# Gambling Dealers

> Operate table games. Stand or sit behind table and operate games of chance by dispensing the appropriate number of cards or blocks to players, or operating other gambling equipment. Distribute winnings or collect players' money or chips. May compare the house's hand against players' hands.

- **SOC code:** 39-3011.00
- **Canonical URL:** https://singulariki.com/roles/role-39-3011-00
- **Also known as:** Blackjack Dealer, Casino Dealer, Table Games Dealer, Twenty-One Dealer, Black Jack Dealer, Card Dealer, Dealer, Dual Rate Dealer
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Pay winnings or collect losing bets as established by the rules and procedures of a specific game.
- Greet customers and make them feel welcome.
- Exchange paper currency for playing chips or coin money.
- Check to ensure that all players have placed bets before play begins.
- Inspect cards and equipment to be used in games to ensure that they are in good condition.
- Deal cards to house hands, and compare these with players' hands to determine winners, as in black jack.
- Stand behind a gaming table and deal the appropriate number of cards to each player.
- Apply rule variations to card games such as poker, in which players bet on the value of their hands.
- Receive, verify, and record patrons' cash wagers.
- Conduct gambling games, such as dice, roulette, cards, or keno, following all applicable rules and regulations.
- Work as part of a team of dealers in games, such as baccarat or craps.
- Supervise staff and monitor gambling tables to ensure security of the game.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Mathematics _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Speech Recognition _(ability)_
- Speech Clarity _(ability)_
- Active Listening _(essential_skill)_
- Speaking _(essential_skill)_
- Near Vision _(ability)_
- English Language _(knowledge)_
- Problem Sensitivity _(ability)_
- Social Perceptiveness _(transferable_skill)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Active Listening _(Common Skill)_
- English Language _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Apache Spark _(Specialized Skill)_
- Apache Hadoop _(Specialized Skill)_

**Tools & technology:**
- Apache Hadoop _(hot technology)_
- Apache Spark _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Slack _(hot technology)_
- Email software

## AI exposure & outlook

- **AI task-overlap index:** 51st percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 49th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 18th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 86th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 91st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -0.6% growth (Declining); 14.1k annual openings; 88.7k → 88.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $33,280; 82,980 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Autonomy median:** 3.5 (higher = AI acts more independently).

**Tasks most handed to AI here:**
- Answer questions about game rules and casino policies. _(0.3% of measured AI use)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me answer questions about game rules and casino policies.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-39-3011-00_