[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