Urban and Regional Planners

Life, Physical & Social Sciences

AI exposure

  • Data source: BLSPublished: 2026-08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.096

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.41

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 36% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 1 hidden task

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Design, promote, or administer government plans or policies affecting land use, zoning, public utilities, community facilities, housing, or transportation.

O*NET Task ID 220

0.5
Recommend approval, denial, or conditional approval of proposals.

O*NET Task ID 222

0.5
Create, prepare, or requisition graphic or narrative reports on land use data, including land area maps overlaid with geographic variables, such as population density.

O*NET Task ID 225

0.5
Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives.

O*NET Task ID 226

0.5
Conduct field investigations, surveys, impact studies, or other research to compile and analyze data on economic, social, regulatory, or physical factors affecting land use.

O*NET Task ID 227

0.5
Discuss with planning officials the purpose of land use projects, such as transportation, conservation, residential, commercial, industrial, or community use.

O*NET Task ID 228

0.5
Keep informed about economic or legal issues involved in zoning codes, building codes, or environmental regulations.

O*NET Task ID 229

0.5
Mediate community disputes or assist in developing alternative plans or recommendations for programs or projects.

O*NET Task ID 230

0.5
Coordinate work with economic consultants or architects during the formulation of plans or the design of large pieces of infrastructure.

O*NET Task ID 231

0.5
Review and evaluate environmental impact reports pertaining to private or public planning projects or programs.

O*NET Task ID 232

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information