Will AI replace Gambling Dealers?

Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Gambling Dealers (United States, SOC 39-3011), 2% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 3–9, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Gambling Dealers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare collection reports for submission to supervisors” (69/100, high); “Compute amounts of players' wins or losses, or scan winning tickets presented by patrons to calculate the amount of money won” (22/100, low); “Answer questions about game rules and casino policies” (18/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Gambling Dealers” stay human?

About 98% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Greet customers and make them feel welcome” (0/100, minimal); “Supervise staff and monitor gambling tables to ensure security of the game” (0/100, minimal); “Seat patrons at gaming tables” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Gambling Dealers” do about AI?

Start from the ledger rather than the headline: 2% of this job's weighted core work is exposed, and roughly 98% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Gambling Dealers calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Gambling Dealers

paying winnings or collecting losing bets as established by the rules and procedures of a specific game, applying rule variations to card games and receiving, verifying and recording patrons' cash wagers. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: greeting customers and making them feel welcome is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 2%
changing shape: 0%
staying human: 98%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 5 out of 100 (3–9 allowing for uncertainty): minimal exposure, across 21 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for gambling dealers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is preparing collection reports for submission to supervisors: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

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. The job title says “gambling dealers”. The real job is the part underneath: greeting customers and making them feel welcome. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But gambling dealers is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is greeting customers and making them feel welcome, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 2%
changing shape: 0%
staying human: 98%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Preparing collection reports for submission to supervisors
This is reading one thing and writing another: collection reports in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare collection reports for submission to supervisors.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Collection reports are routine paperwork built from table figures, which software compiles quickly.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

0 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

20 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Greeting customers and making them feel welcome
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Greet customers and make them feel welcome.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Greeting players at a table is about a person being there with them.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Checking to ensure that all players have placed bets before play begins
This work happens in the physical world: ensure, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check to ensure that all players have placed bets before play begins.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Checking that everyone has bet before play starts requires watching the table in person.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Paying winnings or collecting losing bets as established by the rules and procedures of a specific game
This work happens in the physical world: winnings, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Pay winnings or collect losing bets as established by the rules and procedures of a specific game.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Paying winnings and taking losing bets means handling chips at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting cards and equipment to be used in games to ensure that they are in good condition
This work happens in the physical world: cards, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect cards and equipment to be used in games to ensure that they are in good condition.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Inspecting cards and equipment for damage means handling them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Conducting gambling games, such as dice, roulette, cards or keno, following all applicable rules and regulations
This work happens in the physical world: games, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Conduct gambling games, such as dice, roulette, cards, or keno, following all applicable rules and regulations.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running a dice, roulette or card game means being at the table operating it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Exchanging paper currency for playing chips or coin money
This work happens in the physical world: paper currency, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Exchange paper currency for playing chips or coin money.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Exchanging cash for chips is a hands-on transaction at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Dealing cards to house hands
This work happens in the physical world: cards, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Deal cards to house hands, and compare these with players' hands to determine winners, as in black jack.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Dealing the house hand and comparing it with players hands happens with real cards at a table.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Standing behind a gaming table and deal the appropriate number of cards to each player
This work happens in the physical world: behind a gaming table, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Stand behind a gaming table and deal the appropriate number of cards to each player.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Standing at a table dealing cards is physical work by definition.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Answering questions about game rules and casino policies
This work happens in the physical world: questions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Answer questions about game rules and casino policies.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Rules and casino policies are well documented and easy to look up, though players ask the dealer in front of them.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.

Show the other 11 tasks

Computing amounts of players' wins or losses
staying human
This work happens in the physical world: amounts of players' wins, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Compute amounts of players' wins or losses, or scan winning tickets presented by patrons to calculate the amount of money won.” (O*NET task statement)
How this row was scored
Exposure score: 22 out of 100 (15–29 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The arithmetic is trivial for software, but the tickets and chips are handled at the table by a licensed dealer.
The five ratings: output a model can produce 4/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Referring patrons to gaming cashiers to collect winnings
staying human
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Refer patrons to gaming cashiers to collect winnings.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (5–19 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Pointing a player toward the cashier is simple information, but it happens face to face at the table.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Applying rule variations to card games
staying human
This work happens in the physical world: rule variations, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Apply rule variations to card games such as poker, in which players bet on the value of their hands.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (0–20 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Game rules are fully documented and easy for software to apply, but they are applied live at a table by a licensed dealer.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Training new dealers
staying human
This work happens in the physical world: new dealers, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Train new dealers.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Training new dealers means watching their hands and correcting them at a real table.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Starting and controlling games and gaming equipment
staying human
This work happens in the physical world: games, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Start and control games and gaming equipment, and announce winning numbers or colors.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Starting and controlling games and equipment and calling results is done at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Opening and closing cash floats and game tables
staying human
This work happens in the physical world: cash floats, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Open and close cash floats and game tables.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Opening and closing cash floats and tables is physical handling of money and equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Receiving, verifying and recording patrons' cash wagers
staying human
This work happens in the physical world: patrons' cash wagers, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Receive, verify, and record patrons' cash wagers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Taking and recording cash wagers happens hand to hand at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Working as part of a team of dealers in games
staying human
This work happens in the physical world: part of a team of dealers, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Work as part of a team of dealers in games, such as baccarat or craps.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Working as part of a dealing team at a live table requires being there.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Participating in games for gambling establishments to provide the minimum complement of players at a table
staying human
This work happens in the physical world: games, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Participate in games for gambling establishments to provide the minimum complement of players at a table.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Sitting in on a game to make up the numbers requires a body at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Seating patrons at gaming tables
staying human
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Seat patrons at gaming tables.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Seating patrons at tables is done in person.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Supervising staff and monitoring gambling tables to ensure security of the game
staying human
This work happens in the physical world: staff, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Supervise staff and monitor gambling tables to ensure security of the game.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Watching tables for security means eyes on the game in the room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $34,320a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 83,910in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: collection reports in, a record out. The rows above are exactly that shape: preparing collection reports for submission to supervisors. What it cannot do is be there in the room, and that is still where customers get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: greeting customers and making them feel welcome is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 2% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 98% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Preparing collection reports for submission to supervisors is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to collection reports, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether customers are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near greeting customers and making them feel welcome. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here

