Working Paper: NBER ID: w31461
Authors: Daron Acemoglu; Todd Lensman
Abstract: Transformative technologies like generative artificial intelligence promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and convex. If social damages are proportional to the productivity gains from the new technology, a higher growth rate leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.
Keywords: transformative technologies; generative artificial intelligence; technology adoption; optimal regulation; social damages
JEL Codes: H21; O33; O41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| higher growth rates of new technology (O39) | slower optimal adoption (D15) |
| time progresses without a disaster (C41) | belief in probability of a disaster declines (H84) |
| belief in probability of a disaster declines (H84) | increased adoption rates (D16) |
| socially optimal adoption should be gradual (D15) | society learns about potential risks (D18) |
| equilibrium adoption can be inefficiently fast (D50) | firms do not internalize all social damages (D62) |
| regulatory schemes like Pigovian taxes (H23) | align private incentives with social welfare (D69) |