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:我们调查了基于生成式预训练转换器(GPT)模型及相关技术对美国劳动力市场的潜在影响。使用新的评估标准,我们根据其与GPT能力的对应程度来评估职业,同时考虑人类专业知识和GPT-4的分类。我们的研究发现,约80%的美国劳动力市场可能会受到GPT引入的影响,他们的工作任务中至少有10%可能受到影响,而约19%的工人可能会看到至少50%的任务受到影响。影响范围涉及各个薪资水平,高收入工作面临更大的暴露风险。值得注意的是,影响不限于近期生产率增长较高的行业。我们得出结论:生成式预训练转换器具有通用技术(GPTs)的特征,表明随着这些模型可能带来显著的经济、社会和政策影响。