Gambling Dealers
In short: today's best AI models could do about 6% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 94% of the time, stays with people.
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.
83,910 people do this job in the US. $34,320 a year.
Reviewed on October 1, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work3 h
- Desk work AI can't do yet0 h
- Stays with people37 h
Gambling Dealers, now: of a 40-hour week, AI could take on 0 hours, 3 hours become checking its work, 0 hours are desk work it can't do yet, and 37 hours stay with people.
The largest pieces: Prepare collection reports (about 1 h, a person checks); Open and close table floats (about 1 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 2%
- 5%
- 0%
- 23%
- 71%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 6% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 1%. With Claude Mythos Preview (early), measured in April 2026, 6%. Today's estimate: 7%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.032% went to this job's tasks in May 2026, the 266th largest of the 447 jobs that showed up at all; 1 of its 21 tasks showed up. Within each of Anthropic's methods, never across a change: none to 0.0034% (August 2025 to February 2026); 0.023% to 0.032% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 21 |
| Released March 2025 | none | n/a | 0 of 21 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 21 |
| The week of Nov 13, 2025 (released January 2026) | 0.0032% | 615 of 672 | 1 of 21 |
| The week of Feb 5, 2026 (released March 2026) | 0.0034% | 602 of 677 | 1 of 21 |
| April 2026 (released June 2026) | 0.023% | 305 of 434 | 1 of 21 |
| May 2026 (released June 2026) | 0.032% | 266 of 447 | 1 of 21 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 83% of this job's working time as physical work. For 72% of the working time, a robot has been shown doing most of the task somewhere: 53% in a purpose-built cell, 19% in a structured facility. 0.91 of 3, the 171st highest of the 923 jobs.
- Not physical 17%
- No robot shown 10%
- In a purpose-built cell 53%
- In a structured facility 19%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
94% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Conduct games, the hands-on work, 27%
- Deal as part of a team, the hands-on work, 20%
- Supervise tables, the in-person work, 10%
- Play as a house player, the in-person work, 10%
- Train new dealers, the in-person work, 4%
- Inspect cards and equipment, the hands-on work, 3%
What becomes checking the machine
6% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it.
- Prepare collection reports, costly if wrong, 3%
- Open and close table floats, costly if wrong, 3%
What to learn
- The hands-on craft. About 71% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- The conversations. About 23% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 21 tasks from .
| Task | ||||
|---|---|---|---|---|
| Conduct gambling games, such as dice, roulette, cards, or keno, following all applicable rules and regulations.Running the game at the table.Done hourly or more by most who do it. Robots: in a structured facility, G4 Organic Card Blackjack (Elektroncek / Interblock) (sold). Anthropic's rating. | 27% | 38 min17 min to 1.7 h | Hands-on 100% | Stays with people |
| Work as part of a team of dealers in games, such as baccarat or craps.Dealing a team game.Done hourly or more by most who do it. Robots: in a purpose-built cell, Interblock UC Craps and MiniStar Roulette (automated ETGs) (deployed). Anthropic's rating. | 20% | 33 min17 min to 1.3 h | Hands-on 100% | Stays with people |
| Supervise staff and monitor gambling tables to ensure security of the game.Watching the tables and the dealers.Done hourly or more by most who do it. | 10% | 38 min13 min to 1.7 h | In person 50%, hands-on 50% | Stays with people |
| Participate in games for gambling establishments to provide the minimum complement of players at a table.Sitting in at the table.Done hourly or more by most who do it.Robots: no robot shown. Anthropic's rating. | 10% | 50 min25 min to 1.7 h | In person 100% | Stays with people |
| Open and close cash floats and game tables.Counting the float and balancing it against the records.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 6% | 17 min8 min to 33 min | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Train new dealers.Teaching at the table, side by side.Done more than yearly by most who do it. | 4% | 3.3 h50 min to 6.7 h | In person 100% | Stays with people |
| Prepare collection reports for submission to supervisors.The table's figures written up.Done daily by most who do it. | 3% | 33 min13 min to 1.3 h | Documents 50%, systems 50% | 100%Half by now |
| Inspect cards and equipment to be used in games to ensure that they are in good condition.Checking the decks and the wheel by hand.Done daily by most who do it. Robots: in a purpose-built cell, Casino Card Deck Checker (TCSJOHNHUXLEY) (sold). Anthropic's rating. | 3% | 7 min3 min to 13 min | Hands-on 100% | Stays with people |
| Exchange paper currency for playing chips or coin money.Cash taken and chips counted out.Done hourly or more by most who do it. Robots: in a structured facility, SuzoHapp Chip Dispensing Machine (CDM) (sold). Anthropic's rating. | 2% | 3 min1 min to 7 min | Hands-on 100% | Stays with people |
| Apply rule variations to card games such as poker, in which players bet on the value of their hands.Dealing the game by its variant's rules.Done hourly or more by most who do it. Robots: no robot shown. Anthropic's rating. | 2% | 3 min2 min to 4 min | Hands-on 100% | Stays with people |
| Receive, verify, and record patrons' cash wagers.Cash checked at the table, with the entry.Done hourly or more by most who do it. Robots: in a structured facility, Video/stadium blackjack terminals with bill acceptors (Multiple gaming suppliers) (deployed). Anthropic's rating. | 2% | 3 min2 min to 7 min | Hands-on 80%, systems 20% | 20%Desk part half by now |
| Check to ensure that all players have placed bets before play begins.A look at the layout before the deal.Done hourly or more by most who do it. | 2% | 2 min1 min to 3 min | Hands-on 70%, in person 30% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Gambling and Sports Book Writers and Runners26% now, 26% by 2028
- Gambling Managers57% now, 74% by 2028
- First-Line Supervisors of Gambling Services Workers30% now, 32% by 2028
- Gambling Cage Workers44% now, 44% by 2028
- Gambling Change Persons and Booth Cashiers50% now, 50% by 2028
- Tellers48% now, 48% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Gambling Dealers is within reach of AI now?
- An estimated 7% of the working time is within reach of AI models now and 7% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Gambling Dealers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Prepare collection reports (100% of the task's time now); Open and close table floats (50% of the task's time now); and Take and record cash wagers (20% of the task's time now).
- Which tasks of Gambling Dealers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Conduct games; Deal as part of a team; and Supervise tables. Together such tasks are about 89% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Gambling Dealers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; dealing with people outside the organization; and the conversations. By the end of 2028 on the long-run pace at four in five, about 94% of the working time stays with people, 6% becomes checking a model's work and 0% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Gambling Dealers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.032% went to this job's tasks, the 266th largest share of the 447 jobs that showed up; 1 of its 21 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Gambling Dealers?
- Anthropic's robot exposure rating (September 2026) finds 83% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 72% of the working time, 19% in a structured facility. Its index is 0.91 of 3. One example: G4 Organic Card Blackjack for "conduct games". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 21 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 1, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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