Will AI replace Gambling and Sports Book Writers and Runners?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Gambling and Sports Book Writers and Runners (United States, SOC 39-3012), 10% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 11–18, 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 and Sports Book Writers and Runners” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record the number of tickets cashed and the amount paid out after each race or event” (75/100, high); “Prepare collection reports for submission to supervisors” (69/100, high); “Answer questions about game rules or casino policies” (35/100, low). 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 and Sports Book Writers and Runners” stay human?

About 90% 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: “Sell food, beverages, or tobacco to players” (0/100, minimal); “Deliver tickets, cards, and money to bingo callers” (0/100, minimal); “Operate games in which players bet that a ball will come to rest in a particular slot on a rotating wheel, performing actions such as spinning the wheel and…” (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 and Sports Book Writers and Runners” do about AI?

Start from the ledger rather than the headline: 10% of this job's weighted core work is exposed, and roughly 90% 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 and Sports Book Writers and Runners 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 18 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 and Sports Book Writers and Runners

conducting gambling tables or games, collecting bets in the form of cash or chips and starting gaming equipment that randomly selects numbered balls and announce winning numbers and colors. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: collecting bets in the form of cash or chips 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: 10%
changing shape: 0%
staying human: 90%

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

Whole-job exposure score 13 out of 100 (11–18 allowing for uncertainty): minimal exposure, across 18 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 and sports book writers and runners 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

Post information enabling patrons to wager on various races and sporting events. Assist in the operation of games such as keno and bingo. May operate random number-generating equipment and announce the numbers for patrons. Receive, verify, and record patrons' wagers. Scan and process winning tickets presented by patrons and pay out winnings for those wagers. The job title says “gambling”, “sports book writers” or “runners”: officially one job, several names. The real job is the part underneath: collecting bets in the form of cash or chips. 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 and sports book writers and runners is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is collecting bets in the form of cash or chips, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 10%
changing shape: 0%
staying human: 90%

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.

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

2 tasks

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.
Recording the number of tickets cashed and the amount paid out after each race or event
This is reading one thing and writing another: the number of tickets in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Record the number of tickets cashed and the amount paid out after each race or event.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): 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: Recording tickets cashed and amounts paid out is simple bookkeeping software does automatically.
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 4/4.

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

16 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.

Computing and verifying amounts won or lost
This work happens in the physical world: amounts won, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Compute and verify amounts won or lost, paying out winnings or referring patrons to workers, such as gaming cashiers, so that winnings can be collected.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 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: The sums are trivial for software, but pushing winnings across the table happens in person.
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 3/4.
Collecting bets in the form of cash or chips
This work happens in the physical world: bets, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Collect bets in the form of cash or chips, verifying and recording amounts.” (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 cash and chips from players and counting them is hands-on 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.
Answering questions about game rules or casino policies
This work happens in the physical world: questions, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Answer questions about game rules or casino policies.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Game rules and casino policies are fully documented, though the questions come from players at the table.
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 4/4.
Collecting cards or tickets from players
This work happens in the physical world: cards, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect cards or tickets from players.” (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: Picking up cards or tickets from players is pure hands-on work 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 2/4.
Paying off or moving bets as established by game rules and procedures
This work happens in the physical world: or moving bets, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Pay off or move bets as established by game rules and procedures.” (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: Moving chips and settling bets on a live table is hands-on work at the table itself.
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 1/4 · how much data exists 3/4.
Checking to ensure that all players have placed their bets before play begins
This work happens in the physical world: ensure, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Check to ensure that all players have placed their bets before play begins.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 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.
The rating behind it: Checking that everyone has bet before play means watching the table in front of you.
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 1/4 · how much data exists 3/4.
Inspecting cards or equipment to be used in games to ensure they are in proper condition
This work happens in the physical world: cards, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect cards or equipment to be used in games to ensure they are in proper 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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Inspecting cards and equipment for damage or tampering is done by hand.
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.
Conducting gambling tables or games
This work happens in the physical world: tables, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Conduct gambling tables or games, such as dice, roulette, cards, or keno, and ensure that game rules are followed.” (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 and running a live table game is physical work with cards, chips and dice.
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.

