Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Occupation · SOC 39-1013.00
Directly supervise and coordinate activities of workers in assigned gambling areas. May circulate among tables, observe operations, and ensure that stations and games are covered for each shift. May verify and pay off jackpots. May reset slot machines after payoffs and make repairs or adjustments to slot machines or recommend removal of slot machines for repair. May plan and organize activities and services for guests in hotels/casinos.
Also called: Casino Shift Manager (CSM) · Floor Supervisor · Slot Supervisor · Table Games Supervisor · Casino Manager · Pit Boss · Pit Supervisor · Slot Floor Person · Slot Shift Manager · Slot Shift Supervisor · Blackjack Pit Boss · Blackjack Supervisor
Job family: Personal Care and Service Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-39-1013-00/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.
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.
64th-percentile task overlap — yet about 3,300 openings a year (+2% projected, BLS) . 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 |
|---|---|---|---|
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 47th | 0.6 | |
| AI assistant applicability (Microsoft) High | 83rd | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.4), and including AI-powered software (γ 0.6). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
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.
| Greet customers and ask about the quality of service they are receiving. | 2.3% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +2.0% by 2034 |
| Projected annual openings | 3,300 |
| Employment 2024 → 2034 | 32,500 → 33,100 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
All 30 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).
| Oral Comprehension | 4.0 | |
| Oral Expression | 3.9 | |
| Problem Sensitivity | 3.8 | |
| Speech Recognition | 3.8 | |
| Speech Clarity | 3.8 | |
| Deductive Reasoning | 3.6 | |
| Near Vision | 3.6 | |
| Written Comprehension | 3.5 | |
| Written Expression | 3.5 | |
| Selective Attention | 3.5 | |
| Information Ordering | 3.4 | |
| Inductive Reasoning | 3.3 | |
| Far Vision | 3.3 | |
| Memorization | 3.1 |
| Monitoring | 3.9 | |
| Active Listening | 3.8 | |
| Speaking | 3.8 | |
| Reading Comprehension | 3.6 | |
| Critical Thinking | 3.6 | |
| Writing | 3.4 |
| Service Orientation | 3.9 | |
| Social Perceptiveness | 3.5 | |
| Time Management | 3.5 | |
| Coordination | 3.4 | |
| Management of Personnel Resources | 3.4 | |
| Instructing | 3.3 | |
| Complex Problem Solving | 3.3 | |
| Negotiation | 3.1 | |
| Judgment and Decision Making | 3.1 |
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 In demand |
| Microsoft Office software | Office suite software | Hot technology In demand |
| Microsoft Access | Data base user interface and query software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Project | Project management 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: Business, Management, Marketing, and Related Support Services , 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 | 47.7% | |
| Some College Courses | 17.2% | |
| Post-Secondary Certificate | 11.9% | |
| Associate's Degree (or other 2-year degree) | 10.6% | |
| Bachelor's Degree | 6.5% | |
| Less than a High School Diploma | 6.1% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 8.0 | |
| Attention to Detail | 7.0 | |
| Integrity | 6.0 | |
| Cautiousness | 5.0 | |
| Social Orientation | 4.0 | |
| Self-Control | 3.0 | |
| Leadership Orientation | 2.4 |
| Enterprising | 5.9 | |
| Conventional | 5.5 | |
| Realistic | 3.7 | |
| Social | 3.0 |
| Management/Administration | 5.7 | |
| Personal Service | 3.3 | |
| Protective Service | 3.2 | |
| Human Resources | 3.0 | |
| Accounting | 3.0 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $38,360 |
| 25th percentile | $49,190 |
| Median (50th) | $61,590 |
| 75th percentile | $74,080 |
| 90th percentile | $82,370 |
| People employed | 25,530 |
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 |
|---|---|---|
| Accommodation and Food Services · Sector | 13,900 | $63,120 |
| Casino Hotels · National industry | 13,870 | $63,270 |
| Arts, Entertainment, and Recreation · Sector | 10,740 | $60,220 |
| Other Services (except Public Administration) · Sector | 370 | $41,650 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 60 | $54,210 |
| Health Care and Social Assistance · Sector | 60 | $78,050 |
| Information · Sector | — | $43,090 |
| Real Estate and Rental and Leasing · Sector | — | $57,500 |
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 | 248.57× | 13,870 |
| Arts, Entertainment, and Recreation · Sector | 24.55× | 10,740 |
| Accommodation and Food Services · Sector | 5.9× | 13,900 |
| Other Services (except Public Administration) · Sector | 0.5× | 370 |
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 First-Line Supervisors of Gambling Services 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.
See where this work sits in the bigger picture.
First-Line Supervisors of Gambling Services Workers show 64th-percentile AI task overlap — and about 3,300 annual U.S. openings
First-Line Supervisors of Gambling Services Workers show 64th-percentile AI task overlap — and about 3,300 annual U.S. openings • First-Line Supervisors of Gambling Services Workers rank in the 64th 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 3,300 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 about average (+2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $61,590, across about 25,530 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "First-Line Supervisors of Gambling Services Workers". https://singulariki.com/roles/role-39-1013-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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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. "First-Line Supervisors of Gambling Services 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. Accessed June 7, 2026. https://singulariki.com/roles/role-39-1013-00
Singulariki. (2026). First-Line Supervisors of Gambling Services Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-39-1013-00
@misc{singulariki-role-39-1013-00,
title = {First-Line Supervisors of Gambling Services 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. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-39-1013-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.