Abstract
Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system. We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising. Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation. We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information. Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.
Abstract (translated)
鉴于生成式人工智能的迅速采用及其对各种任务潜在影响，理解AI对经济的影响是社会面临的重要问题之一。在这项研究中，我们通过分析人们使用AI的工作活动、这些活动的成功程度和广泛性，并结合有关从事此类活动的职业的数据，朝着这个目标迈出了一步。我们分析了一个包含20万条匿名且隐私已清除的用户与微软Bing Copilot（一个公开可用的生成式AI系统）之间的对话数据集。我们发现人们寻求AI协助最常见的工作活动包括收集信息和写作，而AI本身最常执行的任务是提供信息和支持、写作、教学和建议。结合这些任务分类以及对任务成功度和影响范围的测量，我们为每个职业计算了AI适用性得分。研究结果表明，在计算机和数学、办公室及行政支持等知识型工作群体中，以及销售等需要提供和传递信息的职业中，AI的适用性得分最高。此外，我们还描述了哪些类型的工作活动执行得最成功，并探讨了工资与教育程度如何影响AI适用性，以及实际应用与职业AI影响预测之间的比较情况。
URL
https://arxiv.org/abs/2507.07935
