Headline risk
55%
Very High RiskGambling change persons and booth cashiers
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
Weighted task overlap from O*NET
Median annual from BLS OEWS
BLS employment projections
O*NET job zone level
Occupation profile
Exchange coins, tokens, and chips for patrons' money. May issue payoffs and obtain customer's signature on receipt. May operate a booth in the slot machine area and furnish change persons with money bank at the start of the shift, or count and audit money in drawers.
Task evidence
100% weighted task match · 0% effective coverage
Method contract
structural_pressure = exposure × (1 - bottleneck)
headline_risk = structural_pressure × (1 - country_demand_resilience)
United States Now
Median Wage
USD 34,810
Employment 2024
22.6K
Projected Change
-6.4%
Openings
4.0K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Exchange money, credit, tickets, or casino chips and make change for customers. AI 0%
- 2. Count money and audit money drawers. AI 0%
- 3. Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations. AI 0%
- 4. Check identifications to verify age of players. AI 0%
- 5. Maintain cage security according to rules. AI 0%
- 6. Reconcile daily summaries of transactions to balance books. AI 0%
Technologies
Requirements
Work context
Worker profile
Median age 41.0 · 962K employed
Under 25: 16% · 25–54: 59% · 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
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.