Locomotive Engineers
Transportation & Material MovingAI exposure
- Data source: BLSPublished: 2026-08
Moderate· 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
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.20
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, 86% 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 |
|---|---|
Interpret train orders, signals, or railroad rules and regulations that govern the operation of locomotives.O*NET Task ID 12752 | 1.0 |
Confer with conductors or traffic control center personnel via radiophones to issue or receive information concerning stops, delays, or oncoming trains.O*NET Task ID 12754 | 1.0 |
Prepare reports regarding any problems encountered, such as accidents, signaling problems, unscheduled stops, or delays.O*NET Task ID 12760 | 1.0 |
Observe tracks to detect obstructions.O*NET Task ID 12751 | 0.5 |
Inspect locomotives after runs to detect damaged or defective equipment.O*NET Task ID 12762 | 0.5 |
Monitor gauges or meters that measure speed, amperage, battery charge, or air pressure in brake lines or in main reservoirs.O*NET Task ID 12750 | 0.0 |
Receive starting signals from conductors and use controls such as throttles or air brakes to drive electric, diesel-electric, steam, or gas turbine-electric locomotives.O*NET Task ID 12753 | 0.0 |
Operate locomotives to transport freight or passengers between stations or to assemble or disassemble trains within rail yards.O*NET Task ID 12755 | 0.0 |
Respond to emergency conditions or breakdowns, following applicable safety procedures and rules.O*NET Task ID 12756 | 0.0 |
Check to ensure that brake examination tests are conducted at shunting stations.O*NET Task ID 12757 | 0.0 |
Call out train signals to assistants to verify meanings.O*NET Task ID 12758 | 0.0 |
Inspect locomotives to verify adequate fuel, sand, water, or other supplies before each run or to check for mechanical problems.O*NET Task ID 12759 | 0.0 |
Check to ensure that documentation, such as procedure manuals or logbooks, are in the driver's cab and available for staff use.O*NET Task ID 12761 | 0.0 |
Drive diesel-electric rail-detector cars to transport rail-flaw-detecting machines over tracks.O*NET Task ID 12763 | 0.0 |
Monitor train loading procedures to ensure that freight or rolling stock are loaded or unloaded without damage.O*NET Task ID 12764 | 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