Martin Beraja on AI-tocracy, the Benefits of Unclear Property Rights, and the Massive Potential of AI to Optimize Firms
Martin: The demand for surveillance that the Chinese state had was allowing firms to have access to large quantities of data that they wouldn’t have had otherwise, and hence allowed them to innovate more in this type of technology — facial recognition — that needed a lot of data to be successful.
Andrey: I’m gonna push back on this.
Martin: I like that Seth is the good cop and Andrey is the bad cop in this. [laughs]
Seth: Welcome to Justified Posteriors. Today we’re joined by Martin Beraja, a top economist at Berkeley who has exploded onto the scene with a raft of influential macroeconomics of AI papers. He’ll be talking to us about AI-enabled dictatorship, economic growth when no one knows how AI companies are allowed to use the data they hoover up, and so much more. I’m Seth Benzell, always ready to fight to appropriate a share of the world’s knowledge, coming to you from Chapman University in sunny Southern California.
Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. Welcome to the show, Martin.
Martin: Hi, Seth, Andrey. Thank you for having me. It’s a pleasure.
“Growth at the Resource Frontier” — what the property-rights tradition misses 1:10
Seth: It’s lovely to have you on. We saw you present some of this work at NBER Summer Institute, and we were already talking about having you on, but it was so exciting we just had to get you scheduled. The first thing we wanted to talk to you about is the work you presented there — a paper in progress called “Growth at the Resource Frontier.” Maybe a good place to start us off: what do you think the previous literature misses about economic growth that this paper is designed to capture?
Martin: I would say that something we teach undergrads, and even later on, is the idea that having secure property rights is good for innovation and economic growth. There’s been plenty of evidence and plenty of theoretical models in that tradition. In fact, when you read reports from the IMF or the World Bank or any international organization — I come from Argentina, a middle-income country — a lot of those reports say, “The problem with these countries is that they do not have good property rights institutions.” That’s a very strong tradition in economics. Then there’s another tradition, more on the innovation side, that asks what incentivizes innovators to innovate. The key idea there is that you need to give innovators some form of monopoly rents, either through patents or through trade secrets. They need to be able to exploit their ideas in the market, and those rents must not be competed away — otherwise they wouldn’t innovate. That idea, that rents in the product market drive innovation, is in basically all the models of innovation we have, in one way or another.
Seth: So what’s missing? [laughs]
Martin: When we started with this paper — it’s kind of the story — we began with a conversation with Noam Yuchtman, my co-author. He’s in London, and I think it was just about when the New York Times had filed its complaint against OpenAI, accusing them of appropriating all the Times articles and using them to train ChatGPT. So we’re debating this, and Noam was more like, “Obviously this is terrible that they appropriated all this.” I’m caricaturing the conversation a bit, but— [laughs]
Seth: No, I love that. Make your co-author the straw man. That’s good.
Martin: Yeah, there’s some drama. And obviously, it was illegal, blah. And I said, “Well, Noam, surely you think the innovation wasn’t just the algorithms behind the LLMs and the training, but also the idea that we could use all this public data” — in this case, public or not, that’s the question — “under some notion of fair use, to train LLMs at scale.” That was the big conceptual and technological breakthrough: that more data was enough, at the end of the day. So we started there, thinking about what that conceptual breakthrough was. It was the idea that something previously not recognized as valuable — online text, images, whatever was out there — could suddenly become a new resource, namely training data for LLMs. And once we made that connection, we started to realize: hold on, this has been going on forever in human history. A lot of the history of human progress was really about expanding the resource frontier. In fact, historians name ages after their materials — the Iron Age, the Bronze Age— —the Stone Age— —and so on, up to more modern eras where knowledge became important and, through the Enlightenment, the idea that we could master nature.
Seth: If there were secure property rights over iron ore, the Hittites would have never conquered the Near East.
Martin: Right. Well, we’re gonna get there—
Seth: We’ll get there. [laughs]
Martin: We’ll get there. And then we say: oil was another example, where we brought oil into the resource frontier. The electromagnetic spectrum for radio and TV broadcasting. Data for digital advertising in more recent years. So we started coming up with all these stories and examples of expansions of the resource frontier being really important for human progress. And once we were there — I think this was our moment of inspiration, if you want — we said, “What’s different about these new resources that previously were not in use and didn’t have any economic value is that no one has really bothered to establish who could own them, who could use them, who they belong to.” Property rights. And the reason was that most of the time it wasn’t worth doing it, or it’s just impossible to think in advance how a particular resource could be used and write all the contracts about exactly how that resource could be used.
Seth: Or maybe I’d add to that: it was also kind of non-excludable. If we’re thinking about electromagnetic spectrum, it’s not excludable until you can make electromagnetic waves, right?
Martin: Well, until you can exclude the frequencies in that case. You can say you can transmit on that frequency and not on that one, by regulation.
Seth: Okay. Fair enough. So excludable in terms of offense. Fair enough.
Andrey: It strikes me that the New York Times example doesn’t fit into this very neatly. One interpretation is that we do have great property rights for New York Times content. It has been recognized for ages that it is very valuable. That is what copyright is. It’s not like someone else can, for free, create newspapers with New York Times content, or display their archive. So what’s happening seems to me a bit more like Uber entering the taxi market without a medallion and hoping for the best.
Seth: It’s regulatory arbitrage, right?
Andrey: It’s regulatory arbitrage. Yeah.
Seth: Right. The New York Times story is: there are clear property rights, you’re not supposed to do this, but Sci-Hub exists and it gets on a torrent site.
Martin: Absolutely. This is why in the paper we don’t talk about the New York Times anymore — for the reason that I think it’s not the best example. That said, one way we end up thinking about it is: the Times articles were obviously valuable for consumption, for readers. What wasn’t understood at the time, because the technology didn’t exist, is whether one could use these articles as training data. And the price of an article as training data is very different from the price of an article for consumption. So the ambiguity in the property rights is: did I still need to license these articles if I was going to use them for training LLMs? And the answer is, if it wasn’t at scale, yes — I could learn something from the Times articles and then teach it in class. That fell under fair use. So really the ambiguity here is about the concept of fair use in a world where intellectual goods can be used at scale to train large language models.
