[Submitted on 17 Mar 2023]
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of
Large Language Models
Tyna Eloundou
<https://arxiv.org/search/econ?searchtype=author&query=Eloundou%2C+T>,
Sam Manning
<https://arxiv.org/search/econ?searchtype=author&query=Manning%2C+S>,
Pamela Mishkin
<https://arxiv.org/search/econ?searchtype=author&query=Mishkin%2C+P>,
Daniel Rock
<https://arxiv.org/search/econ?searchtype=author&query=Rock%2C+D>
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.
Subjects: General Economics (econ.GN); Artificial Intelligence (cs.AI);
Computers and Society (cs.CY)
Cite as: arXiv:2303.10130 <https://arxiv.org/abs/2303.10130> [econ.GN]
(or arXiv:2303.10130v1 <https://arxiv.org/abs/2303.10130v1> [econ.GN]
for this version)
https://doi.org/10.48550/arXiv.2303.10130
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