Materials 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

    0.000

    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

Show 4 hidden tasks

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
Perform managerial functions, such as preparing proposals and budgets, analyzing labor costs, and writing reports.

O*NET Task ID 9007

1.0
Write for technical magazines, journals, and trade association publications.

O*NET Task ID 9018

1.0
Replicate the characteristics of materials and their components, using computers.

O*NET Task ID 9019

1.0
Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.

O*NET Task ID 9001

0.5
Monitor material performance, and evaluate its deterioration.

O*NET Task ID 9002

0.5
Supervise the work of technologists, technicians, and other engineers and scientists.

O*NET Task ID 9003

0.5
Design and direct the testing or control of processing procedures.

O*NET Task ID 9004

0.5
Evaluate technical specifications and economic factors relating to process or product design objectives.

O*NET Task ID 9005

0.5
Conduct or supervise tests on raw materials or finished products to ensure their quality.

O*NET Task ID 9006

0.5
Solve problems in a number of engineering fields, such as mechanical, chemical, electrical, civil, nuclear, and aerospace.

O*NET Task ID 9008

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