Why not just Coase it? DuPont, Conoco, and the plastics counterexample 9:13
Andrey: So let’s think a little bit about the counterfactual. Let’s say OpenAI respected the property right. Do you think that would have substantially slowed down the progress of LLMs? Don’t we believe in Coase’s arguments—
Seth: Yeah, what about Coase?
Andrey: [laughs] —that we would come to an efficient agreement? I don’t know.
Martin: I do not. In this case, I do not. Once you understand that whenever you’re innovating to use new resources, and these resources do not have clearly defined property rights — which does not mean there were no property rights, just that they’re ambiguous enough that it could lead to ex post contestation if I try to appropriate them — you’re right to ask the question: why, instead of just taking and then hiring lawyers ex post, or figuring it out with the government, why not try to contract with the parties that have a potential claim? Honestly, history has seen both. There are examples where innovators brought new resources into use but contracted with the claimants. One example is plastics. The way it worked was that DuPont, one of the main innovators behind plastics, ended up— So plastics are made out of waste from oil refining. That was one example where obviously the waste wasn’t valuable — hence the word “waste” — for refineries like Conoco. But the waste clearly belonged to Conoco.
So it’s not that if DuPont had gone and taken that waste out of Conoco’s deposits, that would have been all right. They would have lost the lawsuit. Instead they contracted. They vertically integrated with Conoco to produce plastics with their waste. So the question is: why wasn’t that the case in these other examples — the electromagnetic spectrum, where people just started transmitting; or data for digital advertising, where platforms just appropriated; or the New York Times, and so on?
Andrey: Those are completely different cases, right?
Martin: How so?
Andrey: Well, no one owned the electromagnetic spectrum. So that one is truly a case where we don’t have property rights at all. The case with the waste is pretty clear who owned it, so it was never a question — it was just a question of contract. And the New York Times, in my mind, it’s pretty clear who owns the rights to the content. It’s the New York Times. And then the data — consumers are willing to give up their data for nothing. Experiment after experiment shows they don’t value the data. I don’t see some sort of nefarious uncertainty-grabbing from the company. It’s more just that you collect data from users, even before the internet — you look at who’s coming into your store and what they like to buy.
Seth: I don’t think Martin’s got a problem in this model with there being a continuum from better-defined to less well-defined property rights. Theoretically, we didn’t need to pass a regulation about electromagnetic spectrum. Common law would have come to an answer. It just would have been a very uncertain answer. There would have been an answer before a judge eventually.
Andrey: Well, I think the electromagnetic spectrum is the clearest example for what you’re arguing. I’m just disputing that the other cases are what you’re arguing.
Seth: Fair enough. Martin, feel free to answer, but I think your answer would just be that it’s a continuum of how well-defined the right is.
Martin: I think different people will disagree on how obviously defined property rights over data were. In fact, we had a lot of conflict and litigation and regulation around it, I think precisely because of that ambiguity. But I agree there’s a continuum from very clearly defined to not so clearly defined, and these technologies fall in different parts of that spectrum.
Seth: Now let me ask the better version of Andrey’s question. Clearly in some situations we have very well-defined property rights, and in some situations less well-defined ones. Is the spicy take of your paper that we might get more growth and innovation if we had less well-defined property rights?
Martin: Yes and no. No, in the sense that the point of the paper is not— I don’t think we would ever say having secure property rights is bad for innovation.
Seth: I remember there’s a result in your paper that if you’re below the optimal rate of growth and monopoly rents aren’t out of control, you want the innovator to be able to appropriate. Did I misread that?
Martin: Correct. But that’s different. Obviously you want secure property rights ex post. Innovators must be able to innovate and then reap the returns from that innovation. What we’re saying here is really not normative. It’s a question about how you design institutions to be more or less permissive toward this type of appropriation. Do you want institutions where, whenever we see anything that looks appropriative and conflictual, instantaneously you give the right to the counterclaimant and not the innovator? Versus institutions that are a bit more permissive, as they have been in the US for most of history, where again and again these instances of appropriation have been legitimized ex post by the courts and the regulators in some form. In that world, there’s a question of a continuum: where do you want to fall on the line of how much appropriation you allow? And the answer may not be zero. Every time we see anything appropriative, we’re just going to shut it down — that would be bad for growth.
Ethan Allen, the Green Mountain Boys, and conflict at the literal frontier 15:10
Seth: I believe this is part of a long American tradition. I was recently visiting Vermont, where I learned about Ethan Allen and the Green Mountain Boys. Are you familiar with these guys?
Martin: I am not, no. You told me about it before, but— [laughs]
Seth: [laughs] I think this illustrates both the excitement of your approach and maybe a factor that’s left out of your model of conflict. Vermont was not a state. It was not one of the original 13 colonies. There was a dispute between New Hampshire and New York about whether the territory that is now Vermont would be part of the two states. New York sold land grants to the area, and New Hampshire sold land grants to the area. Ethan Allen was a real character — free thinker, deist, wrote a book I was skimming the other day that I would describe as Hume, if Hume were a much worse writer [laughs], trying to talk about reason above faith. Anyway, he basically puts together a mob called the Green Mountain Boys and starts harassing people who had bought land under the New York land grants, because they themselves had bought under the much cheaper New Hampshire grants. So they’re basically a reckless mob attacking New Yorkers. Like most good things in America, this is a tradition that comes out of people trying to avoid New York state property taxes.
That institution, the Green Mountain Boys, during the Revolutionary War ends up joining the Revolutionary Army. They seize Fort Ticonderoga, an important early battle. So the innovation Ethan Allen made in reckless violence ended up contributing to the growth of America. Okay — so what do I think this example indicates?
Andrey: What do you mean, it contributed to the growth of America? You have no evidence of that.
Seth: What? They won an important battle, this mob. If he hadn’t made the investment in the complementary good of local civic defense, maybe we don’t have an America today.
Andrey: You don’t see the counterfactual.
Seth: Well, of course. Yeah, dude.
