a16z Podcast

Companies, Networks, Crowds

Erik Brynjolfsson, Andrew McAfee, Frank Chen, and Sonal Chokshi

Posted June 28, 2017

Is a network — whether a crowd or blockchain-based entity — going to replace the firm anytime soon? Not yet, argue Andrew McAfee and Erik Brynjolfsson in the new book Machine, Platform, Crowd. But that title is a bit misleading, because the real questions most companies and people wrestle with are more “machine vs. mind”, “platform vs. product”, and “crowd vs. core”. They’re really a set of dichotomies.

Yet the most successful systems are rarely all one or all the other. So how then do companies make choices, tradeoffs in designing products between humans and machines, whether it’s sales people vs. chatbots, or doctors vs. AIs? How can companies combine the fundamental building blocks of businesses — such as network effects, platforms, crowds, and more — in a way that lets them get ahead on the chessboard against the Red Queen? And then finally, at a macro level, how do we plan for the future without falling for the “fatal conceit” (which has now, arguably flipped from radical centralization to radical decentralization) … and just run a ton of experiments to get there?

We (Frank Chen and Sonal Chokshi) discuss all this and more with Brynjolfsson and McAfee, who also founded MIT’s Initiative on the Global Economy — and previously wrote the popular The Second Machine Age and Race Against the Machine. Maybe there’s a better way to stay ahead without having to run faster and faster just to stay in place like Alice in a tech Wonderland.

Show Notes

The basic economics of networks and the concept of complements [1:08]

Discussion of whether “the firm” will survive [10:08], and our inability to simulate all possible outcomes [15:34]

The ability of crowds to problem-solve [20:47], with caveats related to biases in AI [23:34]

Advice for businesses today [30:45]


Hi, everyone. Welcome to the “a16z Podcast.” I’m Sonal. Today we’re doing one of our book podcasts around the new book just out, “Machine, Platform, Crowd.” The authors previously wrote the popular book, “The Second Machine Age.” And before that, their book was “Race Against the Machine.” Sensing a bit of a theme here. So in this episode, we cover those themes, first starting with a bit of Econ 101 around network effects, complements, and other key concepts. Then we discuss how this all plays out organizationally, especially given trends like machine learning, blockchain, and crowds — and tackle the tricky question of whether networks can replace the firm. And where are we in the classic question around the future of the firm? And finally, what can companies do more concretely?

Frank Chen joins the conversation in between, as well, to share his perspective on what he sees, given his role as head of investing and research at a16z. But our main guests on the episode, both from MIT, are Erik Brynjolfsson and Andrew McAfee, who I’m gonna call Andy, is that okay?

Andrew: Otherwise, I’m gonna mistake you for my mom.

Sonal: Good, I don’t wanna be mistaken for your mom.

Andrew: That would be weird.

Sonal: I’m way too young to be your mom. We kind of go way back in the sense that I met you years ago and…

Andrew: Not as far back as I go with my mom.

Sonal: No, no. Let’s be very clear about that.

Andrew: So, you and I will do Andy. Okay?

Networks and complements

Sonal: All right. Good, we’re doing Andy. So, this is your third book together. The real thrust of your work is that this is unprecedented in the speed at which we’re changing and what the effects are. And I think a great theme for this conversation is to sort of break down how those changes are going to play out, and where they’re happening.

Andrew: Yeah.

Erik: Yeah. Well, but let me just push back on that first part a little bit, because in Silicon Valley, everybody agrees with that. And we agree with it. That’s very clear. But we were reading people who didn’t. One of the things that got us writing our first book, “Race Against the Machine,” was there were people who were talking about “the great stagnation,” and how there were no good inventions anymore. Nothing good was invented. In particular, Tyler Cowen, he was spot on that median income had been stagnating. And that was kind of troubling for us. Because, you know, I had been taught the slogan that productivity isn’t everything. But in the long run, if we just have tech progress, everything else takes care of itself. And when Tyler showed us that evidence, we were like, “Oh, this is a real problem.” But we refused to give up on the idea that technology was just doing amazing things…

Andrew: We weren’t gonna let a little evidence get in the way of all the <inaudible>, for God’s sake.

Sonal: Yeah, no. No way. No dammit, we’re not letting that happen.

Erik: But fortunately, we figured out a way out of it. And the way out of it is that even though technology is making the pie bigger, there’s no economic law that everyone’s going to benefit from it. It’s possible for some people to get left behind. Now, to be clear, that’s not what happened for most of the past 200 years. But the past 10, 20 years, there really have been more and more people being left behind. And so you could get stagnating median incomes, even as some people, maybe in the top 1%, got fabulously wealthy. And that helped us reconcile these different perspectives. And it led to a whole broader set of discussions about the way that organizations, and society, and business processes aren’t keeping up with these amazing technologies, and some of the dysfunctions that can create and some of the opportunities that can create.