Show the other 8 tasks

Comparing the house hand with players' hands to determine the winner
staying human
This work happens in the physical world: the house hand, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Compare the house hand with players' hands to determine the winner.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 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: Comparing hands follows fixed rules, but the cards sit on a physical table in front of you.
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 0/4 · how much data exists 4/4.
Supervising staff and games and mediating disputes
staying human
This work happens in the physical world: staff, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Supervise staff and games and mediate disputes.” (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; the value is that a specific person does it.
The rating behind it: Running the floor and calming a dispute between players depends on being there and being respected.
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 2/4.
Exchanging paper currency for playing chips or coins
staying human
This work happens in the physical world: paper currency, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Exchange paper currency for playing chips or coins.” (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: Swapping bills for chips means handling cash and chips.
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 or closing cash floats or game tables
staying human
This work happens in the physical world: cash floats, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Open or close cash floats or 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 a table's cash float is handled physically and under watch.
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.
Starting gaming equipment that randomly selects numbered balls and announce winning numbers and colors
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Start gaming equipment that randomly selects numbered balls and announce winning numbers and 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 the ball machine and calling numbers happens 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 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Operating games in which players bet that a ball will come to rest in a particular slot on a rotating wheel
staying human
This work happens in the physical world: games, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Operate games in which players bet that a ball will come to rest in a particular slot on a rotating wheel, performing actions such as spinning the wheel and releasing the ball.” (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: Spinning the wheel and releasing the ball is done by hand.
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.
Delivering tickets, cards and money to bingo callers
staying human
This work happens in the physical world: tickets, cards and money, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Deliver tickets, cards, and money to bingo callers.” (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: Carrying tickets, cards and money to the caller is a physical errand.
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.
Selling food, beverages or tobacco to players
staying human
This work happens in the physical world: food, beverages or tobacco, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Sell food, beverages, or tobacco to players.” (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: Serving food, drinks or tobacco to players 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 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $34,980a 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: 8,950in 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 and recording the number of tickets cashed and the amount paid out after each race or event. What it cannot do is be there in the room, and that is still where bets 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: collecting bets in the form of cash or chips is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 10% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 90% in rows it is nowhere near. That is the position, measured across 18 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 amounts won is 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 computing and verifying amounts won or lost. 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 and Sports Book Writers and RunnersGambling Dealers
a year or morematched on shared tasks
You already check to ensure that all players have placed their bets before play begins. In that job the same thing shows up as check to ensure that all players have placed 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. But you would be doing it for slightly less money, so want it for the work.
The work the two jobs share
- You already do
  “Check to ensure that all players have placed their bets before play begins.”
  They do
  “Check to ensure that all players have placed bets before play begins.”
- You already do
  “Exchange paper currency for playing chips or coins.”
  They do
  “Exchange paper currency for playing chips or coin money.”
- You already do
  “Conduct gambling tables or games, such as dice, roulette, cards, or keno, and ensure that game rules are followed.”
  They do
  “Conduct gambling games, such as dice, roulette, cards, or keno, following all applicable rules and regulations.”
What you would not already have: Nothing in your task list touches “greet customers and make them feel welcome”, “apply rule variations to card games” or “receive, verify, and record patrons' cash wagers”. 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
A pay cut: $34,320 against your $34,980 (OEWS May 2025 (both)). I am saying the words: you would earn less. Decide that on purpose, not by accident.
- 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: $34,320 against your $34,980, 1.9% less (OEWS May 2025 (both)). I am saying the words: you would earn slightly less. Decide that on purpose.
- 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.
- Is the target job itself holding up: Gambling Dealers scores 5/100 on this site’s own exposure measure (minimal), with 2% 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 dealers

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 coins, 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 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
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 coins, 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 →
Cashiers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer questions about game rules or casino policies, and their equivalent is to answer customers' questions, and provide information on procedures or policies. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% 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: 10% of its task weight, across 18 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 collecting bets in the form of cash or chips 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:

In UK official statistics this job is counted as Sports and leisure assistants, Other elementary services occupations n.e.c. 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.

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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for gambling / sports book writers / runners, 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 10% of the work on this page is already inside what they can do.

Try The AI Authority free →

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 / sports book writers / runners. 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.

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 Dealers →About 49% of it you could do on Monday. But you would be doing it for slightly less money, so want it for the work.

Noted, and thank you. We’ll email you if a Space for gambling / sports book writers / runners launches. Nothing else.

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No Space for gambling / sports book writers / runners 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 / sports book writers / runners existed, with researched problems, courses and people in the same boat, would you want in?

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We use your email address for one thing: to tell you if a Space for gambling / sports book writers / runners 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 and Sports Book Writers and Runners?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Gambling and Sports Book Writers and Runners (United States, SOC 39-3012), 10% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 11–18, 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 and Sports Book Writers and Runners” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record the number of tickets cashed and the amount paid out after each race or event” (75/100, high); “Prepare collection reports for submission to supervisors” (69/100, high); “Answer questions about game rules or casino policies” (35/100, low). 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 and Sports Book Writers and Runners” stay human?: About 90% 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: “Sell food, beverages, or tobacco to players” (0/100, minimal); “Deliver tickets, cards, and money to bingo callers” (0/100, minimal); “Operate games in which players bet that a ball will come to rest in a particular slot on a rotating wheel, performing actions such as spinning the wheel and…” (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 and Sports Book Writers and Runners” do about AI?: Start from the ledger rather than the headline: 10% of this job's weighted core work is exposed, and roughly 90% 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 and Sports Book Writers and Runners 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 18 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.
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.

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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.