry to — right.

Prof. Arthur: So, technologies don’t come out of nowhere. They come out of a very deep understanding of what’s in the Lego box and how to put those things together.

Marc: Well, so the pessimistic view on that would be — boy, that means by implication, there really aren’t the kind of eureka moments that people think about. And the pessimistic view on that is — then, therefore, there’s really not gonna be anybody sitting around in the next 20 years who’s gonna say, “I wanna build warp drive,” and therefore faster than light travel. And they’re just gonna come up with it. Or immortality, or whatever, you know, these — and so, in a sense, it’s an argument against, kind of, dramatic innovation. Let’s just say determined innovation. On the other hand, it’s an optimistic argument, because it says the number of combinations of the Lego blocks are combinatorially effectively infinite over time.

Prof. Arthur: Well, I did argue that there are breakthroughs, you know, there are eureka moments. They tend to work that — I’m sitting here wondering how I could get some effect. How could I transmit images by radio wave? And I could be sitting there thinking for months, “Well, I could use this combination, that combination, and another combination,” and then suddenly I realize, if I can get this in place, and that in place, and the other thing in place, that’s gonna work. And the interesting thing is — and I’ve read individual accounts by the dozen from inventors, even lab books. You see this again and again, “Can’t do it. Can’t do it. Can’t do it,” and then, “Oh. Oh. Oh.” One of my favorite stories is that the steam engine already existed way before James Watt. And James Watt, in the 1760s — I think it was in Glasgow — was brought in to see if he could improve it.

So Watt thinks it over and he thinks, “Oh, well, you know, you’re heating the steam, you’re expanding it in a cylinder, then you’re suddenly cooling it again, and all of this is pretty slow. What if I allowed the steam to expand the cylinder, and then that steam is ejected into a second cylinder that’s kept at [a] very low temperature?” Suddenly, the steam collapses, there’s a vacuum, etc. So he invented an independent cold cylinder. He thought of it passing the village green on a Sunday, the Sabbath day. He was properly Scottish. It nearly killed him. He says, “And there I was…” And he knows it’s gonna work, but he can’t get into his workshop until Monday. You can just read this stuff and see that it’s half killing him, that he can’t prove the concept until the next day. He’s a machinist, and he got it to work fairly readily.

Sonal: So he’s using the building blocks to basically — people are using existing building blocks to do this sort of combinatorial innovation, combinatorial evolution…

Prof. Arthur: That’s right. Yeah, yeah. The point I’m making is that new technologies don’t build up as just pure inventions. There’s plenty of breakthrough insights, but they build out of what’s already there, the components. And quite often, then, new things come along, some key breakthrough technologies. Deep learning is one. CRISPR is another.

Sonal: Right. These aren’t just isolated components. They themselves are tools, and literally recombine or create other technologies. And, by the way — in that sense, I think it is very much like evolution. I mean, we had Yuval Harari on the podcast too. And basically, in his book “Sapiens,” he argues that tech helps mankind leapfrog natural evolution. And, only in that context — we were talking about it across a much larger timescale. But in this context, I do think of it as a primordial soup for the next phase.

On that note, you mentioned deep learning, which — we think of it as, basically, machine learning, distributed computing, artificial intelligence. I mean, just for this purpose, we can broadly clump that into one category. And I remember a big piece you did for “McKinsey Quarterly,” right before I left PARC. It was around 2011, and it was on the second economy. Basically an autonomy economy. And actually, you should summarize this, because then I’d like to talk to you about how you might update that today, given all the advances in AI since.

Prof. Arthur: Sure. Yeah. What I was pointing out was that there’s a familiar physical economy, the one we all know about. It has to do with retail stores, and factories, and banks — all the stuff that we see in the physical world. I was checking into a flight in the San Jose airport, sometime around 2011, and when I put my frequent flyer card in, suddenly, it was triggering a lot of processes. Certainly, the flight was being alerted that I was now there, maybe TSA was being alerted. So I began to realize that somehow there’s a huge second economy out there of machines talking to machines. I was thinking of it as a very large underground, unseen, invisible economy — could be in the cloud — of servers talking to servers, of software and algorithms talking to servers, talking to other servers — all being transmitted and in conversation. Always on, and occasionally then, putting out shoots up into the physical world.

