Will AI replace Coin, Vending, and Amusement Machine Servicers and Repairers?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Coin, Vending, and Amusement Machine Servicers and Repairers (United States, SOC 49-9091), 23% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 24 out of 100 (range 22–29, 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.

What should someone working in “Coin, Vending, and Amusement Machine Servicers and Repairers” do about AI?

Start from the ledger rather than the headline: 23% of this job's weighted core work is exposed, and roughly 68% 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.

US dataswitch to UK

Coin, Vending, and Amusement Machine Servicers and Repairers

keeping records of merchandise distributed and money, testing machines to determine proper functioning, recording transaction information on forms or logs and disassembling and assembling machines. 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: maintain records of machine maintenance and repair. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 23%
changing shape: 10%
staying human: 68%

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

Whole-job exposure score 24 out of 100 (22–29 allowing for uncertainty): low 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 coin, vending, and amusement machine servicers and repairers 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.

That slice is not coming back; the core of the job, inspecting machines and meters to determine causes of malfunctions and fix minor problems, stays yours. New tools, same person answering for it.

Your week, as this page understands it

Install, service, adjust, or repair coin, vending, or amusement machines including video games, juke boxes, pinball machines, or slot machines. The job title says “coin”, “vending”, “amusement machine servicers” or “repairers”: officially one job, several names. The real job is the part underneath: inspecting machines and meters to determine causes of malfunctions and fix minor problems. 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 coin, vending, and amusement machine servicers and repairers 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 inspecting machines and meters to determine causes of malfunctions and fix minor problems, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 23%
changing shape: 10%
staying human: 68%

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.

1 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

4 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.

Maintain records of machine maintenance and repair
This is reading one thing and writing another: records of machine maintenance in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain records of machine maintenance and repair.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Keeping maintenance and repair records is routine logging that software already handles very well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Recording transaction information on forms or logs
This is reading one thing and writing another: transaction information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record transaction information on forms or logs, and notify designated personnel of discrepancies.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Logging transactions and flagging discrepancies is form-filling and comparison that software does very well.
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.
Ordering parts needed for machine repairs
This is reading one thing and writing another: parts in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Order parts needed for machine repairs.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Parts ordering is a documented purchasing task, though identifying parts for older machines takes know-how.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Keeping records of merchandise distributed and money
This is reading one thing and writing another: records of merchandise in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Keep records of merchandise distributed and money collected.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Tracking product sold and cash collected is record-keeping that modern machines increasingly report automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

2 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.

Contacting other repair personnel or making arrangements for the removal of machines in cases where major repairs
The software now makes the first pass at other repair personnel, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Contact other repair personnel or make arrangements for the removal of machines in cases where major repairs are required.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial 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: Arranging cover or machine removal is scheduling and messaging that software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Referring to manuals and wiring diagrams to gather information needed to repair machines
The software now makes the first pass at manuals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Refer to manuals and wiring diagrams to gather information needed to repair machines.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial 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: Looking up manuals and wiring diagrams is document search, though many manuals are not publicly available.
The five ratings: output a model can produce 3/4 · 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.

Staying human

12 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.

Inspecting machines and meters to determine causes of malfunctions and fix minor problems
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products.” (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: Finding why a machine jammed and clearing it needs hands inside the machine.
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.
Filling machines with products, ingredients, money and other supplies
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fill machines with products, ingredients, money, and other supplies.” (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: Loading products, ingredients and coins into a machine has to be 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 1/4.
Testing machines to determine proper functioning
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Test machines to determine proper functioning.” (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: Testing that a machine works properly means operating it 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 2/4.
Cleaning and oiling machine parts
This work happens in the physical world: machine parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean and oil machine parts.” (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: Cleaning and oiling machine parts is hands-on maintenance.
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.

