First-Line Supervisors of Food Preparation and Serving Workers
In short: today's best AI models could do about 48% 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 52% of the time, stays with people.
Directly supervise and coordinate activities of workers engaged in preparing and serving food.
1,223,240 people do this job in the US. $44,080 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 work16 h
- Desk work AI can't do yet0 h
- Stays with people21 h
First-Line Supervisors of Food Preparation and Serving Workers, now: of a 40-hour week, AI could take on 3 hours, 16 hours become checking its work, 0 hours are desk work it can't do yet, and 21 hours stay with people.
The largest pieces: Develop objectives, budgets and policies (about 3 h, a person checks); Forecast staff and supply needs (about 3 h, a person checks); Specify portions, sequences and station layouts (about 2 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.
- 37%
- 11%
- 3%
- 30%
- 19%
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 48% 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, 1%. With Claude Mythos Preview (early), measured in April 2026, 35%. Today's estimate: 48%.
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 26 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0079% to 0.0034% (released February and March 2025); 0.029% to 0.05% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0079% | 557 of 708 | 2 of 26 |
| Released March 2025 | 0.0034% | 617 of 703 | 2 of 26 |
| The week of Aug 4, 2025 (released September 2025) | 0.029% | 316 of 655 | 3 of 26 |
| The week of Nov 13, 2025 (released January 2026) | 0.034% | 301 of 672 | 3 of 26 |
| The week of Feb 5, 2026 (released March 2026) | 0.05% | 250 of 677 | 3 of 26 |
| April 2026 (released June 2026) | none | n/a | 0 of 26 |
| May 2026 (released June 2026) | none | n/a | 0 of 26 |
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 42% of this job's working time as physical work. For 28% of the working time, a robot has been shown doing most of the task somewhere: 21% in a purpose-built cell, 7% in a structured facility. 0.35 of 3, the 366th highest of the 923 jobs.
- Not physical 58%
- No robot shown 14%
- In a purpose-built cell 21%
- In a structured facility 7%
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
51% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Train workers in food and safety procedures, the in-person work, 10%
- Supervise and join in cleaning, the hands-on work, 6%
- Plan and oversee patient diet trays, the in-person work, 4%
- Prepare and serve food, the hands-on work, 3%
- Inspect supplies, equipment and work areas, the hands-on work, 3%
- Hire, schedule and supervise staff, the in-person work, 3%
What becomes checking the machine
41% 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 7% is desk work that is small if wrong, which can be handed over with lighter checks.
- Develop objectives, budgets and policies, costly if wrong, 9%
- Forecast staff and supply needs, costly if wrong, 6%
- Specify portions, sequences and station layouts, costly if wrong, 5%
- Analyze theft and waste, and set procedures, costly if wrong, 5%
- Handle cash, deposits and payroll, costly if wrong, 3%
- Hire, schedule and supervise staff, costly if wrong, 3%
What to learn
- 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 37% 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.
- The conversations. About 33% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- The hands-on craft. About 19% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Running AI inside the business systems. About 11% 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 . 26 tasks from . One has a worked example.