And it reminded me as a metaphor of aspen trees. Aspen trees, apparently, are one huge organism — that is, they’re all connected underground with the same root system. And what you see on the surface is the trees themselves, but there is a very, very large underground root system that’s all connected. These roots are all talking to each other, and this would be like the second economy. I now think I should have chosen the term “virtual economy,” or better still, the “autonomous economy,” because all of this is happening without our knowing. It’s autonomous. It’s things talking to things. So I don’t emphasize an internet of things. It’s more like an internet of conversations. Things triggering things, things switching off things, and querying.

Sonal: I mean, just to give it a quick picture. If you have that image of you putting the card in the kiosk at the airport, and you have all these machines talking to each other, if you were to light up all those machines at once, they’d be all around the world. There’d be servers on Amazon’s cloud, there’d be something local. The local printer. There’d be something else, like, a processing payment thing, maybe in Palo Alto. There could be all these different pieces kind of coming together to drive that one transaction.

Prof. Arthur: Yes. And not just a few dozen computers or servers lighting up, because those servers would be lighting up other servers. <Right.> And so, in the end, there could be hundreds of thousands of servers that were lighting up very briefly, maybe only for a few fractions of a second, and then shutting down again, and then passing messages. So I was interested in this autonomous economy. There was general conversation about automation and robots, and 3D printing. And I thought, no, they’re missing the point. I tend to think that the digital revolution — I believe there is such a thing, and I believe it keeps morphing or changing. About every 20 years, the digital revolution gets a new theme. <Right.> And the latest revolution comes almost by accident — that in the 2010s or so, we started to get huge numbers of sensors. Sensing chemicals, sensing visual pixels, sensing images, sensing temperatures — by the hundreds and dozens and hundreds of thousands. And all these sensors out there — and they were maybe feeding back from smartphones or from your car, and huge amounts of data.

About the same time, and this was no coincidence, along comes a new generation of neural networks powered by deep learning. But more than anything, powered by all the data that the sensors are bringing us. And these algorithms started to be able to do one thing very well, and that was pattern recognition. Could recognize your voice much better than before, because of all the data, all the training. It could recognize faces. So, suddenly, we got the ability of algorithms to do things that we thought only humans could do.

As recently as 20 years ago, or 10 years ago, we would have said, “Oh, yeah, computers are great, but they’ll never be good at what humans are good at.” What are humans good at? We’re good at recognizing things, we’re good at fast association. Computers, they can do deduction or logic. We’re not much good at logic, so it seemed that the whole world was nicely divided.

Sonal: But now…

Prof. Arthur: But now, computers have learned to do associative thinking. These patterns mean such and such. And so, suddenly, we’re in an area that we thought only human beings were gonna be good at, and we’re seeing industry after industry change as a result. It’s not just automation, it’s much more than that. It’s redoing or restructuring whole areas of the economy. So, I was looking for an analogy in history that even vaguely resembles what’s happening. The printing revolution, starting around the 1450s — suddenly information went from being very closely guarded by monasteries and abbeys and libraries — these big vellum books chained to desks. And with printing, it became publicly available. So, printing made information externally available, and that changed everything. It very much changes the way people are thinking. Copernicus, for example, had at his disposal data that he could not have got hold of if it just existed in monasteries. It made a huge difference. It brought in modern science, it helped the Renaissance, and this brought us our modern world.

Sonal: I mean, I would agree — but is that the big transformation now? That we have the modern tech equivalent of the printing press?

