Anthropic Economic Index
Confidence 0.85 · 5 sources · last confirmed 2026-06-17
A research initiative by Anthropic that measures real-world AI use through privacy-preserving analysis of Claude conversations on Claude.ai (consumer) and the 1P API (enterprise), and — increasingly — Claude Code agent sessions. Recurring report cadence — through June 2026 the wiki holds the 4th and 5th editions, the March labor-impacts note, and the June agentic-coding report.
Stated mission (per Anthropic): provide ongoing, empirical measurement of how AI is changing tasks, occupations, and the labor market.
What it tracks
| Domain | Mapped to wiki concept |
|---|---|
| Task speedup, success, complexity | generative-ai |
| Automation vs. augmentation share | automation-vs-augmentation |
| Aggregate productivity impact | ai-employment-effects |
| Task-composition shift | ai-deskilling |
| Cross-country adoption | enterprise-ai-adoption |
| Task-horizon time scaling | ai-benchmarks |
Reports
| Report | Sample period | Status in this wiki |
|---|---|---|
| First | January 2025 | Not separately ingested; numbers cited |
| Second | (early 2025) | Not separately ingested |
| Third | August 2025 | Not separately ingested; numbers carried over |
| Fourth | November 2025 | Ingested — introduces “economic primitives” framework |
| Fifth — Learning curves | February 5–12, 2026 | Ingested — model selection matches task value; high-tenure users have ~3-4 pp higher success after controls; skill-biased technological change framing |
| Labor-market impacts (Massenkoff & McCrory) | March 5, 2026 | Ingested — the analytic/labor-impact branch of the AEI: introduces observed exposure (theoretical capability × usage, weighting automated/work-related uses), validates against BLS 2024–2034 projections, finds no systematic unemployment effect yet but a ~14% young-worker hiring slowdown into exposed occupations. The methodological primary behind the wiki’s “observed exposure” claims. |
| Agentic coding and persistent returns to expertise (Hitzig, Massenkoff, Lyubich, Heller & McCrory) | October 2025 – April 2026 (~400,000 Claude Code sessions) | Ingested — the agentic-coding branch of the AEI: the planning/execution division of labor (people make ~70% of planning decisions, Claude ~80% of execution), persistent returns to domain expertise (not coding skill; every major occupation succeeds within ~7 pts of software engineers), competence-captures-most-of-the-benefit, and the 7-month composition shift (fixing 33%→19%; writing/analysis ~doubled; task value +27%). |
Economic primitives (introduced in fourth report)
Five measurements per conversation, derived by Claude classifying its own conversation samples:
- Task complexity — human time required without AI; whether multiple tasks were handled within one conversation.
- Human and AI skill level — years of education needed to understand prompts and Claude’s responses.
- Use case — work / education / personal.
- AI autonomy — degree of user delegation, from collaboration to fully directive.
- Task success — Claude’s own assessment of whether the task was completed.
See the fourth-report source page for definitions and applications.
Methodology notes
- Privacy-preserving — random samples (typically 1M conversations on Claude.ai + 1M API transcripts).
- Tasks mapped to O*NET taxonomy. O*NET vintage shifted between the 4th and 5th reports (4th used 2010 vintage; 5th uses 2019). Year-over-year comparisons of task-share need this caveat.
- Models change report-to-report — fourth report uses Claude Sonnet 4.5 predominantly; the fifth uses Claude Opus 4.5 / 4.6 in addition. This affects comparability across editions.
Cited by external research
- Brynjolfsson et al. 2025 (Canaries) uses the Anthropic Economic Index automation/augmentation classification at the occupation level for Fact 3.
Open questions
- Earlier reports (1st through 3rd) are referenced indirectly through the fourth-report carry-over data; first-party ingestion of any prior report would clarify the longitudinal methodology.