AGI 之后什么仍稀缺？两位经济学家谈税收与分配

Economics of AGI episode w Alex Imas and Phil Trammell.

There’s a bunch of important questions about how we deal with AI that only economics can answer.

What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn’t explode?

It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong.

It was very helpful to chat through these things with Alex and Phil.

Watch on YouTube; listen on Apple Podcasts or Spotify.

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Timestamps

(00:00:00) – Will capital share increase?

(00:19:36) – Messy Middle scenario

(00:25:57) – How to tax and redistribute AI wealth

(00:30:02) – Why demand collapse is unlikely

(00:39:26) – Human employees would be hard to integrate into the machine economy

(00:43:08) – What if some humans (or AIs) value wealth accumulation intrinsically?

(01:01:28) – What should developing countries do?

Transcript

00:00:00 – Will capital share increase?

Dwarkesh Patel

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?

Alex Imas

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.

Dwarkesh Patel

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?

Alex Imas

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.