Geological and hydrologic technicians: AI exposure and career outlook
Geological and hydrologic technicians (SOC 19-4040) sit at the 70th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 42% of tasks are already automated and 65% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 67% follows an automation pattern and 33% an augmentation pattern, which places it in the Displacement risk group: the routine layer of this work is being automated outright. The judgment layer is not, so the move is to own more of it. In Canada the role maps to NOC 22101 (Geological and mineral technologists and technicians), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 19-4040
- Canada NOC 2021
- 22101
- TEER level
- Not available
- COPS outlook
- Balance
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 17% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 3 detailed roles. |
| Observed AI usage | 19% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). Occupation-group average of 2 detailed roles. |
| Academic AI exposure | 54th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 1 detailed roles. |
| Automation-pattern usage | 67% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). Occupation-group average of 3 detailed roles. |
| Augmentation-pattern usage | 33% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 42% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 65% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
This skill domain faces significant AI substitution. The opportunity is in pivoting to AI orchestration and higher-order human judgment within the domain.
In Canada: NOC 22101
Geological and hydrologic technicians map to NOC 22101 — Geological and mineral technologists and technicians. ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). TEER is the second digit of the NOC code, so it is only as certain as the code is. Several correspondence candidates were available here and we took the closest by title, so we do not report a TEER level for it.
What this job actually involves
These are the O*NET task statements for Geological and hydrologic technicians— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 70, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
4 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 17% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 19-4040 is a BLS occupation group. These statements are pooled across the 3 detailed occupations in it, so a given task may apply to only some of them.
Handling and Moving Objects · 3 tasks
Collect or prepare solid or fluid samples for analysis.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Collect samples of gases, soils, water, industrial wastewater, or asbestos products to conduct tests on pollutant levels or identify sources of pollution.
Core task · Importance 4.1/5 (O*NET) · reported by 1 of 3 occupations in this group
Prepare samples or photomicrographs for testing and analysis.
Core task · Importance 3.9/5 (O*NET) · reported by 1 of 3 occupations in this group
Getting Information · 2 tasks
Investigate hazardous conditions or spills or outbreaks of disease or food poisoning, collecting samples for analysis.
Core task · Importance 4.1/5 (O*NET) · reported by 1 of 3 occupations in this group
Participate in geological, geophysical, geochemical, hydrographic, or oceanographic surveys, prospecting field trips, exploratory drilling, well logging, or underground mine survey programs.
Core task · Importance 3.9/5 (O*NET) · reported by 1 of 3 occupations in this group
Analyzing Data or Information · 2 tasks
Test and analyze samples to determine their content and characteristics, using laboratory apparatus or testing equipment.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Prepare or review professional, technical, or other reports regarding sampling, testing, or recommendations of data analysis.
Core task · Importance 3.8/5 (O*NET) · 76% of observed AI use on this task is automation-pattern · reported by 1 of 3 occupations in this group
Documenting/Recording Information · 2 tasks
Compile, log, or record testing or operational data for review and further analysis.
Core task · Importance 4.2/5 (O*NET) · 93% of observed AI use on this task is automation-pattern · reported by 1 of 3 occupations in this group
Record test data and prepare reports, summaries, or charts that interpret test results.
Core task · Importance 4.0/5 (O*NET) · 55% of observed AI use on this task is automation-pattern · reported by 1 of 3 occupations in this group
Inspecting Equipment, Structures, or Materials · 1 task
Inspect workplaces to ensure the absence of health and safety hazards, such as high noise levels, radiation, or potential lighting hazards.
Core task · Importance 3.7/5 (O*NET) · reported by 1 of 3 occupations in this group
Thinking Creatively · 1 task
Prepare notes, sketches, geological maps, or cross-sections.
Core task · Importance 4.1/5 (O*NET) · reported by 1 of 3 occupations in this group
Communicating with Supervisors, Peers, or Subordinates · 1 task
Discuss test results and analyses with customers.
Core task · Importance 3.8/5 (O*NET) · 52% of observed AI use on this task is automation-pattern · reported by 1 of 3 occupations in this group
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Geological and hydrologic technicians as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Transition to high-value advisory, complex case analysis, or AI system oversight. The pivot is to move from routine execution to AI orchestration and quality assurance.
Cognitive, information-based tasks are highly exposed. Entry-level displacement pressure is high, necessitating transition to advisory and governance roles.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the high risk band, not an occupation-specific research finding about geological and hydrologic technicians.
Sources for this occupation
Only the sources that hold data for geological and hydrologic technicians are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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