The a16z Show - a16z Podcast: Companies, Networks, Crowds

Episode Date: June 29, 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. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hi everyone, welcome to the A6 and Z podcast. I'm Son-Hu. 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, compliments, and other key concepts. Then we discuss how this all plays out organizationally, especially given trends like machine learning, blockchain, 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 in research at A16. But our main guest on the episode, both from MIT, are Eric Brinjolson and Andrew McAfee. Who I'm going to call Andy. Is that okay? Otherwise, I'm going to mistake you for my mom. Good. I don't want to be mistaken for your mom. You're weird. We kind of go way back in the sense that I met you years ago. Not as far back as I go with my mom.

No, no. You and I will do Andy, okay? 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. Yeah. 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, to be clear. But we were reading people who didn't. One of the things that guys writing our first book, Race Against the Machine, was there

were people who were talking about, quote, 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 this 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 on base and face. We weren't going to let a little evidence get in the way of our beliefs for God's sake.

No way, no way. But fortunately, we figured out a way out of it. So, 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 dysfunctiones that can create and some of the opportunities they can create. 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. 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. The classic examples, you know, a telephone or fax machine, WhatsApp, Facebook. And you can have supply-side economies of scale, just to distinguish that. That's when the cost get lower as more people use it. And both of these things lead to the big

companies winning. And just for shorthand, we tend to describe supply-side economies of scale as just economies of scale. Yeah. Yeah. And demand side economies of scale as network effect. That's the more common way. More generically. And we use both sets of terminology. It's sometimes useful to talk about supply side, 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 get at 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 two-sided network. And that's really the platform revolution. A lot of that

has been triggered by the growth of so-called two-sided network. And the idea there is that 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 using it. I want more drivers. And the drivers want more users. So you care about the people on the other side of the network. Except when you're pooling because then you do care. That's right. And that's a case where you do want them on the same side. Exactly. And then to make it even more complicated, you can have two side and one side 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. 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 end-sided network. Lots and lots of different groups of people who value things on the other side, but we don't decide what the sides are in advance of it. 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 Eric just did, start with network effects, one-sided, Two-sided. Two goes to end. Value goes to end. Okay, that's great. And let's probe on one big

thing, which is we talk about network effects, but let's quickly define compliments in this, because that's a term that's frequently used, and I think it has a lot of misconceptions around it. Sure. One of the key economic building blocks that we talk about is compliments. And 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, it's a lot. Well, that's an obvious example. Yeah. Software is more valuable with the right hardware. And so you, Compliments 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 compliments. And you can sell products that are complementary to each other. Razor blade is a lot of examples. 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

like, oh, yeah, we'll just give it away. Like, where is the underlying strategy? Oh, so if you're just saying, like, I need to do freemium without really understanding that or underlying strategy. Exactly. Disasterous. And compliments are weirdly subtle. And Eric just explained. This is why I want to ask about it because it's a very nuanced concept. 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 compliments 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. What made him cave? 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 compliments, each one of which is going to nudge out demand for the iPhone.

And even if each one only nudges that demand outward. Like 99 cents worth. Yeah. Or no, even less. Even less. Just to be clear, we're not talking about the literal money that they get from the news. Yeah, I know. I know.

It's a relationship that makes the entire. It makes people want the phone more. I 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 app. It's all about the app. Bing. It was their line all the time. Angry Birds. 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, well, I got to find all kinds of different ways to do this and play three-dimensional chess with my platform. Is the corollary of all this that clothes will never win then? No. It's not nearly as simple as that. But it does show you that if you can leverage these compliments, you can create not just a one-time win, but in a whole ecosystem. Because Andy Story turns into a virtuous cycle where the more demand for the iPhone. Exactly.

It's a flywheel. So that can work very well. But it's not like you always open up or you always go. Right. Because I don't think a lot of the winners until now have been closed companies. Yeah, absolutely. 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. There are principles. There are principles. And for entrepreneurs who are listening, understanding compliments and the way the people who are creating these ecosystems that have complements is super important. So we've been talking about compliments where the more phones, the more apps in the app store,

the more attractive in 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 compliments 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 X code.

Like, think again because Apple is going to spend as much money as it needs to defend the complement universe. The question that comes to mind for me is what this means for companies. So one thing, the conventional wisdom now is we fund companies whose defenseability. 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 will create the right set of incentives for the network to behave?

So you can imagine in eBay where there is no company. There's just a network coming together with the right set of incentives. That was how we wound up the book, is trying to grab. 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. Like the blockchain. Public distributed ledger. Yeah.

Every transaction. You can stick 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 going to have companies in the future? And as Eric 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, while 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's going to do in every other future state of the world. 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 that's not apportioned apportioned elsewhere in the network. And it starts with Ronald Coase. Of course, the classic nature of the firm paper, what, 1937 or something? He was a geek hero. He was nine years old when he wrote that. He was in his 20s or something. Did you say 90 or 90?

