GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Authors: Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock
Abstract
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.
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Closest papers
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The cybersecurity study applies LLM job-impact analysis to non-managerial cybersecurity roles and certification exams, citing this paper.
Abstract
ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. This study assesses ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. It also explores its potential to pass top cybersecurity certification exams. Findings reveal ChatGPT’s promise to streamline some jobs, especially those requiring memorization. Moreover, this paper highlights ChatGPT’s challenges and limitations, such as ethical implications, LLM limitations, and Artificial Intelligence (AI) security. The study suggests that LLMs like ChatGPT could transform the…
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On Large Language Models in National Security ApplicationsWilliam N. Caballero, Phillip R. Jenkins · 2024 · Cryptography and Security · cited 37 times · Not summarized yet
The article examines LLM benefits and risks in national security, rather than estimating their exposure across the labor market.
Abstract
The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significan…
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The position paper considers how multimodal language models may affect jobs and human workers, focusing on displacement and coexistence.
Abstract
The rapid advancements in artificial intelligence, particularly in large language multi-modal models like GPT-4, have raised concerns about the potential displacement of human workers in various industries. This position paper aims to analyze the current state of job replacement by AI models and explores potential implications and strategies for a balanced coexistence between AI and human workers.
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“Generate” the Future of Work through AI: Empirical Evidence from Online Labor MarketsJin Liu, Xing-Chen Xu, Xiao-Si Nan et al. · 2026 · Information systems research · Not summarized yet
The study measures generative AI’s labor-market effects in online platform submarkets, finding contraction where skills align with LLM capabilities.
Abstract
Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI’s simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller…
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Abstract
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Abstract
This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing inte…
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Abstract
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The survey guides practitioners on LLM models, data, and downstream NLP tasks rather than estimating labor-market impacts.
Abstract
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Prior work it builds on
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The study develops a method for assessing occupational exposure to language-model advances, which this paper cites.
Abstract
Recent dramatic increases in AI language modeling capabilities has led to many questions about the effect of these technologies on the economy. In this paper we present a methodology to systematically assess the extent to which occupations, industries and geographies are exposed to advances in AI language modeling capabilities. We find that the top occupations exposed to language modeling include telemarketers and a variety of post-secondary teachers such as English language and literature, foreign language and literature, and history teachers. We find the top industries exposed to advances in language modeling are legal services and securities, commodities, and investments. We also find a p…
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GPT-4 Technical ReportOpenAI · 2023 · Computation and Language · cited 28,314 times · AI summary available
The technical report describes GPT-4’s model, training, and benchmark performance, and this paper cites it.
Abstract
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What Can Machines Learn, and What Does It Mean for Occupations and the Economy?Erik Brynjolfsson, Tom Michael Mitchell, Daniel Rock · 2018 · cited 634 times · Not summarized yet
The study scores O*NET tasks for machine-learning suitability using a rubric that this paper cites as prior work.
Abstract
Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content.
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The paper shows that scaling language models improves few-shot performance, and this labor-impact study cites it.
Abstract
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Abstract
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Papers that cite it
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The review surveys GPT technologies, applications, challenges, and future directions, and cites this paper.
Abstract
The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related …
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Generative AI for Economic Research: Use Cases and Implications for EconomistsAnton Korinek · 2023 · Journal of Economic Literature · cited 310 times · Not summarized yet
The paper describes LLM use cases for economists and potential productivity gains from automating research tasks, citing this study.
Abstract
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The study tests generative AI’s short-term employment and earnings effects among freelancers in occupations affected by AI, citing this paper.
Abstract
Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured b…
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Assessing the risk of takeover catastrophe from large language modelsSeth D. Baum · 2024 · Risk Analysis · cited 8 times · Not summarized yet
The article analyzes catastrophic takeover risks from LLMs, a concern distinct from this paper’s labor-market exposure estimates.
Abstract
This article presents a risk analysis of large language models (LLMs), a type of “generative” artificial intelligence (AI) system that produces text, commonly in response to textual inputs from human users. The article is specifically focused on the risk of LLMs causing an extreme catastrophe in which they do something akin to taking over the world and killing everyone. The possibility of LLM takeover catastrophe has been a major point of public discussion since the recent release of remarkably capable LLMs such as ChatGPT and GPT‐4. This arguably marks the first time when actual AI systems (and not hypothetical future systems) have sparked concern about takeover catastrophe. The article's a…
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Artificial Intelligence and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor PlatformDandan Qiao, Hua-Xia Rui, Qian Xiong · 2026 · Journal of Management Information Systems · cited 3 times · Not summarized yet
The study estimates ChatGPT’s contrasting displacement and productivity effects across online labor markets, citing this paper.
Abstract
ABSTRACT This study investigates how artificial intelligence (AI) influences various online labor markets (OLMs) over time. Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT’s launch: displacement effects featuring reduced work volume and earnings, exemplified by translation & localization OLM; productivity effects featuring increased work volume and earnings, exemplified by web development OLM. To understand these opposite effects in a unified framework, we developed a Cournot competition model to identify an inflection point for each market. Before this point, human workers benefit from AI enhancements; beyond this point, human workers w…
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Updated on 6 November 2026.