Sonal: So what are some of the big — well, I think we should break down the fundamental building blocks of a lot of the arguments that you make throughout your work. So let’s talk about networks. And one of the biggest questions I had reading your book was, is a network going to displace the firm in the future? We talk a lot about network effects in our business.

Erik: So networks, sometimes economists call them demand side economies of scale — and it’s basically the idea that a product or service becomes more valuable the more other people that are using that product or service. A classic example is, you know, a telephone, or a fax machine, WhatsApp, Facebook. And you can have supply side economies of scale, just to distinguish that — that’s when the costs get lower as more people use it. And both of these things lead to the big companies winning.

Sonal: And just for shorthand, we tend to describe supply side economies of scales as just economies of scale, the demand side economies of scale as network effect.

Erik: That’s the more common way.

Sonal: More generically.

Erik: And we use both sets of terminology. It’s sometimes useful to talk about supply side and demand side, because a lot of the economics become more intuitive once you understand that there’s the demand side and the supply side, and they both can get better as you get bigger. And then to add a little more layer of subtlety to it, you can have traditional single-sided network effects, like other people using the same telephone, or you can have a two-sided network. And that’s what — really the platform revolution, a lot of that has been triggered by the growth of the so-called two-sided networks.

And the idea there is that it’s not necessarily people using the same product as you, but it could be people on the other side using a different product. So, like, drivers and users are using slightly different apps. And me as a user, I don’t really benefit when more users are also, you know — I want more drivers. And the drivers want more users. So you care about the people on the other side of the network.

Sonal: Except when you’re pulling, because then you do care.

Erik: That’s right.

Sonal: And that’s a case where you do want them on the same side.

Erik: Exactly. And then to make it even more complicated, you can have two sided and one sided at the same time, you can have economies of scale. So you can layer them. You mentioned the word building block. Let’s start with these primitives. And then you can start combining them in different ways.

Andrew: This really starts to turn into three-dimensional chess, because the right way to think about the app ecosystem in Apple is not any kind of one or two-sided network. It’s an n-sided network.

Sonal: Multi-sided, yeah.

Andrew: And lots of different groups of people who value things on the other side, but we don’t decide what the sides are, and we let the self-selection happen. And you just watch the vortex form around that ecosystem. And the only way to understand that is by doing what Erik just did — start with network effects, one-sided, two-sided, two goes to N, value goes to N.

Sonal: Okay, that’s great. And then let’s probe on one big thing, which is we talk about network effects. But let’s quickly define complements in this, because that’s a term that’s frequently used. And I think it has a lot of misconceptions around it.

Erik: Sure. One of the key economic building blocks that we talk about is complements. And a complement is a very simple concept. It’s the idea that one product is more valuable in the presence of another. So my left shoe is more valuable if I also have my right shoe.

Sonal: Well, that’s an obvious example.

Erik: Yeah, yeah. Software is more valuable with the right hardware. And so, complements can be physical, they can even be organizational. Well, so you may have a system that taps into the crowd that’s more valuable when you have a global internet that allows you to do that. So you can have organizational, or technical, or physical complements. And you can sell products that are complementary to each other.

Sonal: The razor blade is the classic example.

Erik: Yeah, razors and blades. And sometimes when you have products that are complementary to one another, it actually can be profitable to give one away to increase the demand for the other one. So people famously gave away razors to sell blades. And this can interact with the network effects and the scale economies. It’s not a good strategy if you don’t have those other things. One of the things that, you know, makes us tear our hair out is that, you know, when MBA students are like, “Oh, yeah, we’ll just give it away.” Like, where is the underlying strategy?

Sonal: Oh, so if you’re just saying like, “I need to do freemium,” without really understanding the underlying strategy that we’re trying to accomplish.

Erik: Exactly, disastrous.

Andrew: And complements are weirdly subtle. And Erik just explained…

Sonal: This is why I wanna ask about it. Because it’s a very nuanced concept.

Andrew: Erik just explained them super clearly. The Econ 101 example that I always fall back on is hamburger meat and hamburger buns. And so, if the price of hamburger meat goes down, demand for buns is going to go up, even if the price of buns doesn’t change. That’s the key thing. The price of one good can stay the same and demand for it will go up. The complements are so tricky that they actually tripped up Steve Jobs really badly. This is not lore, this is fact. He did not want to open up the app store to any outside developers. He thought he had to maintain super tight control over that digital environment. And when the iPhone first released, it did not have any external apps on it. He fought boardroom battles for about a year with people who said, “No, you need to open this up.”

Sonal: What made him cave?

Andrew: Pressure from really smart people inside and outside the company, people on his board and executives at the company. What he didn’t fully realize is that if you open up the app store and you curate successfully, you have just opened the door to this massive number of complements, each one of which is going to nudge out demand for the iPhone. And even if each one only nudges that demand outward…

Sonal: Like 99 cents worth.

Andrew: Yeah.

