mlsanityData to 7 Oct 2026

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

arXiv 2303.10130 ↗First posted 17 Mar 2023econ.GN cs.AI cs.CYcited by 174

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Abstract (arXiv)

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.

Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). Builds on 24 of its 91 references that are in the map. First posted on arXiv 17 Mar 2023.

Builds on

24 of 91 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2020Scaling Laws for Neural Language Modelscited by 6,411
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  8. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  9. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  10. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  11. 2021Ethical and social risks of harm from Language Modelscited by 957
  12. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
12 more
  1. 2019Release Strategies and the Social Impacts of Language Modelscited by 505
  2. 2023Augmented Language Models: a Surveycited by 398
  3. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  4. 2021Scaling Laws for Transfercited by 265
  5. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  6. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  7. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 157
  8. 2017When Will AI Exceed Human Performance? Evidence from AI Expertscited by 41
  9. 2022A Hazard Analysis Framework for Code Synthesis Large Language Modelscited by 22
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Citing papers per year: 2023: 70; 2024: 53; 2025: 30; 2026: 21.

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  2. 2023StarCoder: may the source be with you!cited by 790
  3. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
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  6. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 306
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  9. 2023Language Model Tokenizers Introduce Unfairness Between Languagescited by 207
  10. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 195
  11. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 165
  12. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
38 more
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  4. 2023Levels of AGI for Operationalizing Progress on the Path to AGIcited by 94
  5. 2025Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversationscited by 88
  6. 202314 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathoncited by 85
  7. 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
  8. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 76
  9. 2024On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularizationcited by 72
  10. 2023Turning large language models into cognitive modelscited by 66
  11. 2023EdgeMoE: Empowering Sparse Large Language Models on Mobile Devicescited by 65
  12. 2023Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memorycited by 64
  13. 2023Meta-in-context learning in large language modelscited by 59
  14. 2023A Bibliometric Review of Large Language Models Research from 2017 to 2023cited by 56
  15. 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
  16. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
  17. 2025Large Language Models Pass the Turing Testcited by 48
  18. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 46
  19. 2024A Full-duplex Speech Dialogue Scheme Based On Large Language Modelscited by 40
  20. 2025How Do Large Language Monkeys Get Their Power (Laws)?cited by 40
  21. 2024D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Modelscited by 39
  22. 2025Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforcecited by 33
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  24. 2023Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domainscited by 31
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  26. 2024Creativity Has Left the Chat: The Price of Debiasing Language Modelscited by 29
  27. 2023LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMscited by 27
  28. 2024Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?cited by 26
  29. 2026Gym-Anything: Turn any Software into an Agent Environmentcited by 24
  30. 2023Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflowcited by 22
  31. 2024Two Types of AI Existential Risk: Decisive and Accumulativecited by 21
  32. 2024CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technologycited by 21
  33. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  34. 2023An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applicationscited by 20
  35. 2023Enhancing Network Management Using Code Generated by Large Language Modelscited by 20
  36. 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotescited by 20
  37. 2023Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?cited by 19
  38. 2024Bias in Generative AIcited by 19

50 most cited of 174 All 174 (JSON)