Open data

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AI exposure data for every occupation and every state.

Task-level AI exposure for 1,016 occupations, 923 of them scored from O*NET task lists, and the AI readiness index for the 50 states, DC and the nation. Free to download and reuse under CC BY 4.0.

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Downloads

occupations.csv CSV, 156 KB
1,016 O*NET occupations, one row each: employment and median wage, task exposure, tasks AI handles, the recommended model tier and tier mix, and how many task scores are observed.
occupations.json JSON, 479 KB
The same rows as JSON, with the field definitions, the license and the citation inside the file.
states.csv CSV, 6 KB
The 50 states, DC and the United States: share and count of workers in high-exposure occupations, the AI Ready Score and both ranks, and total employment.

Data version 2026-11-02. Method v1.0.0. State index computed July 27, 2026.

Browse every occupation

Search and sort all 1,016 occupations, then open any role for its task-by-task scores, wages and adjacent roles.

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Browse every state

51 jurisdictions ranked by the share of workers in high-exposure occupations, with a page for each state and one for the nation (6.5% nationally).

Open the state index

What each file contains

occupations.csv and occupations.json

Field	Definition
`onet_code`	O*NET-SOC code
`title`	O*NET occupation title
`employment`	US employment, BLS OEWS May 2025 (blank where BLS publishes none)
`median_wage_usd`	Median annual wage in US dollars, BLS OEWS May 2025
`ai_exposure`	Task exposure, 0 to 100: importance-weighted mean of task AI exposure. Higher means more of the work is AI-doable.
`tasks_ai_handles`	Number of tasks with AI exposure of 60 or more
`task_count`	Number of O*NET 30.3 tasks scored
`recommended_tier`	Lowest model tier (efficient, standard, frontier) covering 80% of the importance-weighted AI-addressable work; blank = no tier
`tier_mix_efficient_pct`	Share of importance-weighted task work at the efficient tier
`tier_mix_standard_pct`	Share at the standard tier
`tier_mix_frontier_pct`	Share at the frontier tier
`tier_mix_none_pct`	Share with no model tier (hands-on work)
`aei_observed_tasks`	Number of task scores observed in the Anthropic Economic Index; the rest are estimated from the task's O*NET work activity
`url`	JobRoute page for the occupation

states.csv

Field	Definition
`code`	Postal code (US = national)
`name`	Jurisdiction
`high_exposure_share_pct`	Share of workers in occupations with task exposure of 65 or more
`high_exposure_rank`	Rank of that share across the 50 states and DC (1 = largest share)
`high_exposure_workers`	Workers in high-exposure occupations
`ai_ready_score`	AI Ready Score, 0 to 100, employment-weighted. Higher means a more AI-ready workforce.
`ai_ready_rank`	Rank of the score across the 50 states and DC (1 = most AI-ready)
`total_employment`	Total employment, BLS OEWS State May 2025
`mid_exposure_workers`	Workers in occupations with task exposure from 35 to below 65
`low_exposure_workers`	Workers in occupations with task exposure below 35
`computed_at`	Date the state index was computed
`url`	JobRoute page for the jurisdiction

Per-state files

Each state page links its own CSV at /data/states/<state>.csv, for example texas.csv, with the state's largest and most exposed occupations and their worker counts. Fields: list, rank, onet_code, title, ai_exposure, workers, url.

Each occupation's full task list, with every task's exposure, source and model tier, is also published as JSON at /data/occupations/<O*NET code>.json, for example 23-2011.00.json for paralegals.

License and citation

Use the data for any purpose, including commercial work, as long as you credit JobRoute and keep the source notices below.

Read the methodology

License

JobRoute-derived data is published under the Creative Commons Attribution 4.0 International license (CC BY 4.0). This dataset includes information from the O*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. O*NET is a trademark of USDOL/ETA. JobRoute has modified some of this information. Employment and wages: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2024 to 2034). Observed AI use by task: Anthropic Economic Index (June 2026 release). CC BY 4.0 license text.

How to cite

JobRoute (2026). AI exposure by occupation and state. Data version 2026-11-02, method v1.0.0. Built on O*NET 30.3, BLS OEWS May 2025 and the Anthropic Economic Index (June 2026). CC BY 4.0. https://www.jobroute.ai/data

What to know before you use it

Exposure describes occupations, not individual people. A higher task exposure means more of the work is AI-doable today; a higher AI Ready Score means a more AI-ready workforce.
The Anthropic Economic Index observes 1,147 of the 18,796 task scores directly. The rest are estimated from each task's O*NET work activity, and the per-occupation count is in aei_observed_tasks.
O*NET occupations that share a SOC code carry the same BLS employment and wage figures.

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