Prof. Arthur: What’s gone external now is not information. What’s gone external is intelligence. I may be driving in a convoy of 50 driverless cars, and the whole idea of the car adjusting — the car is talking to roadside sensors and servers, it’s talking to other cars, it’s talking to the highway patrol servers, and so on. And it’s basically farming out its intelligence into this other economy, and then getting back intelligent actions in return. So it’s a bit like phone-a-friend, only the friend is incredibly smart, and the friend consists of, again, these hundreds of thousands of servers talking to each other and then adjusting what you do. So suddenly, intelligence doesn’t just exist in human beings. Suddenly, intelligence exists in the cloud, or in this autonomous economy, and we can farm out not just getting information, but getting smart moves back. And this is making all the difference.

Sonal: So it’s not about the form intelligence takes, it’s that intelligence is no longer housed internally in the brains of human workers. Because it’s moved outward into the virtual economy.

Prof. Arthur: Yes, that’s right.

Impact of computer intelligence

Sonal: So, when intelligence is not just information, but, sort of, decision-making, or being able to externalize a lot of this — I mean, one of the things you mentioned earlier is about these building blocks of technology. What happens when all of these things are available to everybody equally? Like, is there not, like, a sort of a Red Queen effect, where everyone’s accessing the same building blocks and tools? So how do companies, how do industries find competitive advantage in that kind of a world?

Prof. Arthur: I think the answer to that question is timing. If I’m a retail bank, whatever that might be, I might be quite a large bank. And I’m saying all these externally intelligent technologies and algorithms are suddenly available. How can I make use of that, and how can I bring those into my operations and combine them with what I’m doing? I’m making mortgage loans, I’m acting as escrow or something, you know — all these various different types of financial operations. I can make a lot of them automatic and autonomous, and get an advantage. The trouble is that that can be rapidly commoditized.

Sonal: So what does that mean for jobs? In this podcast, we talk a lot about how whenever industries are changed in this way — you know, through tech and other shifts — that other new jobs — classic examples include more designers in the age of Adobe design, that new jobs that never existed before, like social media managers — that can only exist today. What’s your take here?

Prof. Arthur: So, what I’m seeing is — about 90 years ago or so, John Maynard Keynes pointed out that he thought by 100 years’ time, 2030, we’d be in an economy where the production problem was largely solved. There’d be enough, in principle, to go around for everyone. There might be plenty, in principle — goods and services around — but getting access to them meant you needed wages, which you needed a job for, and that was not possible. I think that what Keynes said in that regard is becoming true. In other words, the trough is full, but how do the piggies get their share of the trough? So we’re now in a new distributive era.

What’s counting is not how much is produced, but who gets what. The whole question of growth and getting more economic product out there — physical product and services — that’s a job for entrepreneurs, and it’s a job for engineers. Who gets what is much more a political issue, and that’s not quite a job just for politicians, but it’s a job for society to solve. And we haven’t solved it in Europe or anywhere else, so it’s a new era.

Marc: The problem with that theory is the same problem as that theory in Keynes’s era, right? Which is, sort of, Milton Friedman’s observation in the 1950s, 1960s, when that issue came up again. Which is that human wants and needs are infinite, right? One of the things we are best at as a species is coming up with new things that we want. <crosstalk> And then the things that we want in one generation become the things that we need in the next generation, right? Air conditioning goes from being a luxury to being something that we expect…

Sonal: Cell phones…

Marc: …and we get outraged when we don’t have [it], and cell phones and everything else. And, you know, he speculated as a thought experiment — he said, “Look, you know, we have no way of envisioning the wants and needs of what people will have in the future. We just know they’ll be there.” And he said, “Look, maybe it’ll be that, like, you know, right now, psychiatry is a luxury good. And maybe in the future, it’ll be a basic human right to have access to a psychiatrist, and then we’ll employ half the population being psychiatrists to the other half.” It just is one example, right, of…

Prof. Arthur: I’m looking forward to this new economy.

Sonal: I like that one. The best, actually, among the examples, so far.

Marc: Exactly. And in economic terms, of course, the problem with Keynes’s analysis was, it overlooks the role of productivity growth, right? Which is the scenario that you were describing — is the scenario of, like — rapidly increase your productivity growth. And in a world of rapidly increasing productivity growth, you have gigantic gains in economic welfare. You have gigantic growth in underlying industries, right? You have gigantic amounts of entrepreneurial activity that come out of that. And that, then, generates a fountain of new jobs to satisfy all those new wants and needs. And then, finally, I can’t resist pointing out that you’re making this argument on a day when the unemployment rate in the U.S. dropped below 4%.

