About AI Exposure
AI Exposure is a free, open-data platform that tracks how artificial intelligence and automation are reshaping the American labor market — with evidence, not hype.
Our Mission
To give every American worker free access to the data they need to understand how AI is changing their career — and to make informed decisions about their future. We believe public data should serve the public, and that actionable intelligence about automation risk should not be locked behind paywalls or buried in academic papers.
Why We Built This
The conversation around AI and jobs is dominated by two extremes: breathless predictions of mass unemployment and dismissive reassurances that "new jobs will appear." Neither is useful if you're a worker, educator, or policymaker trying to make real decisions today.
We built AI Exposure to fill the gap between academic research and public understanding. Every risk score, every trend line, every projection on this site is grounded in publicly available government data and peer-reviewed methodology.
What We Track
- 925 occupations with composite AI risk scores
- 22 industries with sector-level automation trends
- 51 states & territories with regional employment vulnerability
- 393 metro areas with local AI risk profiles
- 3,220 counties with industry-mix risk scores
- 324 companies tracked for AI adoption and layoffs
- Real-time layoff signals from WARN Act filings and news reporting
- FRED labor market indicators (unemployment, JOLTS, participation)
- Indeed hiring trend data
Methodology Overview
Our composite risk score combines five weighted components to produce a single 0–100 metric for each occupation:
For complete details including normalization formulas, data sources, academic citations, and comparison with other indices, see our full methodology page.
Our Data Sources
Every data point on AI Exposure is traceable to a public source. We don't use proprietary datasets, internal surveys, or anonymous tips. If we can't cite it, we don't publish it.
- Bureau of Labor Statistics (BLS) — Occupational Employment & Wage Statistics
- O*NET — Task-level occupation data from the Department of Labor
- Frey & Osborne (2017) — Oxford automation probability estimates
- OECD — Task-based automation risk framework
- Federal Reserve Economic Data (FRED)
- State WARN Act filings — Real-time layoff notifications
- SEC filings — Corporate restructuring and AI-related workforce changes
- Census Bureau — County Business Patterns employment data
- Indeed Hiring Lab — Job posting trends
- layoffs.fyi — Tech layoff tracker
Data Partnerships
Bureau of Labor Statistics
Primary source for occupational employment, wages, and 10-year projections
O*NET Resource Center
Task-level occupation data powering our OECD analysis and skill-matching algorithms
Federal Reserve (FRED)
Macroeconomic indicators for our labor market dashboard
Indeed Hiring Lab
Job posting trends and hiring demand signals by occupation and region
layoffs.fyi
Verified tech layoff event data supplementing our WARN Act tracking
Team & Background
AI Exposure is built by a small team of data analysts, journalists, and engineers who believe the public deserves clear, honest information about how technology is reshaping work. We're not funded by tech companies, consulting firms, or advocacy groups — our independence is what makes our analysis credible.
Our team combines expertise in labor economics, data science, and investigative journalism. We've collectively worked with BLS data for over 15 years and have published research on automation, employment trends, and workforce development.
A Project by TheDataProject.ai
AI Exposure is built and maintained by TheDataProject.ai, an independent initiative focused on making complex data accessible and actionable. We believe public data should serve the public — not sit locked in PDFs and government databases. Our portfolio includes 60+ data-driven websites covering healthcare, transportation, finance, education, and more.
Media Mentions
The Wall Street Journal
Cited AI Exposure occupation risk scores in coverage of white-collar job displacement trends
NPR Marketplace
Featured as a resource for workers evaluating career transition options
The Economist
Referenced composite methodology in comparative analysis of automation risk models
Bloomberg
Used layoff tracker data in reporting on AI-driven corporate restructuring
Wired
Profiled as one of the leading public-interest AI impact tracking tools
Harvard Business Review
Cited in analysis of augmentation vs. displacement patterns across industries
How to Cite
If you use AI Exposure data in research, reporting, or analysis, please cite as:
AI Exposure. (2026). AI Automation Risk Scores [Dataset].
TheDataProject.ai. https://www.aiexposure.org
For academic papers, include the methodology version (currently v2.1) and data access date.
Our Principles
Data-First
Every claim is backed by verifiable data sources. No speculation.
Open & Free
All data is freely available. No paywalls, no premium tiers.
Balanced
We show risks AND opportunities. Automation isn't all doom.
Living Data
Scores update as new BLS data, research, and signals arrive.
Project Timeline
From our initial launch with 702 occupations to today's comprehensive platform covering 925 occupations, 393 metro areas, and real-time layoff tracking — here's how AI Exposure has evolved.
AI Exposure v1.0 launched with 702 occupations and 4-component risk scoring model
Added real-time WARN Act layoff tracking from all 50 states
Reached 100,000 monthly visitors; added career transition planner tool
Expanded to 925 occupations via O*NET task similarity imputation (v1.1)
Launched industry deep dives for 22 sectors with company-level AI adoption tracking
Added 393 metro area profiles and 3,220 county-level risk scores
v2.0: Added GenAI Exposure Index as 5th scoring component
API launched — free REST access to all datasets
v2.1: Updated with 2024-2034 BLS projections and 2025 LLM capability assessments
Stay Updated
Get monthly updates on AI's impact on the job market — new data releases, methodology updates, and featured analysis.
Coming soon — newsletter launching Q3 2026
Questions, feedback, or data partnership inquiries?
Reach us at info@thedataproject.ai
For press inquiries: press@thedataproject.ai
For API & data licensing: api@thedataproject.ai