Andrey: [laughs] I know, I don’t understand.
Seth: Wow. Sorry, man. We don’t have an America.
Andrey: I’m just saying that if Vermont was part of New York and that was universally acknowledged, maybe the politicians in New York would have organized to defend. I feel like it’s a bit of a stretch of an argument.
Seth: Okay, well, believe it or not, that’s actually where I’m going with this, Andrey. It’s unclear whether you’d want to use Martin’s framework for thinking about this question of unsecured property rights literally at the frontier. This is the frontier. There are literally unsecured property rights. And do we want to think about unsecured property rights as leading to lawless violence, which is destructive? One thing, Martin, that I didn’t see in this model is conflict as destructive. Usually when economists think about conflict, it’s destructive — all-pay auctions where we end up wasting resources. So I didn’t see that in the model. Is that a natural extension? We’re not seeing people literally show up with militias to fight over their electromagnetic spectrum, but paying for lawyers can certainly be destructive waste.
Martin: No, absolutely. There are a number of things once you go to the normative. That’s why the paper so far is about the idea, and more on the positive side — trying to make predictions and describe this process of growth. Once you get to the normative part, you need to start thinking about whether conflict is inherently destructive, whether there’s some destruction of resources in the process of conflict. And the question Andrey was alluding to as well, which goes back to the traditions in intellectual property protection: if the resource is not just magically there but actually needs to be created somehow — and I think data is much closer to that, where there needs to be effort put into creating the content that later on may or may not be appropriated. All those things would move the optimal policy toward less lax permission for appropriation than you otherwise would have. Now, does it move you all the way to no appropriation? In my mind, no. And so now it’s a question of quantifying these trade-offs, as we usually do in economics. The same way that for patents we don’t say patents should be infinitely lived, nor do we say there should be no patents whatsoever — there is some optimal patent length. I think this is similar.
Andrey: Just a question about that argument. Let’s say we did have defined property rights, but a company just chose to break the law anyway, with the rational calculation that eventually they’ll settle in court. That process might take some years, but by that point the uncertainty about the value of their seizure of the resource will have been resolved. Is that inconsistent with your model? So the property rights are defined, but you’re just going to say “screw it” and go for it anyway.
Martin: There we are much closer to the case where property rights are really well-defined, and then it looks much more like expropriation than appropriation. I guess that’s the difference in my mind. Whereas in the case of truly novel resources — and again, the world is complicated — that’s not the case, because the novelty is what makes the property rights ill-defined to begin with. So the question in reality is: to what extent is the resource truly novel and not owned by someone before?
Andrey: Maybe I’m anchoring back to the New York Times, which you said is not what you want to be talking about, but that’s what I want to be talking about. OpenAI probably sat in a room and said, “We’re probably gonna get sued. We’re gonna do it anyway. We’ll settle at a reasonable price, and we’ll have a better sense of how much it’s worth three years from now, once the model is trained.” To me that really doesn’t seem like uncertainty over property rights. It just seems like a rational calculation. There’s uncertainty over the final bargain.
Martin: Yeah. Imagine that all the value of OpenAI was just this training data. And imagine it wasn’t just the New York Times — imagine it was collective litigation where you had every single piece of data from the internet, everyone behind it. Now we’re talking about whether OpenAI could essentially be expropriated of their company. That’s what would be decided in court. If you’re thinking about that calculation, it’s very different. Of course, if you’re thinking about just one piece of data, like the New York Times, the argument you’d make in court is, “Look, the Times data wasn’t pivotal for training ChatGPT,” which is obviously true. So I should compensate these people somehow, but not with the full value of my company, because I have good substitutes — which I appropriated from somewhere else. One example where that’s not the case, which is interesting, is YouTube videos. When you train on YouTube videos that were already subtitled, you wouldn’t say there are good substitutes for YouTube. Somehow that litigation has not happened. There’s no court case, YouTube against any of these companies, for obvious reasons.
Seth: Wait, what’s the obvious reason? [laughs]
Martin: Well, some of them own them. [laughs] Essentially, some of them own them.
Andrey: Why wouldn’t Google sue OpenAI over training on YouTube videos?
Martin: I don’t know. I imagine that could have knock-on effects where they don’t want that to become—
Seth: On net, Google wants appropriation. [laughs]
Martin: Yeah, so they don’t want to start suing each other, I imagine.
Seth: They should sue and then intentionally lose to set the case law their way. Galaxy brain.
Martin: [laughs] But look, you have the view that the New York Times case is clear-cut. I imagine OpenAI had the view that “we may pay something, but this is sufficiently valuable to us that it’s worth it.” That’s the rational calculation. So it’s really a question of how strong the penalties are ex post and how much they’ll have to pay. There are plenty of reasons why they may choose not to contract. Some are obvious — first-mover advantage, or the fact that you’d have to contract with every possible claimant, which would be a nightmare and impossible. You’d never launch the product. And there’s another reason that’s plausible: deniability. Imagine you went out and tried to contract with the New York Times, Reddit, Wikipedia, and they said, “No, you can’t have our data.” And then you still go ahead and do it. That would travel badly in court ex post. They’d say, “Look, they tried to do this, we said no, they still did it.” Not good.
Andrey: It’s also interesting, because I do think they have now contracted with the New York Times, right?
Martin: They have now done so, yes, for the new stuff. But part of it is that in these bargaining situations, when the two parties don’t exactly know what it is that they have, the bargaining can become hard. The New York Times cannot really assess what the value of their own data is, and OpenAI at the time maybe didn’t know exactly what they had. They thought it was great — but how great?
Seth: So this is another thing that’s not explicitly modeled but you’d imagine as an elaboration. Crisis bargaining models suggest you fight when there’s disagreement about the underlying value of the thing you’re fighting over— —and the likelihood of winning, right?
Martin: Yep. And that’s one reason you’d choose not to contract. There’s no way to contract, and then the decision becomes: either I don’t develop the technology, or I go ahead and appropriate, and we figure it out in court ex post.
