Gans (2024): Market Power in Artificial Intelligence

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High quality. Established institution or organization with editorial oversight and accountability.

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Useful reference for AI governance discussions around data monopolies and market concentration; relevant to policymakers and researchers studying structural risks from AI industry consolidation, though not directly focused on safety or alignment.

Metadata

Importance: 52/100working paperanalysis

Summary

Joshua Gans surveys economic literature on competition and market power across three AI market segments: training data, input data, and AI predictions. The paper argues that the functioning of data markets—specifically whether data can be traded across firm boundaries—is the central determinant of whether dominant positions will emerge and persist in AI industries. It provides a framework for researchers and policymakers to analyze competitive dynamics in AI.

Key Points

•Surveys existing economic literature to identify drivers of competition in AI product markets across three sectors: training data, input data, and AI predictions.
•Data market operability—the ability to trade data across firm boundaries—is identified as the key factor in determining AI market power emergence and persistence.
•Market power in AI is not inevitable; its trajectory depends heavily on institutional and regulatory choices about data access and tradability.
•Relevant to antitrust analysis and AI governance, offering an economic framework for policymakers evaluating concentration risks in AI industries.
•Published as an NBER working paper (March 2024) by a prominent economist with extensive AI economics research background.

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# Market Power in Artificial Intelligence

[Joshua S. Gans](https://www.nber.org/people/joshua_gans)

Working Paper 32270


DOI 10.3386/w32270


Issue DateMarch 2024

This paper surveys the relevant existing literature that can help researchers and policy makers understand the drivers of competition in markets that constitute the provision of artificial intelligence products. The focus is on three broad markets: training data, input data, and AI predictions. It is shown that a key factor in determining the emergence and persistence of market power will be the operation of markets for data that would allow for trading data across firm boundaries.

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- Acknowledgements and Disclosures




Joshua Gans has drawn on the findings of his research for both compensated speaking engagements and consulting engagements. He has written the books Prediction Machines, Power & Prediction, and Innovation + Equality on the economics of AI for which he receives royalties. He is also chief economist of the Creative Destruction Lab, a University of Toronto-based program that helps seed stage companies, from which he receives compensation. He conducts consulting on anti-trust and intellectual property matters with an association with Charles River Associates and his ownership of Core Economic Research Ltd. He also has equity and advisory relationships with a number of startup firms. Thanks to Andrei Haigu, Chad Jones and the participants at the Rochester Antitrust Workshop for their helpful comments. All errors remain my own. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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Joshua S. Gans, "Market Power in Artificial Intelligence," NBER Working Paper 32270 (2024), https://doi.org/10.3386/w32270.



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