You did not come here for a career change and I am not selling you one. But one road out of here is worth knowing about, so here it is with the bill attached.

Gambling DealersGambling and Sports Book Writers and Runners
a year or morematched on shared tasks
You already check to ensure that all players have placed bets before play begins. In that job the same thing shows up as check to ensure that all players have placed their bets before play begins. Take both published task lists together and about 49% of the work in that job that the software is not taking is work you are doing today.
About 49% of it you could do on Monday. The catch is not the work, it is how few of those jobs there are.
The work the two jobs share
- You already do
  “Check to ensure that all players have placed bets before play begins.”
  They do
  “Check to ensure that all players have placed their bets before play begins.”
- You already do
  “Exchange paper currency for playing chips or coin money.”
  They do
  “Exchange paper currency for playing chips or coins.”
- You already do
  “Conduct gambling games, such as dice, roulette, cards, or keno, following all applicable rules and regulations.”
  They do
  “Conduct gambling tables or games, such as dice, roulette, cards, or keno, and ensure that game rules are followed.”
What you would not already have: Nothing in your task list touches “collect bets in the form of cash or chips, verifying and recording…”, “operate games in which players bet that a ball will come to…” or “deliver tickets, cards, and money to bingo callers”. That is the part you would be learning from scratch, and it is roughly the 51% of their durable work you do not already hold.
The honest bill
Pay: $34,980 against your $34,320 (OEWS May 2025 (both)).
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $34,980 against your $34,320, 1.9% more (OEWS May 2025 (both)). A real difference, not a life-changing one. Take the move for the work, not the raise.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- How many seats there are: About 8,950 of those jobs against 83,910 of yours (OEWS May 2025), 11% as many seats. A real door, not a wide one.
- Is the target job itself holding up: Gambling and Sports Book Writers and Runners scores 13/100 on this site’s own exposure measure (minimal), with 10% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 51% of their work you would be learning, not by any one course.
See what AI is doing to gambling and sports book writers and runners

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Gambling Cage Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange paper currency for playing chips or coin money, and their equivalent is to supply currency, coins, chips, or gaming checks to other departments as needed. Across both published task lists that is about 13% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 14,430 of those jobs against 83,910 of yours (OEWS May 2025), 17% as many seats.
Look at that job’s page anyway →
Gambling Change Persons and Booth Cashiers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange paper currency for playing chips or coin money, and their equivalent is to exchange money, credit, tickets, or casino chips and make change for customers. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
First-Line Supervisors of Gambling Services Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange paper currency for playing chips or coin money, and their equivalent is to exchange currency for customers, converting currency into requested combinations of bills and coins. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 2% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like greeting customers and making them feel welcome arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Sports and leisure assistants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Financial administrative occupations n.e.c.

In UK official statistics this job is counted as Sports and leisure assistants and Financial administrative occupations n.e.c.. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Nearby moves

The jobs above, as pages you can read the same way as this one. Your job shares its core work with these. That is what the match is, and it is all it is.

Gambling and Sports Book Writers and Runners →About 49% of it you could do on Monday. The catch is not the work, it is how few of those jobs there are.

Noted, and thank you. We’ll email you if a Space for gambling dealers launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for gambling dealers yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for gambling dealers existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for gambling dealers launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Gambling Dealers?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Gambling Dealers (United States, SOC 39-3011), 2% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 3–9, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Gambling Dealers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare collection reports for submission to supervisors” (69/100, high); “Compute amounts of players' wins or losses, or scan winning tickets presented by patrons to calculate the amount of money won” (22/100, low); “Answer questions about game rules and casino policies” (18/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Gambling Dealers” stay human?: About 98% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Greet customers and make them feel welcome” (0/100, minimal); “Supervise staff and monitor gambling tables to ensure security of the game” (0/100, minimal); “Seat patrons at gaming tables” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Gambling Dealers” do about AI?: Start from the ledger rather than the headline: 2% of this job's weighted core work is exposed, and roughly 98% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Gambling Dealers calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

How we score a job Download this release Look up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.