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Gambling Change Persons and Booth Cashiers

Occupation · SOC 41-2012.00

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.

Also called: Cage Cashier · Cashier · Change Person · Slot Attendant · Booth Cashier · Casino Banker · Casino Cashier · Slot Floor Person · Slot Technician · Vault Cashier · Bingo Cashier · Booth Monitor

Job family: Sales and Related Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-41-2012-00/context.md directly.

AI work map

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.

40th-percentile task overlap — yet about 4,000 openings a year (-6.4% projected, BLS) . What exposure means →

AI & job outlook

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.

Exposure to current AI

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 47th -0.1
LLM task exposure, γ (OpenAI / Eloundou) Low 31st 0.3
AI assistant applicability (Microsoft) Moderate 47th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.3), and including AI-powered software (γ 0.3). 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.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

Historical automation estimate (2013)

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 0.8 · 68th percentile among occupations · High

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Declining · -6.4% by 2034
Projected annual openings 4,000
Employment 2024 → 2034 22,600 → 21,100

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

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.

39% mean task exposure (2025)
77th percentile of 427 placed occupations
+14 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Cashiers and Ticket Clerks · 5230 39% Gradient 1

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.

Tasks

All 15 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Customer and Personal Service 4.2
Mathematics 3.7
English Language 3.0
Public Safety and Security 2.8
Administrative 2.7
Computers and Electronics 2.6

Abilities

Oral Comprehension 3.5
Oral Expression 3.4
Near Vision 3.4
Problem Sensitivity 3.3
Information Ordering 3.3
Speech Recognition 3.3
Written Comprehension 3.1
Mathematical Reasoning 3.1
Number Facility 3.1
Speech Clarity 3.1
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Perceptual Speed 3.0
Arm-Hand Steadiness 3.0
Finger Dexterity 3.0
Written Expression 2.9
Category Flexibility 2.9
Selective Attention 2.9
Manual Dexterity 2.9
Far Vision 2.9
Time Sharing 2.8
Auditory Attention 2.8

Essential skills

Reading Comprehension 3.1
Active Listening 3.0
Speaking 3.0
Mathematics 2.9
Critical Thinking 2.9
Monitoring 2.9
Writing 2.8

Transferable skills

Social Perceptiveness 3.0
Coordination 3.0
Service Orientation 3.0
Complex Problem Solving 2.9
Judgment and Decision Making 2.8

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology In demand
Microsoft Word Word processing software Hot technology

Work context

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.

Contact With Others 4.9
Importance of Being Exact or Accurate 4.8
Face-to-Face Discussions with Individuals and Within Teams 4.7
Dealing With Unpleasant, Angry, or Discourteous People 4.6
Spend Time Making Repetitive Motions 4.6
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.5
Importance of Repeating Same Tasks 4.5
Spend Time Standing 4.4
Deal With External Customers or the Public in General 4.3
Impact of Decisions on Co-workers or Company Results 4.2
Frequency of Decision Making 4.2
Indoors, Environmentally Controlled 4.0
Telephone Conversations 4.0
Physical Proximity 3.8
Work With or Contribute to a Work Group or Team 3.7
Exposed to Contaminants 3.6
Time Pressure 3.4
Conflict Situations 3.4
Determine Tasks, Priorities and Goals 3.3
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.2
Freedom to Make Decisions 3.1
Written Letters and Memos 3.0
Level of Competition 2.9
Coordinate or Lead Others in Accomplishing Work Activities 2.8
Spend Time Bending or Twisting Your Body 2.8
Spend Time Walking or Running 2.7
Work Outcomes and Results of Other Workers 2.6
Health and Safety of Other Workers 2.6
Consequence of Error 2.5
Dealing with Violent or Physically Aggressive People 2.3
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.1
Exposed to Disease or Infections 2.1
Degree of Automation 2.0
Pace Determined by Speed of Equipment 2.0
Public Speaking 1.9
E-Mail 1.9
Spend Time Sitting 1.9
Spend Time Keeping or Regaining Balance 1.8
Exposed to Minor Burns, Cuts, Bites, or Stings 1.7
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.6

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
No formal educational credential · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 96.5%
Some College Courses 2.4%
Post-Secondary Certificate 1.2%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Conventional 6.4
Enterprising 4.2
Realistic 3.1
Social 2.4

Work styles

Dependability 4.0
Attention to Detail 3.0
Integrity 2.8
Cautiousness 2.1
Self-Control 1.5

Interest areas

Accounting 3.8
Office Work 2.8
Finance 2.6
Personal Service 2.3
Sales 1.7
Management/Administration 1.6
Mathematics/Statistics 1.6

Wages & employment

U.S. · annual wages (BLS OEWS)

$23k10th$29k25th$35kMedian$39k75th$49k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
23k202421k2034 (proj.)-6.4% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $22,810
25th percentile $28,870
Median (50th) $34,810
75th percentile $39,350
90th percentile $49,190
People employed 21,930

Industries that employ this occupation

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 12,320 $34,400
Accommodation and Food Services · Sector 8,710 $35,820
Casino Hotels · National industry 8,490 $36,020
Other Services (except Public Administration) · Sector 520 $29,270

Where this work is most concentrated

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 177.13× 8,490
Arts, Entertainment, and Recreation · Sector 32.78× 12,320
Accommodation and Food Services · Sector 4.3× 8,710
Other Services (except Public Administration) · Sector 0.83× 520

Part of the Hospitality, Events, & Tourism career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Gambling Change Persons and Booth Cashiers sits at the 40th percentile of AI task-overlap and the 4th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Gambling Change Persons and Booth Cashiers Gambling Surveillance Officers and Gambling Investigators Gambling and Sports Book Writers and Runners First-Line Supervisors of Gambling Services Workers Gambling Managers Tellers New Accounts Clerks Bookkeeping, Accounting, and Auditing Clerks AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Gambling Change Persons and Booth Cashiers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 77th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Gambling Change Persons and Booth Cashiers show 40th-percentile AI task overlap — and about 4,000 annual U.S. openings

  • Gambling Change Persons and Booth Cashiers rank in the 40th 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 4,000 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 (-6.4%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $34,810, across about 21,930 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Gambling Change Persons and Booth Cashiers show 40th-percentile AI task overlap — and about 4,000 annual U.S. openings

• Gambling Change Persons and Booth Cashiers rank in the 40th 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 4,000 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 (-6.4%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $34,810, across about 21,930 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Gambling Change Persons and Booth Cashiers". https://singulariki.com/roles/role-41-2012-00
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.

Sources for this page

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.

Cite this page
Plain

Singulariki. "Gambling Change Persons and Booth Cashiers." 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; 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-41-2012-00

APA

Singulariki. (2026). Gambling Change Persons and Booth Cashiers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-41-2012-00

BibTeX 
@misc{singulariki-role-41-2012-00,
  title  = {Gambling Change Persons and Booth Cashiers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-41-2012-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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