Professionals · code 2146

Mining engineers, metallurgists and related professionals

AI can model the ore body. Deciding how to mine it safely is still an engineer's call.

Eight of the nine tasks are mostly out of AI's reach, including planning how water, oil and gas get moved and sizing up mines. Setting safety standards underground it barely touches.

What they actually do (and what else they get called)

Mining engineers, metallurgists and related professionals conduct research on, design, develop and maintain commercial-scale methods of extracting metals from their ores, or minerals, water, oil or gas from the earth, and of developing new alloys, ceramic and other materials, or study and advise on mining or metallurgical aspects of particular materials, products or processes. Tasks include - (a) determining the location and planning the extraction of coal, metallic ores, non-metallic minerals, and building materials such as stone and gravel; (b) determining the most suitable methods of efficient mining and extraction and types of machinery to be used, planning layout and directing construction of shafts and tunnels; (c) determining drilling sites and devising methods of controlling the flow of water, oil or gas from wells; (d) planning and directing storage, initial treatment and transportation of water, oil or gas; (e) establishing safety standards and procedures and first-aid facilities, especially underground; (f) conducting research, developing methods of extracting metals from their ores and advising on their application; (g) investigating properties of metals and alloys, developing new alloys and advising on and supervising technical aspects of metal and alloy manufacture and processing; (h) maintaining technical liaison and consultancy with other relevant specialists such as geologists and geophysicists; (i) examining deposits or mines to evaluate profitability. Examples of the occupations classified here: - Extractive metallurgist - Mining engineer - Petroleum and natural gas extraction engineer Some related occupations classified elsewhere: - Geologist - 2114 - Geophysicist - 2114

quarry engineer
petroleum engineer
drilling engineer
mine development engineer
metallurgist
assayer
liquid fuel engineer
mine planning engineer
mineral processing engineer
mine health and safety engineer
explosives engineer

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

71 / 100

Mostly holds up against AI

AI can take a few chores off your plate, but most of the day is still yours.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

236 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 9 tasks for how much of it AI could do. They average 29.3 out of 100, so it holds up at 100 − 29.3 = 70.7.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

This job rates 23.8 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Mining engineers, metallurgists and related professionals 71

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

Geological and resource modelling.
Mine planning optimisation.
Research and alloy development.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

Safety standards underground.
Accountable engineering decisions.
Judgement about risk and cost.

Both

What's arriving

New work that only exists because the AI does.

Critical minerals for batteries.
Automated and remote mines.
Recycling metals from waste.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

33 could do without you

It could take over very little of the work on its own. It could help with 73 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

24 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 17 out of 100. The higher it is, the longer the robot waits.

24 How
hands-on

17 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 4 US jobs .

finger skill 21.9 · hand skill 10.7 · cramped spaces 16.9 · averaged · rule: hands-on under 50

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 37, against an average of 29. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

37
Planning and directing storage, initial treatment and transportation of water, oil or gas

Low
35
Maintaining technical liaison and consultancy with other relevant specialists such as geologists and geophysicists

Low
31
Determining most suitable methods of efficient mining and extraction and types of machinery to be used, planning layout and directing construction of shafts and tunnels

Low
31
Conducting research, developing methods of extracting metals from their ores and advising on their application

Low
30
Investigating properties of metals and alloys, developing new alloys and advising on and supervising technical aspects of metal and alloy manufacture and processing

Low
28
Determining the location and planning the extraction of coal, metallic ores, non-metallic minerals, and building materials, such as stone and gravel

Low
27
Determining drilling site and devising methods of controlling the flow of water, oil or gas from wells

Low
26
Examining deposits or mines to evaluate profitability.

Low
19
Establishing safety standards and procedures and first-aid facilities, especially underground

Very Low

9 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you: 73
AI does it for you: 33

Sidekick wins, by 40 points.

How good a match is this?

3 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 2336, 2339, 2349

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 0.11% of the conversations.

The data is sorted by US job, and this one matches 3 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should mining engineers and metallurgists be worried?

Short answer: not much. Some of the chores will change, and good riddance.

Parts of the job are changing. Planning, liaison, mining methods, research, alloys, locating deposits, drilling and evaluating profitability are the most exposed tasks, all Low; setting safety standards rates Very Low. Modelling and optimisation tools are powerful, but engineers carry the judgement and accountability.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 92 jobs in the “Professionals” family, averaging 61. This one is above the family average.

Pit this against any other job →