About the Project
About AI Career Stats
Bridging the gap between official U.S. government labor statistics and rapid Generative AI capability changes.
Our Mission
Discussions around artificial intelligence in the workplace often swing between alarming doomsday headlines and dismissive hand-waving. Workers, career switchers, and students are left wondering: Which specific parts of my job can AI actually do, and what human strengths remain durable?
The federal government’s standard labor sources — the U.S. Department of Labor’s O*NET program and the Bureau of Labor Statistics (BLS) — track wages, typical education, and 10-year employment growth. However, official government statistical cycles move slowly, and federal tables do not publish Generative AI automation vulnerability metrics.
AI Career Stats was created to fill that gap. We synthesize standardized O*NET 30.3 task statements with state-of-the-art LLM capability evaluation to produce granular, task-by-task automation scores — weighted by real-world job importance and grounded in official BLS wage and growth data.
Our Editorial & Modeling Principles
Task-Level Granularity
Jobs are not monoliths. A software developer doesn’t just write boilerplate code; they negotiate requirements and debug distributed systems. We evaluate specific O*NET tasks rather than painting entire occupations with a broad brush.
Importance-Weighted Math
Incidental administrative tasks should not skew a doctor's or surgeon's score. We weight each evaluated task by its official O*NET importance rating so core professional responsibilities dominate the index.
Durable Work Highlight
We explicitly identify safe and durable human tasks — physical dexterity, clinical empathy, ethical discretion, and complex interpersonal navigation — to guide workers toward career durability.
Objective Labor Context
We pair AI risk with official BLS median wages and 2025–2035 employment change projections to show real economic realities, not theoretical speculation.
How We Generate Data
Our data pipeline extracts Core work tasks and Detailed Work Activities (DWAs) from O*NET Database 30.3. Each task is evaluated against current Generative AI capabilities using an automated evaluation prompt. Scores range from 0 (completely insulated / physical) to 100 (fully automated by text/code/vision synthesis).
Read the complete mathematical formula and dataset specifications on our Methodology page.
Get in Touch
Contact & Editorial Inquiries
We value feedback from domain practitioners, economists, educators, and working professionals.
Data Corrections & Feedback
Notice an O*NET task weighting or SOC code mismatch? Send your feedback with the occupation title and URL.
General & Press Inquiries
For media questions, research citations, or general platform inquiries:
Mailing Address: AI Career Stats, 1209 Orange St, Wilmington, DE 19801, USA
Response time: We aim to respond to all editorial and research inquiries within 2 business days.