# Gambling and Sports Book Writers and Runners

> Post information enabling patrons to wager on various races and sporting events. Assist in the operation of games such as keno and bingo. May operate random number-generating equipment and announce the numbers for patrons. Receive, verify, and record patrons' wagers. Scan and process winning tickets presented by patrons and pay out winnings for those wagers.

- **SOC code:** 39-3012.00
- **Canonical URL:** https://singulariki.com/roles/role-39-3012-00
- **Also known as:** Casino Floor Runner, Casino Runner, Keno Writer, Racebook Writer, Bingo Clerk, Casino Attendant, Floor Runner, Keno Attendant
- **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):
- Conduct gambling tables or games, such as dice, roulette, cards, or keno, and ensure that game rules are followed.
- Operate games in which players bet that a ball will come to rest in a particular slot on a rotating wheel, performing actions such as spinning the wheel and releasing the ball.
- Exchange paper currency for playing chips or coins.
- Compare the house hand with players' hands to determine the winner.
- Open or close cash floats or game tables.
- Pay off or move bets as established by game rules and procedures.
- Collect bets in the form of cash or chips, verifying and recording amounts.
- Start gaming equipment that randomly selects numbered balls and announce winning numbers and colors.
- Check to ensure that all players have placed their bets before play begins.
- Collect cards or tickets from players.
- Inspect cards or equipment to be used in games to ensure they are in proper condition.
- Compute and verify amounts won or lost, paying out winnings or referring patrons to workers, such as gaming cashiers, so that winnings can be collected.

**Emerging tasks** (O*NET):
- Provide race or game information to patrons.
- Serve drinks to patrons.

## Skills, tools, capabilities

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

**Skills in demand:**
- Mathematics _(Common Skill)_
- Active Listening _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- English Language _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Credit card processing software
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 56th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 59th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 33rd percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 79th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 81st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -6.1% growth (Declining); 1.2k annual openings; 8.2k → 7.7k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $30,460; 7,600 employed.

## 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/
- **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-39-3012-00_