Headline risk
3%
Very Low RiskHelpers--extraction workers
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
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.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 48,400
Employment 2024
7.0K
Projected Change (2024–34)
-1.7%
Openings (2024–34)
0.7K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Observe and monitor equipment operation during the extraction process to detect any problems. AI use: 0%
- 2. Organize materials to prepare for use. AI use: 0%
- 3. Unload materials, devices, and machine parts, using hand tools. AI use: 0%
- 4. Drive moving equipment to transport materials and parts to excavation sites. AI use: 0%
- 5. Clean up work areas and remove debris after extraction activities are complete. AI use: 0%
- 6. Set up and adjust equipment used to excavate geological materials. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 36.4 · 68K employed
Under 25: 26% · 25–54: 49% · 55+: 25%
Related
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
major_group_fallback · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.