Today I'm chatting with Alex Imas, who is Director of AGI Economics at Google DeepMind and Professor of Economics at the University of Chicago, and Phil Trammell, who is Head of Economics at Epoch and research scholar at Stanford. In general, what I want to understand in this interview is what economics tells us about what we can expect in a world with more and more automation and more advanced AI. I want to understand what that tells us about what will happen to wages and the labor share, what the best way to tax and redistribute the wealth generated by AGI will be, and what kinds of things will be scarce. What is scarce tells you where the value will accrue. I want to start there. What are some plausible candidates of what will be scarce?
Something like the relational sector, which is defined as services and goods where the fact that a human was in the loop is part of the value of that product. Because humans are naturally scarce, if we have automation where a lot of other things stop being scarce, we will still have scarcity in the things that humans are involved in and in the loop for.
I'm curious to understand whether humans doing services for other humans can ever be a big part of the economy. Here's maybe one intuition pump. In a world where AI can physically do anything humans can do, there's this whole machine economy where they're building factories and doing research and coming up with new ideas. Humans may or may not be involved in the physical production of those things, but probably not in the ultimate limit, if robotics is solved. If you don't care about humans being involved in that process, why would they be? But then there are these other things you point out where we actually do want the ballerina or the barista to be a human. That's part of the value of going to a cafe or a performance. But only humans have that preference. So there's this human economy where humans are doing services for each other, and part of their wealth is flowing to other humans. But part of their wealth is also flowing out, because they will want some of the automated goods this machine-only economy is creating. This is not a closed loop. A lot of things in the machine-only economy are a closed loop because the machines don't care about getting the human barista to make them a coffee. Within that model, isn't it intrinsic that the human-only economy will become a smaller and smaller share?
I would like to pitch a rephrasing of that question. My view is that the individual forecasts economists like us would make, as individual forecasts, are not necessarily very useful. There was a blog post by Andrey Fradkin, Brian Jabarian, and Andrew Koh that came out yesterday looking at economists' forecasts about the labor market. What they found is that there's a ton of disagreement in every single direction. What they advocate for, and I'm in agreement here, is that rather than thinking about individual forecasts, we should be generating prediction markets where you get aggregate forecasts and wisdom-of-the-crowd effects. The reason I think this is because we have been famously terrible at forecasting. Let's go all the way back to 1820. This debate we've been having is actually 200 years old. David Ricardo is one of the classical economists, not neoclassical. When the Industrial Revolution started happening, he wrote a bunch of stuff saying, 'This is going to be great for everybody. Prices are going to come down.' But then he turned around and said, 'Wait, I can see all these jobs that are creating value are going to be automated by these machines. This is going to be really bad. Everybody's going to become unemployed, and there's going to be political unrest.' And if you look at Ricardo's predictions, they're actually right. All those jobs that made money in Ricardo's time got automated. If David Ricardo woke up and somebody told him all those jobs did get automated, and then asked him, 'What do you think the prime-age employment rate is in 2026?', I think he'd be surprised to be told it was the highest it's ever been other than 2000. We have the highest number of employed people that could potentially be employed since 2000. That was the peak and now it's the second peak basically. What David Ricardo ended up missing is that you have these economics of structural change, where everything that got automated became cheap. People had more money to spend, and then they started spending it on services. This is the lump-of-labor fallacy. David Ricardo didn't consider that new jobs would be created.
But it's not obvious that money would go to services. Why wouldn't it go to more automated goods and something like that?
I'm not using this anecdote to say this is what's going to happen now and that we're going to have full employment. I'm using it to say it's really hard to make predictions. What may be a really useful tool that economists have is to instead start with a premise. Maybe we start today: labor share is zero. Labor share has gone down. What could possibly explain this? Let's write down an economic model of what happened. Phil will talk about this later today. Or you can write down a model that asks, 'What if labor share just stays the same? What can make that happen?' If you don't take anything else out of this conversation from me: We don't have any data. I've been saying we need a Manhattan Project for data. We don't have data on consumer demand elasticities. We don't know what they are. We're not really tracking what jobs are getting created or destroyed. The O*NET database, with all of the tasks and different jobs, has been rarely updated and is super low quality. What is really useful is to think about the potential scenarios, map them out, and say what dimension of scarcity will generate each scenario. If there's full employment, we can talk about the relational sector. If the labor share collapses, we can talk about other sorts of scenarios. That will tell us what data we should be collecting.
