Working with AI: Measuring the Applicability of Generative AI to Occupations

TL;DR

This study analyzes real-world AI usage through a large dataset of conversations to measure AI's applicability across various occupations, revealing widespread use especially in information-related tasks.

Contribution

It introduces a novel methodology using an LLM pipeline to classify AI-assisted activities in occupations based on real-world conversation data.

Findings

Most AI-assisted activities involve information work.

AI applicability spans across sectors and occupations.

The methodology predicts task delegation and assistance likelihood.

Abstract

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.