As AI increases productivity, it will create new jobs in other sectors, and probably not have a massive impact on employment, just like previous tech. The problem comes when it can also do/learn to do those new jobs more quickly than the displaced humans, then we’ll have an actual problem. (Zvi’s take)
Terry Taos recent interview on math and AI gave a nice concrete case of AI enabling greater divisions of Labor via rigidity. Might be a good general model?
People like to point out that AI won’t be a complement to human labor by pointing to the fact that chess AIs beat computer-human pairs pretty quickly. But human+AI might be competitive for longer (assuming in some sense the same rate of general capabilities improvements) in general tasks than in chess because general tasks have out-of-distribution robustness problems, while chess does not. This means humans will maintain an absolute and comparative advantage at checking AI outputs for a while at least.
From the abstract, this looks about right: https://arxiv.org/abs/2312.05481
Re Z’s comments about what model do you adopt when marginal models break down, AGI-pilled people seem to like the horse → internal combustion engine or carrier pigeon → telegraph models. I thought this post was an interesting response to the more extreme pessimistic models. The fact people jump to thinking about nonhuman animals to predict the impacts of AI does seem to support D’s idea that you need a more first principles model of what even counts as an economic agent in the relevant sense.
- Elasticity of labor substitution case studies historically have all used small multiples (how small - ask). this works for marginal analysis. AI probably won’t. So what models do you use. See max tabarrok’s post on horses and AI. Carrier pigeons → telegraphs is the other analogy.