Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

TL;DR

This paper develops a privacy-preserving framework to quantify how AI is used across economic tasks by mapping Claude.ai conversations to the U.S. O*NET task taxonomy. Using Clio, it reveals that AI usage is heavily concentrated in software development and writing tasks, but diffusion extends to about 36% of occupations for at least a quarter of their tasks. The study also characterizes usage as 57% augmentation and 43% automation, and shows wage and barrier-to-entry patterns that favor upper-mid wages and substantial preparation levels. While acknowledging data and taxonomy limitations, the authors offer a dynamic, task-level approach to tracking AI adoption with potential policy implications for guiding diffusion and managing transitions as AI capabilities evolve.

Abstract

Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy. We leverage a recent privacy-preserving system to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor's O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with approximately 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement). While our data and methods face important limitations and only paint a picture of AI usage on a single platform, they provide an automated, granular approach for tracking AI's evolving role in the economy and identifying leading indicators of future impact as these technologies continue to advance.

Figures (27)

• Figure 1: Measuring AI use across the economy. We introduce a framework to measure the amount of AI usage for tasks across the economy . We map conversations from Claude.ai to occupational categories in the U.S. Department of Labor's O*NET Database to surface current usage patterns. Our approach provides an automated, granular, and empirically grounded methodology for tracking AI's evolving role in the economy. (Note: figure contains illustrative conversation examples only.)
• Figure 2: Hierarchical breakdown of top six occupational categories by the amount of AI usage in their associated tasks. Each occupational category contains the individual O*NET occupations and tasks with the highest levels of appearance in Claude.ai interactions.
• Figure 3: Comparison of occupational representation in Claude.ai usage data and the U.S. economy. Results show most usage in tasks associated with software development, technical writing, and analytical, with notably lower usage in tasks associated with occupations requiring physical manipulation or extensive specialized training. U.S. representation is computed by the fraction of workers in each high-level category according to the U.S. Bureau of Labor Statistics bls_website.
• Figure 4: Depth of AI usage across occupations. Cumulative distribution showing what fraction of occupations (y-axis) have at least a given fraction of their tasks with AI usage (x-axis). Task usage is defined as occurrence across five or more unique user accounts and fifteen or more conversations. Key points on the curve highlight that while many occupations see some AI usage ($\sim\!36\%$ have at least 25% of tasks), few occupations exhibit widespread usage of AI across their tasks (only $\sim\!4\%$ have 75% or more tasks), suggesting AI integration remains selective rather than comprehensive within most occupations.
• Figure 5: Distribution of occupational skills exhibited by Claude in conversations. Skills like critical thinking, writing, and programming have high presence in AI conversations, while manual skills like equipment maintenance and installation are uncommon.