Construction and Extraction · United States (national)
Loading and Moving Machine Operators, Underground Mining
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is moderately 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 20.1
Physical / roboticsrange 50.0 to 74.0
When the change is likely
Near-term, highlocal demand declining
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 Excavating and Loading Machine and Dragline Operators, Surface Mining
It is skill-adjacent and less exposed (55.8 vs 62). 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.
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Which of my tasks are exposed
Handle high voltage sources and hang electrical cables.
62.0
Drive loaded shuttle cars to ramps and move controls to discharge loads into mine cars or onto conveyors.
62.0
Pry off loose material from roofs and move it into the paths of machines, using crowbars.
62.0
Move trailing electrical cables clear of obstructions, using rubber safety gloves.
62.0
Control conveyors that run the entire length of shuttle cars to distribute loads as loading progresses.
62.0
Observe hand signals, grade stakes, or other markings when operating machines.
62.0
Examine roadway and clear obstructions from the path of travel.
62.0
Drive machines into piles of material blasted from working faces.
62.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Local demand is declining
Task: Handle high voltage sources and hang electrical cables.
Task: Drive loaded shuttle cars to ramps and move controls to discharge loads into mine cars or onto conveyors.
Lower-risk adjacent roles
Excavating and Loading Machine and Dragline Operators, Surface Mining
55.8
Operating Engineers and Other Construction Equipment Operators
59.2
Industrial Truck and Tractor Operators
61.2
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Loading and Moving Machine Operators, Underground Mining 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 8.1, robotics 62.0.
The timeline comes from the higher axis (62.0), shifted for local demand (declining), 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.