Highway Maintenance Workers
Construction, Maintenance & RepairAI exposure
- Data source: BLSPublished: 2026-08
Low· 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
0.09
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, 100% 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.
| Task | βE1 + 0.5 × E2 |
|---|---|
Inspect markers to verify accurate installation.O*NET Task ID 4883 | 0.5 |
Flag motorists to warn them of obstacles or repair work ahead.O*NET Task ID 4871 | 0.0 |
Set out signs and cones around work areas to divert traffic.O*NET Task ID 4872 | 0.0 |
Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement.O*NET Task ID 4874 | 0.0 |
Drive trucks to transport crews and equipment to work sites.O*NET Task ID 4875 | 0.0 |
Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures.O*NET Task ID 4876 | 0.0 |
Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders.O*NET Task ID 4877 | 0.0 |
Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools.O*NET Task ID 4878 | 0.0 |
Remove litter and debris from roadways, including debris from rock and mud slides.O*NET Task ID 4879 | 0.0 |
Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures.O*NET Task ID 4880 | 0.0 |
Perform roadside landscaping work, such as clearing weeds and brush, and planting and trimming trees.O*NET Task ID 4881 | 0.0 |
Paint traffic control lines and place pavement traffic messages, by hand or using machines.O*NET Task ID 4882 | 0.0 |
Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents.O*NET Task ID 4884 | 0.0 |
Measure and mark locations for installation of markers, using tape, string, or chalk.O*NET Task ID 4885 | 0.0 |
Apply oil to road surfaces, using sprayers.O*NET Task ID 4886 | 0.0 |
Blend compounds to form adhesive mixtures used for marker installation.O*NET Task ID 4887 | 0.0 |
Place and remove snow fences used to prevent the accumulation of drifting snow on highways.O*NET Task ID 4888 | 0.0 |
Perform preventative maintenance on vehicles and heavy equipment.O*NET Task ID 18783 | 0.0 |
Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt and sand.O*NET Task ID 18784 | 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