Civil Engineers

Life, Physical & Social Sciences

AI exposure

  • Data source: BLSPublished: 2026-08

    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

    0.008

    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

    0.30

    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, 67% 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

Exposed tasks only

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
Prepare or present public reports on topics such as bid proposals, deeds, environmental impact statements, or property and right-of-way descriptions.

O*NET Task ID 152

1.0
Compute load and grade requirements, water flow rates, or material stress factors to determine design specifications.

O*NET Task ID 147

0.5
Direct or participate in surveying to lay out installations or establish reference points, grades, or elevations to guide construction.

O*NET Task ID 150

0.5
Estimate quantities and cost of materials, equipment, or labor to determine project feasibility.

O*NET Task ID 151

0.5
Conduct studies of traffic patterns or environmental conditions to identify engineering problems and assess potential project impact.

O*NET Task ID 155

0.5
Analyze manufacturing processes or byproducts to identify engineering solutions to minimize the output of carbon or other pollutants.

O*NET Task ID 19606

0.5
Design energy-efficient or environmentally sound civil structures.

O*NET Task ID 19607

0.5
Design or engineer systems to efficiently dispose of chemical, biological, or other toxic wastes.

O*NET Task ID 19608

0.5
Develop or implement engineering solutions to clean up industrial accidents or other contaminated sites.

O*NET Task ID 19609

0.5
Direct engineering activities, ensuring compliance with environmental, safety, or other governmental regulations.

O*NET Task ID 19610

0.5
Inspect project sites to monitor progress and ensure conformance to design specifications and safety or sanitation standards.

O*NET Task ID 148

0.0
Test soils or materials to determine the adequacy and strength of foundations, concrete, asphalt, or steel.

O*NET Task ID 153

0.0
Manage and direct the construction, operations, or maintenance activities at project site.

O*NET Task ID 20490

0.0

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