| Task | ||||
|---|---|---|---|---|
| Train workers in food preparation, and in service, sanitation, and safety procedures.Showing and teaching the crew in the kitchen.Done daily by most who do it. | 10% | 1.7 h25 min to 5 h | In person 100% | Stays with people |
| Develop departmental objectives, budgets, policies, procedures, and strategies.Planning on paper.Done more than yearly by most who do it. | 9% | 3.3 h1.7 h to 1 work day | Documents 100% | 99%Half by now |
| Analyze operational problems, such as theft and wastage, and establish procedures to alleviate these problems.Analysis of the figures, and new procedures written and explained.Done daily by most who do it. | 7% | 1.3 h38 min to 3.3 h | Documents 70%, in person 30% | 70%Desk part half by now |
| Forecast staff, equipment, and supply requirements, based on a master menu.A forecast from the menu.Done daily by most who do it. | 6% | 1.3 h38 min to 2.5 h | Documents 100% | 100%Half by now |
| Specify food portions and courses, production and time sequences, and workstation and equipment arrangements.A written plan for the kitchen, explained to the crew.Done daily by most who do it. See it done: one graded attempt (passes with named fixes) | 6% | 50 min17 min to 1.7 h | Documents 80%, in person 20% | 80%Desk part half by now |
| Assess nutritional needs of patients, plan special menus, supervise the assembly of regular and special diet trays, and oversee the delivery of food trolleys to hospital patients.Menu planning from diet orders, and overseeing tray assembly in person.Done several times a day by most who do it. | 6% | 1.7 h33 min to 3.3 h | Documents 40%, in person 40%, hands-on 20% | 40%Desk part half by now |
| Supervise and participate in kitchen and dining area cleaning activities.Cleaning alongside the crew.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 6% | 38 min17 min to 1.7 h | Hands-on 60%, in person 40% | Stays with people |
| Perform personnel actions, such as hiring and firing staff, providing employee orientation and training, and conducting supervisory activities, such as creating work schedules or organizing employee time sheets.Interviews and supervision in person, with schedules and time sheets.Done daily by most who do it. | 5% | 1.7 h50 min to 4.2 h | In person 50%, systems 30%, documents 20% | 50%Desk part half by now |
| Control inventories of food, equipment, smallware, and liquor, and report shortages to designated personnel.Counting stock by hand, and keeping the inventory record.Done daily by most who do it. Robots: in a purpose-built cell, Verity autonomous inventory drones (deployed). Anthropic's rating. | 5% | 50 min17 min to 2.5 h | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Perform various financial activities, such as cash handling, deposit preparation, and payroll.Entries in the books and payroll, and cash counted for deposit.Done daily by most who do it. Robots: in a purpose-built cell, Glory CASHINFINITY CI-300 cash recycler (deployed). Anthropic's rating. | 5% | 38 min17 min to 1.7 h | Systems 60%, hands-on 40% | 60%Desk part half by now |
| Recommend measures for improving work procedures and worker performance to increase service quality and enhance job safety.A written recommendation, worked out with the crew.Done daily by most who do it. | 4% | 50 min17 min to 1.7 h | Documents 60%, in person 40% | 60%Desk part half by now |
| Develop equipment maintenance schedules and arrange for repairs.A maintenance schedule, and calls to repair services.Done more than monthly by most who do it. | 4% | 1.7 h38 min to 4.2 h | Documents 50%, conversation 50% | 50%Desk part 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. 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 Entertainment and Recreation Workers, Except Gambling Services27% now, 28% by 2028
- First-Line Supervisors of Housekeeping and Janitorial Workers43% now, 44% by 2028
- First-Line Supervisors of Farming, Fishing, and Forestry Workers23% now, 24% by 2028
- First-Line Supervisors of Non-Retail Sales Workers46% now, 49% by 2028
- First-Line Supervisors of Production and Operating Workers48% now, 54% by 2028
- First-Line Supervisors of Office and Administrative Support Workers54% now, 58% 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 Food Preparation and Serving Workers is within reach of AI now?
- An estimated 48% of the working time is within reach of AI models now and 48% 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 Food Preparation and Serving Workers 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: Develop objectives, budgets and policies (99% of the task's time now); Forecast staff and supply needs (100% of the task's time now); and Specify portions, sequences and station layouts (80% of the task's time now).
- Which tasks of First-Line Supervisors of Food Preparation and Serving Workers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Train workers in food and safety procedures; Supervise and join in cleaning; and Prepare and serve food. Together such tasks are about 25% of the working time, and the people and body parts of the other tasks stay with people too.
- What should First-Line Supervisors of Food Preparation and Serving Workers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; briefing a model and checking its drafts; and the conversations. By the end of 2028 on the long-run pace at four in five, about 51% of the working time stays with people, 41% becomes checking a model's work and 7% 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 Food Preparation and Serving Workers?
- 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 First-Line Supervisors of Food Preparation and Serving Workers?
- Anthropic's robot exposure rating (September 2026) finds 42% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 28% of the working time, 7% in a structured facility. Its index is 0.35 of 3. One example: Loomis SafePoint Titan smart safe (Fowler Foods, 88 KFC/Taco Bell units) for "balance cash receipts". 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 26 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. The Work Bench has 1 worked example of its tasks: a brief, a rubric written beforehand, one attempt by an AI model and its grade.
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