“Data-intensive Innovation and the State” — surveillance data and commercial AI 25:25
Andrey: Fascinating discussion. Now we’ll go to a domain where property rights are very different [laughs] — namely, China. You’ve done a bunch of work on AI in China. I’m wondering whether you can first summarize the main lessons you’ve learned from that stream of work.
Martin: This is work also with Noam Yuchtman, and with David Yang. We started off when David was visiting MIT. We were lucky — one of those random things. He was just in the office next door. So we started chatting. He of course knew a lot about China, being from there and having studied it. And we started thinking: this is a great place to study how the state and innovation are intertwined, because it has been so pervasive in modern history, and where we’d have good data. The initial idea was to think about how the fact that the Chinese state cares about AI — and when I say AI here, this meant facial recognition, not the fancy things we have now — is influencing the direction of innovation. That was a very early idea, and we said, okay, this is too big a question. So then we started thinking more narrowly about what exactly we could talk about. The first paper we wrote was on how Chinese facial-recognition AI firms with access to government data — in particular surveillance video feeds from cameras on the streets — actually got a competitive edge in innovation.
We collected data on cameras on the streets to construct a measure of how much data these firms were getting access to, and on the contracts these firms had with municipal police departments. This was more of a positive paper, in the sense of saying: the demand for surveillance that the Chinese state had was allowing firms access to large quantities of data they wouldn’t have had otherwise, and hence allowed them to innovate more in this type of technology, facial recognition, that needed a lot of data to be successful.
Andrey: So I’m gonna push back on this.
Martin: I like that Seth is the good cop and Andrey is the bad cop in this. [laughs]
Andrey: So there’s the simplistic claim that this government data allows for much better facial recognition technology. If I think about it from first principles — yes, more data is better, but the bottleneck to facial recognition is probably more the mapping between an individual and the video, rather than just the video itself. That’s one part. And two, it doesn’t seem like it’s that hard of a problem. The reason the West wasn’t doing it was simply that it was illegal to do it. And in fact, I’m sure the West was doing it in its own way — the CIA, the NSA. It’s not clear this is a very difficult technology to begin with. So why don’t you speak to those?
Martin: Well, I can speak to the evidence that we find. [laughs] The question is: are there very good substitutes for this data in private markets? If the answer were yes, you wouldn’t see any effect from firms contracting with the state and getting access to this data versus not. That said, we can’t really know — we never see how much data they were actually getting access to. We only saw proxies, like how large the surveillance network was in the municipal police departments they contracted with. Now, on whether they’re mapping a face to a name: we don’t know that, but I imagine the answer is yes, because these were municipal police departments that already had the list of people’s names, IDs, and so on.
Andrey: And what is your measure of innovation here? Is it just market success, or something else?
Martin: It’s very crude. It’s just numbers of products, really.
Andrey: So isn’t it — let’s say I win a contract with the local government. Now I have capital. I can continue my business. And, given how business is done in China, I probably have other ways of getting additional capital as a result of that. Maybe some kickbacks.
Martin: Were you referring to Andrey in the paper? [laughs] These are obviously the concerns about identification. The things we can control for are capital, for instance — we know how large the contracts were in monetary terms, so those are easy to control for. Market access was something we really worried about: maybe if you get a contract, that gives you reputation, and the reason you start innovating more and producing more is because now you’re the firm that got a contract. We did a couple of things for that. First, look at firms that were already well-established and producing a lot before, for which these effects would be mitigated — we saw basically the same results. Second, look at second or third contracts instead of just the first, where reputation effects would be weaker.
Andrey: But how can you identify the causal effect of a second or third contract? They already got the first contract.
Martin: Oh, right. Of course, it’s not perfect. You need to control for getting the first contract in that case. All of these are imperfect ways of trying to look at the same thing.
Andrey: All right. So I want to take a step back. I’m the microeconomist here, but one of the themes of the show is how micro evidence doesn’t teach us as much as we hope. [laughs] And you’re a macroeconomist, so I have no problem with the scholarship, Martin.
Martin: But I dabbled in applied microeconomics.
Seth: Dabbled. Dabbled.
Andrey: Dabbled. Maybe we all dabble in the QJE. [laughs] One of the claims is that having access to government data allows you to innovate more with the data. In some sense, obviously true — but the question is how much. And looking at this specific sub-technology, how much does it really inform us? Like a lot of things related to AI, it’s just so obviously swamped by what has happened since. It’s kind of even funny to think that whatever was going on in 2017 in facial recognition has anything to do with AI. [laughs]
Seth: Right. You must admit, Martin, the title is a bit bold — AI-tocracy. If we’re just talking about facial recognition, that seems like a very small piece of the puzzle.
Martin: Right, and that was the second paper. So let me just say — I agree with you, Andrey, and I would say this about a lot of applied micro papers. Is the channel here obvious qualitatively? Yes. You would think more data is better for innovation in things where you need data to train the models. So the question was basically how much. And there was something about testing this idea that data is non-rival, which in theory is obviously true, but in practice we didn’t know. We found that the effects were driven by firms innovating in commercial products, even when the data was surveillance feed. The typical example: you get a contract to provide services to a municipal police department to process their video surveillance feeds, and then you create a product for the commercial market that allows supermarkets to track people as they move along aisles, to know where you need to restock.
Seth: Wait, you’re telling me that the company that has influential political and police connections in China also has an advantage in the private market?
Martin: For producing these types of products, yes. [laughs]
Seth: [laughs] Well, maybe data has something to do with that. [laughs]
Martin: Maybe, or maybe it’s reputation. I agree with you. I think we did a good job trying to convince ourselves and others that it wasn’t the whole story. But as always, it’s not papers, it’s literatures. And then the autocracy paper — that’s the bolder paper.
“AI-tocracy” — is AI a centralizing technology? 34:54
Andrey: So this is a claim that’s made a lot. I think Peter Thiel has famously made it: that AI is a centralizing technology, and this benefits authoritarian governments. Do you think that’s true? How big of an effect is this?
Seth: And who is the Antichrist? [laughs]
Martin: [laughs] So the bold claim — it’s a conjecture, I guess. And somehow I keep writing papers that go against everything Daron Acemoglu says, who was my colleague and whom I love. [laughs] The other one was also—
Andrey: He’s our next podcast guest.
