Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. · 0.7%
Bartenders
Occupation · SOC 35-3011.00
Mix and serve drinks to patrons, directly or through waitstaff.
Also called: Banquet Bartender · Bar Captain · Bartender · Mixologist · Bar Attendant · Bar Tender · Barkeep · Barkeeper · Barmaid · Barman · Catering Bartender · Drink Mixer
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-35-3011-00/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.
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.
- Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. · 100.0% need a human
- Create drink recipes. · 96.7% 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.
- About average employment outlook (+5.9% by 2034)
- 129,600 openings/yr
- Moderate AI exposure
- Observed AI use leans automation
- Median pay $33,530/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (62nd); today's AI task-overlap is comparatively low (39th)
39th-percentile task overlap — yet about 129,600 openings a year (+5.9% projected, BLS), and observed AI use leans 2451% 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 | 31st | -0.6 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 30th | 0.3 | |
| AI assistant applicability (Microsoft) Moderate | 62nd | 0.2 |
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 0.8 · 62nd percentile among occupations · Moderate
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.
| Create drink recipes. | 1.0% | |
| Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. | 0.2% | |
| Check identification of customers to verify age requirements for purchase of alcohol. | 0.2% | |
| Take beverage orders from serving staff or directly from patrons. | 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 | About average · +5.9% by 2034 |
| Projected annual openings | 129,600 |
| Employment 2024 → 2034 | 756,700 → 801,500 |
“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.
Bartenders sits at the 48th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
26% mean task exposure (2025)
48th percentile of 427 placed occupations
+4 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Bartenders · 5132 | 26% | Not exposed |
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 | 24.5% working with AI · 27.4% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 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 |
|---|---|---|
| Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. | Directive | 0.7% |
| Create drink recipes. | — | 0.3% |
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.
| Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. | 100.0% | |
| Create drink recipes. | 96.7% |
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 mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks.From: Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. · 0.7% of measured AI use · directive
-
Help me create drink recipes.From: Create drink recipes. · 0.3% of measured AI use
Tasks
All 20 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Clean glasses, utensils, and bar equipment. Core · importance 4.8
- Collect money for drinks served. Core · importance 4.8
- Balance cash receipts. Core · importance 4.7
- Check identification of customers to verify age requirements for purchase of alcohol. Core · importance 4.7
- Clean bars, work areas, and tables. Core · importance 4.7
- Attempt to limit problems and liability related to customers' excessive drinking by taking steps such as persuading customers to stop drinking, or ordering taxis or other transportation for intoxicated patrons. Core · importance 4.6
- Take beverage orders from serving staff or directly from patrons. Core · importance 4.6
- Serve wine, and bottled or draft beer. Core · importance 4.5
- Plan, organize, and control the operations of a cocktail lounge or bar. Core · importance 4.5
- Stock bar with beer, wine, liquor, and related supplies such as ice, glassware, napkins, or straws. Core · importance 4.5
- Serve snacks or food items to customers seated at the bar. Core · importance 4.4
- Supervise the work of bar staff and other bartenders. Supplemental · importance 4.4
- Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks. Core · importance 4.3
- Order or requisition liquors and supplies. Supplemental · importance 4.1
- Slice and pit fruit for garnishing drinks. Core · importance 4.0
- Ask customers who become loud and obnoxious to leave, or physically remove them. Core · importance 3.8
- Arrange bottles and glasses to make attractive displays. Core · importance 3.8
- Plan bar menus. Supplemental · importance 3.6
- Create drink recipes. Core · importance 3.4
- Prepare appetizers such as pickles, cheese, and cold meats. Supplemental · importance 3.1
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Provide customers with directions or answers to questions.
Work activities
- Stock serving stations or dining areas with food or supplies.
- Process customer bills or payments.
- Enforce rules or regulations.
- Balance receipts.
- Communicate with customers to resolve complaints or ensure satisfaction.
- Clean tableware.
- Take customer orders.
- Serve food or beverages.
- Clean food service areas.
- Mix ingredients.
- Order materials, supplies, or equipment.
- Prepare foods for cooking or serving.
- Arrange tables or dining areas.
- Manage food service operations or parts of operations.
- Coordinate activities of food service staff.
- Plan menu options.
- Cook foods.