Erik: Oh, no, even less.

Andrew: Even less.

Erik: Just to be clear, we’re not talking about the literal money…

Sonal: Yeah, I know. I know. Exactly.

Erik: Yeah, we’re talking about the fact that it makes the…

Sonal: It’s a relationship that makes the entire…

Erik: It makes the phone…

Sonal: It makes people want the phone. Remember the early days of the iPhone — I still don’t have an iPhone, I have an Android. But I still remember to this day, the first thing people would say I’m like, “I don’t really like the iPhone that much.” And they’re like, “Oh, it’s not about the phone. It’s all about the apps. It’s all about the apps.” That was the line all the time.

Erik: Angry Birds.

Andrew: Yeah. And the only way to understand the value of opening up that app store is to understand that you are unleashing this tidal wave of complementary goods that were priced at all different price points, including zero, which is awesome. So zero is a really great price. But the more fundamental thing, I think, is that it shifted out demand. It nudged demand upward for the other complementary good, the iPhone itself. And once you grok into that, then you say, “Oh, I got to find all kinds of different ways to do this and play three-dimensional chess with my platform.”

Sonal: Is the corollary of all this that “closed” will never win then?

Erik: No, it’s not nearly as simple as that. But it does show you that if you can leverage these complements, you can create not just a one-time win, but in a whole ecosystem, because Andy’s story turns into a virtuous cycle where the more demand for the iPhone…

Sonal: Right, flywheel.

Erik: Exactly. It’s a flywheel. So that can work very well. But it’s not like you always open up, or you always build complements.

Sonal: Right. Because I was gonna say, a lot of the winners until now have been closed companies.

Erik: Yeah, absolutely.

Andrew: Yeah. And Apple was comparatively closed against Google and the Android ecosystem. One of the things we say is, there is not one right answer. There is not one recipe that you follow for success with machines, platforms, or crowd.

Erik: There are principles.

Frank: And for entrepreneurs who are listening, understanding complements, and the way the people who are creating these ecosystems that have complements is super important. So we’ve been talking about complements where the more apps in the app store, the more attractive an iPhone. So think about that when you’re thinking about development tools for these platforms. Xcode, Visual Studio are so important to Microsoft and Apple, because they’re creating these complements and therefore the desirability for their iPhone. That’s where they make all their money. So if you think, “Hey, I’m going to create a better development tool. I’m going to create a better Xcode.” Like, think again, because Apple is going to spend as much money as it needs to defend a complement universe.

The future of the firm

Sonal: Crushing you. The question that comes to mind for me is what this means for companies.

Frank: So one thing that — conventional wisdom now is, we fund companies whose defensibility is a network effect. In other words, we’re in Lyft and Airbnb precisely because once you have all the hosts, you’re going to get all of the renters, right? And so, one thing to think about is, maybe in the future, even the firm that creates the network effect gets decentralized. Who needs a firm? Why don’t people just come together and we’ll create the right set of incentives for the network to behave? So you can imagine an eBay where there is no company. There’s just a network coming together with the right set of incentives.

Andrew: That was how we wound up the book, is trying to grapple honestly with this question of in the universe that can be turbocharged by the fact that everyone’s got a device, that we’ve got this completely decentralized cryptocurrency system you could pay people with, that we’ve got these technologies of radical decentralization.

Frank: Like the blockchain.

Andrew: Like the blockchain.

Frank: Like the blockchain. Public distributed ledger. Every transaction…

Andrew: Where you could stick…

Frank: …everybody’s <crosstalk>

Andrew: …contracts and code into those things. You can do a lot of the stuff that we used to need a company for. The question gets teed up, are we still gonna have companies in the future? And as Erik and I started to think about all the stuff that we’d learned and tried to digest, our answer was an unequivocal yes. And the main reason for that is that ownership of a thing matters, simply because almost — well, every economist, I think, that we’ve talked to would agree that you can never write a complete contract that will specify exactly what everybody is going to do in all future states of the world.

Sonal: Every possible contingency cannot be accounted for.

Andrew: And the reason for a firm is it gets to make the decisions that are not contractually specified elsewhere. And it gets all the value that’s not apportioned elsewhere in the network.

Erik: It starts with Ronald Coase…

Sonal: Of course, the classic “Nature of the Firm,” 1937 or something.

Andrew: ’37. He’s a hero. He was 9 years old when he wrote that. He was in his 20s or something…

Sonal: Did you say 90 or 9?

Andrew: No, he was in his 20s.