Seth: Next guest.
Martin: Okay, well, he knows. [laughs] That’s perfect sequencing, actually. So: the tradition in economics is that autocratic regimes are bad for innovation. The reason being that property rights are insecure, innovators are afraid of being expropriated by the autocrat, and even if they become wealthy, they can be incarcerated or killed because they become a threat to the regime. All these things essentially tax innovation under autocracy. I very much agree with all of that. In the AI-tocracy paper, what we said is: there’s a countervailing point, that sometimes we’ve seen autocracies actually innovate a lot, at least in particular technological domains. Russia is an obvious example.
Andrey: In terms of poisons for assassinations? [laughs]
Martin: [laughs] I would say in terms of space technology. Imperial Germany is another example of a less-than-democratic regime — in that case it was more about chemicals. Those observations don’t quite fit the other story. So the story we tell, and some of the evidence we find: first, we do see that in those places in China adopting more facial recognition technology, these are precisely the places where we were seeing more protests before. That’s a revealed-preference argument that the reason municipal police departments were adopting facial recognition is to squash different types of protests in those regions.
Seth: This is 1984. The only innovation that is allowed is innovation in technologies of social control.
Martin: Right. Now, if that’s the whole story, then you would see very narrow innovation — innovation in this particular technology would not spill over to other parts of the economy. The conjecture in the autocracy paper is that if there are indeed spillovers, either in terms of the data you accumulated, which was the point of the previous paper, or more traditional spillovers in terms of ideas, then innovating a lot for surveillance technology because the government demands it may end up boosting innovation more broadly in the rest of the economy. And you get into a feedback loop where there’s more innovation in this other part of the economy, which again makes the surveillance technology better. So instead of autocratic regimes being detrimental to growth, you get that in fact they boost it. And instead of growth being destabilizing for autocratic regimes, they’re stabilizing, because they produce the very technology that allows them to repress dissent. That’s the story in that paper.
Andrey: I’m gonna make the same comment here. In some sense I buy the argument, but I just don’t update at all from the data. Okay, so there’s a protest and then those local places get more facial recognition. Sure. China doesn’t have any problems with facial recognition to begin with. I’m sure the federal government of China is using facial recognition a lot already, so it’s really a question about local police departments. And in terms of innovation in AI, it’s hard to imagine that the important dimension of innovation here is facial recognition.
Seth: Yeah, let me tell a story. You’re a democracy, right? That means you have a lot of political parties always trying to innovate in tools of social persuasion. So places that have close elections get more invested in tools of social persuasion, and then there are spillovers from that public good that boost adjacent innovation.
Martin: Again, as an applied microeconomist, one needs to be able to extrapolate a bit [laughs] from the particular context we’re studying to a broader type of story. Seth mentioned one. In China, obviously, control over social media and the digital world more generally is important, and AI has been a big part of that too, and will continue to be. So the broader story is that innovations in technologies of social control that then spill over into other domains can create feedbacks by which the regime both becomes more stable and grows. And at which point the more traditional autocratic tax on innovation starts to kick in — that’s more of a quantitative question. I don’t know. The two things are both there, and it’d be impossible to quantify, I would say.
Seth: All right. Impossible to quantify. Let’s move on to the next topic.
Andrey: [laughs] That’s a theme of our podcast.
Seth: The question that we care about is the one we can’t know.
“The Value of Organizational Learning Technologies” — AI as a codification technology 40:40
Seth: So, Martin, excellent stuff. I’m now going to ask you about another paper that has a really clever theoretical argument about economic growth, but some challenges in bringing it to the data: your paper “The Value of Organizational Learning Technologies,” with Eduard Talamás. I’ve seen him present it. It’s a really cool paper. The reason I love it is that it’s attacking a really underappreciated angle of how AI may change firms. Everybody wants to talk about automation, but that’s a little overexposed compared to this idea that AI might help organizational learning, or what I might call managerial expertise. So how do you think about organizational learning or managerial expertise in this paper, and why do you think AI is going to help with it?
Martin: I’m really excited about this line of work too. I think there’s a lot to be done at the intersection of AI and org econ — where you start thinking about AI as not just a fancy robot. By the way, the title of the paper is now “The Value of Codifying Organizational Knowledge,” and it will become clear why in a second. The idea is that organizations and firms take a long time to grow, to scale, because there’s a process of trial and error and experimentation they all have to go through until they figure out exactly what the right processes are, what the right products are, who the customer is, who the market is, how to expand, how to hire, how to price. All these business decisions take time to learn how to do well for the particular thing a business is producing and selling.
Seth: Let me pause you right there to understand why it takes firms a long time to grow. In our macroeconomic models we usually have some parameter for firms growing over time — a capital adjustment cost, which explains why average Q is different from marginal Q. When somebody says there is an adjustment cost in capital, are we talking about something like the learning you’re describing, or are we talking about the fact that when you decide to build a factory, it’s still going to be five years until you get the factory?
Martin: It’s neither of those two, actually. The factory taking five years is more like an adjustment cost — a time lag for building things. The way I think about this is more a form of learning-by-doing and experimentation that firms go through to increase their productivity. It doesn’t show up in the physical investments you see. It shows up in productivity, or in the quality of the products the firms have.
Seth: I guess what I’m pointing at is that the one parameter we have in a lot of models is just a capital adjustment cost. And what you’d say is: some of that’s organizational learning, and some of it is that it takes five years to build the factory.
Martin: Yeah, if you bundle everything under “capital, intangible and tangible,” then yes. But we do have models of that intangible buildup that have to do with learning as well. Now, the question that comes up: different from capital — which you don’t need to build yourself, you can rent it in the market or buy it from a different firm — this idea of organizational capital or knowledge is something the firm needs to accumulate internally. It cannot just buy it from the market. That’s why it takes so many decades to build up that knowledge. That said, we have had, in different eras, different technologies that can codify that knowledge and then diffuse it to the rest of the economy. One very early codification technology was manuals — scientific manuals. Someone figured out how to do a particular chemical process, and by writing the manual of how to do that, the knowledge accumulated through that person’s learning could be codified, because they could spell it out and transfer it to everyone else in the economy.