No, he was in his 20s. Yeah, he was in his 26, I think he was. But then, and then more recently, Oliver Hart, who was my thesis advisor, and Banked Holmstrom, 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 to that engineering mindset. We'll write a complete contract that covers all contingencies.

And the reality is that 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 what are called the residual rights of control. Everything that's not specified to contract. You want to change the color of it. That's what ownership means. And ultimately, you take that to the level of a firm. A firm is an aggregator of a bunch of assets and owns certain things. And that means that gives them certain power. It gives them certain incentives of how those objects are used. As Eric 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 Tao. Yeah. And let's do a quick terminology thing. When you say the Dow, you mean the corporation that was foreign. but that's very different than a DAO, which is a decentralized autonomous organization or decentralized autonomous corporation.

Yes, there is a thing called the DAO, the proper noun, not the generic noun, right. The DAO, which was intended to be a completely owner-free, completely decentralized organization along the lines that you just described. And it got hacked. 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 to me, to reset the clock on the entire Dow. They became de facto owners, and they asserted those rights in a way. And de novo, they said, okay, we're going to 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. I mean, I love you're saying something counterintuitive, which is a firm is not going to go away. It's going to actually look the same as it does now then. But when we talk about the cost of the transaction cost of all this coordination and why you need management or even you have these 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 like we don't, we're basing our theories right now on what we know already. We don't know how it's going to play out in the future. Well, we'll never say never.

And yeah, if there's an AI that has magical properties that we can't imagine, you know, all bets are off, of course. But we're talking about a world right now where the blockchain and related technologies is 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 matters. That leads to something like company being an enduring part of the economic landscape. I would even make it more basic, which is this 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. Right. And they're fractious, right? And they want to break down in the Bitcoin community. Yes. There's forks.

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. Yeah. 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. And 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. Why is it called a red queen phenomenon? Oh, um, so from Victoria of your arts novel? From Alice in Wonderland. Oh, from Allison Wonderland, of course. You have to run faster and faster just to stay in place. So if you get more sophisticated, all those other parties are getting more

specifically to, you're not going to be able to completely anticipate what they all do because they'll be even more clever. 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 a almost 180-degree reverse, perhaps fatal conceit going on among the fans of radical decentralization as opposed to radical centralization. Right. So you're saying the same phenomena at play just in the different direction, but I'm going to add something too, because I was going to 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, machine learning, you know, sort of signals and whatnot. 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. better, but all the companies are reacting that much faster as well. And so it's exactly this red queen phenomenon that however much they, 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 one, two, ten steps ahead of them, but that's not the way the world works. 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. The fifth generation. The fifth generation supercomputer built around expert systems, optimized all the way down into silicon. So you can imagine silicon optimized for Lisp, right, so that we could build. Not just imagine they built it, did it, right? 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. Yeah. 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. 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, blah, blah, blah, blah, we already know that. But why not? A lot of things that were tried before, it didn't work because it was a wrong time. Why wouldn't that be possible now? Like, can't simulation work there? Yeah. I mean, speaking as an investor that, you know, is trying to predict the future and often gets it wrong. as you should. 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.

Exactly. On businesses to figure out what people will want. Because until you have it in the world, you're not sure what people will want. And that's not called simulation in the face of massive computational power. That's called entrepreneurship and capitalism. That's a very different approach. I agree with you guys.

I find that. So if anything, the data is going the opposite direction. Which is. We're seeing less planning and predicting, less five-year plans. We're going to do this. And a lot more experimenting. testing, fail fast. That seems to be a model that works a lot better. But the other thing I was going to say is, like, I look at countries like China, and they're

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 disillusioned me of that because help me let it go. And our colleagues, Daronne SMO, or James Robinson, wrote this amazing book called Why Nations Fail. Okay. Yeah. And their answer was really straightforward. it. Nations fail because they have extractive institutions. Extractive institutions. We're an elite grabs power and they just

suck up the value from all. Arguably, that's why companies that fail. Exactly. Exactly. And they make sure that their descendants. That's a good analogy. You should write the next book. 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. 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. For sure. In different ways. And that is where I think this topic of ICOs and token launches is really interesting. Part of the power is, as Hayek would have

said, is that you decentralize some of the local knowledge. They have information that nobody else has. That's right. Or resources, like if it's a computer power. 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, so hopefully if you do it right, you get a better match between the incentives and the decision rights. The whole 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 crowd funding, with crowd source. Sourcing with different ways to tap into what people can do, to give them an ownership stake, to get them bought in and pointed in the right direction. Have we scratched the surface of that? 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, Kareem Lakanis now at Harvard Business School, it was a very unlikely. It was a diversity. a PhD 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. 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. Core in the sense of core versus crowd. 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. Kareem then worked with the NIH and top coder 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. Four hours to 10 seconds. So we called up Kreme and he gets 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.

Oh, that's the best part of the story. There was not a biologist among them. So crowds and prediction markets are similar. What's a difference? I would say a prediction market is one way to harness crowd. Right. Markets do a really good job overall on aggregating knowledge. Markets tap into the crowd.

