Agronomists
Life, Physical & Social SciencesAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-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
- Data source: AnthropicPublished: 2026-03
0.051
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.29
0.09Range of values carried here0.70Group-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, 69% score at or above this value.
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 onlyValues 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.
| Task | βE1 + 0.5 × E2 |
|---|---|
Communicate research or project results to other professionals or the public or teach related courses, seminars, or workshops.O*NET Task ID 9021 | 0.5 |
Provide information or recommendations to farmers or other landowners regarding ways in which they can best use land, promote plant growth, or avoid or correct problems such as erosion.O*NET Task ID 9022 | 0.5 |
Investigate responses of soils to specific management practices to determine the use capabilities of soils and the effects of alternative practices on soil productivity.O*NET Task ID 9023 | 0.5 |
Develop methods of conserving or managing soil that can be applied by farmers or forestry companies.O*NET Task ID 9024 | 0.5 |
Conduct experiments to develop new or improved varieties of field crops, focusing on characteristics such as yield, quality, disease resistance, nutritional value, or adaptation to specific soils or climates.O*NET Task ID 9025 | 0.5 |
Investigate soil problems or poor water quality to determine sources and effects.O*NET Task ID 9026 | 0.5 |
Study soil characteristics to classify soils on the basis of factors such as geographic location, landscape position, or soil properties.O*NET Task ID 9027 | 0.5 |
Develop improved measurement techniques, soil conservation methods, soil sampling devices, or related technology.O*NET Task ID 9028 | 0.5 |
Conduct experiments investigating how soil forms, changes, or interacts with land-based ecosystems or living organisms.O*NET Task ID 9029 | 0.5 |
Identify degraded or contaminated soils and develop plans to improve their chemical, biological, or physical characteristics.O*NET Task ID 9030 | 0.5 |
Conduct experiments regarding causes of bee diseases or factors affecting yields of nectar or pollen.O*NET Task ID 9041 | 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