Later on we had scientific management in factories. And later, actual management — the idea that management is a technology that can translate best practices across businesses. And franchising. I love franchising as an example of a codification technology, because it’s literally that. McDonald’s took a long time to figure out exactly how to produce a Big Mac. But once they figured it out, they could write down the instructions for how you set up a new McDonald’s.
Seth: There’s a binder. You go and open up your franchise, you get the binder, and the binder gives you the step-by-step of what to do. I think it’s optimistic if you think that chemistry instructions are at that level of instructiveness, but— [both laugh]
Martin: So now the new McDonald’s that opens up doesn’t need to go through the same process of learning. It can be born as an old, experienced McDonald’s. That said, these codification technologies still remain pretty imperfect, because they require humans to go ahead and spell out explicitly and describe what’s going on. That process can be very hard. That’s why we still see learning curves in many industries. So the conjecture in the paper is: AI is somewhat different from these other codification technologies, in that it does not require a human to explicitly say in natural language what’s going on. Instead, it can learn from instances and experiences of the firm that are captured in operational logs, emails, different parts of the organization that probably need to be digitized. And if new AI tools can learn from this type of operational data, then that is knowledge that can be codified and transferred to newcomers.
Seth: Let me pause you there again. You’re emphasizing the ability of AIs to replicate the knowledge that’s already inside the firm. But one of your thought experiments is: what if you could raise each firm’s productivity to its expected long-term productivity immediately? To me, that doesn’t sound like “we get some knowledge and then spread it faster.” That sounds like the AI actually helps us do the iteration and makes the right decision without the experimentation.
Martin: Great question. There are two parts to this, and that’s why I was building up. One is a blunt experiment we do. Forget about AI. Imagine we could somehow codify all the knowledge that mature firms have and just give it to new entrants right away. Instead of taking ten years to build up, they’d start with it. There would be no learning curves at all. What’s the value of that? We can quantify that by looking at actual learning curves of firms in the US economy — in particular, how large a mature firm is compared to a new firm.
Seth: We’ll come back to how plausible this is. Keep going. [laughs]
The number: if entrants were born old, US GDP would roughly double 48:19
Martin: So mature firms are about seven times larger, and they also exit much less, which is something else that contributes to increasing total output in the economy. So think about that ridiculous experiment. That number tells you: if you just did that, and new entrants were born old — like a forty-year-old firm — then US GDP would about double.
Seth: [whistles]
Martin: That’s a huge number, and we think of it as a bound. Now, what does AI have to do with all of this, at least in the long run? Here’s where we talk about a way of implementing this in practice with an AI system. And it doesn’t need to learn from the forty-year-old firms. All you need is the following. Imagine that each year you set up a system that learns from the data that one-year-old firms produce. Each year a new cohort of firms comes in. They’re one year in. They produce some data. Some of them succeeded, some failed and exited, some did really well. From all of that data, there must be some information about what are good business practices and bad business practices, things that work and things that don’t. Imagine — this is the conjecture, we don’t know this — that AI could distill that knowledge just one year ahead. I’m not talking about twenty or forty. One year ahead: what makes a startup grow past their first year versus just be a failure?
Now you pass that knowledge to the new entrants in the next cohort. Repeat. Now the new cohort enters with the experience of a one-year-old firm. So it grows again, and that new cohort goes from being like a one-year-old firm to like a two-year-old firm, but only in their first year. So now set up the system again. Learn from that new data, pass it on to the newcomers, and so on and so forth. So just the existence of this technology that can learn from data of firms one year ahead eventually manages to codify all the organizational knowledge in the economy. And you get to that same ridiculous exercise, where maybe in fifty years the newcomers are born with the experience of very old firms in the economy.
Seth: I think you’re thinking a little too small ball here. People are talking about these AIs becoming superhuman. Why couldn’t the AI executive or manager make better decisions than the optimal firm from forty years ago?
Martin: Oh, absolutely. That’s why I say this is what we can quantify, instead of just speculate. This was a paper that was very different from the others. The others had some evidence and then a kind of crazy extrapolation; this is the opposite. Everyone wants to put a number on the macroeconomic impact of AI. If you think about it as a fancy robot, you can get away with some numbers, and there have been papers trying to do that. But if you think about it as what you’re describing — really increasing the productivity of firms in the long run, or improving science, or improving innovation—
Seth: Strategic decision-making.
Martin: —strategic decision-making [laughs] — I just don’t know what the number is, because we don’t have evidence on that, and we won’t for a long time. So our approach was to ask: what is the question we can answer somewhat convincingly, just from data that already exists in the economy, by knowing how important learning and knowledge are in the economy today?
Andrey: Just a question here. Let’s say it’s 2028. What in the data would we see to know if your story is first-order important or not?
Martin: Great question. 2028 may be too early, but let’s say 2030-plus, who knows. What you’d start seeing is a flattening of the age-size profiles. Right now, an entrant is about seven times smaller than a thirty-four-year-old firm. The profile is fairly flat initially, then it starts to kick off at around five to ten years. You’d want to see that profile flatten — old firms and new firms being somewhat similar in size.
Seth: And I love this. This is a decentralizing hypothesis, because a lot of the concentration in the economy comes from older firms. So this is very interesting. If my mechanism is right — it just makes all the managers smarter — you’d expect that, if anything, to boost the big firms more. So we’ll bring you back on in five years to see which is happening. [laughs]
Andrey: The other story that would make that prediction a little tricky, and it’s not in your paper really, is that AI makes the optimal firm size smaller.
Seth: Well, it’s productivity. Productivity and size go together, but I think there’s a reading of this where it’s just about productivity.
Martin: Yeah. There’s another question about whether AI changes the span of control. Can a manager now have a firm that’s twice as large because we have ten agents and two workers? That’s another channel, which would lead to more concentration.