Google taps into the crowd because their search algorithm basically exploits the link structure that all of us contribute wherever 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. It's a lot of work to be done in that. 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 and that there's biases that you bring to your decision-making that you don't even understand. Right. So when you're thinking it through, you're still going to make the same mistake because you don't understand that you have that bias. After all, we're walking you through your decision. making process is your brain that came up that flawed decision making process in the first place. It's not going to catch its own mistakes. Right. So it's a permanent blind spot. And by contrast, you would

assume that machine learning algorithm trained with a carefully selected broad set of data sets will have a decision-making efficiency or effectiveness better than 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 maybe the talent is better outside your company than inside your company. So what should you do? Should you join a company? 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. I was a little shocked when Picasso was alive when computers were. He actually said that. We went down. Yeah. Yeah. And he said that. I know. I just never associated Picasso and computer. Well, he's a brilliant guy in a lot of different ways. And obviously, he didn't know much about the latest neural networks. Right. But his understanding was spot on that simply giving the answer isn't necessarily the most interesting or important part of solving a problem. Kevin Kelly actually makes this argument in an 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 should 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 new questions that make us want to answer differently. I mean, there's all kinds of interesting things. Our brains are made of atoms and so are computers. I'm not going to say that there's some things that they just can never touch. 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. 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. Yeah, algorithms are biased by definition. Not just 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 going to be one of the other that 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 with human than it is with humans. 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 now. There's a concept. The story of 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 but what I think some of the most durable human advantage 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. You just hear it in your head. 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. Rather in trying to build a system that does everything from soup to nuts, you get some kind of of a division of labor. Sebastian Thrun described the assistant to us recently. That was just fascinating. He's at Udacity and a lot of, he gets incoming traffic. Another A-Satency company. Yeah. Rock on. All right. We've been a good investment. Listeners at home, we are not, we don't have a list of A-66. Yeah, I'm going to see. This is all natural organic. We do have a list of cool companies,

which seems to overlap for some reason. But, 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 talk 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. So it's kind of getting at the augmentation idea. It's absolutely augmentation. Because there's a long tale 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.

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? Look, 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 a course of treatment and get me to buy into what's going on. because that AI in the background that's got access to my test and my lab results and can assess the jaundice in my skin and, you know, how white the scler of my eyes are, that's going to be the diagnostic expert in the not too much and future.

That's exactly right. And I want AI not in the back room. I want it in the room with me when I'm doing the conversation with the doctor. You're right. It's a theme that comes up again and again that we talk about mind and machine, product and platform, core and crowd. And we don't want to give people a mistaken idea that you just cross off the first word of each of those lists and only do the second. 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. So the playbook is in favor of machine, platform, and crowd. As opposed to mine, product, and core. Right. So each of them is really a dichotomy. And the most successful systems are rarely all one or all the other. That's right. 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, et cetera. 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? I think way too many, even successful companies today are overweighing 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. It's part of the same lesson we learned from the mind machine tradeoff 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. Right. Tell us you. Yeah. You give them a really precise well-upy. Which is what people used to do with the olden days.

Remember when we used to do these crowd sorts of innovation boxes? Yeah, I 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 at each other. And you can't, you're always, it's always a red queen. Right. You have to run faster than everybody else. But if everyone has access to the same crowd, how does the company get advantage in the space? Then honestly, it's a matter of where your leadership throws its attention, how firmly 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. 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. There's a set of weapons out there and some people have a better strategy. Some people have better techniques. 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. Right. The failure mode was they refused to rethink what a factory could be and they refused to. And how to really absorb and they refused to take seriously the idea of an overhead crane or an assembly line or a

conveyor belt. I'm just thinking about the statistic because when you said this thing about this antibody that organizations naturally have, which is essentially they just immediately reject this non-invented hair syndrome, basically. Those antibodies are the best news possible for your industry. But the research has shown over and over and over again that it is practically impossible for big companies to absorb startup 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. I would look to do that in some of the most forward-thinking parts of the organization,

as Eric 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. There's a role for the core to be able to define that. In our world, a perfect example of the core leveraging cloud is the classic enterprise software company. Yeah. 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. Right. Right. So the open source becomes like that. And the core needs to be there because they got to, what's the project? Exactly. What problem are we trying to solve? But the crowd comes in into it to basically lend legitimacy and support and enthusiasm for the project. 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, that it's capturing a lot of the value as well as creating it. Ultimately, we are in 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 less, fewer start startups, less innovation, fewer young firms in America today than there were 10 or 20 years ago. Oh, yeah, we talk about this phenomenon. It worries us too. Absolutely. We are all four trying to make the bigger companies more nimble and I understand this. 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've got to have both. And we'd like to see progress on both dimensions. That's great. Thank you for joining the A6 and Z podcast. Thanks for having us on. This is fantastic. It's a real pleasure.