There’s certainly no trace — and remember, once there was a day in the American economy — you actually had very low productivity growth, not very high productivity growth. Which counters against the argument that there’s some level of unprecedented technological disruption that’s happening, because you certainly can’t see it in the numbers. And then you have unprecedented levels of job growth and employment. So the facts seem to be on the other side of this argument.

Prof. Arthur: Well, let me both agree and disagree here. I certainly agree that there will be whole new categories of jobs. I very much like the idea that half of us will be therapists.

Sonal: I love that one too.

Prof. Arthur: And the other half — and we can swap couches.

Marc: Oh, yeah, no, no, the therapists will need therapists.

Prof. Arthur: I think there’ll be plenty of new jobs invented. At the same time, though, not just through automation and not just through algorithms, but over the last 20 or 30 years, we’ve had a huge amount of globalization. Jobs have been off-shored, and that’s not just due to the rise of China. It’s due to the rise of telecommunications. Of — I can keep track of all the suppliers in China, all the factories in China, the inventories, and so on, in real time. Couldn’t have done that much in the 1980s because the technology wasn’t there. And that hollowed out an enormous amount of traditional workers in the middle of America, and certainly in Britain and in other countries. So, where I would come out on this question — I like your observation. I agree, yes, we will get new jobs, but quite often there’s a big lag in between the original happening of hollowed-out industries, and then something taking its place.

An analogy that I like is that in Britain in the 1850s. The economy was going gangbusters. New textile companies, the railways were just starting to kick in. There was all kinds of possibilities — steelworks, everything got suddenly very serious. And at the same time — so, there were people getting very rich, but at the same time, there was child labor, there were…

Sonal: The Dickensian world…

Prof. Arthur: The whole Dickensian world of people almost being worked to death. Both are true. The economy is going gangbusters. Some people are not doing well out of this. It took about 30 to 60 years before the whole thing equalized and workers had safe conditions, they had much better conditions — and eventually, they were able to partake in a decent way in all this wealth creation. So what I would say is that the digital economy, through globalization — and now through algorithms — is pressing us into a scramble to invent new categories of jobs. I’m optimistic. I think eventually, we’ll get on top of this. And I’m hoping we do it in a good way, where we have creative pursuits, not just rote jobs, like we might’ve had 100 years ago. I think things are going quite well.

Marc: Good.

Globalization and international affairs

Sonal: So, it is a global world now, and it depends on what your frame of reference is. For me, my frame of reference is — I have relatives in India, who are now increasing in their middle class. If your frame of reference is global, you see this as a very different kind of shift. It really depends on where you, sort of, put the square, the rectangle of the frame, and where you zoom in. Because there is Africa, you know, another great example, Cambodia. You have all these countries — there’s something interesting happening there. So, speaking of that, I’d love to hear — because you spent a lot of time in Singapore. I’d love to hear your thoughts on, sort of, the evolution of that, because we’ve often made the argument that this kind of form — top-down, government-planned innovation cluster — never works out, and Singapore is a rare exception. How would you distill it, having been on the ground there?

Prof. Arthur: I’m a watcher of countries that look as if they’re in trouble and then make their way out of trouble. Finland’s a good example, because The Cold War shuts down, Finland was a broker — a bit like Hong Kong, in between the West and the East. Then around 1990, suddenly the bridge is there, but the river ceases to exist. And so then they invented their way out of that with Nokia and other companies. Their back was to the wall. And I could say the same thing in Singapore. When the country was set up, about 51 or 52 years ago, they felt very much as if they had been set adrift — so, like a little rowing boat that was being towed behind Malaysia, and then somebody cut the rope. So, I think, again, it was a matter of desperation, very good planning. People like Lee Kuan Yew, who led the government.