Erik: Yeah, he was in his 20s, 26 I think he was. But then and then more recently, Oliver Hart, who was my thesis advisor, and Bengt Holmström, one of our other colleagues at MIT, elaborate on that, as Andy was saying, with this so-called incomplete contracts theory. One of the blinders that a lot of people, especially technologists have, is they say, “Hey, we can just write everything down under an engineering mindset. We’ll write a complete contract that covers all contingencies.” And the reality is, the world is just too complicated to cover every possible contingency. So when you own a car, you can sell that to someone else. And whoever owns the car gets to have all of what are called the residual rights of control, everything that’s not specified in the contract. You want to change the color of it, that’s what ownership means. And ultimately, you take that to the level of the firm. A firm is an aggregator of a bunch of assets and owns certain things. And that means, that gives them a certain power, that gives them certain incentives of how those objects are used.

Andrew: As Erik and I were trying to reason our way through this and convince ourselves to one view of the world here, this amazing real-life experiment happened, which was the Dow.

Sonal: Yeah. And let’s do a quick terminology thing. When you say the Dow, you mean the corporation that was formed, but that’s very different than a DAO which is a decentralized autonomous organization or decentralized autonomous corporation. This is the Dow, the entity.

Andrew: This is the thing called the Dow.

Sonal: The proper noun, not the generic noun. Yes.

Andrew: The Dow, which was intended to be a completely owner-free, completely decentralized organization along the lines that you just described. And it got hacked, and somebody found out how to treat it like an ATM essentially. So, to the extent there was a group of people, kind of, behind it, they collectively freaked out and thought about what to do. And then they made this fairly autocratic decision — looks a lot like an ownership decision for me, to reset the clock on the entire Dow.

Erik: They became de facto owners, they asserted those rights in a way.

Sonal: That’s right.

Andrew: A de novo. They said, “Okay, we’re gonna do this. And if enough of you go along with this, then this is what’s going to happen.” It was extraordinary for a very decentralized organization, it was kind of heavy.

Sonal: I mean, I love you’re saying something counterintuitive, which is a firm is not going to go away. It’s gonna actually look the same as it does now, then. But when we talk about the transaction cost of all this coordination, and why you need management — or even you have this incomplete contract theory, and people — you can’t predict every contingency. What if we have an algorithmic AI who’s able to then account for every one of those contingencies versus — we’re basing our theories right now on what we know already. We don’t know how it’s gonna play out in the future.

Andrew: Amen.

Erik: Well, we’ll never say never. And yeah, if there’s an AI that has magical properties that we can imagine, you know, all bets are off, of course. But we’re talking about a world right now, where the blockchain and related technologies are allowing radical decentralization of lots of types of decisions. And that’s really important. It’s changing, creating a lot of new opportunities, but it doesn’t change everything. And there are still some core things like this concept of incomplete contracts. Anything that’s not explicit that you can’t write down, maybe you can’t anticipate, and maybe the current AIs can’t anticipate, then those are the residual and that’s where ownership actually…

Andrew: That leads to something like “company” being an enduring part of the economic landscape.

Sonal: I mean, I would even make it more basic, which is, it’s human nature that people — at the end of the day, systems of networks that are online, or in a company, or any other form, are made up of people, and people are fallible and are emotional.

Andrew: And fractious, right?

Sonal: Yes, they wanna fight.

Andrew: If we look at the breakdown in the Bitcoin community and the civil war going on there. Okay, one reason you have management is to say, “Gang, we’re going to go this way and not that way.” And disagree and then commit, as opposed to disagree and then disagree.

Erik: And we all have bounded rationality. Friedrich Hayek called it — was the fatal conceit, the idea that we could plan everything in excruciating detail. The world is far too complicated for any one person or any one group of people to do that. There’s even a, kind of, a Red Queen phenomenon, that the more sophisticated you are, the more sophisticated your competitors are, your customers are, your suppliers are.

Sonal: Why is it called the Red Queen phenomenon?

Erik: Oh, so Alice was…

Sonal: From Victoria Aveyard’s novel, or…

Erik: No, from “Alice in Wonderland.”

Sonal: Oh, from “Alice in Wonderland.” Of course.

Erik: You have to run faster and faster…

Andrew: Faster and faster just to keep up.

Erik: …just to stay in place.

Sonal: Got you.

Erik: So if you get more sophisticated, all those other parties are getting more sophisticated too. You’re not going to be able to completely anticipate what they all do, because they’ll be even more clever.

Andrew: But think about how crazy this is. Hayek brought up the term “the fatal conceit” to demolish this idea that we could centrally plan an economy. And at the time, when a lot of intellectuals in the West were excited about Soviet-style central planning, Hayek wrote one paper and just demolished it. There’s an almost 180-degree reverse — perhaps “fatal conceit” going on — among the fans of radical decentralization as opposed to radical centralization.

Sonal: Right. So you’re saying the same phenomena is at play, just in a different direction. But I wanted to add something, too, because I was gonna say, there’s now some claims out there that the power of simulation has gotten so good that we might be able to actually move to that fatal conceit of being able to centrally plan an economy, because of all these data and machine learning, you know, sort of, signals and whatnot.

