AI Risk Index
AI job exposure, measured task by task
A transparent, task-based index scoring 923 U.S. occupations from 18,796 O*NET tasks rated by a multi-model LLM panel.
923
occupations scored
18,796
O*NET tasks rated
~225k
dimension ratings
32%
of U.S. wage bill under pressure
194.2M
workers covered (BLS OEWS)
Overview
AI Risk Index scores every U.S. occupation on its exposure to AI-driven automation with a task-based methodology: each of the ~18,800 O*NET task statements is rated individually by a multi-model LLM panel across five weighted dimensions, and occupation scores are derived from importance-weighted task scores. Every number is transparent, versioned, and reproducible.
The index never collapses to one number. Three separate scores answer three different questions — Exposure (is AI technically capable?), Substitution (does AI actually replace the human after cost, barriers, and adoption?), and Augmentation (does AI assist without replacing?). Each ships with a confidence interval derived from rater disagreement, because single-model exposure ratings can vary by an order of magnitude.
The headline run finds 32% of the U.S. wage bill — roughly $4.7T of $14.5T — under substitution pressure, while 47.8M workers sit in high-augmentation occupations against 5.3M in high-substitution ones. The index is explicitly built as adaptation guidance, not doom, and its scores are published under CC BY 4.0.
Key Features
Task-based methodology
Every occupation score is built bottom-up from individually rated O*NET task statements, aggregated with official importance weights — never a vibes-based occupation guess.
Multi-model LLM rater panel
Claude Sonnet 5 and Haiku 4.5 rate every task independently; disagreement between raters becomes the published confidence interval.
Three scores, never collapsed
Exposure, Substitution, and Augmentation answer distinct questions — technical capability, actual replacement, and productivity assistance — and are always reported separately.
Confidence intervals on everything
CI bounds are a worst/best-case envelope over the rater panel, making multi-model disagreement visible instead of hiding it behind a single number.
Pure, deterministic scoring engine
The TypeScript scoring core has no I/O, clock, or randomness — pinned by property-based tests and a published worked example reproduced to three decimals.
Full audit trail
Every rating stores model, prompt version, raw response, parsed score, and rationale — visible on every occupation page by expanding any task.
Versioned public API
Health, machine-readable methodology, occupation search, and full score breakdowns with sub-scores, CI bounds, tasks, and wages — index_version in every payload.
Immutable score runs
Weight, formula, or prompt changes bump INDEX_VERSION with a changelog entry; every published score traces to an immutable run that stays queryable forever.
Colorblind-safe data design
Charts follow a validated palette with CI whiskers, data-table fallbacks, and a dark mode with selected rather than flipped color steps.
How It Works
ETL pipeline (apps/etl)
Python 3.12 downloads O*NET 30.3 and BLS OEWS, transforms them with committed manifests of hashes and row counts, and loads 1,016 occupations and 18,796 tasks into Postgres.
Rating worker (apps/worker)
A BullMQ pipeline drives Anthropic Message Batches — one request per task per model with deterministic custom IDs — making runs idempotent and resumable at a 50% batch discount.
Scoring engine (packages/scoring)
The auditable core: five weighted dimensions, inverted adoption-barriers orientation, importance-weighted aggregation, and CI envelopes — pure, deterministic, and property-tested.
Web and API (apps/web)
Next.js 14 serves the ranking, insights, occupation detail pages with expandable per-task audit trails, and the versioned public JSON API.
Immutable runs (packages/db)
Prisma schema for occupations, tasks, the full rating audit trail, and immutable score_runs rows — historical runs stay queryable forever.
Tech Stack
Frontend
Data
AI Rating
ETL & Sources
Highlights
- Multi-model replication shows single-model exposure ratings can vary by an order of magnitude — so a single-model index is an artifact; this one makes disagreement visible.
- Five dimensions with published weights: task automatability (0.35), technical feasibility (0.20), adoption barriers (0.20, inverted — strong barriers protect), cost vs. wage (0.15), and adoption velocity (0.10).
- Headline finding: 32% of the U.S. wage bill (~$4.7T) overlaps with what AI can plausibly take over — framed explicitly as adaptation guidance, not a payroll forecast.
- Scores and derived data are CC BY 4.0; the methodology is served live as machine-readable JSON at /api/v1/methodology.
- Raw data dumps are immutable and never committed — derived artifacts commit manifests only, with hashes and row counts.
- Grounded in a public research corpus reviewing 20+ existing AI-exposure indices, with a roadmap for human review samples, sensitivity analyses, and EU/France coverage via ESCO and ROME.
Explore AI Risk Index
Task-based AI job-exposure index — the full source is on GitHub.