First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services
In short: today's best AI models could do about 27% 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 28% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 72% of the time, stays with people.
Directly supervise and coordinate activities of entertainment and recreation related workers.
103,190 people do this job in the US. $48,560 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 on3 h
- A person checks AI's work8 h
- Desk work AI can't do yet0 h
- Stays with people29 h
First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services, now: of a 40-hour week, AI could take on 3 hours, 8 hours become checking its work, 0 hours are desk work it can't do yet, and 29 hours stay with people.
The largest pieces: Supervise recreation activities (about 3 h, a person checks); Plan event programs with staff (about 2 h); Assign work schedules (about 2 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.
- 24%
- 4%
- 20%
- 49%
- 4%
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 27% 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, 0%. With Claude Mythos Preview (early), measured in April 2026, 16%. Today's estimate: 27%.
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, 0.053% went to this job's tasks in May 2026, the 228th largest of the 447 jobs that showed up at all; 3 of its 19 tasks showed up. Within each of Anthropic's methods, never across a change: 0.015% to 0.014% (released February and March 2025); 0.0015% to 0.015% (August 2025 to February 2026); 0.057% to 0.053% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.015% | 483 of 708 | 7 of 19 |
| Released March 2025 | 0.014% | 463 of 703 | 5 of 19 |
| The week of Aug 4, 2025 (released September 2025) | 0.0015% | 650 of 655 | 2 of 19 |
| The week of Nov 13, 2025 (released January 2026) | 0.012% | 453 of 672 | 6 of 19 |
| The week of Feb 5, 2026 (released March 2026) | 0.015% | 436 of 677 | 5 of 19 |
| April 2026 (released June 2026) | 0.057% | 218 of 434 | 3 of 19 |
| May 2026 (released June 2026) | 0.053% | 228 of 447 | 3 of 19 |
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 12% of this job's working time as physical work. For 6% of the working time, a robot has been shown doing most of the task somewhere: 6% in a structured facility. 0.12 of 3, the 499th highest of the 923 jobs.
- Not physical 88%
- No robot shown 6%
- In a structured facility 6%
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
72% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Supervise recreation activities, the in-person work, 17%
- Recruit and hire staff, the conversation, 9%
- Continue education, the in-person work, 6%
- Evaluate workers' appearance and performance, the in-person work, 5%
- Plan event programs with staff, the conversation, 4%
- Meet with managers on changes, the conversation, 4%
What becomes checking the machine
20% 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 8% is desk work that is small if wrong, which can be handed over with lighter checks.
- Supervise recreation activities, severe if wrong, 7%
- Apply customer feedback, costly if wrong, 3%
- Direct recreation workers, costly if wrong, 3%
- Record data and write activity reports, costly if wrong, 2%
- Recruit and hire staff, costly if wrong, 2%
- Requisition safety supplies, costly if wrong, 1%
What to learn
- The conversations. About 68% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 24% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
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 . 19 tasks from .
| Task | ||||
|---|---|---|---|---|
| Plan, direct, or supervise recreational and entertainment activities led by staff, such as sports, aquatics, games, or performing arts.Overseeing sports, aquatics and games on site, with the planning.Done hourly or more by most who do it. | 24% | 2.5 h50 min to 6.7 h | In person 70%, documents 30% | 29%Desk part half by now |
| Recruit and hire staff members.Interviews in person and by call, with the paperwork.Done more than yearly by most who do it. AI observed on this task. | 11% | 5 h1.7 h to 1.6 work days | Conversation 40%, in person 40%, documents 20% | 17%Desk part half by now |
| Collaborate with staff members to plan or develop programs of events or schedules of activities.Programs planned with the staff and written up.Done more than weekly by most who do it. | 9% | 1.7 h50 min to 5 h | Documents 50%, conversation 50% | 50%Desk part half by now |
| Direct or coordinate the activities of entertainment and recreation related workers.Leading the workers on site, with the planning behind it.Done daily by most who do it. | 6% | 50 min25 min to 2.5 h | In person 60%, documents 40% | 40%Desk part half by now |
| Participate in continuing education to stay abreast of industry trends and developments.Attendance and learning only the supervisor can do.Done more than yearly by most who do it. | 6% | 2.5 h50 min to 6.7 h | In person 100% | Stays with people |
| Assign work schedules, following work requirements, to ensure quality and timely delivery of service.Building the schedule in a system, with some planning and telling people.Done daily by most who do it. | 5% | 50 min25 min to 1.7 h | Systems 50%, documents 30%, conversation 20% | 80%Desk part half by now |
| Observe and evaluate workers' appearance and performance to ensure quality service and compliance with specifications.Watching the workers on the floor.Done daily by most who do it. | 5% | 50 min13 min to 1.7 h | In person 100% | Stays with people |
| Apply customer feedback to service improvement efforts.Feedback read and changes made with the team.Done more than monthly by most who do it. AI observed on this task. | 5% | 1.3 h17 min to 3.3 h | Documents 60%, in person 40% | 58%Desk part half by now |
| Train workers in proper operational procedures and functions and explain company policies.Training in person, with the material.Done more than monthly by most who do it. | 4% | 1.7 h38 min to 4.2 h | In person 80%, documents 20% | 20%Desk part half by now |
| Meet with managers or other supervisors to stay informed of changes affecting workers or operations.Meetings with managers and supervisors.Done more than weekly by most who do it. | 4% | 50 min25 min to 1.7 h | Conversation 100% | Stays with people |
| Provide staff with assistance in performing difficult or complicated duties.Working through hard tasks with staff, in person and by call.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 38 min13 min to 1.7 h | Conversation 50%, in person 50% | Stays with people |
| Inspect work areas or operating equipment to ensure conformance to established standards in areas such as cleanliness or maintenance.Walking the areas to check them.Done daily by most who do it. Robots: in a structured facility, Boston Dynamics Spot + Orbit 5.0 (sold). Anthropic's rating. | 4% | 38 min13 min to 1.7 h | Hands-on 100% | 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.
- First-Line Supervisors of Personal Service Workers31% now, 32% by 2028
- First-Line Supervisors of Office and Administrative Support Workers54% now, 58% by 2028
- First-Line Supervisors of Mechanics, Installers, and Repairers47% now, 57% by 2028
- First-Line Supervisors of Non-Retail Sales Workers46% now, 49% by 2028
- First-Line Supervisors of Housekeeping and Janitorial Workers43% now, 44% by 2028
- First-Line Supervisors of Passenger Attendants32% now, 33% 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 First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services is within reach of AI now?
- An estimated 27% of the working time is within reach of AI models now and 28% 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 First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services 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: Supervise recreation activities (29% of the task's time now); Plan event programs with staff (50% of the task's time now); and Assign work schedules (80% of the task's time now).
- Which tasks of First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Continue education; Evaluate workers' appearance and performance; and Meet with managers on changes. Together such tasks are about 31% of the working time, and the people and body parts of the other tasks stay with people too.
- What should First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services 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 conversations; judgment where errors are severe; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 72% of the working time stays with people, 20% becomes checking a model's work and 8% 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 First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.053% went to this job's tasks, the 228th largest share of the 447 jobs that showed up; 3 of its 19 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 4%. 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 First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services?
- Anthropic's robot exposure rating (September 2026) finds 12% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 6% of the working time, 6% in a structured facility. Its index is 0.12 of 3. One example: Boston Dynamics Spot + Orbit 5.0 for "inspect work areas and equipment". 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 19 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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