Will AI replace Continuous Mining Machine Operators?
AI reaches little of this job’s work.
5/100
Low exposure
More exposed to AI than 11% of US workers.
- AI already used in
- 0% of the work
- As AI tools mature: up to
- 10% of the work
What the job involves
Operate self-propelled mining machines that rip coal, metal and nonmetal ores, rock, stone, or sand from the mine face and load it onto conveyors, shuttle cars, or trucks in a continuous operation.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Within reach(2)
- Observe and listen to equipment operation to detect binding or stoppage of tools or other equipment malfunctions.
- Apply new technologies developed to minimize the environmental impact of coal mining.
Out of reach(13)
- Check the stability of roof and rib support systems before mining face areas.
- Conduct methane gas checks to ensure breathing quality of air.
- Hang ventilation tubing and ventilation curtains to ensure that the mining face area is kept properly ventilated.
- Drive machines into position at working faces.
- Reposition machines to make additional holes or cuts.
and 8 more
Also called
Bore Miner Operator · Heavy Equipment Operator · Loader Operator · Miner Operator · Continuous Mining Machine Coal Miner · Continuous Mining Machine Company Miner · Continuous Mining Machine Contract Miner · Continuous Mining Machine Lode Miner
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics