Construction and Extraction · United States (national)
Highway Maintenance Workers
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is highly exposed to automation, and the bigger shift looks near-term.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 12.0
Physical / roboticsrange 55.2 to 79.2
When the change is likely
Near-term, highlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Act within the next 1 to 2 years
Displacement for this role is near-term. Start a concrete reskilling plan now, while you still have income and leverage.
2
Move up from the task to the system
Robotics targets the physical work. Shift toward supervising, maintaining, or programming the automation instead of competing with it.
3
Pivot toward Construction Laborers
It is skill-adjacent and less exposed (58.9 vs 67). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
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Which of my tasks are exposed
Set out signs and cones around work areas to divert traffic.
67.2
Flag motorists to warn them of obstacles or repair work ahead.
67.2
Perform preventative maintenance on vehicles and heavy equipment.
67.2
Drive trucks to transport crews and equipment to work sites.
67.2
Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools.
67.2
Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures.
67.2
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.
67.2
Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders.
67.2
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Set out signs and cones around work areas to divert traffic.
Task: Flag motorists to warn them of obstacles or repair work ahead.
Lower-risk adjacent roles
Construction Laborers
58.9
Operating Engineers and Other Construction Equipment Operators
59.2
Septic Tank Servicers and Sewer Pipe Cleaners
60.2
Pile Driver Operators
62.4
Paving, Surfacing, and Tamping Equipment Operators
62.5
Rail-Track Laying and Maintenance Equipment Operators
65.8
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Highway Maintenance Workers to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 0.0, robotics 67.2.
The timeline comes from the higher axis (67.2), shifted for local demand (stable), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.