Will AI replace Helpers--Extraction Workers?
Estimated risk score: 7 out of 100
Very low replacement risk
Estimate from published task research, not reviewed by a person · how we estimate
Within about 19 points for 4 in 5 of the jobs we checked (0 to 26).
For the occupation as a whole, from ratings of its 14 O*NET tasks published in 2023 and calibrated to our hand scores. A specialty inside it can sit higher or lower.
O*NET-SOC 47-5081.00 · Construction and Extraction
Tasks rated in 2023 as ones AI could do or speed up
Tasks a GPT-4-class model could do, or do in half the time or less: 0 of its 14 rated tasks. In O*NET’s words.
None of its rated tasks.
Tasks rated in 2023 as not sped up by AI
Tasks such a model was rated as not speeding up or taking over: 14 of its 14 rated tasks; the first 6 are shown. In O*NET’s words.
- Clean and prepare sites for excavation or boring.
- Clean up work areas and remove debris after extraction activities are complete.
- Drive moving equipment to transport materials and parts to excavation sites.
- Load materials into well holes or into equipment, using hand tools.
- Organize materials to prepare for use.
- Repair and maintain automotive and drilling equipment, using hand tools.
What the job is
Help extraction craft workers, such as earth drillers, blasters and explosives workers, derrick operators, and mining machine operators, by performing duties requiring less skill. Duties include supplying equipment or cleaning work area.
Also called: Miner Helper, Driller Helper, Driller's Assistant, Roughneck, Ore Digger, Salt Miner, Jack Setter, Drill Helper.
Closest jobs we score by hand
In the same field (Construction and Extraction). Different jobs, each scored by a person.
Source. This site includes information from the O*NET 30.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not approved, endorsed, or tested these modifications. (O*NET, CC BY 4.0.) Task ratings: Eloundou, Manning, Mishkin and Rock (2023), “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, data used under the MIT licence (licence texts). Our change: we averaged the ratings over this occupation’s tasks and calibrated them to our 0–100 scale. It is not a prediction about any one person and not career advice. Disagree? Write to hello@yourstopwashit.com. How we estimate