It's probably worth defining labor share and capital share real quick. The whole economy, the total sum of goods and services sold, is either paid out to people in wages or it's paid out to capital, which is to say, there's rent on buildings and shareholders of companies that get paid out. For many hundreds of years, ~60% of the economy basically gets paid out to humans in wages, and the other 30-40% gets paid out to people who own machines and land and claims on companies. The question is, if 60% is going to wages right now, does that shrink as AIs get smarter and better? This is a Kaldor fact. We should stress this. It's incredibly surprising that it's over 60% after the Industrial Revolution and all of the automation we've ever seen. Some people are worried it's an accounting error that it's been so constant. There's even a controversy right now. Some might say labor share has been falling in the last 20 to 30 years. But there have been a lot of accounting changes in the last 30 to 40 years. For example, Atkinson has a paper showing that if you keep the accounting constant over the years, labor share hasn't even fallen ever. But it's not that surprising, right? Phil, you made this point that if labor and capital are complements, you need both to do anything. It would make sense that you'd need to pay both of them to get something done. You have had stuff be completely automated.
There's a sense in which nothing has yet been completely automated. Look at the network-adjusted factor shares of a good. Look down the supply chain and not just the final step and how much of that is done by capital and labor, but what went into the machines that can automate that final step. You'll find that labor is adding a lot of value down the supply chain. Computer and electronic products in the US have a very stable network-adjusted capital share of around 50%. It's not 100%. I do think there's this qualitative shift that I think we agree is coming, which is that there will be at least some goods whose network-adjusted capital share goes to one. The whole supply chain can be automated, and there's no part in it that we care intrinsically about having a human do. That will be a qualitative shift. Interestingly, the implications of that shift for the overall capital share are ambiguous. Let's say we've got two sectors: the human-intrinsic sector with the ballerinas, and everything else. Right now, everything else has been scarce because of the lack of labor in it. But if we fully automate the supply chains for everything else, and we satiate in everything else really fast, then the quantity of everything that's not a ballerina goes to infinity, but the marginal utility in that stuff goes to zero faster than the quantity is rising.
I also want to move away from the ballerina example. The point I was trying to make in my post—working backwards from a particular scenario—was that the ballerina and the performer are the wrong reference class. Right now we have a lot of jobs where you have different tasks. This is the task-based model of jobs. Take a doctor, what is their job? They're filling out insurance documents. They're going and calling different pharmaceutical companies. One of their tasks is to see the patient and talk to them, but that's not the main part of the job. You could have a job and a service or a good be a product of different types of tasks, and you can automate a ton of those tasks. If the consumer is willing to pay more for a product or service where every single task is automated except for that one part where the doctor is delivering the diagnosis and providing support, we would call that job part of the relational sector. People are willing to pay more for the human to stay in the loop in the job. We don't have data to say, 'Here are relational jobs, here are not.' You literally need to collect data of the following sort. Do a conjoint analysis of your willingness to pay for this service or good. Here's the counterfactual where everything is produced by machine. Here's the counterfactual where this one task is not produced by a machine. What is your willingness to pay? What is your elasticity for the human to not be in the loop? If I don't have that data, what prediction am I going to make in this story?
Isn't there another point, which is that there are a lot of fully automated goods that don't even exist yet? And you can't collect any data right now about, say, how much people will want to keep buying more and more of some drug that makes you healthier that is fully produced by the AIs.
Absolutely. That's kind of Phil's point. You could have an increase in variety in capital where you don't get the satiation. You're increasing variety, so you're not hitting that diminishing marginal utility point where most of your income is going to the human sector. If that increasing variety is fast enough, and there is no such increasing variety in the human sector, then you can get all of the relational goods you want, but it doesn't matter for labor share. It goes to zero.
Phil, I liked your analogy to some Mongolian economist sitting around in 1400 thinking about what will be scarce and the limits of that kind of analysis. I think you should talk about that.
Just look at the goods available to a Mongolian of the distant past. I'm no expert on this society, but I know that they didn't have nearly the variety that we have now. Look at the jobs that were intrinsically human, like being a singer. And then you look at the things that were not intrinsically human, like the transportation services provided by their horses or the different kinds of food they had. If they just held the varieties fixed in both categories and asked, 'What will happen once we have a lot more automation?', they might have said, 'We'll just satiate in horse-like transportation and in yogurt and in yurts. Those shares will all go to zero, and we'll be left spending all of our money on singers.' But of course, that's not what happened. As we've accumulated more wealth and more advanced machines, we've expanded the range of things other than singers to spend our money on, and the share spent on singers has stayed negligible. Likewise, that's my central prediction about how the future unfolds, though it could go either way.
