We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implications.

翻译：我们研究了生成式预训练Transformer模型（GPTs）及相关技术对美国劳动力市场的潜在影响。通过建立新的评估框架，结合人类专家判断与GPT-4的分类结果，对职业与GPT能力的对应关系进行系统评估。研究显示：约80%的美国劳动力中至少10%的工作任务将受到GPTs引入的影响，约19%的工人可能面临超过50%的任务变化。该影响覆盖所有薪资水平群体，其中高收入职业面临更大冲击。值得注意的是，这种影响并不局限于近期生产率增长较快的行业。我们得出结论：生成式预训练Transformer展现出通用技术（GPTs）的特征，表明这些模型可能产生显著的经济、社会及政策影响。