Headline risk
32%
High RiskCoin, vending, and amusement machine servicers and repairers
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
Install, service, adjust, or repair coin, vending, or amusement machines including video games, juke boxes, pinball machines, or slot machines.
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 47,350
Employment 2024
32.5K
Projected Change (2024–34)
-2.9%
Openings (2024–34)
3.5K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products. AI use: 0%
- 2. Fill machines with products, ingredients, money, and other supplies. AI use: 0%
- 3. Test machines to determine proper functioning. AI use: 0%
- 4. Clean and oil machine parts. AI use: 0%
- 5. Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers. AI use: 0%
- 6. Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 42.6 · 132K employed
Under 25: 9% · 25–54: 64% · 55+: 27%
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
title_match · 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.