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Regulating transformative technologies Daron Acemoglu and Todd Lensman

By: Acemoglu, Daron.
Contributor(s): Lensman, Todd.
Material type: ArticleArticleSubject(s): IMPUESTO SOBRE EL VALOR AÑADIDO | DESARROLLO TECNOLOGICO | INTELIGENCIA ARTIFICIAL | PRODUCTIVIDAD In: The American Economic Review v. 6, n. 3, September 2024, p. 359-376.Summary: Transformative technologies like generative AI promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multisector 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 typically convex. If social damages are large and proportional to the new technology's productivity, a higher growth rate paradoxically 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.
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Bibliografía.

Transformative technologies like generative AI promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multisector 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 typically convex. If social damages are large and proportional to the new technology's productivity, a higher growth rate paradoxically 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.

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