Occupation · SOC 43-3041.00
In a gambling establishment, conduct financial transactions for patrons. Accept patron's credit application and verify credit references to provide check-cashing authorization or to establish house credit accounts. May reconcile daily summaries of transactions to balance books. May sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons. May convert gambling chips, tokens, or tickets to currency upon patron's request. May use a cash register or computer to record transaction.
Also called: Cage Cashier · Casino Cage Cashier · Casino Cashier · Vault Cashier · Cage and Players Club Rep (Cage and Players Club Representative) · Casino Services Rep (Casino Services Representative) · Dual Rate Banker · Gaming Cage Worker · Gaming Cashier · Mutuel Clerk · Casino Gaming Worker · Casino Worker
Job family: Office and Administrative Support Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-43-3041-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
56th-percentile task overlap — yet about 1,300 openings a year (-5% projected, BLS), and observed AI use leans 3977% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 58th | 0.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 41st | 0.5 | |
| AI assistant applicability (Microsoft) High | 73rd | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.4), with simple added tooling (β 0.4), and including AI-powered software (γ 0.5). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.4 · 43rd percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Prepare reports, including assignment of company funds or recording of department revenues. | 0.9% | |
| Provide customers with information about casino operations. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -5.0% by 2034 |
| Projected annual openings | 1,300 |
| Employment 2024 → 2034 | 14,100 → 13,400 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Bookmakers, Croupiers and Related Gaming Workers · 4212 | 45% | Gradient 2 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 39.8% working with AI · 44.9% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.8 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 29.6% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Provide customers with information about casino operations. | Directive | 1.2% |
| Prepare reports, including assignment of company funds or recording of department revenues. | Directive | 0.6% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Provide customers with information about casino operations. | 100.0% | |
| Prepare reports, including assignment of company funds or recording of department revenues. | 96.4% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me provide customers with information about casino operations. From: Provide customers with information about casino operations. · 1.2% of measured AI use · directive
Help me prepare reports, including assignment of company funds or recording of department revenues. From: Prepare reports, including assignment of company funds or recording of department revenues. · 0.6% of measured AI use · directive
All 17 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Customer and Personal Service | 4.7 | |
| Mathematics | 3.8 | |
| English Language | 3.2 | |
| Administration and Management | 3.1 | |
| Administrative | 3.0 | |
| Economics and Accounting | 2.8 | |
| Computers and Electronics | 2.6 |
| Oral Comprehension | 3.9 | |
| Oral Expression | 3.9 | |
| Near Vision | 3.9 | |
| Speech Clarity | 3.8 | |
| Problem Sensitivity | 3.6 | |
| Mathematical Reasoning | 3.6 | |
| Number Facility | 3.6 | |
| Deductive Reasoning | 3.3 | |
| Information Ordering | 3.3 | |
| Speech Recognition | 3.3 | |
| Written Comprehension | 3.1 | |
| Selective Attention | 3.1 | |
| Written Expression | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Category Flexibility | 2.9 | |
| Perceptual Speed | 2.9 | |
| Trunk Strength | 2.9 | |
| Memorization | 2.6 |
| Speaking | 3.5 | |
| Active Listening | 3.3 | |
| Mathematics | 3.3 | |
| Reading Comprehension | 3.0 | |
| Writing | 3.0 | |
| Critical Thinking | 3.0 | |
| Monitoring | 3.0 |
| Social Perceptiveness | 3.0 | |
| Service Orientation | 3.0 | |
| Time Management | 3.0 | |
| Coordination | 2.9 | |
| Persuasion | 2.9 | |
| Negotiation | 2.9 | |
| Instructing | 2.9 | |
| Judgment and Decision Making | 2.9 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Corel WordPerfect Office Suite | Office suite software |
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Culinary, Entertainment, and Personal Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 84.8% | |
| Some College Courses | 13.7% | |
| Associate's Degree (or other 2-year degree) | 1.4% |
The interests and personal qualities O*NET associates with people who do this work.
| Conventional | 6.0 | |
| Enterprising | 4.7 | |
| Social | 2.9 | |
| Realistic | 2.9 |
| Accounting | 4.8 | |
| Office Work | 4.4 | |
| Finance | 3.0 | |
| Management/Administration | 2.3 | |
| Personal Service | 2.0 | |
| Sales | 1.9 | |
| Mathematics/Statistics | 1.6 |
| Dependability | 4.0 | |
| Attention to Detail | 3.0 | |
| Integrity | 2.8 | |
| Cautiousness | 2.5 | |
| Self-Control | 1.9 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $27,940 |
| 25th percentile | $31,870 |
| Median (50th) | $36,990 |
| 75th percentile | $43,840 |
| 90th percentile | $49,350 |
| People employed | 13,490 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 7,610 | $35,880 |
| Accommodation and Food Services · Sector | 5,490 | $38,460 |
| Casino Hotels · National industry | 5,480 | $38,460 |
| Administrative and Support and Waste Management and Remediation Services · Sector | — | $32,370 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Casino Hotels · National industry | 185.86× | 5,480 |
| Arts, Entertainment, and Recreation · Sector | 32.92× | 7,610 |
| Accommodation and Food Services · Sector | 4.41× | 5,490 |
Part of the Hospitality, Events, & Tourism career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Gambling Cage Workers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 83rd percentile of 427 international occupations.
Gambling Cage Workers show 56th-percentile AI task overlap — and about 1,300 annual U.S. openings
Gambling Cage Workers show 56th-percentile AI task overlap — and about 1,300 annual U.S. openings • Gambling Cage Workers rank in the 56th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 1,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $36,990, across about 13,490 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 40% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Gambling Cage Workers". https://singulariki.com/roles/role-43-3041-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Gambling Cage Workers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-43-3041-00
Singulariki. (2026). Gambling Cage Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-43-3041-00
@misc{singulariki-role-43-3041-00,
title = {Gambling Cage Workers},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-43-3041-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.