Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Describe and recommend wines to customers. · 1.4%
Waiters and Waitresses
Occupation · SOC 35-3031.00
Take orders and serve food and beverages to patrons at tables in dining establishment.
Also called: Food Server · Server · Waiter · Waitress · Banquet Server · Busser · Cocktail Server · Food Runner · Restaurant Server · Room Service Server · Banquet Waiter · Banquet Waitress
Job family: Food Preparation and Serving Related Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-35-3031-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Use as a copilot
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
- Provide guests with information about local areas, including giving directions. · 0.5%
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Provide guests with information about local areas, including giving directions. · 100.0% need a human
- Describe and recommend wines to customers. · 98.6% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-0.7% by 2034)
- 456,700 openings/yr
- High AI exposure
- Observed AI use leans augmentation
- Median pay $33,760/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (86th); today's AI task-overlap is comparatively low (44th)
44th-percentile task overlap — yet about 456,700 openings a year (-0.7% projected, BLS), and observed AI use leans 4294% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 20th | -0.9 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 31st | 0.3 | |
| AI assistant applicability (Microsoft) High | 83rd | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.2), and including AI-powered software (γ 0.3). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.9 · 86th percentile among occupations · High
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Describe and recommend wines to customers. | 0.6% | |
| Explain how various menu items are prepared, describing ingredients and cooking methods. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -0.7% by 2034 |
| Projected annual openings | 456,700 |
| Employment 2024 → 2034 | 2,329,700 → 2,313,500 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Waiters and Waitresses sits at the 52nd percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
28% mean task exposure (2025)
52nd percentile of 427 placed occupations
−4 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Waiters · 5131 | 28% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 42.9% working with AI · 42.9% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.5 / 5 · higher = AI acts more independently |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Describe and recommend wines to customers. | Directive | 1.4% |
| Provide guests with information about local areas, including giving directions. | Iteration | 0.5% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Provide guests with information about local areas, including giving directions. | 100.0% | |
| Describe and recommend wines to customers. | 98.6% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me describe and recommend wines to customers.From: Describe and recommend wines to customers. · 1.4% of measured AI use · directive
-
Help me provide guests with information about local areas, including giving directions.From: Provide guests with information about local areas, including giving directions. · 0.5% of measured AI use · task iteration
Tasks
All 25 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Collect payments from customers. Core · importance 4.9
- Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages. Core · importance 4.9
- Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff. Core · importance 4.8
- Check with customers to ensure that they are enjoying their meals, and take action to correct any problems. Core · importance 4.8
- Take orders from patrons for food or beverages. Core · importance 4.8
- Prepare checks that itemize and total meal costs and sales taxes. Core · importance 4.7
- Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning. Core · importance 4.7
- Clean tables or counters after patrons have finished dining. Core · importance 4.7
- Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as required. Core · importance 4.6
- Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom. Core · importance 4.5
- Present menus to patrons and answer questions about menu items, making recommendations upon request. Core · importance 4.5
- Prepare tables for meals, including setting up items such as linens, silverware, and glassware. Core · importance 4.4
- Stock service areas with supplies such as coffee, food, tableware, and linens. Core · importance 4.4
- Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties. Core · importance 4.4
- Inform customers of daily specials. Core · importance 4.3
- Explain how various menu items are prepared, describing ingredients and cooking methods. Core · importance 4.3
- Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests. Core · importance 4.1
- Fill salt, pepper, sugar, cream, condiment, and napkin containers. Core · importance 4.1
- Garnish and decorate dishes in preparation for serving. Supplemental · importance 4.0
- Perform food preparation duties, such as preparing salads, appetizers, and cold dishes, portioning desserts, and brewing coffee. Core · importance 4.0
- Prepare hot, cold, and mixed drinks for patrons, and chill bottles of wine. Core · importance 4.0
- Escort customers to their tables. Core · importance 3.8
- Bring wine selections to tables with appropriate glasses, and pour the wines for customers. Core · importance 3.8
- Describe and recommend wines to customers. Core · importance 3.8
- Provide guests with information about local areas, including directions. Core · importance 3.5
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Check with customers to see if they want to apply any rewards to their purchase.
- Perform routine tasks, such as refilling syrups, sanitizer bottles, and other essential supplies.
Work activities
- Arrange tables or dining areas.
- Collect dirty dishes or other tableware.
- Schedule dining reservations.
- Assist customers with seating arrangements.
- Clean food preparation areas, facilities, or equipment.
- Provide customers with general information or assistance.
- Enforce rules or regulations.
- Process customer bills or payments.
- Communicate dining or order details to kitchen personnel.
- Take customer orders.
- Communicate with customers to resolve complaints or ensure satisfaction.
- Serve food or beverages.
- Cook foods.
- Present food or beverage information or menus to customers.
- Clean food service areas.
- Prepare hot or cold beverages.
- Prepare foods for cooking or serving.
- Stock serving stations or dining areas with food or supplies.
