Well Drillers
United States · BLS Employment Projections 2024–34: this SOC code is not among the published items.
AI exposure in published research
Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.
Data provider: OpenAI · "GPTs are GPTs"
Time basis: 2023 baselineex-ante estimate
Scored against GPT-4-generation capability.
- Human rater basis
- —
- GPT-4 rater basis
- —
This occupation code is not included in this dataset.
This occupation code is not included in this dataset.
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
— This occupation code is not included in this dataset.
- License:
- MIT License · Copyright (c) 2024 OpenAI
Data provider: Anthropic Economic Index
Time basis: Published 2026-03-05composite index
- Observed exposure
- —
This occupation code is not included in this dataset.
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
28 tasks · usage observed in 0 · mean 0.0%
Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex
AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
- Version:
- hf-lmi-2026-03
- License:
- CC BY 4.0
- Observation period:
- — (not applicable to this release)
- Model:
- — (not stated by the source)
- Definition source:
- https://www.anthropic.com/research/labor-market-impacts
2023 prediction vs observation-based index published 2026-03-05
One card (GPTs are GPTs) is a 2023 estimate of what AI could theoretically do; the other (Anthropic Economic Index) is built from observed usage and was published on 2026-03-05 — that is its publication date, not the period it observed. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.
Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.
The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.
The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.
These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.
Task Breakdown
- Operate and monitor drilling equipment and machinery
- Assemble and disassemble drill rigs and components
- Analyze geological data to determine optimal drilling locations
- Record and log drilling progress and soil conditions
- Maintain and repair drilling tools and equipment
About This Occupation
If you work as a Well Driller, AI is beginning to influence data-driven aspects of your trade. With an automation risk of 9/100 and overall exposure at 13%, this role faces low transformation. The highest-impact area is recording and logging drilling progress at 45% automation. This is classified as an 'augment' role. BLS projects +2% growth through 2034. Physical drilling operations remain manual, but AI-powered geological analysis and automated logging are gaining adoption.
ISCO-08 classification
Well Drillers and Borers and Related Workers
Indirect mapping — this occupation is linked to the ISCO unit group by a rule-based fallback.
Definition
ILO original text (English)
Well drillers and borers and related workers position, assemble and operate drilling machinery and related equipment to sink wells, extract rock samples, liquids and gases or for a variety of other purposes.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Frequently Asked Questions
They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.