Geological Technicians

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

    0.229

    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

    0.26

    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, 76% 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 8 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
Assemble, maintain, or distribute information for library or record systems.

O*NET Task ID 22272

1.0
Test and analyze samples to determine their content and characteristics, using laboratory apparatus or testing equipment.

O*NET Task ID 22262

0.5
Compile, log, or record testing or operational data for review and further analysis.

O*NET Task ID 22264

0.5
Prepare notes, sketches, geological maps, or cross-sections.

O*NET Task ID 22265

0.5
Prepare or review professional, technical, or other reports regarding sampling, testing, or recommendations of data analysis.

O*NET Task ID 22267

0.5
Read and study reports in order to compile information and data for geological and geophysical prospecting.

O*NET Task ID 22269

0.5
Interview individuals, and research public databases in order to obtain information.

O*NET Task ID 22270

0.5
Plot information from aerial photographs, well logs, section descriptions, or other databases.

O*NET Task ID 22271

0.5
Plan and direct activities of workers who operate equipment to collect data.

O*NET Task ID 22274

0.5
Record readings in order to compile data used in prospecting for oil or gas.

O*NET Task ID 22276

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