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

arXiv: https://arxiv.org/abs/2507.07935 · HTML: https://mlsanity.com/paper/2507.07935/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Towards Accurate Differential Diagnosis with Large Language Models](https://mlsanity.com/paper/2312.00164/) (2023)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce](https://mlsanity.com/paper/2506.06576/) (2025)
- [The Use of Generative Search Engines for Knowledge Work and Complex Tasks](https://mlsanity.com/paper/2404.04268/) (2024)
- [The Potential Impact of AI Innovations on U.S. Occupations](https://mlsanity.com/paper/2312.04714/) (2023)

## Built on by (most cited first, 19 of 19)

- [LLMs Corrupt Your Documents When You Delegate](https://mlsanity.com/paper/2604.15597/) (2026)
- [DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces](https://mlsanity.com/paper/2509.13444/) (2025)
- [Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context](https://mlsanity.com/paper/2512.03671/) (2025)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research](https://mlsanity.com/paper/2604.21006/) (2026)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [AI Fiction in the Wild](https://mlsanity.com/paper/2606.22748/) (2026)
- [Google's AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy](https://mlsanity.com/paper/2608.00038/) (2026)
- [Crashing Waves vs. Rising Tides: Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks](https://mlsanity.com/paper/2604.01363/) (2026)
- [How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions](https://mlsanity.com/paper/2604.22503/) (2026)
- [Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild](https://mlsanity.com/paper/2605.29018/) (2026)
- [AI Technologies in Language Access: Attitudes Towards AI and the Human Value of Language Access Managers](https://mlsanity.com/paper/2605.19234/) (2026)
- [Generative AI and the Reorganization of Labor Demand](https://mlsanity.com/paper/2605.23159/) (2026)
- [Measuring the Occupation-Level Impact of AbbVie Intelligence: AI Applicability Analysis, 2024-2025](https://mlsanity.com/paper/2606.20635/) (2026)
- [The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies](https://mlsanity.com/paper/2607.05404/) (2026)
- [How Organizations Use AI: Evidence from ChatGPT](https://mlsanity.com/paper/2608.12236/) (2026)
- [Who Delegates to AI? Evidence from Agent Configurations in Github](https://mlsanity.com/paper/2608.20425/) (2026)
- [Sophistication in GenAI Use: Field Evidence from a Large Firm](https://mlsanity.com/paper/2608.27364/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

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.