I was going to make a point and I realize it's a fallacy, but the reason it's a fallacy is interesting. It's just hard to imagine a world where there are trillions upon trillions of robots, but there's only some billion-odd humans, and the cumulative amount we're spending on robots is less than what we're spending to pay Magnus Carlsen or—
Financial advisors or doctors or tutors.
Right, or podcasters or whatever. But then I realized why it's a fallacy. The number of transistors in the world has literally trillion-X'd, maybe quadrillion-X'd. Your colleague Chad Jones has a very interesting result about how the share of the economy that is going towards paying for computing, paying for the transistors, has been decreasing. The point you made is that one way to think about Moore's law is… What sets price? Supply and demand. So not only are we producing more transistors more cheaply, but also the value of the marginal transistor is decreasing. As you were saying, another way of saying Moore's law is…
I like the pessimistic framing of Moore's law: every 18 months, the value of computation halves. We're running out of uses for computation so fast that it's sustaining Moore's law.
This is relevant to a conversation about AI where maybe for the first time, this is no longer true. The famous fact here is that an H100 costs more to rent now than it did three years ago, even though we have much superior technology and much more compute in the world. Because as models get smarter, the opportunity cost of compute gets higher. This is Phil's point about increasing variety. What we have done is increased the types of things that people demand from capital. Now all of a sudden you have a new variety that you could be using capital for, and you jump back up. You could imagine we just never satiate demand for compute. As long as that stays the case, the share of the economy that is going towards compute would keep increasing. That's the big question. That is the ultimate question that we need to be looking at. What number of new uses are we finding for that compute where you have the demand for these uses? What I want to emphasize is that a lot of models in economics, especially in the space that we're talking about, take demand as almost exogenous. They don't unpack the psychology of what people actually want.
What got me thinking about the idea of the relational sector is work that I was doing on the fact that there does seem to be this intrinsic value. It's not just because it's scarce; it's because there's some intrinsic preference that people have for empathy, connection, and interacting with another person. One of the experiments that we ran involved an art print. We have an incentive-compatible way of asking, 'How much are you willing to pay for this art print?' People are actually paying real money for it. Then we say, 'Look, there's only one of those art prints, and it's either made by AI or by a person.' These are between-subject conditions. With one, you get the effect that the person-produced art print is valued much higher than the AI version. Then, in a set of other conditions, we say there's 500 of these being produced. For the human-made one, the price goes down a lot because it's no longer seen as making a connection with this one artist. With AI there's no difference. AI is already viewed as a commodity. We need to do a lot more research on this, but it seems that's the key difference between this and something like a horse. A horse was an input into an output, where you can replace the horse with something else. You only care about the output. The only way this relational story works—and this is what we need more data on—is if a human is not a horse in the sense that they are providing value from the output, where if you replace the human, the value of the output decreases. If that's not strong enough, and if it doesn't hold for enough sectors or enough jobs, then this story doesn't work anymore.
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There's one possibility which Molly Kinder has written about, this 'Messy Middle' scenario. That possibility made me think about whether it might be better to have—at least as far as wealth distribution and redistribution go—a much faster AI takeoff. I want to ask you whether the following possibility is at all likely, or if there's any set of assumptions that can make it so. AI makes it possible to automate jobs such that many people are losing their jobs, but it doesn't create enough wealth, while the process of automation is happening, to basically pay off the people who are getting laid off and create a Pareto improvement, where everybody's getting better as a result of AI automation.
Of course, there's a trivial sense in which that must be true. Whatever money the company is saving by not paying the humans instead of just paying the AIs, those resources still exist in the economy and can just be paid out to people. But there's going to be some allocative inefficiency. The government doesn't know exactly who got laid off because of AI. There's a political problem. If the Meta worker gets laid off first and they were making $200,000 a year, is there a politically sustainable situation where you give them a $200,000 check a year when there are many working people making much less?
Do you find this scenario plausible, where AI is automating a bunch of things, but there isn't as much wealth creation as there is automation?
I think it's possible. To me, it does seem like a pretty narrow window. My guess is that if we have the technology to automate so many jobs that it becomes a new kind of political problem, then the pie will also be growing really fast.