Erik: So, Alan Greenspan, of all people, I asked him about computers and the ability to simulate the economy. And he was a chairman of the Federal Reserve, you know, set interest rates and everything. And he said, “Well, yeah, we can understand a lot, lot better. But all the companies are reacting that much faster as well.” And so it’s exactly this Red Queen phenomenon, that however much the Federal Reserve advanced, each company advanced, all the other guys are doing the same thing. If you could freeze the rest of the world, and you were the only party that had access to cloud computing and Moore’s Law, etc. Yeah, maybe you could stay 1 to 10 steps ahead of them. But that’s not the way the world works.

Frank: There’s a great story from the early days of AI on this fatal conceit idea, which was in the late ’80s, Japan tried to organize their entire industrial policy around creating artificial intelligence.

Erik: Fifth generation.

Frank: The fifth generation. Supercomputer.

Sonal: Like what’s happening in China right now.

Frank: Built around expert systems, optimized all the way down in the silicon. So you can imagine, silicon optimized for Lisp, right, so that we can build apps. <crosstalk> And it was a complete failure, precisely to this idea of — you actually can’t plan anything, right? What happened out of the ’80s was more the rise of client server computing and Microsoft Windows. Nobody anticipated that.

Andrew: And the idea that we’re out of that world because of Moore’s Law, because we have much more computational power now, I find that ludicrous.

Sonal: Well, tell me why? If we have this accelerating, growing, fast-happening thing — and I don’t want to make it a crutch to say, like, we can’t predict the future, dot-dot-dot, blah, blah, blah. We already know that. But why not? A lot of things that were tried before didn’t work because it was the wrong time. Why wouldn’t that be possible now? Like, can’t simulation work there?

Frank: Yeah. I mean, speaking as an investor, you know, who’s trying to predict the future and often gets it wrong.

Sonal: As you should.

Frank: You know, it’s hard to imagine a better system than the one we have, which is, let’s spend a little money and run a ton of experiments on businesses to figure out what people want. Because until you have it in the world, you’re not sure what the people will want.

Andrew: And that’s not called simulation in the face of massive computational power. That’s called entrepreneurship and capitalism. It’s a very different approach.

Sonal: I agree with you guys. I find that.

Erik: So, if anything, the data is going the opposite direction.

Sonal: Which is?

Erik: We’re seeing less planning and predicting, less five-year plans, we’re gonna do this, and a lot more experimenting, testing, fail fast. That seems to be a model that works a lot better.

Sonal: But the other thing I was gonna say is, like, I look at countries like China and their incredibly coordinated efforts. And while I agree that past central industrial planning efforts have failed, for various reasons, I don’t know, I think there might be something to it this time. I just want to make sure you guys really disillusion me of that. Help me let it go.

Andrew: And our colleagues, Daron Acemoglu and James Robinson wrote this amazing book called “Why Nations Fail.” And their answer was really straightforward. Nations fail because they have extractive…

Erik: Extractive institutions.

Andrew: Extractive institutions, where an elite grabs power and they just suck up the value of more.

Sonal: Arguably, that’s why companies fail too. <crosstalk>

Andrew: Exactly. And they make sure that their descendants…

Erik: Yes, that’s a good analogy. You should write the next book.

Andrew: And they hand down power to their descendants, and they just make sure that they pervert the rules of the game to benefit themselves. That’s as opposed to inclusive institutions, where you have an honest shot of making the most of your human capital. Now, which one is China? They took big steps in the direction of inclusion by opening up to a market economy. Would we call that authoritarian state, one of actually inclusive institutions? I would not.

Sonal: I think that’s the legitimate thing to say. Okay, so just going back to this idea of extractive institutions. So, I do think it’s interesting that there are now networks that are coming up that are letting people participate differently as owners…

Erik: For sure.

Sonal: …in different ways. And that is where I think this topic of ICOs and token launches is really interesting.

Erik: Part of the power, as Hayek would have said, is that you decentralize some of the local knowledge. They have information that nobody else has. And if you could…

Sonal: That’s right, or resources, like if it’s a computing power…

Erik: Yeah, they have skills. Exactly. If you can move the decision rights to where that knowledge is, you’re going to be better off. And one of the great things that technology has allowed us to do is move around decision rights, move around ownership. So hopefully, if you do it right, you get a better match between the incentives and the decision rights.

The power of crowds

Andrew: The entire third section of our book is about this rebalancing necessary between the core institutions of a company, and the crowd available over the internet now. How much more room there’s very likely ahead of us, with crowdfunding, with crowdsourcing, with different ways to tap into what people can do to give them an ownership stake, to get them bought in and pointing the right direction. Have we scratched the surface of that?

Erik: Let’s talk a little bit about Joy’s law, that no matter what company you work for, most of the smart people in the world work for somebody else. It used to be limited what you could do about that, because there’s only so far you can communicate. But now for the first time in history, a majority of the world’s people are connected with a digital network. So they can access all the world’s knowledge. And part of it isn’t necessarily that they’re smarter out there, part of it just comes from the raw variety, the diversity, the variance. Within a company, you tend to have people who are like-minded, they’ve trained the same way. That’s who they get hired. And maybe the way to solve a problem is with an entirely different approach. And that may be somebody from a different culture, a different way of looking at the world.

