How We Calculate AI Risk Scores

Data version 2026-11 · Maintained by the AIProofMe Team

The short version

Every occupation’s risk level comes from the U.S. Bureau of Labor Statistics (BLS), which rates how exposed each occupation is to AI. Three studies from OpenAI, Anthropic and Microsoft then decide where the job sits within that level. A score measures how much of a job AI can affect, not the chance that you will lose it.

BLS AI exposureScore rangeRisk level
Very high75–95HIGH RISK
High55–74MEDIUM RISK
Moderate45–54LOW RISK
Low40–44LOW RISK

Step 1: the official BLS rating sets the level

In August 2026 the BLS published four categories of relative AI exposure (low, moderate, high, very high) for the 831 occupations it projects. To build them, BLS combined five external studies: three that score what AI could do in theory and two that measure how people actually use AI. It converted each study to a percentile rank, took the median of the theoretical and observed measures, and grouped occupations into the four categories.

We use the BLS category as published. It decides the score range in the table above, so a job BLS rates “very high” is always high risk on AIProofMe.

Step 2: three studies rank jobs within the level

Inside each range, we place occupations using three of the studies BLS also relies on, with equal weight:

  • OpenAI “GPTs are GPTs” (Science, 2024): whether large language models could cut the time a work task takes by at least half.
  • Anthropic, Labor market impacts of AI (2026): which work tasks people actually use Claude for, in conversations and through the API.
  • Microsoft Research, Working with AI (NeurIPS 2026): which work activities 200,000 Bing Copilot conversations helped with, and how successfully.

Each study is converted to a percentile rank across all occupations, the available ranks are averaged, and the job is placed within its range by that average: a higher average means a higher score within the same level.

When data is missing

Some occupations in our list are broad groups or “all other” categories that the studies do not score directly. For 129 of them we use the data of the closest occupations in the same Standard Occupational Classification (SOC) family. Each occupation page shows how many studies cover it.

Military occupations are not covered by BLS projections or by any of the three studies, so we do not give them a score. Their pages explain why and show official Department of Defense figures instead.

What the score is not

  • It is not the probability of losing your job. BLS states that exposure does not imply job loss, and many highly exposed jobs are projected to grow.
  • It is not an employment forecast. Read it alongside the BLS 2025–35 employment projection shown on each occupation page.
  • It is relative: it compares occupations with one another, not against an absolute standard.
  • It covers U.S. occupations and U.S. data only.
  • AI changes fast. The studies were published in 2024–2026 and reflect AI capabilities and usage from 2023 to 2025; we update scores when the sources update.

Your personal score

The free assessment starts from your occupation’s score. You pick the tasks you actually do from your occupation’s O*NET task list and mark the ones that take most of your time (they count double). We compare the AI exposure of your tasks, from OpenAI’s task-level ratings, with the average for all of your occupation’s tasks, multiply the difference by 80 and cap it at 15 points either way. The result stays within 40–95. Your age, how you work, your employer’s plans and your own AI use do not change the score; where research applies to them, the result page shows it separately with sources.

Task lists are based on O*NET 30.0 by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. We shortened the task wording; USDOL/ETA has not approved, endorsed or tested these modifications.

Sources

  1. U.S. Bureau of Labor Statistics, “Artificial Intelligence (AI) exposure categories,” Employment Projections, August 27, 2026.
  2. Eloundou, T., Manning, S., Mishkin, P., & Rock, D., “GPTs are GPTs: Labor market impact potential of LLMs,” Science 384(6702), June 21, 2024.
  3. Massenkoff, M., McCrory, P., et al., “Labor market impacts of AI: A new measure and early evidence,” Anthropic, March 5, 2026.
  4. Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S., “Working with AI: Measuring the Applicability of Generative AI to Occupations,” NeurIPS 2026 (arXiv:2507.07935).

The other two studies in the BLS rating (Felten, Raj & Seamans; Eisfeldt et al.) are included through the BLS category; we do not use them separately.

Download the data: BLS AI exposure categories (XLSX) · Microsoft Working with AI scores.

Update log

  • November 5, 2026: the personal assessment now adjusts the score only by the tasks you pick (up to 15 points either way). It previously used fixed adjustments for task type, employer AI use, years of experience and company size.
  • November 4, 2026: risk levels now come from the official BLS rating, and three studies (OpenAI, Anthropic, Microsoft) rank jobs within each level. Earlier scores combined four sources, including Andrej Karpathy’s “jobs” project, which we no longer use because it is an informal LLM rating rather than published research.

Who is behind these scores

AIProofMe is built and maintained by the AIProofMe Team at Funovate Technology LLC. Questions or corrections: [email protected]. See also our editorial policy and legal notice.