- Create new recipes or food presentations.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Customer and Personal Service | 4.4 | |
| English Language | 3.6 | |
| Education and Training | 3.2 | |
| Sales and Marketing | 3.1 | |
| Administration and Management | 3.0 | |
| Public Safety and Security | 3.0 | |
| Mathematics | 3.0 |
Abilities
| Oral Expression | 3.9 | |
| Oral Comprehension | 3.8 | |
| Information Ordering | 3.3 | |
| Near Vision | 3.3 | |
| Speech Recognition | 3.3 | |
| Arm-Hand Steadiness | 3.1 | |
| Manual Dexterity | 3.1 | |
| Speech Clarity | 3.1 | |
| Written Comprehension | 3.0 | |
| Written Expression | 3.0 | |
| Problem Sensitivity | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Memorization | 3.0 | |
| Selective Attention | 3.0 | |
| Finger Dexterity | 3.0 | |
| Trunk Strength | 3.0 | |
| Auditory Attention | 3.0 |
Essential skills
| Active Listening | 3.8 | |
| Speaking | 3.1 | |
| Critical Thinking | 3.1 | |
| Reading Comprehension | 3.0 | |
| Active Learning | 3.0 | |
| Monitoring | 3.0 | |
| Learning Strategies | 2.9 |
Transferable skills
| Service Orientation | 3.6 | |
| Social Perceptiveness | 3.3 | |
| Coordination | 3.1 | |
| Persuasion | 3.1 | |
| Instructing | 3.0 | |
| Complex Problem Solving | 3.0 | |
| Time Management | 3.0 | |
| Negotiation | 2.9 |
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.
What to study: Culinary, Entertainment, and Personal Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 43.0% | |
| Less than a High School Diploma | 37.0% | |
| Some College Courses | 16.4% | |
| Post-Secondary Certificate | 2.6% | |
| Bachelor's Degree | 1.1% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 4.7 | |
| Conventional | 4.5 | |
| Enterprising | 3.9 | |
| Social | 3.7 | |
| Artistic | 2.3 |
Interest areas
| Personal Service | 4.6 | |
| Sales | 3.6 | |
| Culinary Art | 3.1 | |
| Physical/Manual Labor | 3.1 | |
| Management/Administration | 2.8 | |
| Protective Service | 2.1 | |
| Accounting | 2.0 | |
| Marketing/Advertising | 2.0 |
Work styles
| Dependability | 3.0 | |
| Social Orientation | 2.4 | |
| Self-Control | 2.0 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $19,930 |
| 25th percentile | $25,790 |
| Median (50th) | $33,530 |
| 75th percentile | $46,790 |
| 90th percentile | $71,920 |
| People employed | 745,610 |
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 | 588,740 | $34,020 |
| Full-Service Restaurants · National industry | 308,430 | $35,090 |
| Arts, Entertainment, and Recreation · Sector | 55,000 | $31,950 |
| Other Services (except Public Administration) · Sector | 42,310 | $27,580 |
| Manufacturing · Sector | 38,190 | $35,030 |
| Casino Hotels · National industry | 12,690 | $37,440 |
| Retail Trade · Sector | 5,600 | $34,080 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 4,530 | $39,970 |
| Real Estate and Rental and Leasing · Sector | 3,290 | $31,760 |
| Temporary Help Services · National industry | 2,830 | $40,150 |
| Information · Sector | 2,750 | $32,160 |
| Theater Companies and Dinner Theaters · National industry | 2,250 | $33,340 |
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 | 11.9× | 308,430 |
| Accommodation and Food Services · Sector | 8.55× | 588,740 |
| Casino Hotels · National industry | 7.79× | 12,690 |
| Theater Companies and Dinner Theaters · National industry | 6.43× | 2,250 |
| Arts, Entertainment, and Recreation · Sector | 4.3× | 55,000 |
| Other Services (except Public Administration) · Sector | 1.98× | 42,310 |
| Manufacturing · Sector | 0.62× | 38,190 |
| Fitness and Recreational Sports Centers · National industry | 0.57× | 1,730 |
Related occupations
Part of the Hospitality, Events, & Tourism career cluster.
- Waiters and Waitresses · 35-3031.00 Compare →
- Dining Room and Cafeteria Attendants and Bartender Helpers · 35-9011.00 Compare →
- Fast Food and Counter Workers · 35-3023.00 Compare →
- Baristas · 35-3023.01 Compare →
- Food Preparation Workers · 35-2021.00 Compare →
- Chefs and Head Cooks · 35-1011.00 Compare →
- Food Service Managers · 11-9051.00 Compare →
- First-Line Supervisors of Food Preparation and Serving Workers · 35-1012.00 Compare →
- Cooks, Restaurant · 35-2014.00 Compare →
- Cooks, Short Order · 35-2015.00 Compare →
- Food Servers, Nonrestaurant · 35-3041.00 Compare →
- Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop · 35-9031.00 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 Bartenders — 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
- Culinary, Entertainment, and Personal Services · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 48th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Bartenders show 39th-percentile AI task overlap — and about 129,600 annual U.S. openings
- Bartenders rank in the 39th 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 129,600 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 about average (+5.9%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $33,530, across about 745,610 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 25% 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
Bartenders show 39th-percentile AI task overlap — and about 129,600 annual U.S. openings • Bartenders rank in the 39th 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 129,600 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 about average (+5.9%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $33,530, across about 745,610 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 25% 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 — "Bartenders". https://singulariki.com/roles/role-35-3011-00 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. "Bartenders." 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-35-3011-00
APA
Singulariki. (2026). Bartenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-35-3011-00
BibTeX
@misc{singulariki-role-35-3011-00,
title = {Bartenders},
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-35-3011-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.