First-Line Supervisors of Passenger Attendants
In short: today's best AI models could do about 32% 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 33% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 67% of the time, stays with people.
Supervise and coordinate activities of passenger attendants.
$62,890 a year (for the broader BLS group this job sits in).
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 on1 h
- A person checks AI's work12 h
- Desk work AI can't do yet0 h
- Stays with people27 h
First-Line Supervisors of Passenger Attendants, now: of a 40-hour week, AI could take on 1 hours, 12 hours become checking its work, 0 hours are desk work it can't do yet, and 27 hours stay with people.
The largest pieces: Improve methods and service (about 4 h, a person checks); Estimate cash and staffing needs (about 3 h, a person checks); Record data and write reports (about 3 h, a person checks).
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.
- 30%
- 4%
- 19%
- 41%
- 7%
Anthropic's data does not cover this job.
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 32% 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, 18%. Today's estimate: 32%.
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, 0.016% went to this job's tasks in May 2026, the 338th largest of the 447 jobs that showed up at all; 1 of its 19 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0068% to 0.0054% (released February and March 2025); 0.0016% to 0.0028% (August 2025 to February 2026); 0.017% to 0.016% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0068% | 571 of 708 | 5 of 19 |
| Released March 2025 | 0.0054% | 572 of 703 | 4 of 19 |
| The week of Aug 4, 2025 (released September 2025) | 0.0016% | 649 of 655 | 2 of 19 |
| The week of Nov 13, 2025 (released January 2026) | 0.003% | 618 of 672 | 4 of 19 |
| The week of Feb 5, 2026 (released March 2026) | 0.0028% | 622 of 677 | 3 of 19 |
| April 2026 (released June 2026) | 0.017% | 328 of 434 | 1 of 19 |
| May 2026 (released June 2026) | 0.016% | 338 of 447 | 1 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 10% of this job's working time as physical work. For 0% of the working time, a robot has been shown doing most of the task somewhere. 0.00 of 3.
- Not physical 90%
- No robot shown 10%
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
67% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Recruit and hire attendants, the conversation, 8%
- Improve methods and service, the conversation, 7%
- Evaluate attendants' performance, the in-person work, 7%
- Attend continuing education, the in-person work, 5%
- Train attendants on procedures, the in-person work, 5%
- Resolve customer complaints, the in-person work, 5%
What becomes checking the machine
32% 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 2% is desk work that is small if wrong, which can be handed over with lighter checks.
- Improve methods and service, costly if wrong, 11%
- Estimate cash and staffing needs, costly if wrong, 7%
- Record data and write reports, costly if wrong, 6%
- Turn feedback into improvements, costly if wrong, 3%
- Recruit and hire attendants, costly if wrong, 2%
- Train attendants on procedures, costly if wrong, 1%
What to learn
- The conversations. About 60% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- 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 30% 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.
- Handling protected records. Much of the work touches protected personal records: knowing what may be shared with a model, and how, is part of the job.
- 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 | ||||
|---|---|---|---|---|
| Recommend and implement measures to improve worker motivation, work methods, or customer services.Recommendations written and put in place.Done daily by most who do it. | 19% | 2.5 h50 min to 6.7 h | Documents 60%, conversation 40% | 57%Desk part half by now |
| Recruit and hire staff members.Interviews and calls, with the paperwork.Done more than weekly by most who do it. AI observed on this task. | 10% | 5 h1.7 h to 1.6 work days | Conversation 40%, in person 40%, documents 20% | 17%Desk part half by now |
| Observe and evaluate workers' appearance and performance to ensure quality service and compliance with specifications.Watching the attendants at work.Done daily by most who do it. | 7% | 50 min13 min to 1.7 h | In person 100% | Stays with people |
| Compute or estimate cash, payroll, transportation, or personnel requirements.Working the numbers.Done daily by most who do it. | 7% | 50 min17 min to 2.1 h | Documents 100% | 100%Half by now |
| Analyze and record personnel or operational data and write related activity reports.Analysis and entries, with the report written.Done daily by most who do it. | 6% | 1.3 h25 min to 3.3 h | Documents 70%, systems 30% | 99%Half by now |
| Train workers in proper operational procedures and functions and explain company policies.Sessions in person, with the material.Done daily by most who do it. | 6% | 1.7 h38 min to 4.2 h | In person 80%, documents 20% | 20%Desk part half by now |
| Participate in continuing education to stay abreast of industry trends and developments.Learning only the supervisor can do for themselves.Done more than yearly by most who do it. | 5% | 2.5 h50 min to 6.7 h | In person 100% | Stays with people |
| Resolve customer complaints regarding worker performance or services rendered.Face to face and by phone with customers.Done daily by most who do it. | 5% | 38 min13 min to 1.3 h | In person 60%, conversation 40% | Stays with people |
| Apply customer feedback to service improvement efforts.Reading the feedback and changing the service with the crew.Done daily 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 |
| Inspect work areas or operating equipment to ensure conformance to established standards in areas such as cleanliness or maintenance.Walking the areas.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 5% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Enforce safety rules and regulations.Holding the attendants to the rules in person.Done daily by most who do it. | 4% | 17 min8 min to 38 min | In person 100% | Stays with people |
| Direct or coordinate the activities of workers, such as flight or car attendants.In person with the crew.Done daily by most who do it. | 4% | 1.3 h25 min to 3.3 h | 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.
- First-Line Supervisors of Mechanics, Installers, and Repairers47% now, 57% by 2028
- First-Line Supervisors of Office and Administrative Support Workers54% now, 58% by 2028
- First-Line Supervisors of Material-Moving Machine and Vehicle Operators45% now, 45% by 2028
- First-Line Supervisors of Helpers, Laborers, and Material Movers, Hand33% now, 34% by 2028
- First-Line Supervisors of Production and Operating Workers48% now, 54% by 2028
- First-Line Supervisors of Non-Retail Sales Workers46% now, 49% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
Questions about this job
- How much of the work of First-Line Supervisors of Passenger Attendants is within reach of AI now?
- An estimated 32% of the working time is within reach of AI models now and 33% 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 Passenger 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: Improve methods and service (57% of the task's time now); Estimate cash and staffing needs (100% of the task's time now); and Record data and write reports (99% of the task's time now).
- Which tasks of First-Line Supervisors of Passenger Attendants stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Evaluate attendants' performance; Attend continuing education; and Resolve customer complaints. Together such tasks are about 42% of the working time, and the people and body parts of the other tasks stay with people too.
- What should First-Line Supervisors of Passenger 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 conversations; reviewing work you did not do yourself; and briefing a model and checking its drafts. By the end of 2028 on the long-run pace at four in five, about 67% of the working time stays with people, 32% becomes checking a model's work and 2% 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 Passenger Attendants?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.016% went to this job's tasks, the 338th largest share of the 447 jobs that showed up; 1 of its 19 tasks appeared. 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 Passenger Attendants?
- Anthropic's robot exposure rating (September 2026) finds 10% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 0% of the working time. Its index is 0.00 of 3. 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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