Seth: So, some data questions about these extrapolations. One concern Andrey raised about the facial recognition work in China is that maybe some of what you’re measuring isn’t a supply-side effect, it’s a demand-side effect. When I look at your list of businesses where you have the largest premium for being an established business, number two on the list is apparel. Number three is performing arts. Is this age advantage in productivity really about supply-side efficiency, or is it really about a reputation effect?
Martin: Great question. With the data we use, we can’t tell, because we’re using data on size. So really the question is: when we talk about knowledge — such a big word — is this thing creating size differences something AI can help codify and then spread? If it’s something like status, I don’t think AI can do anything about that, because it’s sort of zero-sum. Either you have status or you don’t. Building reputation — I don’t know, maybe there is some way of building reputation, and if the AI can codify what are good versus bad ways of building reputation, that’s a type of knowledge that can be diffused. And then I’d say, yeah, that’s the right number. So it need not be just something you’d associate with productivity, like processes inside the company or how you price — operational knowledge. It can be knowledge about how you build a brand. Is that captured in the operational logs and emails companies have? Then you’d say, well, that will not show up in productivity, it will show up in prices and quality. And if that knowledge can be transferred, then I’m fine with that story. But I think we’ll see.
Seth: Yeah, you just don’t want something that’s pure demand side. Anything that’s plausibly controlled from the supply side, you could tell an AI story about how you’d accelerate it. I guess the final thing is: I led with this idea that maybe older firms are bigger just because it takes time to grow. There are frictions in getting the license to build the factory, capital adjustment. Is that really— I look at the top one on this list, couriers and messengers, and it seems pretty plausible that couriers face long adjustment costs because you’ve got to build out this network, and AI is not really going to accelerate that.
Martin: No, I agree. The main thing we push against is not just capital adjustment costs, but really financial frictions. Maybe small young firms are small not because they’re not productive and haven’t accumulated a lot of knowledge, but simply because they can’t scale up due to financial frictions.
Seth: They don’t have collateral, right?
Martin: Yeah, exactly. So we do an exercise where we imagine that the first ten years are really just a form of capital adjustment cost or financial friction — so a zero-year-old firm and a ten-year-old firm should be the same size, and the only reason they’re not is an adjustment cost. Even then, the numbers we come up with are still pretty sizable. Two is a very large number.
Seth: Final question here. If we get, say, five percentage points of economic growth a year because all of our tech friends are right and AI is a really powerful technology, what percentage of that additional GDP growth is coming from automation versus a mechanism like this? Do you think automation would be relatively more important in a productivity boom, or a mechanism like this?
Martin: You’re asking me to speculate now.
Andrey: Well, yeah, that’s what the listeners want to hear. [laughs]
Seth: That’s right. We want to know.
Martin: [laughs] This is what the audience demands. In the short to medium run, I think automation, because it’s the easiest thing to implement. Just keep doing what we’re doing, but now with agents, especially in some industries. Where the channel I’m describing really kicks in is as you churn through new companies that can take advantage of setting up their whole processes to be able to learn in this way with these new tools. So we may see this filtering through the economy in ten, twenty, thirty years — not before that, until we really see the new cohorts coming in with a completely different organizational setup that can leverage this technology of codification. But I would say the reason I started studying this side of AI is because I do think what makes it different is not that it’s an automation technology, or that it can automate cognitive rather than manual and routine tasks. That’s just a different matrix of exposures. I don’t find that the most exciting aspect of AI.
The most exciting aspect to me — and we’re all experiencing this already — is that it can somehow codify knowledge from the experiences of others, first people, and then that knowledge becomes available to us or to other organizations, say through a chatbot. That seems really special about AI, and not something a robot could do before, where you needed to give the instructions by hand.
Andrey: Is it fair to say — Seth assumed we get to 5% GDP growth per capita.
Seth: I said conditional. It’s a conditional expectation. [laughs]
Martin: I should have pushed back on that to begin with.
Andrey: Is it fair to say that you don’t think we can get to that level with automation alone?
Martin: Automation of what? Of production? If it’s automation of production, yes. At the end of the day, a lot of this is also automation — you could call this the automation of codification. Before, you needed to actually go ahead, read stuff, document it, write it down, make it explicit. Whereas now you have this machine that can automatically read off all those experiences and come up with something that looks like — it’s not really instructions, but you can chat with the chatbot and it will tell you what to do. It is a form of automation, just not of a task of production, but of how you organize and run businesses.
Lightning round, and the Pacing the Frontier letter 59:26
Andrey: All right. Let’s move on to our lightning round. First question.
Martin: [laughs] Can I pass? Do I get to pass?
Andrey: No.
Seth: No, don’t pass.
Martin: No passing. [laughs] Okay.
Andrey: What does the Argentina soccer team’s success — or this year’s lack thereof — teach us about efficient organizational learning?
Martin: [laughs] Somehow I haven’t connected those two dots. First of all, I’d say it was a pretty successful year. We made it to the final, so I’d say that’s successful. [laughs] I think there’s been something about team building — how you manage to create a cohesive team — that our coach, Scaloni, was really good at, especially in the previous one. We were just talking about one of our colleagues, Damián, getting into football and thinking about whether we can use AI to figure out decisions on the pitch. When should we press? When should we stay back? When should we attack? These types of things. That’s the business of football in that case. This is just to get free tickets for the next World Cup. [laughs]
Andrey: Is there anything specific that the coach did that you think made a difference?
Martin: Two things. One, you hear that he really consulted with the players who knew — like Messi — about what the strategy should or should not be. I appreciated that. The second was that in the last World Cup, in the first match, when things didn’t work out, he realized early that he needed to sub in the younger players, disregarding names and reputation. Which is something I think he did not manage to do in this World Cup.
Andrey: Yeah, just from watching the World Cup, that seems like a very interesting consistent tension where some teams decided to bench their old stars and some decided to play them. I couldn’t figure out a pattern of what the optimal strategy was there.
Martin: But with AI, we will. [laughs]
Andrey: With AI, yeah, we’ll figure it out.
