Occupational Heterogeneity in Exposure to Generative AI

69 Pages Posted: 19 Apr 2023

See all articles by Edward W. Felten

Edward W. Felten

Princeton University - Center for Information Technology Policy; Princeton University - Woodrow Wilson School of Public and International Affairs; Princeton University - Department of Computer Science

Manav Raj

University of Pennsylvania - Management Department

Robert Seamans

New York University (NYU) - Leonard N. Stern School of Business

Date Written: April 10, 2023

Abstract

Recent dramatic increases in generative Artificial Intelligence (AI), including language modeling and image generation, has led to many questions about the effect of these technologies on the economy. We use a recently developed methodology to systematically assess which occupations are most exposed to advances in AI language modeling and image generation capabilities. We then characterize the profile of occupations that are more or less exposed based on characteristics of the occupation, suggesting that highly-educated, highly-paid, white-collar occupations may be most exposed to generative AI, and consider demographic variation in who will be most exposed to advances in generative AI. The range of occupations exposed to advances in generative AI, the rapidity with its spread, and the variation in which populations will be most exposed to such advances, suggest that government can play an important role in helping people adapt to how generative AI changes work.

Keywords: artificial intelligence, ChatGPT, language modeling, occupation, technology

JEL Classification: J23, J24, O31, O33

Suggested Citation

Felten, Edward W. and Raj, Manav and Seamans, Robert, Occupational Heterogeneity in Exposure to Generative AI (April 10, 2023). Available at SSRN: https://ssrn.com/abstract=4414065 or http://dx.doi.org/10.2139/ssrn.4414065

Edward W. Felten

Princeton University - Center for Information Technology Policy ( email )

Sherrerd Hall, Third Floor
Princeton, NJ 08544
United States

Princeton University - Woodrow Wilson School of Public and International Affairs ( email )

Princeton University
Princeton, NJ 08544-1021
United States

Princeton University - Department of Computer Science ( email )

35 Olden Street
Princeton, NJ 08540
United States

Manav Raj (Contact Author)

University of Pennsylvania - Management Department ( email )

The Wharton School
Philadelphia, PA 19104-6370
United States

Robert Seamans

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

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