Well, unless in all of those professions it's automating, it's just a hair more productive. So the cost of all the capital to replace all the software engineers is just a hair less than the cost of what we've been paying the software engineers. Why is it implausible that a company can save money by laying off a bunch of software engineers? And in the long run, there's a Jevons paradox, and we can't anticipate in advance what we'd do with more software, and surely there will be more uses. But in the short run, the effect is just that a lot of people are laid off, and they still need to figure out how they can use a million times more JavaScript tokens.
Phil and I have been writing about these things, and we have mathematical models in the back of these things. We don't have any political economy in any of our models. Andy Hall wrote a really nice blog post about the politics of AGI, and he made a really interesting observation. If there's a 2% increase in unemployment, the political winds completely change. Unemployment has a huge effect on what happens politically. Referring to Molly's excellent essay, I think in some ways one of the worst scenarios is a drip scenario because of the political economy piece. What you might see is people not really being unemployed en masse, but moving into sectors that pay them less money. This is what happened with phone operators between 1920 and 1940. Phone operators were completely automated, but it took 20 years, even though the technology existed. There was this drip. It wasn't like this giant sector just disappeared. There's a really nice QJE paper on this showing that they got reabsorbed into the economy, but at lower salaries, and they were mostly underemployed. That's the scenario Molly was writing about, this messy middle where things aren't a disaster. We saw with COVID that the fiscal response can move quickly if there's an emergency. An emergency is a quick uptick in unemployment, which could even look like 2-3%. That becomes a national emergency if it happens fast.
The concern is that whatever you're saving on those white-collar workers, if that's not growing the economy but just creating saved resources that can be allocated elsewhere, is that enough to do a broad-based redistribution scheme? You have the money you've saved off a couple of people. Unless you can figure out exactly how to get it to them specifically, you have the problem of, 'Can I do a UBI off the money I saved by laying off…?' You're basically saying the pie did not grow that much. You're just displacing a bunch of people, but that didn't grow the technological frontier of what the economy can produce. Then there's a question of whether every time this has happened in history, the technological frontier has expanded a bunch. I think that's the case. Simply in history, the technological frontier has expanded. I think Phil made the same point. It's hard to imagine that sort of scenario where you are getting intelligence that's just enough to replace the software engineer but still costs a lot of money. It's just a hair less expensive than the software engineer, so you're not getting this abundance effect. Where is the redistribution going to happen because the pie didn't grow?
This is very helpful. Many different things have to be true for this scenario to come to pass, each of which seem unlikely. One, it has to be the case that it is possible to automate entire white-collar jobs, but only in a piecemeal way. That is to say that you can only automate software engineers, but that same program can't also automate an accountant and an analyst and whatever. My model of intelligence is such that—both the breadth of tasks it requires to do something like software engineering and what intelligence is—if you can really just lay off all the software engineers, you've got enough in the bucket there that you could automate all kinds of white-collar work. There are huge amounts of potential savings that have happened as a result of these layoffs, and also AI is going to be cheaper than human labor. If both of those things are true, this messy middle scenario where we literally don't have the wealth to go around seems unlikely.
Then the question is, what is the best way to tax it and redistribute it?
I have some thoughts. I think it's really important to outline the costs and benefits. First, there's differential complexity in implementing these things. Two, they differ in the timeline of being actually helpful. Something like universal basic capital is not going to generate returns for something that happens in six months. You probably are going to end up with a layer of things. Take a negative income tax, for example. You implement it, and the day it turns into law, you already have this insurance that there's a floor where everybody gets a certain amount of money, and if you earn more money, you get taxed more. But there are positives and negatives to a negative income tax. With UBI, for example, I worry a lot about the political economy implications. If people are just dependent on a check, it really matters who's in power. Right now, we're endowed with labor that can turn into income. When that is no longer the case and we are at the mercy of the elected official for basic needs, that feels like a power-sharing arrangement that's really dangerous.
But wouldn't that be true of any sort of government redistribution program? With something like universal basic capital, where you have an ownership share and property rights for capital, you just have a share. You're a normal shareholder. You're just a normal person.
But this goes back to the question of indexing, because if indexing is hard, then universal basic capital is hard. That's the problem of universal basic capital: targeting. What do you target to put into people's portfolios?
Like, what if Anthropic goes to zero, but some random robotics company takes all this over?