Seth: You’ve spent part of your academic career in San Francisco, part in Cambridge, Massachusetts, part in Chicago — all very highly charged intellectual climates. How would you say they differ in how they think about AI in the economy? And are you saltwater or freshwater?
Martin: What’s the name for when the rivers meet the sea? The edge?
Seth: Oh, you’re in the littorals? [laughs]
Martin: Yes. That water.
Seth: You’re in the floodplains. [laughs]
Martin: Yes. [laughs] And truly, I think I am — precisely because I’ve spent so much time in both. One thing I’ve seen since I’ve been in Berkeley is how the agglomeration externalities are really here when it comes to AI. Even at MIT in Cambridge, where of course people are interested in it and studying it, I did not feel the externalities from the outside world. Whereas here in the Bay Area — just coming to your podcast, even when we’re online, dinners that others have organized, conferences, random encounters with people. You feel this around you in a way that in Cambridge, despite it being intellectually amazing and people being interested, that ecosystem is not quite there. The only conjecture I have for that is that the venture capitalists are the ones that create the externalities. It’s not the engineers alone. It’s the venture capitalists who are interested in networking and making people come together. I don’t know if that’s your experience too.
Andrey: I think engineer founders are certainly part of this as well. But I’m wondering — one of the worries I have spending time in San Francisco is that maybe it’s biasing me in the wrong direction. We have an intuition that studying things from afar has some advantages, whereas once you’re in the middle of it, you’re hopelessly tied up with everything else going on. Have you thought about this?
Martin: I moved here because I wanted to be here for that reason. So I bought into the hype a bit too, and I think we all have to some extent — that this is an important moment in history, where this technology is going to change the economy a lot. Hence why we’re studying it. I don’t know if I would say it would bias me. I think it would bias me in terms of overestimating how important it will be, and saying, “Maybe I look back ten years and I just wasted ten years of my academic career studying this thing that was not that important.”
Seth: Do you feel that way about your work before working on AI now?
Martin: [laughs] Are my co-authors listening to this? [laughs]
Seth: No, no one listens to this. Should you have been working on AI sooner? Is that your current sense?
Andrey: You were working on AI pretty early.
Martin: I was working on facial recognition since 2016 or ‘17, so I was pretty early. But my previous work was more on business cycles, monetary and fiscal policy.
Seth: Who cares about monetary policy these days? [laughs]
Martin: [laughs] Let’s talk about that. The reason I shifted is that I was lacking good ideas and new questions in those domains. One thing I’d say is that every time I do research and write my paper, it’s not just about AI. AI is prompting a re-examination of certain more classical issues in economics. That’s what I find exciting. AI was what prompted me to study property rights. But then when you start thinking about property rights, you realize, “Well, it’s not like we understand everything perfectly.” And the same for org econ. Once I started thinking about AI and knowledge codification, I was like, “it’s not like our models are great models of this process that was going on even before AI.”
Andrey: Final question. You’ve thought about potentially inefficient automation. How big of a risk is this with AI? Are we going to automate too much on the margin, in terms of GDP losses or other metrics? Is this a first-order concern or not?
Martin: I don’t think so. And here I’m not going to speculate so much — this is one answer I’m perhaps the most confident about. People are extrapolating a lot from the experience of robots, a technology that automated people in the lower parts of the income distribution who were both relatively poor and didn’t have the financial means to smooth out the transition to new jobs or to retirement.
Seth: And geographically concentrated.
Martin: And geographically concentrated. Absolutely. In the “Inefficient Automation” paper, we get at the question of when we know that automation is not just bad for redistribution but also inefficient — inefficient in the sense that you could engineer a slowdown of the transition that would make everyone better off and no one worse off. Based on that, the reason I’m more optimistic about AI is that when you look at the exposure, it’s hitting parts of the income distribution that are more middle-to-high income — cognitive workers in cognitive-intensive occupations — who you would not think of as poor, so the redistribution is going the opposite way, nor as financially vulnerable. They probably have the means to smooth out transitions to new occupations or into retirement. And they’re not that geographically concentrated. So all the things that made me think maybe for the robot transition we screwed it up and should have slowed things down a bit — those arguments are not that powerful in this case. Except for some examples.
Call centers is one where that’s obviously not the case. Truckers, probably another. But by and large those are specific examples, not the broad pattern. That said, some of these groups are politically powerful. That may mean we see the same thing that happened with the forgiveness of college debt — another thing where the redistribution went the wrong way [laughs], but they managed to pass it because they were a politically powerful group. So now that makes me wonder whether the same thing will happen with AI, even when it’s not the right thing, just because of where the political power lies.
Seth: We almost closed on a happy note.
Martin: You can cut it there. [laughs]
Andrey: We could have cut there, but there was this recent Pacing the Frontier letter from the labs, essentially calling for a coordinated slowdown. Usually you don’t think about slowdowns in automation as a possible policy, but this does have that flavor — people asking for the government to essentially tell everyone not to train as much, or to slow—
Seth: Chill out. [laughs]
Andrey: Just chill out, guys. We have a lot to process here. And I don’t think it’s driven by what you’re saying. A lot of people are already talking about regulatory capture and stuff. I just don’t think it really comes from that place. But I do think that, more so than protectionism, we might just get slowed down for other reasons.
Martin: Yeah. And again, there are many reasons you may worry about. One is the automation of young people in entry-level jobs — I think the jury’s still out on that. And reasons that have to do with the social rather than the economic side: people asking chatbots for things that may be harmful to them, these types of things.
Seth: That was a really diplomatic way of saying child porn.
Martin: [laughs] Actually, I was thinking more about psychology-type things. [laughs] Asking for therapy or advice and things like that. And to me, that’s more like — we need some guardrails, and not so much halting or slowing down automation the way we have for other technologies.
Andrey: All right. Well, thanks for coming on the show, Martin. This was a really delightful episode, and we look forward to further conversations in the future.
Seth: Yeah, we’ll bring you back in five years to see if new firms are growing faster.
Martin: [laughs] No, you should bring me back with Daron, and then you can put us together. I think I can fight with Daron. [laughs] All right. It was great, guys.
Seth: Thank you so much.