GPTs are GPTs: An early look at the labor market impact potential of large language models

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Abstract

We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT‑4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implications.

* GPT

* Language

* Reasonings & Policy

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Tyna Eloundou (OpenAI), Sam Manning (OpenAI, OpenResearch), Pamela Mishkin (OpenAI), Daniel Rock (University of Pennsylvania)

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