SAFE
The modeling half of this job — ore reserve estimation, geostatistical block models, pit optimization runs, MSHA paperwork and incident reports — is exactly the structured numeric and document work AI is eating fastest. The other half is standing in an active pit or underground heading assessing ground conditions, ventilation adequacy, slope stability and blast placement, then signing a plan that determines whether people go home alive; that half is not automatable with current robotics or acceptable to regulators. Small occupation (about 6,000 jobs) tightly coupled to commodity cycles, which matters more to headcount than AI does.
Roughly flat across the period, with year-to-year wobble.
Median pay $91,160 → $106,220 -6.8% 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
+0.7%
Percentage only. The projection counts a different population from the 6,080 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Hard to automate, and growing
The work resists current AI and the BLS projects +0.7% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.
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.
~400 openings a year on average, including replacing people who leave.
EngineerMine ExpertMine AnalystMine EngineerField EngineerSafety AnalystSafety MonitorMining EngineerSafety EngineerMineral EngineerProject EngineerSafety InspectorSeismic EngineerMining ConsultantPlanning EngineerTailings EngineerCoal Mine InspectorGeological EngineerExploration EngineerGeophysical EngineerMetal Mine InspectorMine Safety EngineerGeotechnical EngineerOre Dressing Engineer
Holding it up: judgment & accountability . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier Whittle/Deswik pit optimisations, Datamine block model runs and MSHA Part 48 training records are already largely software-driven, but walking a bench to read a fresh highwall crack, judging whether a rib is taking weight, or re-sequencing a blast pattern after the drillers hit unexpected water keeps this at 13 rather than the low end.
Hands-on in uncontrolled environments You are underground or on active benches in dust, diesel, water and unsupported ground on inspection rounds, standing behind the drill rig checking hole depth and burden, and going into the mine after a fall of ground — an environment where robots are used to avoid humans precisely because it cannot be controlled, which is why this sits at 15.
Licensed human required and personally liable Mine plans, ventilation plans and impoundment designs go out under a state PE seal and the engineer of record is personally answerable to MSHA and state inspectors, but a meaningful share of mining engineers work under the industrial exemption without a stamp, which holds this at 13 instead of the high teens.
Exists to be accountable for ambiguous calls Calling whether a slope with a moving prism is still safe to mine, whether ventilation quantity is adequate for the extended heading, or whether to stop production and evacuate is a decision made on incomplete monitoring data with fatalities and a Section 107(a) imminent danger order on the other side of it — 17 reflects that the ambiguity is irreducible and the consequence is bodies.
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 (13/20 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 (13/20) is whether the law requires a licensed human to sign. Trust premium (10/20) is whether buyers specifically pay for a person. Judgment and accountability (17/20) 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 40 of this occupation's 68 points (59%).
Embodiment (15/20) 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 80/100, still SAFE.
Global Industry Standard on Tailings Management (GISTM) adoption becoming a lending/insurance precondition, requiring a named Engineer of Record and Independent Tailings Review Board for each facility — already contractually required by ICMM member companies and increasingly by mine insurers
Task-mix shift: once block modeling, pit optimization and MSHA report drafting are automated, the residual role is ground-condition judgment, blast design sign-off and incident causation determination — the genuine judgment tier of this two-tier job. This raises effective resistance without any new rule, though it may shrink headcount at the same time
MSHA or state mine boards extending the existing ground-control-plan and ventilation-plan signature requirement (30 CFR Part 75/77 plan approvals) to explicitly name a licensed Professional Engineer as the responsible signer, with personal liability, and barring AI-generated plans from submission without PE certification of the underlying model — a pattern already visible in Nevada/Arizona PE practice acts and in post-Brumadinho tailings rules requiring a named Engineer of Record
State or federal rule making the mine engineer a named accountable party in fatality investigations (analogous to the responsible-person provisions in Australian WHS mining regulations, where a Statutory Mine Manager or Ventilation Officer holds personal legal duty)
The limit. Judgment accountability at 17 is near saturated. Trust premium has no realistic route: buyers are mining companies purchasing regulatory compliance, not a human relationship, and they will accept whatever the cheapest compliant signature is. Headcount is set by commodity prices, not by any of these levers.
| Denver-Aurora-Centennial, CO | 520 | $93,100 -12% |
| Phoenix-Mesa-Chandler, AZ | 250 | $114,290 +8% |
| Los Angeles-Long Beach-Anaheim, CA | 180 | $144,230 +36% |
| Sacramento-Roseville-Folsom, CA | 160 | $166,770 +57% |
| Salt Lake City-Murray, UT | 160 | $131,770 +24% |
| Beckley, WV | 140 | $81,370 -23% |
| Tucson, AZ | 140 | $106,220 +0% |
| Anchorage, AK | 80 | $141,080 +33% |
| San Francisco-Oakland-Fremont, CA | 30 | $173,790 +64% |
| Sacramento-Roseville-Folsom, CA | 160 | $166,770 +57% |
| Los Angeles-Long Beach-Anaheim, CA | 180 | $144,230 +36% |
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 68. 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.