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