Occupation data (employment, wages, education, growth outlook) comes from the Bureau of Labor Statistics Occupational Outlook Handbook (2024 edition), covering 342 occupations representing 160M+ American workers.
Each occupation is scored 0–10 for AI exposure by two independent LLMs — Gemini 3 Flash and Claude Sonnet 4.6 — then averaged. Both models analyze each job's core tasks, work environment, and required skills against the same calibrated rubric. The two models agreed within ±1 point on 91% of occupations.
"Paths Forward" suggestions use BLS Similar Occupations data (expert-curated relationships between jobs), weighted by education match, pay similarity, and growth outlook.
The two models agreed within ±1 point on 91% of occupations. Where they disagree, Sonnet tends to score knowledge work lower, giving more weight to human judgment and physical presence. Gemini leans toward "if it's digital, it's exposed."
Biggest disagreements:
Both models receive the same calibrated rubric with anchor examples at each level. The full prompt and scoring pipeline are in the source code.
AI impact scores reflect a single model's assessment and should be treated as directional estimates, not precise predictions. AI capabilities are evolving rapidly — scores may shift as the technology matures. Growth projections are BLS 10-year estimates (2023–2033).
Originally created by Andrej Karpathy. Extended by Chrona with career path analysis, interactive filtering, and additional analytics.
Source code: github.com/chrona-nyc/jobs