基于真实对话数据，量化生成式AI在各职业中的适用性。
Working with AI: Measuring the Applicability of Generative AI to Occupations
- 用大模型分析20万条Bing Copilot对话，识别职业中被AI辅助的任务。
- 信息类工作（如内容创作、信息处理）最易被AI协助，多数职业都涉及此类任务。
- 可预测哪些职业更可能将任务交给AI或用于现有流程优化。
随着生成式AI成为通用技术，理解其经济影响成为社会最紧迫的问题之一。现有研究多依赖对AI能力的预测或聚焦单一企业。本文基于真实世界中的AI使用数据，分析了20万条匿名的Microsoft Bing Copilot对话，以衡量生成式AI在职业中的适用性。我们采用基于LLM的流水线，对O*NET工作活动进行分类，判断其是否由AI协助或执行。结果发现，最常见的且最成功的AI辅助工作集中在信息类任务——即信息的创建、处理与传播。在职业层面，跨行业普遍存在高适用性，因为大多数职业都包含信息工作成分。本方法还能预测哪些职业更可能将任务委派给AI，或更倾向于用AI辅助现有工作流。
原文摘要 · Abstract (English)
With generative AI emerging as a general-purpose technology, understanding its economic effects is among society's most pressing questions. Existing studies of AI impact have largely relied on predictions of AI capabilities or focused narrowly on individual firms. Drawing instead on real-world AI usage, we analyze a dataset of 200k anonymized conversations with Microsoft Bing Copilot to measure AI applicability to occupations. We use an LLM-based pipeline to classify the O*NET work activities assisted or performed by AI in each conversation. We find that the most common and successful AI-assisted work activities involve information work--the creation, processing, and communication of information. At the occupation level, we find widespread AI applicability cutting across sectors, as most occupations have information work components. Our methodology also allows us to predict which occupations are more likely to delegate tasks to AI and which are more likely to use AI to assist existing workflows.
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