Share and intensity of work current AI systems can materially affect.
SOC 39-3011
Gambling Dealers AI displacement risk
Electronic table games and AI surveillance handle game mechanics and integrity monitoring automatically. Live tables persist because players pay for the human game — the dealing ritual, table talk, and trusted handling of real money.
Likely potential for exposed tasks to move to software after workflow integration.
ETGs keep taking floor share at the value end, and surveillance AI watches every hand. The live dealer survives on experience economics — high-limit and social players want a human game — but this is a genuinely narrowing occupation.
Distribution
Where Gambling Dealers sits across 620 tracked roles
Displacement pressure 48 — higher than 78% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
21 O*NET task statements matched to SOC 39-3011. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $34,320 (May 2025, US national). The latest BLS row matched SOC 39-3011.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Gambling Dealers
SOC 39-3011 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 48/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Gambling Dealers
The current evidence import matched 21 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks21
SOC39-3011
- Core task / ID 4446
Pay winnings or collect losing bets as established by the rules and procedures of a specific game.
- Core task / ID 21151
Greet customers and make them feel welcome.
- Core task / ID 4445
Exchange paper currency for playing chips or coin money.
- Core task / ID 4449
Check to ensure that all players have placed bets before play begins.
- Core task / ID 4451
Inspect cards and equipment to be used in games to ensure that they are in good condition.
- Core task / ID 4447
Deal cards to house hands, and compare these with players' hands to determine winners, as in black jack.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Deal cards and run table games
Exposure 48, automation 38%, augmentation 20%.
O*NET evidence: Stand behind a gaming table and deal the appropriate number of cards to each player. (ID 4450)
Compute and pay winnings
Exposure 62, automation 50%, augmentation 26%.
O*NET evidence: Pay winnings or collect losing bets as established by the rules and procedures of a spe... (ID 4446)
Exchange currency and verify wagers
Exposure 58, automation 44%, augmentation 28%.
O*NET evidence: Exchange paper currency for playing chips or coin money. (ID 4445)
Engage players and answer questions
Exposure 24, automation 8%, augmentation 40%.
O*NET evidence: Answer questions about game rules and casino policies. (ID 4457)
TaskExposureAutomationAugmentation
Deal cards and run table games4838%20%
Compute and pay winnings6250%26%
Exchange currency and verify wagers5844%28%
Engage players and answer questions248%40%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateTable Games Supervisor
Training horizon: 2-5 months. Skill overlap 72. Wage preservation signal 138.
- Oversee multiple tables
- Review surveillance flags
- Manage dealer schedules
adjacent role
ModerateCasino Cage Cashier Supervisor
Training horizon: 2-4 months. Skill overlap 58. Wage preservation signal 106.
- Own cash operations accuracy
- Handle high-value transactions
- Track compliance reporting
Comparison guides
Compare the next move before you commit
2-5 months
Gambling Dealers to Table Games Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Gambling Dealers into Table Games Supervisor.
2-4 months
Gambling Dealers to Casino Cage Cashier Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Gambling Dealers into Casino Cage Cashier Supervisor.
What the AI risk score means for Gambling Dealers
The displacement pressure score for Gambling Dealers is 48. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Compute and pay winnings carries 50% automation pressure, while Engage players and answer questions carries 40% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $34,320 (May 2025, US national). Employment context: Casino table role with electronic-game competition. Typical education: Dealer training program; state gaming license required.
Wage vulnerability is 72, while transition feasibility is 60. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Electronic tables gain floor share
- Live-game experience retains value players
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Gambling Dealers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Game rules and compliance
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Cash handling accuracy
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Customer engagement
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Rule compliance
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Table Games Supervisor, such as oversee multiple tables.
- By 90 days, compare internal openings and external postings for Table Games Supervisor or Casino Cage Cashier Supervisor and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Gambling Dealers
Will AI replace Gambling Dealers?
Electronic table games and AI surveillance handle game mechanics and integrity monitoring automatically. Live tables persist because players pay for the human game — the dealing ritual, table talk, and trusted handling of real money. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Gambling Dealers work are most exposed to AI?
Compute and pay winnings and Exchange currency and verify wagers show the strongest automation pressure in this model. Engage players and answer questions and Exchange currency and verify wagers are better treated as AI-augmented work.
What should Gambling Dealers learn next?
Start with Game rules and compliance, Cash handling accuracy, Customer engagement. The most practical adjacent paths in this model are Table Games Supervisor and Casino Cage Cashier Supervisor.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics