US dataswitch to UK
Gambling Cage Workers
maintaining confidentiality of customers' transactions, preparing reports and supplying currency, coins, chips or gaming checks to other departments. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: verifying accuracy of reports, such as authorization forms, transaction reconciliations or exchange summary reports. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, converting gaming checks, coupons, tokens or coins to currency for gaming patrons, stays yours. New tools, same person answering for it.
Your week, as this page understands it
In a gambling establishment, conduct financial transactions for patrons. Accept patron's credit application and verify credit references to provide check-cashing authorization or to establish house credit accounts. May reconcile daily summaries of transactions to balance books. May sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons. May convert gambling chips, tokens, or tickets to currency upon patron's request. May use a cash register or computer to record transaction. The job title says “gambling cage workers”. The real job is the part underneath: converting gaming checks, coupons, tokens or coins to currency for gaming patrons. 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 cage workers is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is converting gaming checks, coupons, tokens or coins to currency for gaming patrons, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 21%
- changing shape
- 4%
- staying human
- 75%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 28 out of 100 (23–34 allowing for uncertainty): low exposure, across 17 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 cage workers 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.
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.
- 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
4 tasksTasks 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.
Verifying accuracy of reports, such as authorization forms, transaction reconciliations or exchange summary reports
This is reading one thing and writing another: accuracy of reports in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Verify accuracy of reports, such as authorization forms, transaction reconciliations, or exchange summary reports.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high 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: Checking reports and reconciliations for accuracy is exactly what accounting software is built to do.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining cash requirements for windows and ordering all necessary currency
This is reading one thing and writing another: cash requirements in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Determine cash requirements for windows and order all necessary currency, coins, or chips.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 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: Forecasting how much cash each window needs and placing the order is a routine calculation from past figures.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing reports, including assignment of company funds or recording of department revenues
This is reading one thing and writing another: reports, including assignment of company funds in, a record out. That is the shape today's tools are built for.
importance 5 · SupplementalSource: “Prepare reports, including assignment of company funds or recording of department revenues.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high 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: Pulling department figures into a report is straightforward document work for software.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Establishing new computer accounts
This is reading one thing and writing another: new computer accounts in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Establish new computer accounts.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 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: Setting up an account in the system is routine screen work, with identity usually confirmed in person.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
1 taskTasks 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.
Recording casino exchange transactions, using cash registers
The software now makes the first pass at casino exchange transactions, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · SupplementalSource: “Record casino exchange transactions, using cash registers.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial 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: Entering exchange transactions is data entry, but it happens at a register with a customer present.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
12 tasksTasks 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.
Maintaining confidentiality of customers' transactions
The ratings behind this row put confidentiality of customers' transactions well outside what today's tools can do on their own.
importance 5 · CoreSource: “Maintain confidentiality of customers' transactions.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 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.
The rating behind it: Access controls keep records private, but the discretion happens face to face at the cage window.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Following all gaming regulations
This work happens in the physical world: all gaming regulations, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Follow all gaming regulations.” (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; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: The rules are written down and checkable, yet casinos must have a licensed person handling the transactions.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Converting gaming checks, coupons, tokens or coins to currency for gaming patrons
This work happens in the physical world: checks, coupons, tokens or coins, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Convert gaming checks, coupons, tokens, or coins to currency for gaming patrons.” (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; someone qualified has to answer for it.
The rating behind it: Exchanging chips and coins for cash across a counter is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Maintaining cage security
This work happens in the physical world: cage security, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Maintain cage security.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Keeping the cage secure means a licensed person physically controlling access to cash.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cashing checks and processing credit card advances for patrons
This work happens in the physical world: checks, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Cash checks and process credit card advances for patrons.” (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; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Checking a customer’s details is systematized, but handing over cash at the window is physical.
The five ratings: output a model can produce 3/4 · 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 3/4.
Show the other 7 tasks
Preparing bank deposits, balancing assigned funds
staying humanThis work happens in the physical world: bank deposits, balancing assigned funds, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Prepare bank deposits, balancing assigned funds as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 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: Balancing funds and preparing a deposit is standard cash accounting, with the notes still counted by hand.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing customers with information about casino operations
staying humanThis work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Provide customers with information about casino operations.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 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: Answering questions about the casino is well-documented information, though patrons usually ask at the window.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Counting funds and reconciling daily summaries of transactions to balance books
staying humanThis work happens in the physical world: funds, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Count funds and reconcile daily summaries of transactions to balance books.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
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: Reconciling the day’s transactions is standard bookkeeping, though the cash itself still gets counted by hand.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing assistance in the training and orientation of new cashiers
staying humanThis work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Provide assistance in the training and orientation of new cashiers.” (O*NET task statement)
How this row was scored
Exposure score: 15 out of 100 (8–22 allowing for uncertainty): minimal exposure, medium 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: Training material is easy to produce, but showing a new cashier the window takes a person beside them.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Selling gambling chips, tokens or tickets to patrons or to other workers for resale to patrons
staying humanThis work happens in the physical world: chips, tokens or tickets, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Sell gambling chips, tokens, or tickets to patrons or to other workers for resale to patrons.” (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; someone qualified has to answer for it.
The rating behind it: Selling chips and tickets across the counter is physical cash handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Performing removal and rotation of cash
staying humanThis work happens in the physical world: removal, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Perform removal and rotation of cash, coin, or chip inventories as necessary.” (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; someone qualified has to answer for it.
The rating behind it: Rotating cash and chip inventories means physically moving money around the cage.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Supplying currency, coins, chips or gaming checks to other departments
staying humanThis work happens in the physical world: currency, coins, chips or gaming checks, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Supply currency, coins, chips, or gaming checks to other departments as needed.” (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; someone qualified has to answer for it.
The rating behind it: Moving currency, chips and coins between departments is physical handling of cash.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · 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
- $37,580a 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
- 14,430in 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: accuracy of reports in, a record out. The rows above are exactly that shape: verifying accuracy of reports and determining cash requirements for windows and ordering all necessary currency. What it cannot do is be answerable: checks, coupons, tokens or coins need a named person the rules will accept, and software cannot be that person. 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
Your week is splitting in two, and which half fills it is the whole question. Verifying accuracy of reports, such as authorization forms, transaction reconciliations or exchange summary reports is going; converting gaming checks, coupons, tokens or coins to currency for gaming patrons is not.
So, given all that: 21% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 75% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (converting gaming checks, coupons, tokens or coins to currency for gaming patrons) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles converting gaming checks, coupons, tokens or coins to currency for gaming patrons, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to gambling cage workers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was gambling change persons and booth cashiers: only about 20% of its durable work is work you already do. And on the numbers you do not need one. This job scores 28/100 here, with only 21% of the task list in the top band, and “maintain confidentiality of customers' transactions” is not work that hands over cleanly. None of them beats deepening what you already have.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
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 Change Persons and Booth Cashiers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “maintain cage security”. Across the whole of both lists that adds up to about 20% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 20% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Tellers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supply currency, coins, chips, or gaming checks to other departments as needed, and their equivalent is to count currency, coins, and checks received, by hand or using currency-counting machine, to…. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Gambling Dealers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already determine cash requirements for windows and order all necessary currency, coins, or chips, and their equivalent is to exchange paper currency for playing chips or coin money. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
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: 21% of its task weight, across 17 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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:
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:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for gambling cage workers, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 21% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for gambling cage workers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for gambling cage workers 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 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 Cage Workers?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Gambling Cage Workers (United States, SOC 43-3041), 21% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 28 out of 100 (range 23–34, band: low). 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 Cage Workers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Verify accuracy of reports, such as authorization forms, transaction reconciliations, or exchange summary reports” (81/100, very high); “Prepare reports, including assignment of company funds or recording of department revenues” (81/100, very high); “Determine cash requirements for windows and order all necessary currency, coins, or chips” (61/100, high). 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 Cage Workers” stay human?
- About 75% 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: “Supply currency, coins, chips, or gaming checks to other departments as needed” (0/100, minimal); “Perform removal and rotation of cash, coin, or chip inventories as necessary” (0/100, minimal); “Sell gambling chips, tokens, or tickets to patrons or to other workers for resale to patrons” (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 Cage Workers” do about AI?
- Start from the ledger rather than the headline: 21% of this job's weighted core work is exposed, and roughly 75% 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 Cage Workers 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 17 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
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 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.
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}
}Data as of release 2026-q4.1, published . 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.