Ushers, Lobby Attendants, and Ticket Takers
In short: today's best AI models could do about 5% 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 95% of the time, stays with people.
Assist patrons at entertainment events by performing duties, such as collecting admission tickets and passes from patrons, assisting in finding seats, searching for lost articles, and helping patrons locate such facilities as restrooms and telephones.
121,770 people do this job in the US. $32,910 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 on2 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people38 h
Ushers, Lobby Attendants, and Ticket Takers, now: of a 40-hour week, AI could take on 2 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 38 hours stay with people.
The largest pieces: Schedule volunteer ushers (under an hour); Sell and stock merchandise (under an hour); Count and record tickets (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.
- 0%
- 5%
- 2%
- 32%
- 61%
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 5% 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, 3%. Today's estimate: 5%.
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 23 tasks showed up. Within each of Anthropic's methods, never across a change: 0.008% to 0.016% (released February and March 2025); none to 0.0018% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.008% | 555 of 708 | 2 of 23 |
| Released March 2025 | 0.016% | 457 of 703 | 2 of 23 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 23 |
| The week of Nov 13, 2025 (released January 2026) | 0.0042% | 594 of 672 | 2 of 23 |
| The week of Feb 5, 2026 (released March 2026) | 0.0018% | 658 of 677 | 1 of 23 |
| April 2026 (released June 2026) | none | n/a | 0 of 23 |
| May 2026 (released June 2026) | none | n/a | 0 of 23 |
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 71% of this job's working time as physical work. For 50% of the working time, a robot has been shown doing most of the task somewhere: 21% in a purpose-built cell, 29% in a structured facility. 0.79 of 3, the 229th highest of the 923 jobs.
- Not physical 29%
- No robot shown 21%
- In a purpose-built cell 21%
- In a structured facility 29%
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
94% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Clean facilities, the hands-on work, 30%
- Run refreshment stands, the hands-on work, 19%
- Lead tours, the in-person work, 9%
- Sell and stock merchandise, the hands-on work, 6%
- Keep order and safety, the hands-on work, 5%
- Schedule volunteer ushers, the conversation, 4%
What becomes checking the machine
No desk work within reach by then in tasks where a mistake costs something. Another 5% is desk work that is small if wrong, which can be handed over with lighter checks.
What to learn
- The hands-on craft. About 61% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 34% 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.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
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 . 23 tasks from .
| Task | ||||
|---|---|---|---|---|
| Clean facilities.Cleaning the venue.Done hourly or more by most who do it. Robots: no robot shown. Anthropic's rating. | 30% | 50 min17 min to 2.5 h | Hands-on 100% | Stays with people |
| Operate refreshment stands during intermission or obtain refreshments for press box patrons during performances.Serving at the stand.Done hourly or more by most who do it.Robots: in a purpose-built cell, ADAM (Automated Dual Arm Mixologist), Richtech Robotics (deployed). Anthropic's rating. | 19% | 38 min13 min to 1.7 h | Hands-on 70%, in person 30% | Stays with people |
| Lead tours and answer visitors' questions about the exhibits.Tours and questions in person.Done several times a day by most who do it. | 9% | 50 min25 min to 2.1 h | In person 100% | Stays with people |
| Manage inventory or sale of artist merchandise.Selling at the stand and keeping the stock, with the counts entered.Done hourly or more by most who do it.Robots: in a structured facility, Vidir Merchandise Vending Machine (ASRS) (deployed). Anthropic's rating. | 8% | 50 min25 min to 3.3 h | Hands-on 50%, in person 30%, systems 20% | 20%Desk part half by now |
| Schedule or manage staff, such as volunteer usher corps.Schedules in a system, with calls and a word on the floor.Done daily by most who do it. | 6% | 1.7 h25 min to 5 h | Systems 40%, conversation 30%, in person 30% | 38%Desk part half by now |
| Maintain order and ensure adherence to safety rules.Presence among the crowd.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 5% | 13 min4 min to 38 min | Hands-on 60%, in person 40% | Stays with people |
| Count and record number of tickets collected.Tickets counted and the total recorded.Done daily by most who do it.Robots: in a structured facility, Alvarado MSTT full-height turnstiles + TAS12 admission devices + GateWatch counting software (deployed). Anthropic's rating. | 3% | 13 min4 min to 38 min | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Manage informational kiosks or displays of event signs or posters.Kiosks and posters kept up.Done more than weekly by most who do it.Robots: in a purpose-built cell, Digit (Agility Robotics) at GXO Logistics (deployed). Anthropic's rating. | 3% | 25 min8 min to 1.7 h | Hands-on 100% | Stays with people |
| Refuse admittance to undesirable persons or persons without tickets or passes.Turning away people without tickets.Done more than yearly by most who do it. | 2% | 7 min2 min to 25 min | In person 100% | Stays with people |
| Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events.Tickets taken or sold at the door.Done daily by most who do it. Robots: in a structured facility, Axess self-scanning ticketing pedestals (deployed). Anthropic's rating. | 2% | 4 min2 min to 13 min | Hands-on 70%, in person 30% | Stays with people |
| Examine tickets or passes to verify authenticity, using criteria such as color or date issued.Looking at the ticket with the patron there.Done hourly or more by most who do it. Robots: in a structured facility, Alvarado TAS12-AS1 admission pedestal + GateLink10 + full-height turnstiles (deployed). Anthropic's rating. | 2% | 3 min1 min to 8 min | Hands-on 60%, in person 40% | Stays with people |
| Greet patrons attending entertainment events.Greeting at the door.Done hourly or more by most who do it. | 2% | 3 min1 min to 7 min | In person 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.
- Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop23% now, 23% by 2028
- Baggage Porters and Bellhops8% now, 8% by 2028
- Hotel, Motel, and Resort Desk Clerks45% now, 45% by 2028
- Passenger Attendants5% now, 5% by 2028
- Reservation and Transportation Ticket Agents and Travel Clerks36% now, 36% by 2028
- Locker Room, Coatroom, and Dressing Room Attendants3% now, 3% 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 Ushers, Lobby Attendants, and Ticket Takers is within reach of AI now?
- An estimated 5% of the working time is within reach of AI models now and 5% 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 Ushers, Lobby Attendants, and Ticket Takers 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: Schedule volunteer ushers (38% of the task's time now); Sell and stock merchandise (20% of the task's time now); and Count and record tickets (40% of the task's time now).
- Which tasks of Ushers, Lobby Attendants, and Ticket Takers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Clean facilities; Run refreshment stands; and Lead tours. Together such tasks are about 83% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Ushers, Lobby Attendants, and Ticket Takers 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; the conversations; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 94% of the working time stays with people, 0% becomes checking a model's work and 5% 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 Ushers, Lobby Attendants, and Ticket Takers?
- 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 Ushers, Lobby Attendants, and Ticket Takers?
- Anthropic's robot exposure rating (September 2026) finds 71% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 50% of the working time, 29% in a structured facility. Its index is 0.79 of 3. One example: Vidir Merchandise Vending Machine (ASRS) for "sell and stock merchandise". 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 23 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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