The Simple Macroeconomics of AI

Will AI's impact be as modest as predicted, or could it exceed expectations in reshaping economic productivity? In this episode, hosts Seth Benzell and Andrey Fradkin discuss the paper “The Simple Macroeconomics of AI” by Daron Acemoglu, an economist and an institute professor at MIT.

Additional notes from friend of the podcast Daniel Rock of Wharton, coauthor of “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” one of the papers cited in the show, and a main data source for Acemoglu’s paper: (1) Acemoglu does not use the paper’s ‘main’ estimates of the feasibility of using GPTs to dramatically increase productivity in tasks, rather it uses more ‘experimental’ estimates from the appendix about which tasks are fully automatable. These numbers are smaller than the main texts’ which is one reason for Acemoglu’s small productivity impact estimates (2) For a paper that uses the main estimates from his paper, Daniel recommends the OECD working paper “Miracle or Myth?"🔗Links to the paper for this episode’s discussion: https://economics.mit.edu/sites/default/files/2024-05/The%20Simple%20Macroeconomics%20of%20AI.pdf

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Seth and Andrey debate AI's potential effect on economic growth, with reference to Acemoglu's prediction that AI will contribute less than 1 percentage point to total factor productivity (TFP) over the next decade.

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