Competition in Generative AI: Evidence from Consumer Demand

Mark L. Egan, Dominic Russel, Claire Shi and Vincent Yao

No 35953, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: How competitive will the market for generative AI be as it matures? Using novel transaction level credit and debit card data covering 174 million consumers and 114 billion transactions, we study the adoption of paid services and the demand-side foundations of competition in this market. As of April 2026, approximately 2.9% of U.S. consumers pay for at least one major generative AI service, with adoption highest among younger and higher-income consumers. Nearly all of these paid users subscribe to basic plans that cost $30 or less per month. Three patterns suggest limited differentiation and low switching costs. First, although multihoming is common, it has declined as providers’ capabilities have expanded. Second, annual customer retention rates range from 20% to 45% across major AI providers, lower than those for many other subscription products. Third, switching rises around major model releases. Motivated by these findings, we develop and estimate a structural model of AI service demand and supply that reveals strong competitive pressure. Consumers are highly price sensitive, have much weaker preferences for particular providers than for AI overall, and view AI services as increasingly close substitutes. Our results suggest that, absent a substantial increase in AI model quality differentiation, growing substitutability among services will constrain providers’ pricing power and allow consumers to capture a large share of the surplus generated by these services.

JEL-codes: D12 L13 O33 (search for similar items in EconPapers)
Date: 2026-10
New Economics Papers: this item is included in nep-ain, nep-bec and nep-reg
Note: CF IO PR
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