EXPOSED
The modal worker splits time between rough field settings — collecting rock, soil and core samples, running downhole and surface instruments, mudlogging at active drill sites — and screen work compiling logs, plotting data and drafting reports, which is the automatable half. Automated core scanners, real-time telemetry, and ML lithology/log interpretation are already eating the description and data-reduction tasks, while the sample handling and rig-side presence stay human because there is no robot that will hike a claim block or babysit a drill floor. No license shields the role and geologists sign the deliverables, so the protection is physical, not legal.
Most of this decline happened after 2021 — it is not the pandemic dip.
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.
BLS projection, 2024–2034
+1.5%
Percentage only. The projection counts a different population from the 6,980 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +1.5% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~1,300 openings a year on average, including replacing people who leave.
TesterLocaterLocatorObserverGeologistOil ScoutGas ProverGas TesterMud LoggerOil TesterOre TesterAcid TesterField ScoutGas AnalystOil AnalystWell LoggerWell TesterChalk TesterCrude TesterField TesterLine LocatorScout LeaserGeotechnicianSeismographer
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Sample prep, core description, log plotting and report compilation — roughly half the shift — are already being handled by hyperspectral core scanners and ML log-interpretation packages, but rig-side mudlogging, sample chain-of-custody, geophysical instrument setup and troubleshooting a stuck downhole tool keep it out of the single-digit band.
Hands-on in uncontrolled environments The job happens on drill floors, claim blocks, quarry benches and offshore rigs with H2S monitors and hardhats, hauling core boxes and running trenching and sampling gear in weather nobody controls; it sits at 15 rather than 18 because a real share of hours are spent in a core shack or trailer at a screen.
No licence, no signature requirement There is no license or registration to hold — the Professional Geologist stamp on the report belongs to the geologist you report to, and the 3 reflects only site-specific tickets like MSHA Part 46/48, HAZWOPER or offshore safety training that gate access to the location, not to the work itself.
Meaningful discretion You make real calls on the fly — where to cut the sample interval, whether a gas show is worth flagging to the mudlogger, whether an instrument reading is a formation or a bad calibration — but the interpretation, the well decision and the resource estimate belong to the supervising geologist, which caps this at 8.
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 (11/20 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 (3/20) is whether the law requires a licensed human to sign. Trust premium (6/20) is whether buyers specifically pay for a person. Judgment and accountability (8/20) 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 43 points (40%).
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 54/100, still EXPOSED.
Task-mix shift: as ML lithology/log interpretation and automated core scanning absorb description and data-reduction work, what remains is field QA/QC — sample chain-of-custody, contamination judgment, deciding when an instrument reading is an artifact vs. real, and re-sampling calls at the rig. The occupation genuinely has a routine tier (plotting, log compiling) and a judgment tier (sample validity, rig-side anomaly calls); the second is what the job becomes.
If assay-fraud enforcement (post-Sino-Forest, post-Rangold style cases) leads exchanges or the SEC to demand named, documented sign-off on the specific field sampling events feeding a resource estimate, the technician who took the sample becomes an identified accountable party in the audit trail rather than an anonymous input.
If mineral exploration shifts further toward greenfield/brownfield drilling in terrain where drone and autonomous-rig coverage is poor (steep claim blocks, permafrost, jungle), and if mudlogging remains required onsite under state oil-and-gas commission witnessing rules rather than moving to remote operations centers, the physical share of the day rises. The counter-motion is real: Halliburton/SLB remote-ops centers already pull mudloggers off the rig floor.
NI 43-101 / SK-1300 (SEC Subpart 1300) sampling and QA-QC provisions currently place the attestation on the Qualified Person/licensed geologist, not the technician. A concrete raise would require a rule change making a registered geologist or certified sampling technician personally attest to chain-of-custody and field duplicate/blank insertion — for example, an ASBOG-linked technician registration tier, or a state board rule (Texas BPG, California PG) extending practice-act coverage to field sampling. Nothing like this is currently in motion; treat as unlikely.
The limit. There is no realistic route to a trust premium — buyers of geological data are mining companies and E&P operators who pay for defensible numbers, not for human hands. And the liability route is genuinely blocked: the entire regulatory architecture (NI 43-101, SK-1300, state practice acts) is built to place liability on the geologist above the technician, and no professional body is lobbying to change that. Realistic ceiling is low-to-mid 50s, driven almost entirely by task-mix shift plus terrain, and remote-operations centers are actively eroding the embodiment floor at the same time.
| Houston-Pasadena-The Woodlands, TX | 680 | $59,630 +12% |
| Dallas-Fort Worth-Arlington, TX | 240 | $47,710 -11% |
| Oklahoma City, OK | 200 | $47,510 -11% |
| San Antonio-New Braunfels, TX | 160 | $49,280 -8% |
| New York-Newark-Jersey City, NY-NJ | 140 | $57,150 +7% |
| Pittsburgh, PA | 80 | $43,440 -19% |
| Raleigh-Cary, NC | 80 | $47,920 -10% |
| San Diego-Chula Vista-Carlsbad, CA | 80 | $82,350 +54% |
| San Diego-Chula Vista-Carlsbad, CA | 80 | $82,350 +54% |
| Denver-Aurora-Centennial, CO | 60 | $70,280 +32% |
| Las Vegas-Henderson-North Las Vegas, NV | 60 | $62,520 +17% |
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 43. 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.