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.

翻译：我们研究了生成式预训练Transformer（GPTs）等大语言模型（LLMs）对美国劳动力市场的潜在影响，重点关注基于LLM的软件相比LLM本身所增强的能力。通过制定新的评估框架，我们结合人类专家判断与GPT-4分类结果，依据各类职业与LLM能力的契合度对其进行评估。研究发现，约80%的美国劳动力可能至少有10%的工作任务受到LLM引入的影响，而约19%的劳动者可能面临至少50%的工作任务被影响。我们未对LLM的开发或采用时间表进行预测。预期影响覆盖所有薪资水平，其中高收入岗位可能面临LLM能力及LLM驱动软件更强的暴露程度。值得注意的是，这些影响并不局限于近期生产率增长较快的行业。我们的分析表明，在可访问LLM的条件下，美国约15%的劳动者任务能够以同等质量水平显著加速完成。若结合基于LLM构建的软件与工具，该比例将提升至全部任务的47%至56%。这一发现意味着，LLM驱动软件将对底层模型的经济影响产生显著的规模放大效应。我们得出结论：GPTs等LLM展现出通用目的技术的特征，预示其可能对经济、社会及政策产生深远影响。