And what they did was, they decided that they would go into what was then tech manufacturing. They had inherited shipyards from the British, etc., so they were able to station themselves as a very early manufacturer, a bit like Hong Kong or Taiwan. Produce cheap goods, and take great advantage that the oil tankers had to stop at — and become a commercial and brokerage hub for shipping. Since that, they’ve moved into finance. What I’m finding — and let me broaden into Asian countries, including China — we tend to think of — as recently as 10 years ago, we would have thought of China as being not fully developed. Not at all like Japan, which is developed. Singapore is quite developed.

What we’re now seeing in Asia is that a lot of countries in Asia, including China, their digital revolution is not much more than two to three years behind what’s happening in California or in the West. They’re extremely well-advanced, they’re paying a huge amount of attention to technical education, and it’s not just that they’re following in China. They’re not just following, say, genomics or AI. They’re inventing their own. Singapore, by dint of strong will and going techie, has managed to do that already. What I do notice in Singapore is that they tend to — not so much initiate perfectly new technologies, but they’re very quick to take them up. China, though, is able to initiate things…

Sonal: Initiate them as well, right.

Prof. Arthur: …especially in things like genomics.

Sonal: Do you think the initiation thing matters? Because part of your thesis around there being these building blocks that are widely available, which leads to this combinatorial innovation, combinatorial evolution, as you describe it. I wonder if that even matters so much anymore, because if these building blocks — open source, APIs — all are available. Like, application programming interfaces that people can combine into entirely new companies. It seems like you can actually draw on the best of the best expertise.

Prof. Arthur: I think so. That’s been a long debate, actually, in economics. Why put all the effort into initiating something when you can just position yourself to learn the technology quickly? The other case is, it’s good to be first. I think it’s debatable. What I would say though in China is that when it comes to a country digitizing everything, China isn’t going to be far behind.

Sonal: It’s especially true of AI, actually.

Prof. Arthur: Yes. Especially in artificial intelligence and in genomics, and probably in several other industries.

Sonal: Well, genomics is particularly interesting, because they were the first to do human-scale studies of CRISPR. Because we, regulatorily — rightly so — may not be able to, or maybe not. I don’t know. I don’t have an opinion on that.

Prof. Arthur: What I see is this sort of technology expanding rapidly into the rest of the world — and the other country, of course, to mention is India. For several decades, technological education in India has been excellent. IIT, places like that, Bangalore — and India is not very far behind. China’s in a better position because China is top-down hierarchical. They can quickly reorganize and change their economy.

Sonal: We go back and forth around this all the time, but every past industrial planning, top-down, centralized model of coordination has eventually eaten its own, and fallen on its own, like — hoisted on its own petard, to use that expression. Which is kind of the thing that inevitably seems to happen. That’s what happened in Japan, and it’ll inevitably happen with China.

Prof. Arthur: Well, it may inevitably happen in the United States, too.

Sonal: That’s true. That’s a very good point.

Prof. Arthur: I do think that, occasionally, economies get a bit tired, people get complacent, etc. I was in India — I’ve been there several times. But a long time ago, like 1975, when there were old English cars — Morris Minors driving around, taxis that you wouldn’t have seen since the 1950s in London. And the Indian economy has gone light years beyond that.

Sonal: Well, I would say that one of the other shifts there, which is important to note here for this part of the conversation, is that India, China, Singapore — they’ve moved away. Well, India went through an outsourcing phase, as you know, you described this, to being originators of their own innovation. They’re not just a copycat narrative. And we’ve written about this when it comes to China as well. I mean, just yesterday, Walmart announced it’s buying Flipkart — which that’s, kind of, an inversion of the typical model that would have happened before. So, anyway, I think that’s an important shift that this is playing out against.

Prof. Arthur: Yeah. The rest of the world is very rapidly catching up. I still think that the U.S. economy is going to do extremely well.

Sonal: That’s great. It’s optimistic.

