AI exposure by occupation, O*NET 41-2012.00

Will AI replace Gambling Change Persons and Booth Cashiers?

Gambling Change Persons and Booth Cashiers: AI can already handle 12 of 13 tasks; the work mostly needs an efficient-tier model.

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Every task, scored

The 13 tasks O*NET 30.3 lists for Gambling Change Persons and Booth Cashiers, from most to least exposed. 12 reach 60 or more, the level at which AI already handles a task.

Task AI exposure Evidence Model tier Importance
Calculate the value of chips won or lost by players. 80 Estimated Standard 4.8
Check identifications to verify age of players. 80 Estimated Efficient 4.8
Accept credit applications and verify credit references to provide check-cashing authorization or to establish house credit accounts. 80 Estimated Efficient 4.4
Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations. 75 Estimated Efficient 4.9
Obtain customers' signatures on receipts when winnings exceed the amount held in a slot machine. 75 Estimated Efficient 4.8
Exchange money, credit, tickets, or casino chips and make change for customers. 75 Estimated Efficient 4.8
Count money and audit money drawers. 75 Estimated Efficient 4.8
Reconcile daily summaries of transactions to balance books. 75 Estimated Efficient 4.7
Furnish change persons with a money bank at the start of each shift. 75 Estimated Efficient 4.4
Listen for jackpot alarm bells and issue payoffs to winners. 75 Estimated Efficient 4.4
Sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons. 75 Estimated Frontier 4.3
Clean casino areas. 75 Estimated Efficient 3.9
Maintain cage security according to rules. 20 Estimated Efficient 4.8

Observed: scored from AI use recorded in the Anthropic Economic Index (June 2026 release), 0 of 13 tasks. Estimated: scored from the task's O*NET 30.3 work activity, 13 of 13. Importance is O*NET's 1 to 5 rating of how central the task is. Model tier is the lowest tier that can do the task under the published rubric; "No tier" marks hands-on work.

The model tier this role needs

An efficient-tier model or lower covers 85% of the role's importance-weighted task work. 100% of the work is something AI can address at some tier.

Efficient, standard and frontier tiers come from the JobRoute model tier reference. The recommended tier is the lowest one that covers 80% of the work AI can address. How the method works.

Wages, employment and outlook

US employment
21,530
Median annual wage
$36,220
Middle half of earners
$29,630 to $41,530
Projected change, 2024 to 2034
-6.4%

Employment and wages: BLS Occupational Employment and Wage Statistics, May 2025, national. Projection: BLS Employment Projections 2024 to 2034.

Adjacent roles

The occupations that reuse the most of this role's skills, with the usual time to move. These are the first places to look when you plan to redeploy or retrain people in this role.

Skill overlap and transition time from the JobRoute role adjacency graph on O*NET 30.3 skills.

Questions about Gambling Change Persons and Booth Cashiers and AI

Will AI replace Gambling Change Persons and Booth Cashiers?

AI already handles 12 of the 13 tasks O*NET lists for this occupation, at a task exposure of 60 or more, and the role's importance-weighted exposure is 72 out of 100. JobRoute scores tasks, not whole jobs, so the score shows how much of the work changes rather than whether the job disappears. BLS projects employment to change by -6.4% from 2024 to 2034.

What is the AI exposure of Gambling Change Persons and Booth Cashiers?

72 out of 100 on JobRoute's task exposure measure, the importance-weighted average of the role's O*NET task scores. Higher means more of the work is AI-doable today. 0 of the 13 task scores are observed in the Anthropic Economic Index (June 2026 release), and the other 13 are estimated from each task's O*NET work activity.

Which Gambling Change Persons and Booth Cashiers tasks can AI already do?

The tasks with the highest exposure are: Calculate the value of chips won or lost by players; Check identifications to verify age of players; Accept credit applications and verify credit references to provide check-cashing authorization or to establish house credit accounts. They are three of the 12 tasks that reach 60 or more.

What AI model tier does this role need?

An efficient-tier model or lower covers 85% of the role's importance-weighted task work. JobRoute assigns each task the lowest tier that can do it under a published rubric and recommends the lowest tier that covers 80% of the work AI can address.

How many people work as Gambling Change Persons and Booth Cashiers, and what do they earn?

21,530 people in the United States, with a median annual wage of $36,220 (BLS Occupational Employment and Wage Statistics, May 2025). BLS Employment Projections expect a change of -6.4% from 2024 to 2034.

How to cite

Data version 2026-11-02, JobRoute method v1.0.0. Download all occupations or this occupation as JSON. Methodology.

JobRoute (2026). AI exposure: Gambling Change Persons and Booth Cashiers. Data version 2026-11-02, method v1.0.0. Built on O*NET 30.3, BLS OEWS May 2025 and the Anthropic Economic Index (June 2026). CC BY 4.0. https://www.jobroute.ai/jobs/gambling-change-persons-and-booth-cashiers

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