Data sources & attribution
The score is built on public, peer-reviewed data — not opinion. Here's every source and what it contributes.
O*NET 29.1 database
CC BY 4.0The backbone: the catalog of occupations, their real tasks, skills, and work activities that the score is computed over.
Anthropic Economic Index
Open datasetObserved, real-world Claude usage by occupation. Ingested (release 2026-06-26) to ground the augmentation component: it nudges whether observed use of a role leans toward automation or augmentation. The usage volume is used only as a reliability weight, never as exposure, and it is Claude.ai-specific (a proxy, not a census).
“GPTs are GPTs”
Published researchTask-level exposure modeling. Ingested at the task level (Core tasks weighted higher) and aggregated per occupation to set the automation/exposure magnitude of the score — the empirical backbone of the automation component.
AI Occupational Exposure (AIOE) — Language Modeling
Published research (cited, not redistributed)A second, independent measure of exposure magnitude — built from O*NET abilities mapped to AI capabilities (ability-based), versus the task-based Penn study. The Language-Modeling variant (LLM-specific) is ingested per 6-digit SOC and blended into the automation component so two distinct methods corroborate the magnitude rather than relying on one. Standardized exposure, not a prediction of job loss.
BLS Employment Projections
Public domain (U.S. government)Wages, projected growth or decline, and average annual openings (2024–34 — counts growth plus replacement needs, not just new jobs) by occupation — the labor-market resilience and demand signals, plus the salary data on adjacent roles.
Corroborating research — context, not score inputs
These shape how we frame AI exposure, but are not blended into your score. Exposure measures disagree in magnitude, and none of them equals observed job loss.
- Stanford “Canaries in the Coal Mine” (Brynjolfsson, Chandar & Chen, 2025) — observed early-career strain: a ~16% relative employment decline for workers aged 22–25 in the most AI-exposed roles.
- ILO Working Paper 140 — a global generative-AI occupational-exposure index (ISCO-08; CC BY 4.0).
- OECD AI Exposure Measure (2026) — a forward-looking, occupation-level AI capability-gap measure.
- Yale Budget Lab — AI & the labor market — finds the published exposure metrics disagree in magnitude and do not equal job loss.
- Stanford HAI AI Index — the broad annual benchmark for AI's economic context.
O*NET data is used under the Creative Commons Attribution 4.0 license. Government datasets (BLS, O*NET) are used in accordance with their terms; research is cited, not redistributed. AI-Safe Careers is not affiliated with or endorsed by these organizations.