Artificial Intelligence, Economics, and Industrial Organization

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4/5

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Rating inherited from publication venue: NBER

Relevant to AI governance and policy discussions; offers an economics lens on how AI shapes markets and concentration, complementing technical AI safety perspectives with institutional and industrial organization considerations.

Metadata

Importance: 52/100working paperanalysis

Summary

This NBER working paper by Ajay Agrawal, Joshua Gans, and Avi Goldfarb examines how AI—particularly machine learning as a prediction technology—affects economic structures, firm behavior, and industrial organization. It explores how AI reduces prediction costs, reshapes decision-making, and influences market concentration and competition dynamics. The paper provides an economic framework for understanding AI's broader societal and organizational impacts.

Key Points

•Frames AI advances primarily as reductions in the cost of prediction, enabling new economic analyses of AI adoption and value.
•Examines how cheap prediction reshapes firm decision-making, task organization, and the division of labor between humans and machines.
•Analyzes industrial organization implications: how AI may drive market concentration through data network effects and economies of scale.
•Considers policy implications around data ownership, competition, and the governance of AI-driven markets.
•Provides a bridge between technical AI capabilities and economic/institutional analysis relevant to AI governance debates.

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# Artificial Intelligence, Economics, and Industrial Organization

[Hal Varian](https://www.nber.org/people/hal_varian)

Working Paper 24839


DOI 10.3386/w24839


Issue DateJuly 2018

Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and industries that adopt AI technology. My intent is not to provide an extensive overview of this rapidly-evolving area, but instead to provide a short summary of some of the forces at work and to describe some possible areas for future research.

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- Acknowledgements and Disclosures




I am a full time employee of Google, LLC, a private company. I am also an emeritus professor at UC Berkeley. Carl Shapiro and I started drafting this chapter with the goal of producing a joint work. Unfortunately, Carl became very busy and had to drop out of the project. I am grateful to him for the time he was able to put in. I also would like to thank Judy Chevalier and the participants of the NBER Economics of AI conference in Toronto, Fall 2017. 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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Hal Varian, "Artificial Intelligence, Economics, and Industrial Organization," NBER Working Paper 24839 (2018), https://doi.org/10.3386/w24839.



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## Published Versions

[Artificial Intelligence, Economics, and Industrial Organization](http://www.nber.org/chapters/c14017), Hal Varian. in [The Economics of Artificial Intelligence: An Agenda](https://www.nber.org/books-and-chapters/economics-artificial-intelligence-agenda), Agrawal, Gans, and Goldfarb. 2019

## Related

### Topics

[Industrial Organization](https://www.nber.org/topics/industrial-organization)

### Programs

[Industrial Organization](https://www.nber.org/programs-projects/programs-working-groups/industrial-organization)

[Productivity, Innovation, and Entrepreneurship](https://www.nber.org/programs-projects/programs-working-groups/productivity-innovation-and-entrepreneurship)

### Conferences

[Economics of Artificial Intelligence, Fall 2017](https://www.nber.org/conferences/economics-artificial-intelligence-fall-2017)

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