Coin, Vending, and Amusement Machine Servicers and Repairers
In short: today's best AI models could do about 15% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The people and hands-on work, about 85% of the time, stays with people.
Install, service, adjust, or repair coin, vending, or amusement machines including video games, juke boxes, pinball machines, or slot machines.
26,410 people do this job in the US. $47,450 a year.
A typical 40-hour week
Not yet reviewed task by task- AI could take on6 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people34 h
Coin, Vending, and Amusement Machine Servicers and Repairers, now: of a 40-hour week, AI could take on 6 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 34 hours stay with people.
The largest pieces: Keep records of merchandise distributed and money collected (about 2 h); Record transaction information on forms or logs, and notify designated personnel of discrepancies (about 2 h); Maintain records of machine maintenance and repair (about 1 h).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 15%
- 0%
- 85%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 2%. With Claude Mythos Preview (early), measured in April 2026, 15%. Today's estimate: 15%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 18 tasks showed up. Within each of Anthropic's methods, never across a change: none to 0.0026% (released February and March 2025).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 18 |
| Released March 2025 | 0.0026% | 635 of 703 | 1 of 18 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 18 |
| The week of Nov 13, 2025 (released January 2026) | none | n/a | 0 of 18 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 18 |
| April 2026 (released June 2026) | none | n/a | 0 of 18 |
| May 2026 (released June 2026) | none | n/a | 0 of 18 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 84% of this job's working time as physical work. For 44% of the working time, a robot has been shown doing most of the task somewhere: 44% in a purpose-built cell. 0.44 of 3, the 327th highest of the 923 jobs.
- Not physical 16%
- No robot shown 40%
- In a purpose-built cell 44%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
The tasks
Sorted by . 18 tasks from .
| Task | Kind | |||
|---|---|---|---|---|
| Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 19% | 2.1 h38 min to 4.2 h | Stays with people | |
| Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires.Done hourly or more by most who do it.Robots: in a purpose-built cell, ICB ASAP (Intelligent Cash Box with Automated Secure Asset Processing) (JCM Global) (deployed). Anthropic's rating. | 13% | 33 min17 min to 1.1 h | Stays with people | |
| Fill machines with products, ingredients, money, and other supplies.Done hourly or more by most who do it. Robots: in a purpose-built cell, Vulcan (Amazon Robotics) (deployed). Anthropic's rating. | 12% | 21 min8 min to 50 min | Stays with people | |
| Make service calls to maintain and repair machines.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 9% | 50 min25 min to 1.7 h | Stays with people | |
| Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 8% | 29 min13 min to 1.3 h | Stays with people | |
| Keep records of merchandise distributed and money collected.Done hourly or more by most who do it. | 6% | 17 min8 min to 25 min | 100%Half by now | |
| Clean and oil machine parts.Done more than weekly by most who do it. Robots: in a purpose-built cell, Blackstone-NEY / Cleaning Technologies Group 'Robosonic' robotic ultrasonic cleaning cell (Fanuc CR-7i) (sold). Anthropic's rating. | 6% | 25 min13 min to 50 min | Stays with people | |
| Disassemble and assemble machines, according to specifications and using hand and power tools.Done more than monthly by most who do it.Robots: in a purpose-built cell, Panasonic automated appliance disassembly workcells (six-axis robots, AI machine vision) (demonstrated). Anthropic's rating. | 6% | 1.7 h38 min to 3.3 h | Stays with people | |
| Record transaction information on forms or logs, and notify designated personnel of discrepancies.Done daily by most who do it. | 4% | 13 min4 min to 25 min | 100%Half by now | |
| Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 50 min17 min to 2.5 h | Stays with people | |
| Test machines to determine proper functioning.Done daily by most who do it. Robots: in a purpose-built cell, ABB YuMi (IRB 14000) with Abrantix ATM Test Environment (demonstrated). Anthropic's rating. | 4% | 13 min4 min to 33 min | Stays with people | |
| Maintain records of machine maintenance and repair.Done daily by most who do it. | 3% | 13 min4 min to 25 min | 100%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Computer, Automated Teller, and Office Machine Repairers18% now, 18% by 2028
- Paper Goods Machine Setters, Operators, and Tenders0% now, 0% by 2028
- Industrial Machinery Mechanics13% now, 13% by 2028
- Maintenance Workers, Machinery4% now, 4% by 2028
- Packaging and Filling Machine Operators and Tenders1% now, 1% by 2028
- Machine Feeders and Offbearers5% now, 5% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Coin, Vending, and Amusement Machine Servicers and Repairers is within reach of AI now?
- An estimated 15% of the working time is within reach of AI models now and 15% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Coin, Vending, and Amusement Machine Servicers and Repairers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Keep records of merchandise distributed and money collected (100% of the task's time now); Record transaction information on forms or logs, and notify designated personnel of discrepancies (100% of the task's time now); and Maintain records of machine maintenance and repair (100% of the task's time now).
- Which tasks of Coin, Vending, and Amusement Machine Servicers and Repairers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams; Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires; and Fill machines with products, ingredients, money, and other supplies. Together such tasks are about 85% of the working time.
- Do people already use AI for the work of Coin, Vending, and Amusement Machine Servicers and Repairers?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Coin, Vending, and Amusement Machine Servicers and Repairers?
- Anthropic's robot exposure rating (September 2026) finds 84% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 44% of the working time. Its index is 0.44 of 3. One example: ICB ASAP (Intelligent Cash Box with Automated Secure Asset Processing) for "collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 18 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, each classed as desk, people or body work, and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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