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We investigate the potential implications of large language models (LLMs),such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market,focusing on the increased capabilities arising from LLM-powered softwarecompared to LLMs on their own. Using a new rubric, we assess occupations basedon their alignment with LLM capabilities, integrating both human expertise andGPT-4 classifications. Our findings reveal that around 80% of the U.S.workforce could have at least 10% of their work tasks affected by theintroduction of LLMs, while approximately 19% of workers may see at least 50%of their tasks impacted. We do not make predictions about the development oradoption timeline of such LLMs. The projected effects span all wage levels,with higher-income jobs potentially facing greater exposure to LLM capabilitiesand LLM-powered software. Significantly, these impacts are not restricted toindustries with higher recent productivity growth. Our analysis suggests that,with access to an LLM, about 15% of all worker tasks in the US could becompleted significantly faster at the same level of quality. When incorporatingsoftware and tooling built on top of LLMs, this share increases to between 47and 56% of all tasks. This finding implies that LLM-powered software will havea substantial effect on scaling the economic impacts of the underlying models.We conclude that LLMs such as GPTs exhibit traits of general-purposetechnologies, indicating that they could have considerable economic, social,and policy implications.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models