Which Economic Tasks Are Performed with AI?

Summary

Handa, Tamkin et al. (Anthropic, February 2025) provide the first large-scale empirical measurement of actual AI usage across the US economy, replacing the dominant forecasting-via-exposure approach (Frey & Osborne 2017; Brynjolfsson et al. 2018; Webb 2019; Felten et al. 2023; Eloundou et al. 2023) with privacy-preserving measurement of real user behaviour. Published as the Anthropic Economic Index.

Method. Uses Clio (Tamkin et al. 2024) to classify 4M+ Claude.ai Free/Pro conversations from December 2024–January 2025 against the US Department of Labor's O*NET task taxonomy (~20,000 task statements). Hierarchical traversal handles the taxonomy size; aggregation thresholds enforce privacy. Each conversation is also labelled on an augmentation-vs-automation axis.

Five main findings.

Where AI is used. Highest usage in software engineering (software engineers, data scientists, bioinformatics technicians), writing-heavy roles (technical writers, copywriters, archivists), and analytical roles. Physical-manipulation roles (anesthesiologists, construction workers) show minimal usage.
Depth within occupations. Only ~4% of occupations have AI usage for ≥75% of their tasks; ~36% have usage for ≥25%. AI has begun to diffuse broadly but not yet replaced wholesale task portfolios.
Skill profile. Cognitive skills (Reading Comprehension, Writing, Critical Thinking) dominate AI conversations; physical skills (Installation, Equipment Maintenance) and managerial skills (Negotiation) barely appear. Mirrors human-complementarity theory.
Wage and barrier-to-entry pattern. AI usage peaks in upper-middle wage quartile (software roles), drops at both extremes. Similar inverted-U for required education: peaks at bachelor's-degree occupations, lower at minimal or extensive-training extremes. Contrasts with Webb (2019) prediction that peak exposure is in high-wage occupations.
Automation vs. augmentation. 57% augmentation, 43% automation. Most occupations exhibit a mix of both rather than pure replacement or pure collaboration.

Limitations. Single platform (Claude.ai, skewed toward technical users), cannot observe how outputs are used downstream, static O*NET cannot represent newly created tasks, classification is probabilistic.

Connections

Core bottom-up evidence for concepts/ai-adoption alongside enterprise-side sources/2025-korst-wharton-gen-ai-enterprise-adoption and Microsoft Copilot-side sources/2025-tomlinson-working-with-ai.
Anthropic-methodology sibling of sources/2026-shen-ai-skill-formation (both use internal data / Anthropic infrastructure; both co-authored by entities/alex-tamkin).
Uses the same Clio system described in Tamkin et al. 2024 (referenced-not-ingested).
Complements sources/2024-xu-the-agent-company-benchmark by measuring actual task distribution rather than benchmark coverage.
Feeds concepts/ai-adoption with the augmentation/automation distinction that contrasts with METR's developer-slowdown finding in sources/2025-becker-metr-ai-developer-productivity.
New entities: entities/kunal-handa, entities/esin-durmus.