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

By Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock. 2023

566 citations recorded by the source corpus.

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 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.

Subfields: Artificial Intelligence, Health Informatics, Management Information Systems, Large Language Models, Economics & Operations Research

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Citations in the corpus

GPT-4o System Card (2024, background): “…[26], misuse [27, 23], and loss of control [27]), benefits (for example, in healthcare [28] and real-world challenges in climate and energy [29]), and large-scale transformations (such as economic impacts [30, 31, 32]; acceleration of science and the resulting technological progress [30, 33]).”
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023, background): “Costs, benefits, and influence over RLHF models should be equitably distributed across different communities [Whittlestone et al., 2021, Eloundou et al., 2023].”
Augmenting large language models with chemistry tools (2023, background): “• Then make predictions for the molecules in a selection pool.”
The alignment problem from a deep learning perspective (2022, background): “The effect of this type of recursive improvement may further increase as we develop models with human or superhuman 3 performance in critical domains [Bostrom, 2014] and leverage millions of copies of these systems across the economy [Davidson, 2021, Eloundou et al., 2023].”
A Categorical Archive of ChatGPT Failures (2023, background): “• It is essential to consider the ethical and social consequences of using these models, such as job displacement [22] and the risk of bias and manipulation.”
Executable Code Actions Elicit Better LLM Agents (2024, background): “As extensively discussed in (Eloundou et al., 2023), a fully autonomous agent may transform the current landscape of the labor market and impact the jobs of existing workers.”
Generative AI enhances individual creativity but reduces the collective diversity of novel content (2023, unknown): “Generative AI has the potential to markedly affect most aspects of the economy and society at large ( 27 , 28 ).”
Position: Levels of AGI for Operationalizing Progress on the Path to AGI (2023, unknown): “Such predictions about AGI’s capabilities in turn predict likely societal impacts; AGI may have signiﬁcant economic implications, i.e., reaching the necessary criteria for widespread labor sub-stitution (Ellingrud et al., 2023; Dell’Acqua et al., 2023; Eloundou et al., 2023), as well as geo-politica”
Generative artificial intelligence (2023, unknown): “Hence, the effect on the labor market by the disruption needs to be discussed, and businesses should seek to understand and embrace the potential of generative AI (Eloundou et al., 2023; Willcocks, 2020).”
Large Language Model Alignment: A Survey (2023, unknown): “workforce will be affected by LLMs (Eloundou et al., 2023).”
Generative AI (2023, unknown): “This can reduce costs and open up new opportunities for growth and innovation (Eloundou et al., 2023).”
LLMs Corrupt Your Documents When You Delegate (2026, unknown): “Document editing is among the most common tasks in knowledge work (Siu & Fok, 2025), and one of the primary use cases of LLM-based systems (Handa et al., 2025; Eloundou et al., 2023).”
When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings (2024, unknown): “…Marlow and Wood, 2021; Cabanac et al., 2021), journalism (Petridis et al., 2023), story generation (Valentini et al., 2023; Xie et al., 2023; Zhao et al., 2023), as chatbots on social media (Sadiko˘glu et al., 2023) and in the workplace at large (Brynjolfsson et al., 2023; Eloundou et al., 2023).”