Show the other 8 tasks

Collecting coins and bills from machines
staying human
This work happens in the physical world: coins, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires.” (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: Invoicing and account settlement are largely automated, but emptying the cash box happens at the machine.
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.
Making service calls to maintain and repair machines
staying human
This work happens in the physical world: service calls, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Make service calls to maintain and repair machines.” (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: Traveling out to fix a machine is physical work at the site.
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.
Adjusting machine pressure gauges and thermostats
staying human
This work happens in the physical world: machine pressure gauges, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Adjust machine pressure gauges and thermostats.” (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: Adjusting gauges and thermostats means hands on the 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 2/4.
Replacing malfunctioning parts, such as worn magnetic heads on automatic teller machine, ATM) card readers
staying human
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.” (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: Swapping out a worn card reader head is hands-on repair work.
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.
Adjusting and repairing coin, vending or amusement machines and meters and replacing defective mechanical and electrical parts
staying human
This work happens in the physical world: coin, vending or amusement machines and meters, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams.” (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: Adjusting and repairing machines with hand tools and a soldering iron is physical work.
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.
Disassembling and assembling machines, according to specifications and using hand and power tools
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Disassemble and assemble machines, according to specifications and using hand and power tools.” (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: Taking a machine apart and rebuilding it is hands-on work with tools.
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.
Installing machines, making the necessary water and electrical connections in compliance with codes
staying human
This work happens in the physical world: machines, making the necessary water and electrical connections, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Install machines, making the necessary water and electrical connections in compliance with codes.” (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: Installing a machine and making water and electrical connections is hands-on work.
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.
Transporting machines to installation sites
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Transport machines to installation sites.” (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: Moving machines to a site is physical work.
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 1/4.

What this job pays, and how many people do it

Median pay: $47,450a 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: 26,410in 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: records of machine maintenance in, a record out. The rows above are exactly that shape: maintain records of machine maintenance and repair and recording transaction information on forms or logs. What it cannot do is be there in the room, and that is still where machines 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

Your week is splitting in two, and which half fills it is the whole question. Maintain records of machine maintenance and repair is going; inspecting machines and meters to determine causes of malfunctions and fix minor problems is not.

So, given all that: 23% of this job's task weight sits in rows the software is already learning, 10% in rows that change shape rather than disappear, and 68% in rows it is nowhere near. That is the position, measured across 18 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 (inspecting machines and meters to determine causes of malfunctions and fix minor problems) 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 inspecting machines and meters to determine causes of malfunctions and fix minor problems, 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 coin, vending, and amusement machine servicers and repairers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was computer, automated teller, and office machine repairers: only about 18% of its durable work is work you already do. And on the numbers you do not need one. This job scores 24/100 here, with only 23% of the task list in the top band, and “inspect machines and meters to determine causes of malfunctions and fix minor…” 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.

Computer, Automated Teller, and Office Machine Repairers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already disassemble and assemble machines, according to specifications and using hand and power tools, and their equivalent is to assemble machines according to specifications, using hand or power tools and measuring devices. Across both published task lists that is about 18% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 18% 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 →
Maintenance Workers, Machinery
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already order parts needed for machine repairs, and their equivalent is to collaborate with other workers to repair or move machines, machine parts, or equipment. 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.
Look at that job’s page anyway →
Machinists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already order parts needed for machine repairs, and their equivalent is to fit and assemble parts to make or repair machine tools. 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: 23% of its task weight, across 18 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 Debt, rent and other cash collectors 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

In UK official statistics this job is counted as Debt, rent and other cash collectors. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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

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

Nothing Collab365 runs is built for coin / vending / amusement machine servicers / repairers, 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 23% 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 coin / vending / amusement machine servicers / repairers. 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.

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No Space for coin / vending / amusement machine servicers / repairers yet. Should there be one?

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What a Space actually is, in full

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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 Coin, Vending, and Amusement Machine Servicers and Repairers?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Coin, Vending, and Amusement Machine Servicers and Repairers (United States, SOC 49-9091), 23% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 24 out of 100 (range 22–29, 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 “Coin, Vending, and Amusement Machine Servicers and Repairers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Keep records of merchandise distributed and money collected” (93/100, very high); “Maintain records of machine maintenance and repair” (93/100, very high); “Record transaction information on forms or logs, and notify designated personnel of discrepancies” (69/100, 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 “Coin, Vending, and Amusement Machine Servicers and Repairers” stay human?: About 68% 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: “Transport machines to installation sites” (0/100, minimal); “Install machines, making the necessary water and electrical connections in compliance with codes” (0/100, minimal); “Disassemble and assemble machines, according to specifications and using hand and power tools” (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 “Coin, Vending, and Amusement Machine Servicers and Repairers” do about AI?: Start from the ledger rather than the headline: 23% of this job's weighted core work is exposed, and roughly 68% 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 Coin, Vending, and Amusement Machine Servicers and Repairers 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.
1 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.