- Add garnishes to food.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Customer and Personal Service | 3.9 | |
| English Language | 3.6 | |
| Sales and Marketing | 3.3 | |
| Food Production | 2.6 |
Transferable skills
| Service Orientation | 3.9 | |
| Social Perceptiveness | 3.4 | |
| Coordination | 3.1 | |
| Persuasion | 2.8 | |
| Judgment and Decision Making | 2.8 | |
| Time Management | 2.8 | |
| Complex Problem Solving | 2.6 |
Abilities
| Oral Comprehension | 3.9 | |
| Oral Expression | 3.8 | |
| Speech Recognition | 3.8 | |
| Speech Clarity | 3.8 | |
| Time Sharing | 3.3 | |
| Trunk Strength | 3.3 | |
| Stamina | 3.3 | |
| Problem Sensitivity | 3.1 | |
| Deductive Reasoning | 3.0 | |
| Information Ordering | 3.0 | |
| Arm-Hand Steadiness | 3.0 | |
| Manual Dexterity | 3.0 | |
| Near Vision | 3.0 | |
| Written Comprehension | 2.9 | |
| Selective Attention | 2.9 | |
| Finger Dexterity | 2.9 | |
| Gross Body Coordination | 2.9 | |
| Inductive Reasoning | 2.8 | |
| Static Strength | 2.8 | |
| Extent Flexibility | 2.8 | |
| Far Vision | 2.8 | |
| Written Expression | 2.6 | |
| Category Flexibility | 2.6 | |
| Memorization | 2.6 |
Essential skills
| Active Listening | 3.5 | |
| Speaking | 3.5 | |
| Monitoring | 3.0 | |
| Critical Thinking | 2.9 | |
| Reading Comprehension | 2.8 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- No formal educational credential · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 57.6% | |
| Less than a High School Diploma | 32.3% | |
| Post-Secondary Certificate | 8.3% | |
| Some College Courses | 1.1% | |
| Associate's Degree (or other 2-year degree) | 0.7% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Interest areas
| Personal Service | 5.9 | |
| Sales | 3.4 | |
| Physical/Manual Labor | 2.6 | |
| Culinary Art | 2.6 | |
| Public Speaking | 1.9 |
Career interests (Holland / RIASEC)
| Conventional | 5.0 | |
| Social | 4.7 | |
| Realistic | 4.1 | |
| Enterprising | 4.1 | |
| Artistic | 1.8 |
Work styles
| Dependability | 4.0 | |
| Cooperation | 3.0 | |
| Social Orientation | 2.5 | |
| Optimism | 2.0 | |
| Empathy | 1.9 | |
| Self-Control | 1.8 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $18,500 |
| 25th percentile | $25,690 |
| Median (50th) | $33,760 |
| 75th percentile | $45,350 |
| 90th percentile | $62,510 |
| People employed | 2,302,690 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Accommodation and Food Services · Sector | 2,116,650 | $33,710 |
| Full-Service Restaurants · National industry | 1,735,540 | $33,780 |
| Arts, Entertainment, and Recreation · Sector | 96,680 | $34,350 |
| Casino Hotels · National industry | 26,770 | $32,900 |
| Manufacturing · Sector | 20,180 | $36,120 |
| Health Care and Social Assistance · Sector | 15,640 | $32,610 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 15,470 | $37,440 |
| Other Services (except Public Administration) · Sector | 11,970 | $33,990 |
| Temporary Help Services · National industry | 11,140 | $39,210 |
| Retail Trade · Sector | 7,700 | $29,120 |
| Information · Sector | 4,930 | $29,850 |
| Theater Companies and Dinner Theaters · National industry | 3,120 | $34,480 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Full-Service Restaurants · National industry | 21.68× | 1,735,540 |
| Accommodation and Food Services · Sector | 9.96× | 2,116,650 |
| Casino Hotels · National industry | 5.32× | 26,770 |
| Theater Companies and Dinner Theaters · National industry | 2.89× | 3,120 |
| Arts, Entertainment, and Recreation · Sector | 2.45× | 96,680 |
| Fitness and Recreational Sports Centers · National industry | 0.32× | 3,040 |
| Temporary Help Services · National industry | 0.28× | 11,140 |
| Other Services (except Public Administration) · Sector | 0.18× | 11,970 |
Related occupations
Part of the Hospitality, Events, & Tourism career cluster.
- Dining Room and Cafeteria Attendants and Bartender Helpers · 35-9011.00 Compare →
- Food Servers, Nonrestaurant · 35-3041.00 Compare →
- Bartenders · 35-3011.00 Compare →
- Fast Food and Counter Workers · 35-3023.00 Compare →
- Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop · 35-9031.00 Compare →
- Baristas · 35-3023.01 Compare →
- Food Preparation Workers · 35-2021.00 Compare →
- Food Service Managers · 11-9051.00 Compare →
- Cooks, Short Order · 35-2015.00 Compare →
- First-Line Supervisors of Food Preparation and Serving Workers · 35-1012.00 Compare →
- Cooks, Private Household · 35-2013.00 Compare →
- Chefs and Head Cooks · 35-1011.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Waiters and Waitresses — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- No formal educational credential · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 52nd percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Waiters and Waitresses show 44th-percentile AI task overlap — and about 456,700 annual U.S. openings
- Waiters and Waitresses rank in the 44th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 456,700 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be declining (-0.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $33,760, across about 2,302,690 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 43% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Waiters and Waitresses show 44th-percentile AI task overlap — and about 456,700 annual U.S. openings • Waiters and Waitresses rank in the 44th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 456,700 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-0.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $33,760, across about 2,302,690 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 43% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Waiters and Waitresses". https://singulariki.com/roles/role-35-3031-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Waiters and Waitresses." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-35-3031-00
APA
Singulariki. (2026). Waiters and Waitresses. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-35-3031-00
BibTeX
@misc{singulariki-role-35-3031-00,
title = {Waiters and Waitresses},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-35-3031-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.