Dining Room and Cafeteria Attendants and Bartender Helpers
In short: today's best AI models could do about 0% 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 100% of the time, stays with people.
Facilitate food service. Clean tables; remove dirty dishes; replace soiled table linens; set tables; replenish supply of clean linens, silverware, glassware, and dishes; supply service bar with food; and serve items such as water, condiments, and coffee to patrons.
542,750 people do this job in the US. $33,980 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 on0 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people40 h
Dining Room and Cafeteria Attendants and Bartender Helpers, now: of a 40-hour week, AI could take on 0 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 40 hours stay with people.
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%
- 0%
- 0%
- 6%
- 94%
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 0% 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, 0%. Today's estimate: 0%.
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, 0.021% went to this job's tasks in May 2026, the 303rd largest of the 447 jobs that showed up at all; 1 of its 23 tasks showed up. Within each of Anthropic's methods, never across a change: 0.023% to 0.021% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 23 |
| Released March 2025 | none | n/a | 0 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) | none | n/a | 0 of 23 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 23 |
| April 2026 (released June 2026) | 0.023% | 298 of 434 | 1 of 23 |
| May 2026 (released June 2026) | 0.021% | 303 of 447 | 1 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 98% of this job's working time as physical work. For 98% of the working time, a robot has been shown doing most of the task somewhere: 82% in a purpose-built cell, 16% in a structured facility. 1.15 of 3, the 68th highest of the 923 jobs.
- Not physical 2%
- In a purpose-built cell 82%
- In a structured facility 16%
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
100% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Serve, clean and stock as needed, the hands-on work, 19%
- Clean counters, walls and floors, the hands-on work, 8%
- Keep linens and tableware supplied, the hands-on work, 8%
- Run the cash register, the hands-on work, 7%
- Carry food and dishes to counters, the hands-on work, 6%
- Set tables, the hands-on work, 6%
What becomes checking the machine
No desk work within reach by then in tasks where a mistake costs something.
What to learn
- The hands-on craft. About 94% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- 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 . 23 tasks from .
| Task | ||||
|---|---|---|---|---|
| Perform serving, cleaning, or stocking duties in establishments, such as cafeterias or dining rooms, to facilitate customer service.Hands-on work around the dining room.Done hourly or more by most who do it. Robots: in a purpose-built cell, Servi / Servi Plus (Bear Robotics) (sold). Anthropic's rating. | 19% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Clean and polish counters, shelves, walls, furniture, or equipment in food service areas or other areas of restaurants and mop or vacuum floors.Cleaning by hand.Done daily by most who do it. Robots: in a purpose-built cell, Avidbots Neo (deployed). Anthropic's rating. | 8% | 29 min13 min to 50 min | Hands-on 100% | Stays with people |
| Maintain adequate supplies of items, such as clean linens, silverware, glassware, dishes, or trays.Restocking by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, HOBART fully-automatic warewash system (FTNi flight-type machines with automatic separating unit, tray stacking carriage, cutlery sorting) (sold). Anthropic's rating. | 8% | 13 min4 min to 33 min | Hands-on 100% | Stays with people |
| Run cash registers.A register sale at the counter.Done hourly or more by most who do it. Robots: in a structured facility, Mashgin Touchless Checkout kiosk (deployed). Anthropic's rating. | 7% | 17 min4 min to 50 min | Hands-on 70%, in person 30% | Stays with people |
| Carry food, dishes, trays, or silverware from kitchens or supply departments to serving counters.Carrying by hand.Done hourly or more by most who do it. Robots: in a structured facility, Bear Robotics Servi / Servi Plus (sold). Anthropic's rating. | 6% | 13 min4 min to 21 min | Hands-on 100% | Stays with people |
| Set tables with clean linens, condiments, or other supplies.Laying linens and supplies by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Chef Robotics ChefOS piece-picking robots (sold). Anthropic's rating. | 6% | 10 min4 min to 17 min | Hands-on 100% | Stays with people |
| Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning.Carrying dishes to the kitchen.Done hourly or more by most who do it. Robots: in a purpose-built cell, Armstrong Robotics dishwashing robot (deployed). Anthropic's rating. | 6% | 8 min3 min to 21 min | Hands-on 100% | Stays with people |
| Wipe tables or seats with dampened cloths or replace dirty tablecloths.Cleaning by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Robotic table wiping (RL + whole-body trajectory optimization) on a mobile manipulator (Google Research / Everyday Robots) (demonstrated). Anthropic's rating. | 5% | 7 min3 min to 13 min | Hands-on 100% | Stays with people |
| Fill beverage or ice dispensers.Refilling by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Krones Modulfill VFJ volumetric filler (sold). Anthropic's rating. | 4% | 7 min3 min to 17 min | Hands-on 100% | Stays with people |
| Stock cabinets or serving areas with condiments and refill condiment containers.Restocking by hand.Done daily by most who do it. Robots: in a purpose-built cell, ZoneSun ZS-FAL180G5 ketchup/paste bottle filling line (sold). Anthropic's rating. | 4% | 13 min4 min to 21 min | Hands-on 100% | Stays with people |
| Serve food to customers when waiters or waitresses need assistance.Carrying plates to the table, with a few words.Done hourly or more by most who do it. Robots: in a structured facility, Bear Robotics Servi (sold). Anthropic's rating. | 3% | 8 min3 min to 21 min | Hands-on 80%, in person 20% | Stays with people |
| Clean up spilled food or drink or broken dishes and remove empty bottles and trash.Cleaning by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, π0 (pi-zero) VLA on mobile/bimanual manipulators (Physical Intelligence) (demonstrated). Anthropic's rating. | 3% | 7 min3 min to 17 min | 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.
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 Dining Room and Cafeteria Attendants and Bartender Helpers is within reach of AI now?
- An estimated 0% of the working time is within reach of AI models now and 0% 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 Dining Room and Cafeteria Attendants and Bartender Helpers come within reach first?
- No task has a meaningful share of its time within reach now or by the end of 2028, on the long-run pace at four in five: the work is mostly people work and body work, which the site does not count.
- Which tasks of Dining Room and Cafeteria Attendants and Bartender Helpers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Serve, clean and stock as needed; Clean counters, walls and floors; and Keep linens and tableware supplied. Together such tasks are about 100% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Dining Room and Cafeteria Attendants and Bartender Helpers 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; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 100% of the working time stays with people, 0% becomes checking a model's work and 0% 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 Dining Room and Cafeteria Attendants and Bartender Helpers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.021% went to this job's tasks, the 303rd largest share of the 447 jobs that showed up; 1 of its 23 tasks appeared. 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 Dining Room and Cafeteria Attendants and Bartender Helpers?
- Anthropic's robot exposure rating (September 2026) finds 98% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 98% of the working time, 16% in a structured facility. Its index is 1.15 of 3. One example: Mashgin Touchless Checkout kiosk for "run the cash register". 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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