Waiters and Waitresses
In short: today's best AI models could do about 6% 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 94% of the time, stays with people.
Take orders and serve food and beverages to patrons at tables in dining establishment.
2,270,910 people do this job in the US. $35,230 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
Waiters and Waitresses, 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: Prepare checks (about 1 h); Write or enter food orders (under an hour); Help the host with phones and seating (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%
- 6%
- 2%
- 25%
- 68%
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 6% 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, 5%. With Claude Mythos Preview (early), measured in April 2026, 6%. Today's estimate: 6%.
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, 0.18% went to this job's tasks in May 2026, the 109th largest of the 447 jobs that showed up at all; 1 of its 25 tasks showed up. Within each of Anthropic's methods, never across a change: 0.021% to 0.011% (released February and March 2025); 0.015% to 0.034% (August 2025 to February 2026); 0.16% to 0.18% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.021% | 440 of 708 | 3 of 25 |
| Released March 2025 | 0.011% | 493 of 703 | 3 of 25 |
| The week of Aug 4, 2025 (released September 2025) | 0.015% | 405 of 655 | 2 of 25 |
| The week of Nov 13, 2025 (released January 2026) | 0.024% | 365 of 672 | 3 of 25 |
| The week of Feb 5, 2026 (released March 2026) | 0.034% | 307 of 677 | 4 of 25 |
| April 2026 (released June 2026) | 0.16% | 121 of 434 | 1 of 25 |
| May 2026 (released June 2026) | 0.18% | 109 of 447 | 1 of 25 |
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 62% of this job's working time as physical work. For 58% of the working time, a robot has been shown doing most of the task somewhere: 55% in a purpose-built cell, 4% in a structured facility. 0.62 of 3, the 277th highest of the 923 jobs.
- Not physical 38%
- No robot shown 4%
- In a purpose-built cell 55%
- In a structured facility 4%
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.
- Set up for the next shift, the hands-on work, 14%
- Sweep, mop and tidy, the hands-on work, 13%
- Stock service areas, the hands-on work, 8%
- Collect payments, the hands-on work, 8%
- Do simple food preparation, the hands-on work, 5%
- Help the host with phones and seating, the in-person work, 4%
What becomes checking the machine
No desk work within reach by then in tasks where a mistake costs something. Another 6% is desk work that is small if wrong, which can be handed over with lighter checks.
What to learn
- The hands-on craft. About 68% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 26% 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.
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 . 25 tasks from .
| Task | ||||
|---|---|---|---|---|
| Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties.Rolling silverware and setting the room by hand.Done hourly or more by most who do it. Robots: no robot shown. Anthropic's rating. | 14% | 21 min8 min to 38 min | Hands-on 100% | Stays with people |
| Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom.Cleaning by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Pudu CC1 Pro (sold). Anthropic's rating. | 13% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
| Stock service areas with supplies such as coffee, food, tableware, and linens.Restocking by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, TX SCARA (Telexistence) (deployed). Anthropic's rating. | 8% | 13 min4 min to 25 min | Hands-on 100% | Stays with people |
| Collect payments from customers.Taking the card or cash at the table or register.Done hourly or more by most who do it. Robots: in a purpose-built cell, McDonald's cash-and-coin self-order kiosk (Glory CI-10 cash/coin recycler integrated into the kiosk) (deployed). Anthropic's rating. | 8% | 8 min4 min to 17 min | Hands-on 70%, in person 30% | Stays with people |
| Perform food preparation duties, such as preparing salads, appetizers, and cold dishes, portioning desserts, and brewing coffee.Preparing salads, desserts and coffee by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Barista Bot / Jarvis (Artly Coffee) (sold). Anthropic's rating. | 5% | 13 min4 min to 33 min | Hands-on 100% | Stays with people |
| Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests.Greeting and seating guests, with reservation calls and the booking entry.Done hourly or more by most who do it. Robots: in a structured facility, Servi / Servi Plus (Bear Robotics) (sold). Anthropic's rating. | 5% | 8 min4 min to 13 min | In person 50%, conversation 30%, systems 20% | 20%Desk part half by now |
| Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.Face to face at the table.Done hourly or more by most who do it. | 4% | 4 min2 min to 13 min | In person 100% | Stays with people |
| Prepare tables for meals, including setting up items such as linens, silverware, and glassware.Laying linens, silver and glasses by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, π0 (Physical Intelligence) (demonstrated). Anthropic's rating. | 4% | 7 min3 min to 13 min | Hands-on 100% | Stays with people |
| Clean tables or counters after patrons have finished dining.Cleaning by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, π0 (pi-zero) VLA policy on bimanual/mobile manipulators (Physical Intelligence) (demonstrated). Anthropic's rating. | 4% | 4 min2 min to 10 min | Hands-on 100% | Stays with people |
| Fill salt, pepper, sugar, cream, condiment, and napkin containers.Refilling by hand.Done daily by most who do it. Robots: in a purpose-built cell, All-Fill auger fillers, cappers and unscramblers (e.g., Model TAS/SV/600 twin auger filler) (sold). Anthropic's rating. | 4% | 10 min4 min to 17 min | Hands-on 100% | Stays with people |
| Prepare hot, cold, and mixed drinks for patrons, and chill bottles of wine.Mixing and pouring by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Artly Barista Bot / Jarvis (deployed). Anthropic's rating. | 3% | 7 min3 min to 17 min | Hands-on 100% | Stays with people |
| Present menus to patrons and answer questions about menu items, making recommendations upon request.Talking guests through the menu in person.Done hourly or more by most who do it. | 3% | 4 min2 min to 8 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.
- Dining Room and Cafeteria Attendants and Bartender Helpers0% now, 0% by 2028
- Food Servers, Nonrestaurant10% now, 10% by 2028
- Bartenders20% now, 23% by 2028
- Fast Food and Counter Workers7% now, 7% by 2028
- Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop23% now, 23% by 2028
- Baristas12% now, 12% 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 Waiters and Waitresses is within reach of AI now?
- An estimated 6% of the working time is within reach of AI models now and 6% 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 Waiters and Waitresses 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: Prepare checks (100% of the task's time now); Write or enter food orders (70% of the task's time now); and Help the host with phones and seating (20% of the task's time now).
- Which tasks of Waiters and Waitresses stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Set up for the next shift; Sweep, mop and tidy; and Stock service areas. Together such tasks are about 89% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Waiters and Waitresses 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 6% 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 Waiters and Waitresses?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.18% went to this job's tasks, the 109th largest share of the 447 jobs that showed up; 1 of its 25 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 Waiters and Waitresses?
- Anthropic's robot exposure rating (September 2026) finds 62% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 58% of the working time, 4% in a structured facility. Its index is 0.62 of 3. One example: Servi / Servi Plus (Bear Robotics) for "help the host with phones and seating". 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 25 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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