Scoring 342 US jobs on AI/robot risk: office work scores 94%, hands-on care 17%, and the riskier jobs pay more

Reddit r/ArtificialInteligence Tools
ai-automation job-risk labor-market data-analysis occupational-data side-project

Summary

A side project scores 342 US occupations on AI and robot exposure, finding office and knowledge work at 94% risk while hands-on care jobs sit at 17%, and notably the higher-paying jobs are the most exposed this time around. The methodology is based on Frey & Osborne (2013) adjusted for current AI capability, with a free CSV and site at aifiredyou.com.

https://preview.redd.it/kiyjwvbgddyh1.png?width=3200&format=png&auto=webp&s=eef0df683049672427f6dbacdac29a17fbba81a5 https://preview.redd.it/d3sk40agddyh1.jpeg?width=3200&format=pjpg&auto=webp&s=1ded2efc1d3822ce96986ae738361580996d152d https://preview.redd.it/55i5t1cgddyh1.png?width=3200&format=png&auto=webp&s=128cf93744dc06eb94f470bdced2c75519352ae0 I scored 342 US occupations on how exposed they are to AI and robots. Here's what the data shows and where the method is weak. What it shows The top of the list is office and knowledge work. Customer service reps, general office clerks and software developers all score 94%. 60 jobs land in "Very High Risk", and together they employ 25.2 million people. The bottom is hands-on care. Home health aides, childcare workers and nursing assistants score 17%. Electricians (35%) and registered nurses (39%) also sit well below most office jobs. The riskier jobs pay more. The 60 Very High Risk jobs have a median salary of $79,665, against $51,030 for the 27 lowest-risk ones. That's the opposite of older automation waves, which hit lower-paid routine work first. How it's built I started from Frey & Osborne's 2013 automation probabilities, adjusted each occupation for current AI capability with AI-assisted scoring, and joined BLS pay and headcount data. Weak spots It scores whole occupations, not tasks. Most jobs will change long before they disappear. Scores only take 11 distinct values, so read them as bands, not precision. The "expiration year" is one rough year per job. A 2026 year means the change is already underway, not a layoff date. It's US-only. Which job do you think it gets most wrong, and would a task-level approach fix that? Sources: pay and headcount from the US BLS Occupational Outlook Handbook, and risk from Frey & Osborne (2013) plus AI-assisted adjustment, data from April 2026. The site and the free CSV are at aifiredyou.com. It's my own free, non-commercial side project.
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