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) have posted Occupational Heterogeneity in Exposure to Generative AI on SSRN. Here is the 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.
Highly recommended.