And you’re very unlikely to have that diversity inside of a company. It works against it. But if you can find a way to tap into it. One of our colleagues, Karim Lakhani is now at Harvard Business School, he was a Ph.D student at MIT, has done just case study after case study of examples where tapping into the crowd blew away what companies were able to do internally.

Andrew: He worked with the National Institutes of Health to try to improve the speed and accuracy of sequencing human white blood cell genomes, which are really complicated but important to sequence. The National Institutes of Health, which I would call the core of the medical establishment.

Erik: Core in the sense of core versus crowd.

Andrew: They had an algorithm that could do a run in about four hours with about 70% accuracy. There was a faculty member at Harvard Med School who made a big improvement to that algorithm. He developed one that got them up to about 75% accuracy. Karim then worked with the NIH and Topcoder to make this an algorithmic challenge and open up to the crowd. And the best solutions got down to about 10 seconds and about 80% accuracy.

Erik: From 4 hours to 10 seconds.

Andrew: So we called up Karim and he goes, “About average. When I run a crowdsourcing tournament, this is the magnitude of improvement I expect to see.” The last part of that story that continues to blow us away is that they interviewed the best performers who submitted the top-performing algorithms. None of them had a life sciences background. There was not a geneticist…

Sonal: Oh, that’s the best part of the story.

Andrew: …there was not a biologist among them.

Sonal: So crowds and prediction markets are similar. What’s the difference?

Andrew: I would say a prediction market is one way to harness crowd wisdom. Markets do a really good job, overall, on aggregating knowledge.

Erik: Markets tap into the crowd. Google taps into the crowd because their search algorithm basically exploits the link structure that all of us contribute whenever we make pages. There are lots of ways of tapping into the crowd, but being clever about how to reach them, motivate them, aggregate them — still a lot of work to be done on that.

Frank: Let’s talk about the nature of work. Because I think what people do in that firm, either inside or outside, probably changes a lot. So, we have this idea that human decision-making is sort of fundamentally flawed in that, like, there’s biases that you bring to your decision-making that you don’t even understand. So when you’re thinking through, you’re still going to make the same mistake because you don’t understand that you have that bias.

Andrew: After all, walking you through your decision-making process is your brain that came off that flawed decision-making process in the first place. It’s not going to catch its own mistakes typically.

Frank: Right. So it’s a permanent blind spot. And by contrast, you would sort of assume that a machine learning algorithm, trained with a carefully selected broad set of datasets, will have a decision-making efficiency or effectiveness better than, you know, flawed humans. So if that’s the case, what do people in firms do? Like, how do you prepare for this world, where there’s going to be machine learning algorithms that can, in general, make pretty good decisions. And then there’s this idea that, like, maybe the talent is better outside your company than inside your company. So what should you do? Should you join a company?

Erik: It’s just breathtaking what it can do. But it is far, far from being AI complete, being able to do everything that humans can do. There’s a certain class of problems that it’s kicking butt on, but that’s a tiny sliver of what human decision-making is. Even just defining what the problem is, exactly what needs to be done, that’s half the battle. But you need humans to do that. There’s a quote that we had from the book from Picasso, “Computers are useless. All they do is give you answers.”

Sonal: I was a little shocked Picasso was alive when computers were…

Andrew: He actually said that. We went and wholly investigated that one. He said that.

Sonal: I know. I just never associate Picasso and computers. It’s amazing.

Erik: Well, he’s a brilliant guy in a lot of different ways. And obviously, he didn’t know much about the latest neural network systems. But his understanding was spot on, that simply giving the answer isn’t necessarily the most interesting or important part of solving problems.

Sonal: Kevin Kelly actually makes this argument in “The Inevitable.” We had him on the podcast, that the number one job of the future for humans that humans preserve — and this is I think what you’re getting at — is that we ask the questions and computers answer. But I have to say, I actually disagree with that a little bit. Because I’m seeing a new class of generative AI that makes me wonder if they’re going to be asking you questions that make us want to answer differently. I mean, there’s all kinds of interesting things.

Erik: Our brains are made of atoms and so are computers. You know, I’m not going to say that there’s some things that they just can never touch.

Andrew: But I agree, which is that on average, our wetware is amazing. But it’s got a host of bugs, and biases, and glitches in it, that machine learning systems, and properly-configured algorithms in general do not have. So, if you could only pick one of those two entities to help you — the good news is, that’s a false choice. We don’t have to make that choice. And I think the art going forward is being more clear about, “What are we actually good at?” versus what the machines are actually good at. The happy news is that they have very different failure modes.

