Amusement and Recreation Attendants
In short: today's best AI models could do about 16% 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 conversations, in-person and hands-on work, about 84% of the time, stays with people.
Perform a variety of attending duties at amusement or recreation facility. May schedule use of recreation facilities, maintain and provide equipment to participants of sporting events or recreational pursuits, or operate amusement concessions and rides.
397,830 people do this job in the US. $32,150 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on5 h
- A person checks AI's work1 h
- Desk work AI can't do yet0 h
- Stays with people34 h
Amusement and Recreation Attendants, now: of a 40-hour week, AI could take on 5 hours, 1 hours become checking its work, 0 hours are desk work it can't do yet, and 34 hours stay with people.
The largest pieces: Record attendance and sales (about 4 h); Know the emergency procedures (about 1 h, a person checks); Schedule courts and lanes (under an hour).
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.
- 4%
- 12%
- 1%
- 31%
- 52%
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.
What handing it over would take, now
The 16% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
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, 16%. Today's estimate: 16%.
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 17 tasks showed up. Within each of Anthropic's methods, never across a change: 0.011% to 0.019% (released February and March 2025); 0.0038% to 0.023% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.011% | 525 of 708 | 1 of 17 |
| Released March 2025 | 0.019% | 430 of 703 | 1 of 17 |
| The week of Aug 4, 2025 (released September 2025) | 0.0038% | 561 of 655 | 1 of 17 |
| The week of Nov 13, 2025 (released January 2026) | 0.014% | 432 of 672 | 1 of 17 |
| The week of Feb 5, 2026 (released March 2026) | 0.023% | 379 of 677 | 1 of 17 |
| April 2026 (released June 2026) | none | n/a | 0 of 17 |
| May 2026 (released June 2026) | none | n/a | 0 of 17 |
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 52% of this job's working time as physical work. For 35% of the working time, a robot has been shown doing most of the task somewhere: 9% in a purpose-built cell, 26% in a structured facility. 0.61 of 3, the 279th highest of the 923 jobs.
- Not physical 48%
- No robot shown 17%
- In a purpose-built cell 9%
- In a structured facility 26%
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.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
84% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Store and set up equipment, the hands-on work, 16%
- Watch for safety and order, the in-person work, 13%
- Inspect and maintain equipment, the hands-on work, 8%
- Clean rides and grounds, the hands-on work, 8%
- Operate the rides, the hands-on work, 6%
- Tell patrons about the facilities, the in-person work, 6%
What becomes checking the machine
4% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 12% is desk work that is small if wrong, which can be handed over with lighter checks.
- Know the emergency procedures, severe if wrong, 4%
What to learn
- The hands-on craft. About 52% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- The conversations. About 32% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Running AI inside the business systems. About 12% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 17 tasks from .
| Task | ||||
|---|---|---|---|---|
| Maintain inventories of equipment, storing and retrieving items and assembling and disassembling equipment as necessary.Fetching, putting away and assembling gear.Done daily by most who do it. Robots: in a purpose-built cell, AutoCrib industrial vending systems (Snap-on AutoCrib); CribMaster (Stanley Black & Decker) (sold). Anthropic's rating. | 16% | 38 min17 min to 1.7 h | Hands-on 100% | Stays with people |
| Monitor activities to ensure adherence to rules and safety procedures, or arrange for the removal of unruly patrons.Keeping an eye on the crowd and stepping in when needed.Done daily by most who do it. | 13% | 13 min4 min to 38 min | In person 60%, hands-on 40% | Stays with people |
| Record details of attendance, sales, receipts, reservations, or repair activities.Entries in the log or the system.Done daily by most who do it. | 11% | 13 min4 min to 33 min | Systems 100% | 100%Half by now |
| Inspect equipment to detect wear and damage and perform minor repairs, adjustments, or maintenance tasks, such as oiling parts.Looking rides and gear over and doing small repairs.Done more than monthly by most who do it.Robots: no robot shown. Anthropic's rating. | 8% | 25 min13 min to 50 min | Hands-on 100% | Stays with people |
| Clean sporting equipment, vehicles, rides, booths, facilities, or grounds.Cleaning by hand.Done daily by most who do it.Robots: in a structured facility, Avidbots Neo / Neo 2W (deployed). Anthropic's rating. | 8% | 25 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Keep informed of shut-down and emergency evacuation procedures.Reading and drilling the evacuation steps; the knowing is the attendant's own.Done daily by most who do it. | 7% | 25 min13 min to 50 min | Documents 50%, in person 50% | 50%Desk part half by now |
| Operate, drive, or explain the use of mechanical riding devices or other automatic equipment in amusement parks, carnivals, or recreation areas.Running the ride from its controls with patrons aboard.Done more than monthly by most who do it.Robots: no robot shown. Anthropic's rating. | 6% | 25 min8 min to 1.3 h | Hands-on 80%, in person 20% | Stays with people |
| Provide information about facilities, entertainment options, and rules and regulations.Answering questions in person.Done daily by most who do it. AI observed on this task. | 6% | 4 min2 min to 8 min | In person 100% | Stays with people |
| Sell tickets and collect fees from customers.A register sale at the booth.Done hourly or more by most who do it. | 5% | 4 min3 min to 8 min | Hands-on 70%, in person 30% | Stays with people |
| Announce or describe amusement park attractions to patrons to entice customers to games and other entertainment.Patter to the crowd at the game or the ride.Done hourly or more by most who do it. | 4% | 7 min3 min to 17 min | In person 100% | Stays with people |
| Direct patrons to rides, seats, or attractions.Pointing the way in person.Done hourly or more by most who do it. | 4% | 3 min2 min to 4 min | In person 100% | Stays with people |
| Provide assistance to patrons entering or exiting amusement rides, boats, or ski lifts, or mounting or dismounting animals.A hand for patrons getting on and off the ride.Done hourly or more by most who do it.Robots: no robot shown. Anthropic's rating. | 3% | 4 min2 min to 8 min | Hands-on 70%, in person 30% | Stays with people |
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. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% 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.
- Locker Room, Coatroom, and Dressing Room Attendants3% now, 3% by 2028
- Baggage Porters and Bellhops8% now, 8% by 2028
- Parking Attendants10% now, 10% by 2028
- Counter and Rental Clerks23% now, 23% by 2028
- Ushers, Lobby Attendants, and Ticket Takers5% now, 5% by 2028
- Laborers and Freight, Stock, and Material Movers, Hand9% now, 9% 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 Amusement and Recreation Attendants is within reach of AI now?
- An estimated 16% of the working time is within reach of AI models now and 16% 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 Amusement and Recreation Attendants 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: Record attendance and sales (100% of the task's time now); Know the emergency procedures (50% of the task's time now); and Schedule courts and lanes (70% of the task's time now).
- Which tasks of Amusement and Recreation Attendants stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Store and set up equipment; Watch for safety and order; and Inspect and maintain equipment. Together such tasks are about 80% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Amusement and Recreation Attendants learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; judgment where errors are severe; and the conversations. By the end of 2028 on the long-run pace at four in five, about 84% of the working time stays with people, 4% becomes checking a model's work and 12% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Amusement and Recreation Attendants?
- 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 6%. 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 Amusement and Recreation Attendants?
- Anthropic's robot exposure rating (September 2026) finds 52% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 35% of the working time, 26% in a structured facility. Its index is 0.61 of 3. One example: Avidbots Neo / Neo 2W for "clean rides and grounds". 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 17 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), 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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