Prof. Arthur: Well, it’s not just optimism. I think it’s pretty well inevitable. Let me restate this. I think what’s gonna happen in the next decade or two — the story in the U.S. economy is simply going to be that huge industries are going to reorganize themselves along the lines of autonomous intelligence.

Sonal: When you described that the economy has these, sort of, 20-year beams that you’ve seen, and you’ve described them as morphings in your writings. Like, sort of fundamental sea changes. And you described integrated circuits already, and fast computation as the first — we talked about the connection of digital processes, and now you mentioned these sensors. The cheap and ubiquitous sensors. My question for you, as someone who’s long studied this, is — how do you know when you’re seeing the beginning of one of these revolutions? That it’s a morphing in the making. Is this, sort of, a hindsight view? Because you are, sort of, seeing it early with everything else. What are the signs that tell you this is a morphing — this is a big theme that’s emerging? That gives you the confidence to say that about, say, deep learning, or CRISPR, even?

Prof. Arthur: I think that a change is usually quite well underway before people pick it up. You wake up one day and you say, “Oh, my god. The game has changed.” In the case of sensors, I remember in 2010 or so, sitting down with the CTO of Intel, and I asked him, “Can you tell me when the average sensor is, for example, at a parking meter — that might sense a car being at the meter — the average sensor is gonna drop below about 10 cents per unit?” And he said, “Yeah, that’ll be around 2013, 2015.” He knew pretty well exactly, and so I thought, that’s gonna be a game-changer, because we will now know what’s happening everywhere. What I didn’t see at the time was that the ubiquity of sensors would bring in big data. Some of us saw that in advance, but the big data — [we] didn’t see [it] would bring in all these smart algorithms. <Right.> And so, it’s the combination. Is there a way to see these new things coming along? Yeah. If you’re waiting for them.

Sonal: This reminds me of a story that Alvy Ray Smith tells. He’s a PARC alum as well. He co-founded Pixar back in the day. And he did a piece for me at “WIRED” about how they knew very early on — they had John Lasseter, they had this creative vision — they knew very early on the kinds of things that they wanted to do. And they later mapped out, like, a trajectory of their movies based on Moore’s law, but it was, like, a tool for them. So they saw it, but yeah, they had to wait.

Prof. Arthur: But usually, it’s hard to see. The best I can hope for, at least in my own case, is that within two or three years, you just go, “Oh, the game has changed.” And when the game changes, you realize you’re in a slightly different era. And when you’re in that era, you realize that it’s not gonna last. That in 10 years’ time, 20 years’ time, or 30 years’ time, there’ll be a different version. I wanna make this comment very quickly. I’ve been physically in Silicon Valley — if you count Berkeley, I’ve been in the…

Sonal: I think we should count Berkeley.

Prof. Arthur: Yeah. Okay. I’ve been in the Bay Area now for very close to 50 years. I was a grad student in Berkeley. And then, in Stanford, I’ve been here since 1982. And in all that time, when the game gets a little tired at times, people say, “Oh, the Valley is over,” but it doesn’t. It discovers new technologies and then reinvents itself. That’s the way capitalism works here in Silicon Valley, but in other countries, where it’s more planned, it may have stopped, and places like that can come to a halt as a result.

Sonal: That’s the perfect note to end on. I’m gonna quote a piece from one of your middle-early papers. You talk about whether there’s any hope in complexity, essentially, and you say, “It shows us an economy perpetually inventing itself, perpetually creating possibilities for exploitation, perpetually open to a response. An economy that is not dead, static, timeless, and perfect, but one that is alive, ever-changing, organic, and full of messy vitality.”

Prof. Arthur: It’s not a coincidence that I wrote that, because that’s the way Silicon Valley operates. Inventing and reinventing itself, and morphing and changing, in a way you can’t quite predict, and in a way that I think is delightfully messy, but ordered at the same time.

Sonal: Fabulous. A messy ordered vitality. Brian Arthur, thank you for joining the “a16z Podcast.”

Marc: Yeah. Thank you, Brian. That was really tremendous.

Prof. Arthur: And thank you very much for having me. I’m delighted. Thank you.

Sonal: Thank you.