EXPOSED
Digging, loading haul trucks, and moving overburden happen in mud, dust, and uneven pit walls — physical work language models can't touch, and the machines involved are enormous and expensive. But mining is the single most advanced frontier for industrial autonomy: Rio Tinto, BHP and Komatsu already run driverless haul fleets and remote-operated loaders, and teleoperation centers let one person supervise several machines from a city office, which shrinks seat count fast even where full autonomy lags. MSHA training requirements are safety credentials, not a licensure moat, and no customer pays for a relationship with the operator.
This fall is concentrated in 2020 and has not recovered since.
Median pay $44,800 → $57,430 +2.6% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
-0.4% 35,800 → 35,600 on the projections basis
Shrinking, but not obviously because of AI
The BLS projects -0.4% by 2034, but at 48/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~3,100 openings a year on average, including replacing people who leave.
ExcavatorPit OperatorScoop DriverTram OperatorDragline OilerScoop OperatorShift OperatorLoader OperatorShovel OperatorSteam ShovelmanTrencher DriverBackhoe OperatorBack Hoe OperatorDragline OperatorFoundation DiggerEquipment OperatorExcavator OperatorHarvester OperatorPayloader OperatorSeptic Tank SetterTrack Hoe OperatorAir Shovel OperatorEnd Loader OperatorExcavation Operator
Holding it up: embodiment . Weakest point: trust premium .
Tasks largely resist digitisation Reading a face for slabbing, feeling the bucket load through the sticks, spotting a truck in dust, and digging to grade around unmarked utilities or unstable highwalls are still seat tasks at most non-tier-1 operations, which is why this sits at 14 rather than in the mixed band — but it is nowhere near 18, because autonomous drills and remote-operated dozers/loaders have already left the pilot stage at Pilbara and Chilean copper sites.
Hands-on in uncontrolled environments You are in the cab of a 400-ton shovel or a walking dragline on a bench that changes shape every shift — mud, freeze-thaw slumps, blast dust, night lighting, greasing and track inspections done by hand outdoors — which is uncontrolled-environment work; it is 17 and not 20 only because the operator is seated in an enclosed cab rather than free-climbing the pit.
Certification preferred, not legally required MSHA Part 46/48 new-miner and annual refresher training plus a task-specific hazard sign-off is a documented competency, not a state licence — nobody revokes an individual credential for a bad dig, the operator isn't personally liable for the citation, and the mine operator carries the Part 100 penalty, which puts this at 5 rather than 0 only because the training records are legally mandatory.
Meaningful discretion You call whether a highwall looks like it's going to slough, when to stop loading and radio the shift boss, how to sequence a cut so the pattern drains and the trucks stay loaded — real discretion inside a written mine plan, ground control plan, and blast schedule that someone else authored, which caps it at 9.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (14/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (5/20) is whether the law requires a licensed human to sign. Trust premium (3/20) is whether buyers specifically pay for a person. Judgment and accountability (9/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 17 of this occupation's 48 points (35%).
Embodiment (17/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 63/100, still EXPOSED.
Task-mix shift: once haul-cycle loading and bench-level digging are automated, the surviving seats are the genuinely irregular work autonomy handles worst — highwall scaling, working in soft or saturated ground, recovery of stuck or damaged equipment, pioneering new benches and ramps where no map exists yet, and dragline tub/bucket work in unmapped spoil. This tier is real and already what remaining operators at autonomous mines spend time on
MSHA rulemaking on autonomous and remote-controlled mobile equipment (an area the agency has flagged as needing standards, currently governed only by Program Policy Letters and site-specific petitions for modification) that requires a named, MSHA-certified competent person on site to authorize each autonomous zone entry, sign off on ground-condition and highwall stability before machine operation, and be personally accountable for violations — turning training credentials into a signature requirement
Union contracts (UMWA, or the Australian precedent of automation clauses negotiated with BHP) or insurer conditions that designate the on-machine operator as the person with stop-work authority over ground conditions in the pit, with an obligation to override autonomous fleet dispatch — making the ground-stability call formally the operator's
State surface-mining/reclamation authorities (e.g. under SMCRA state programs) requiring a certified blaster-equivalent or licensed operator attestation for overburden removal and spoil placement near permitted highwall and water boundaries, where the individual signing carries citation liability
The limit. Trust premium has no plausible route: the buyer is a mining company purchasing tons moved, and no customer will ever pay extra for a named human at the controls. Mining is also the sector where capital has the strongest incentive and the deepest pockets for autonomy, so liability gains would have to outrun a capability curve moving faster here than almost anywhere else. Realistic ceiling around 58-62, and the seat count shrinks even if the per-seat score rises.
| Dallas-Fort Worth-Arlington, TX | 990 | $51,780 -10% |
| Houston-Pasadena-The Woodlands, TX | 890 | $49,920 -13% |
| New York-Newark-Jersey City, NY-NJ | 880 | $94,960 +65% |
| Atlanta-Sandy Springs-Roswell, GA | 630 | $56,460 -2% |
| Chicago-Naperville-Elgin, IL-IN | 570 | $87,060 +52% |
| San Antonio-New Braunfels, TX | 540 | $47,970 -16% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 540 | $61,040 +6% |
| Austin-Round Rock-San Marcos, TX | 520 | $51,420 -10% |
| Sacramento-Roseville-Folsom, CA | 40 | $142,970 +149% |
| San Francisco-Oakland-Fremont, CA | 90 | $122,410 +113% |
| New York-Newark-Jersey City, NY-NJ | 880 | $94,960 +65% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 48. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.