Erik: Yeah. And I think that’s exactly the key point. It’s a matter of how we can leverage each of them. Because machines have biases as well.

Andrew: Yeah, algorithms are biased by definition.

Erik: It’s not just [that] somebody designed them, but also the training data that they get. I mean, if you decide to give loans based on all the loans that have been approved or rejected in the past, that could have some biases built into it. And some of these neural nets could have billions of connections. Getting it to sort out how exactly — it’s not gonna be one of those — says, “Okay, discriminate against women.” But there may be some very subtle interactions that are hard to anticipate or explain. That said, at least the machines can be tested and improved. And it’s often easier to do that than it is with humans.

Andrew: We are really resistant to having our wetware tweaked. We really just don’t like to be told that we’re glitchy, and here’s the fix and just go do that, no. There’s a concept…

Sonal: That’s the story of most marriages.

Andrew: Yeah, most marriages and most everything, right? It’s really, really hard to do. There’s a concept from linguistics that I find incredibly helpful for helping understand what I think some of the most durable human advantages in a world full of machines will be. And it’s a concept called the intuition of the native speaker. And what they mean by that is, if I look at any English language sentence, I can immediately tell if it’s grammatically perfect or not.

Erik: You just hear it in your head.

Andrew: Yeah. We are the native speakers of the human-created world. Computers are doing this as their second language. I believe we have a massive advantage. We are the native speakers about this reality around us.

Erik: Rather than trying to build a system that does everything from soup to nuts, you get some kind of a division of labor. Sebastian Thrun described a system to us recently that was just fascinating. He’s at Udacity. And a lot of…

Sonal: Another a16z company.

Erik: Yeah.

Andrew: Rock on.

Erik: All right.

Sonal: We make good investments, hey.

Andrew: Listeners at home, we don’t have a list of a16z companies that we’re ticking off.

Sonal: Yeah. I was gonna say, this is all natural, organic. Nobody’s planned a thing, I was just gonna say.

Erik: We do have a list of cool companies, you know, which seem to overlap for some reason. But, you know, Sebastian described how they get incoming traffic in their chat rooms of people asking about their offerings. They decided, “Let’s take this data and we’ll see which of these conversations lead to sales, which ones don’t lead to sales, and label them that way. And then train a neural net about which replies were successful.” And then what they took with those replies, they didn’t try to have a standalone chatbot that then talked to customers. Instead, they had the human salespeople keep interacting. But when they saw one of these more common error modes, they would gently prompt the not-so-good salesperson, you know, “Maybe you want to give them this set of answers or this other set of responses.”

Sonal: So it’s kind of getting their argumentation idea.

Erik: It’s absolutely argumentation. Because there’s a long tail of other questions that the bot had no clue what they were about. So it could help with the most common sets of queries. And this is, I think, a pattern that you see lots and lots. You see it among radiologists. You combine the two and you end up having fewer false positives and fewer false negatives.

Frank: Yeah, I love this idea of sort of machines and humans working together. And I think it’s only a matter of time before we walk into a doctor’s office or a lawyer’s office, where that isn’t the fundamental interaction, and we’ll just be horrified like, “Where’s your AI companion? Why are you trying to do this yourself with your biases?”

Sonal: Oh, that’s fascinating.

Andrew: I couldn’t agree more. Why on earth would I expect my GP, who’s a really good doctor, to be on top of the accumulated mass of human medical knowledge and keeping up to date with the latest developments in all the fields that might relate to what I walk in the door with? That’s an absurd request on a human being. Now, I want that person to be well trained. Even more, I want them to be able to empathize with me, and get me to go along with the course of treatment and get me to buy-in to what’s going on. Because that AI in the background that’s got access to my test and my lab results, again, assessed jaundice in my skin and, you know, how white the sclera of my eyes are, that’s going to be the diagnostic expert in the not too distant future at all.

Sonal: That everybody wants, right?

Frank: That’s exactly right. And I want AI not in the backroom, I want it in the room with me when I’m doing the conversation with the doctor.

Sonal: A seat at the table.

Andrew: You’re right.

Erik: Well, with a seat at the table. It’s a theme that comes up again and again. We talk about mind and machine, product and platform, core and crowd. And we don’t want to give people the mistaken idea that you just cross off the first words of each of those lists and only do the second one.

Andrew: The mantra that I’ve learned is that tech progress rewrites the business playbook. And what the two of us believe is that the way the playbook is being rewritten these days is in favor of machines, platforms, and crowds. So the balance needs to shift more in those directions.

Sonal: So the playbook is in favor of machine, platform, and crowd.

Erik: As opposed to…

Andrew: Mind, product, and core.

Erik: Right. So each of them is really a dichotomy. And the most successful systems are rarely all one or all the other.

Andrew: That’s right.

Applications for today’s businesses

Sonal: A couple of threads that we didn’t get to pull. One question I had when we were talking about not all the talent is inside your company. And, you know, a lot of people talk about open innovation as a way to kind of get around that, like, open source communities, etc. What does that mean for business concretely? What does that mean for core, in the way that you’re defining core, and deploying the power of the crowd? Like, does a business whose main strategy is their core business, does that mean that all their innovation is now outsourced to the crowd? Or is it the other direction? What’s the ideal framework?

Andrew: I think way too many, even successful companies today are overweighting their core. They’re probably spending too much of their total budget on it, way too much of their managerial bandwidth on it. And at the risk of being a little bit cute, I think a core capability for most organizations going forward is going to be interfacing with the crowd, harnessing its energy and its abilities, and then finding out how to bring that back into the organization without setting off all kinds of antibodies, and resistance, and nonsense.

Erik: It’s part of the same lesson we learn from the mind machine trade off — is that defining the problem is important. Whether you define it for the machine, or whether you define it for the crowd, understanding what the problem is you’re really trying to solve. If you can define it well enough, then these contests work great. The contests don’t work great if you just say, “Hey, guys, you know, tell us stuff.” You give them a really precise…

Sonal: Which is what people used to do with the olden days. Remember when companies used to do these crowdsource and innovation boxes.

Erik: Yeah, and it never worked.

Sonal: Yeah, it never worked for a reason. So then that begs another question, though, for me, which is, if you take the innovation from the crowd, and you said earlier that there’s this escalating effect where everyone has access to the same tools, and they’re all catching up really fast with each other, and you can’t — it’s always the Red Queen. You have to run faster than everybody else. But if everyone has access to the same crowd, how does the company get advantage in this space?

Andrew: Then, honestly, it’s a matter of where your leadership throws its attention, how firmly you believe in these new kinds of energies out there. Not how willing you are to open the checkbook and spend money on technology, but how willing you are, forgive me, to open up your brains and rethink your business model in the face of this craziness.

Erik: Who can use these tools more effectively? Just like who can use the cloud more effectively? I mean, it’s a matter — it’s like what it always is. It’s just a set of weapons out there. And some people have a better strategy. Some people have better techniques.

Andrew: The companies that failed during the transition from steam power over to electric power, almost none of them failed because they refused to invest in electricity. That was not the failure mode. The failure mode was, they refused to rethink what a factory could be.

Sonal: And how to really absorb into the core of their business, yeah.

Andrew: And they refused to take seriously the idea of an overhead crane, or an assembly line, or a conveyor belt.

Sonal: Yeah. I’m just thinking about the statistics. When you said this thing about this antibody that organizations naturally have, which is essentially — they just immediately reject this not-invented-here syndrome, basically, a disease.

Andrew: Yeah. Look, and those antibodies are the best news possible for your industry.

Sonal: Research has shown over, and over, and over again, that it is practically impossible for big companies to absorb startups successfully unless they keep them isolated. And one of the questions I have is — the next follow up, basically, of what happens when you leverage this crowd. How do you then really bring them into the company so that you don’t have these antibodies? Do you have any concrete advice?

Andrew: I would look to do that in some of the most forward-thinking parts of the organization. As Erik said, in parts of the organization where the problem can be most clearly defined, and where you’ve got people at the helm of that part of the organization who are willing to take the innovation, the algorithm, whatever that the crowd comes up with, and slot that into the work of the organization.

Erik: There’s a role for the core to be able to define that.

Frank: In our world, a perfect example of the core leveraging the crowd is the classic enterprise software company. So, in the old days, basically, you wrote software, it was all proprietary. You won Gartner Magic Quadrant, then you sent your Rolex-wearing direct salesperson to go sell it to someone. The new enterprise company is, “Let me create an open source project. Let me get a lot of contributors. Let me get contributors to get downloads. And that’s my path to market.”

Sonal: Right. The open source becomes, like, the…

Frank: And the core needs to be there because they got — what’s the project? And what problem are we trying to solve? But the crowd comes into it to basically lend legitimacy, and support, and enthusiasm for the project.

Erik: So if you can be that scarce complement to the abundant crowd, you can create a lot of value. Then you become the linchpin that is capturing a lot of the value as well as creating it. Ultimately, we are an economy of creative destruction. And one of the strengths of the United States and other dynamic economies is that we have this constant turnover. And one of the things that discourages us is that there’s actually fewer startups, less innovation, fewer young firms in America today than there were 10 or 20 years ago.

Sonal: Oh, yeah. We talk about this phenomenon. That worries us too.

Erik: Absolutely. We are all for trying to make the bigger companies more nimble, understand this.

Andrew: Amen.

Erik: But the bigger way that the economy innovates is by having this innovative set of new startups that rise and adopt some of the new technologies. You got to have both. And we’d like to see progress on both dimensions.

Sonal: That’s great. Thank you for joining the “a16z Podcast.”

Andrew: Thanks for having us on. This is fantastic.

Erik: It’s a real pleasure.