Dwarkesh Patel: prediction

Transcript context

…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. 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.…

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