Exactly. That's the risk of universal basic capital. With a negative income tax, you have the same sort of issues as with UBI, where somebody comes into power and says, 'We're not going to do that anymore,' and people can't work, and then you have the issue of the floor being gone.
One concern with the wealth tax is that there's no politically sustainable equilibrium at a 0.5% wealth tax. This happened with the income tax, of course. It starts low, it's for war or something, and then it slowly escalates until the marginal income tax rate in the US is on the order of 40%, and in certain states, upwards of 50%.
With a capital tax, is there a reason to worry that it would distort investment? Would people just say, 'Why would I invest in Anthropic or Intel? The government is going to take larger and larger shares of it and dilute my share.'
Hold on. It's worth separating how the revenue is raised, what's taxed, and how it's distributed. It could be that the government hands out shares of Anthropic to everyone by a broad-based tax and then buying Anthropic.
Which would probably be the right thing to do.
Hopefully, some populist proposal doesn't interfere with that and expropriate some particular company that everyone happens to know about.
You're suggesting there could be some sort of optimal tax.
We're taxing externalities or we're taxing land.
I guess we probably need to tax something other than just those two things.
Ok, a consumption tax, like a European value-added tax, allows the government to go buy a bunch of stocks, and then they just distribute those stocks to everybody.
That's David Autor's… That's not going to be that different from just redistributing the stocks, but it will be a little different. That was the proposal for Social Security, by the way.
That was privatizing Social Security.
It's worked so far, but there are questions about how long it's going to keep working. Privatizing Social Security was basically giving everybody a basket of stocks.
People talk about whether there's a white-collar apocalypse already. Is there any evidence that suggests there is mass automation or unemployment as a result of AI already?
A lot of people are looking at it. This is an area where there's a lot of eyes and a lot of data being produced. The Budget Lab over at Yale is doing really good analysis on this. They just recently released a report, and you really have to squint to see anything happening. If you want to take an approach across the entire economy, even looking at software engineering, the most exposed sectors, there's just not really anything going on. There might be a little bit of a signal about junior developers getting jobs less than before. But that's a 'less than before' rather than a level shift, as in there's actually an increased demand for senior software engineers, if anything. If you look at the trend, for junior developers, it's a bit below trend.
So you're saying the growth is slower than before, but there is still growth even for entry-level software engineers. What do you think is going on with the anecdotal evidence of graduating college students saying that they're finding it harder to find CS jobs?
I think that's anecdotal evidence.
You think it's always been hard to get jobs for some people, and now it's getting turned into an AI narrative?
Same with the layoffs, where it's probably just a normal layoff, and they turned it into an AI layoff. You have to be careful with all of this. There are these public coordination devices. Let's say we get into a narrative where if you're a firm and you're not laying people off, then you're seen as not adapting AI enough. Then you're going to just get a cascade effect of firms needing to keep up with the Joneses in terms of starting to lay people off. That's super worrying, where the firm might actually be worse off after the layoffs than before, but it's just doing the layoffs to have the perception that, 'Look, we're not behind the times. We're using AI.' You probably heard these anecdotal stories of token counters, where you have to maximize tokens and things like that. Right now, we don't really have any evidence of a white-collar bloodbath.
Is that surprising at all, given all these things AI can do?
This is a story as old as time. If you automate some complementary task, the overall bucket of things—the human labor which complements the automation—will increase in value. One of the statistics that's really important for that argument is elasticity of demand. Take the O-ring model of jobs. A job is a series of tasks. Let's say the AI automates nine out of ten tasks. One task is not automated. If that person can now focus in on that task, the job will become more productive. If that translates into a price effect where the product is actually cheaper, and if demand responds enough where it's being bought more and the service is being used more, that could actually lead to more hiring. A lot of people on the internet have been making that argument very generally, saying, 'Look, if anything in the data, we're seeing an uptick in software engineering demand.' Which suggests that at least for now, given the way that jobs work, it might be elastic enough. I think this elasticity of demand argument is incredibly important for a lot of arguments that people make, or just a lot of labels that people use without understanding what the underlying causation is. People often talk about Jevons paradox. This is the idea that as something gets cheaper, you will want so much more of it that the total amount you spend on the thing increases. Famously, this happened to coal in Britain ~200 years ago. But really this only happens if the demand for something is highly elastic. There are many things for which there is not super elastic demand. If oil, for example, gets super cheap, it's not like magically—
Exactly. It's not like magically there's going to be so many more cars that now we're going to be using way more oil than before. At least not in the short run.
