Heavy and Tractor-Trailer Truck Drivers

Transportation & Material Moving

AI exposure

  • Data source: BLSPublished: '26.08

    Moderate· 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: '26.03

    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: '25

    0.24

    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, 79% 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 21 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
Maintain logs of working hours or of vehicle service or repair status, following applicable state and federal regulations.

O*NET Task ID 10637

1.0
Read bills of lading to determine assignment details.

O*NET Task ID 10643

1.0
Report vehicle defects, accidents, traffic violations, or damage to the vehicles.

O*NET Task ID 10644

1.0
Check all load-related documentation for completeness and accuracy.

O*NET Task ID 20693

1.0
Operate equipment, such as truck cab computers, CB radios, phones, or global positioning systems (GPS) equipment to exchange necessary information with bases, supervisors, or other drivers.

O*NET Task ID 20695

1.0
Read and interpret maps to determine vehicle routes.

O*NET Task ID 10645

0.5
Collect delivery instructions from appropriate sources, verifying instructions and routes.

O*NET Task ID 10647

0.5
Check conditions of trailers after contents have been unloaded to ensure that there has been no damage.

O*NET Task ID 10650

0.5
Inventory and inspect goods to be moved to determine quantities and conditions.

O*NET Task ID 10655

0.5
Plan or adjust routes based on changing conditions, using computer equipment, global positioning systems (GPS) equipment, or other navigation devices, to minimize fuel consumption and carbon emissions.

O*NET Task ID 19923

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

Recent Changes Affecting This Occupation

Apr 2026: Transportation sector saw 32,241 Q1 2026 cuts — 703% increase YoY. Autonomous vehicle testing and AI logistics optimization driving displacement. AI was #1 March cut reason.

[Source: Challenger March 2026]

Mar 2026: Challenger: transportation sector 31,702 cuts YTD (+872% YoY), largest sectoral increase in 2026.

[Source: Challenger Gray Feb 2026 Report]