NBER Working Papers · 2026 · Alexander Bick、Adam Blandin、David J. Deming、Tyler R. Schumacher
中文摘要
我们通过一项全国代表性调查，衡量劳动者如何在工作中使用生成式人工智能（genAI）；该调查将生成式人工智能的采用情况与细分职业和具体任务相联系。我们的数据首次提供了任务层面的生成式人工智能采用指数，并且我们表明，这些指数可为分析生成式人工智能对劳动力市场的影响提供依据。暴露度评分能够解释不同职业和任务之间采用情况的部分差异，但远非全部。我们还将这些指数与基于生成式人工智能平台聊天记录的度量指标区分开来：二者在概念上有所不同，后者往往会将过多聊天记录归入横跨多种职业的通用活动。最后，我们强调，当前生成式人工智能的采用覆盖广泛，但采用程度较浅：它已被用于许多职业和任务，但在其中大多数职业和任务中，采用者尚不足劳动者的一半。这表明，即使从事非常相似的工作，劳动者之间的采用情况也存在相当大的差异；因此，了解谁会采用生成式人工智能，可能与了解生成式人工智能会辅助哪些任务同样重要。
Abstract
We measure how workers use genAI for their jobs in a nationally representative survey linking genAI adoption to detailed occupations and tasks. Our data provide the first task-level genAI adoption indexes, which we show can inform analyses of genAI’s labor market impact. Exposure scores explain some, but far from all, of the variation in adoption across occupations and tasks. We also distinguish our indexes from measures based on genAI platform chat logs, which differ conceptually and tend to over-classify chats into generic activities spanning many occupations. Finally, we highlight that current adoption is widespread but shallow: genAI is used across many occupations and tasks, yet within most of them, fewer than half of workers adopt. This indicates substantial variation among workers doing very similar work, suggesting that understanding who adopts may matter as much as understanding which tasks genAI assists.