Artificial intelligence adoption in a competitive market

Bibliographic Data

ID9724625
AuthorsJoshua S Gans (0000-0001-6567-5859, University of Toronto and NBER Canada, corresponding author)
Year2023
Volume90
Issue358
Pages690-705
Publication date2023-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEconomica (JOURNAL)
Journal identifiersISSN: 0013-0427 • E-ISSN: 1468-0335
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ecca.12458
OpenAlexW4319840035
LanguageEN
Citations received2
References cited24

Economists have often viewed the adoption of artificial intelligence (AI) as a standard process innovation where we expect that efficiency will drive adoption in competitive markets. This paper models AI based on recent advances in machine learning that allow firms to engage in better prediction. Focusing on prediction of demand, it is demonstrated that AI adoption is a complement to variable inputs whose levels are altered directly by predictions and whose use is economized by them (that is, labour). It is shown that in a competitive market, this increases the short‐run elasticity of supply and may or may not increase average equilibrium prices. Generically, there are externalities in adoption, with this reducing the profits of non‐adoptees when variable inputs are important, and increasing them otherwise. Thus AI does not operate as a standard process innovation, and its adoption may confer positive externalities on non‐adopting firms. In the long run, AI adoption is shown to lower prices generally and raise consumer surplus in competitive markets

Competitive equilibrium · Complement (music) · Economics · Externality · Industrial organization · Microeconomics · Network effect · Price elasticity of demand · Process (computing) · Variable (mathematics) · Auction Theory and Applications · Computer Science · Consumer Market Behavior and Pricing · Innovation Diffusion and Forecasting

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