Macroeconomics of AI?

Daron Acemoglu, "The Simple Macroeconomics of AI":

ABSTRACT: This paper evaluates claims about the large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. It establishes that, so long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.71% increase in total factor productivity over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.55%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. AI is also predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

A contrary view, or at least some objections, from Tyler Cowan — including this:

[A]s with international trade, a lot of the benefits of AI will come from “new goods,”  Since the prices of those new goods previously were infinity (do note the degree of substability matters), those gains can be much higher than what we get from incremental productivity improvements.  The very popular Character.ai is already one such new good, not to mention I and many others enjoy playing around with LLMs just about every day.

But there's another thing that neither Acemoglu nor Cowan considers, which is that administrative automation may be different, at least in some settings. I predict that applications of "AI" to administrative functions will decrease productivity more than they increase it — though I'll skip the supporting details to protect the innocent (as well as the guilty…).

[h/t Bob Shackleton]