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Singulariki

Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop

Occupation · SOC 35-9031.00

Welcome patrons, seat them at tables or in lounge, and help ensure quality of facilities and service.

Also called: Greeter · Hospitality Coordinator · Host · Hostess · Buffet Hostess · Dining Coordinator · General Teller · Host Coordinator · Maitre D' (Maitre d'hotel) · Seater · Bar Host · Bar Hostess

Job family: Food Preparation and Serving Related Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-35-9031-00/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.

  • Plan parties or other special events and services. · 16.8%
See collaboration patterns →

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.

  • Plan parties or other special events and services. · 98.7% need a human
See the boundary tasks →

57th-percentile task overlap — yet about 107,700 openings a year (-1.5% projected, BLS), and observed AI use leans 5746% 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.) Moderate 47th -0.1
LLM task exposure, γ (OpenAI / Eloundou) Moderate 38th 0.4
AI assistant applicability (Microsoft) High 89th 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 0.4). 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 1.0 · 94th 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.

Maintain contact with kitchen staff, management, serving staff, and customers to ensure that dining details are handled properly and customers' concerns are addressed. 1.0%

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 · -1.5% by 2034
Projected annual openings 107,700
Employment 2024 → 2034 429,900 → 423,500

“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.

21% mean task exposure (2025)
37th percentile of 427 placed occupations
+10 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Personal Services Workers Not Elsewhere Classified · 5169 21% 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 57.5% working with AI · 41.0% handed to AI
Most common way people use AI here Iteration · you and AI go back and forth
Typical AI autonomy 3.0 / 5 · higher = AI acts more independently
Used for work (vs. personal / coursework) 59.1%

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
Plan parties or other special events and services. Iteration 16.8%

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.

Plan parties or other special events and services. 98.7%

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 plan parties or other special events and services.

    From: Plan parties or other special events and services. · 16.8% of measured AI use · task iteration

Tasks

All 22 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Customer and Personal Service 4.5
English Language 3.7
Food Production 3.0
Psychology 2.5
Computers and Electronics 2.4
Personnel and Human Resources 2.4
Foreign Language 2.4
Administration and Management 2.4
Sales and Marketing 2.4
Public Safety and Security 2.4
Education and Training 2.3

Abilities

Oral Comprehension 4.0
Oral Expression 4.0
Speech Recognition 3.3
Speech Clarity 3.1
Written Comprehension 3.0
Problem Sensitivity 3.0
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Near Vision 3.0
Information Ordering 2.9
Selective Attention 2.9
Time Sharing 2.9
Trunk Strength 2.9
Far Vision 2.9
Category Flexibility 2.6
Written Expression 2.4

Essential skills

Active Listening 3.5
Speaking 3.1
Reading Comprehension 2.9
Monitoring 2.9
Critical Thinking 2.8
Active Learning 2.6

Transferable skills

Service Orientation 3.1
Social Perceptiveness 3.0
Coordination 2.9
Persuasion 2.6
Negotiation 2.6
Time Management 2.6
Instructing 2.4

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

Example Category
Facebook Web page creation and editing software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Windows Operating system software Hot technology
Avenista Table Reservations Data base user interface and query software
GuestBridge Reserve Data base user interface and query software
Hospitality Control Solutions Aloha Point-of-Sale Point of sale POS software
iMagic Restaurant Reservation Calendar and scheduling software
OpenTable Data base user interface and query software
Reservation software Data base user interface and query software

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.

Spend Time Standing 4.8
Contact With Others 4.8
Face-to-Face Discussions with Individuals and Within Teams 4.7
Indoors, Environmentally Controlled 4.7
Work With or Contribute to a Work Group or Team 4.7
Spend Time Walking or Running 4.5
Telephone Conversations 4.2
Physical Proximity 4.2
Spend Time Making Repetitive Motions 4.2
Frequency of Decision Making 4.2
Freedom to Make Decisions 4.2
Deal With External Customers or the Public in General 3.9
Importance of Being Exact or Accurate 3.8
Determine Tasks, Priorities and Goals 3.7
Coordinate or Lead Others in Accomplishing Work Activities 3.6
Dealing With Unpleasant, Angry, or Discourteous People 3.6
Impact of Decisions on Co-workers or Company Results 3.5
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.4
Health and Safety of Other Workers 3.0
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 3.0
Conflict Situations 3.0
Work Outcomes and Results of Other Workers 2.8
Importance of Repeating Same Tasks 2.7
Exposed to Minor Burns, Cuts, Bites, or Stings 2.5
Spend Time Bending or Twisting Your Body 2.5
Written Letters and Memos 2.4
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.4
Level of Competition 2.2
Time Pressure 2.2
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.1
Public Speaking 2.1
Degree of Automation 2.1
Consequence of Error 2.0
Spend Time Sitting 1.7
Exposed to Contaminants 1.7
E-Mail 1.6
Outdoors, Exposed to All Weather Conditions 1.5
Exposed to Very Hot or Cold Temperatures 1.5
Outdoors, Under Cover 1.4
Exposed to Cramped Work Space, Awkward Positions 1.4

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.

Less than a High School Diploma 42.0%
High School Diploma 29.2%
Some College Courses 15.7%
Bachelor's Degree 13.1%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Interest areas

Personal Service 5.8
Management/Administration 2.7
Public Speaking 2.4
Sales 2.3
Culinary Art 2.0
Office Work 1.8
Human Resources 1.8

Career interests (Holland / RIASEC)

Social 5.2
Enterprising 4.4
Conventional 4.1
Realistic 3.2

Work styles

Dependability 4.0
Cooperation 3.0
Social Orientation 2.5
Optimism 2.1
Empathy 1.8

Wages & employment

U.S. · annual wages (BLS OEWS)

$22k10th$27k25th$30kMedian$36k75th$43k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
430k2024424k2034 (proj.)-1.5% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $22,010
25th percentile $26,630
Median (50th) $30,380
75th percentile $35,840
90th percentile $42,600
People employed 427,150

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 404,440 $30,220
Full-Service Restaurants · National industry 357,110 $29,990
Arts, Entertainment, and Recreation · Sector 11,830 $33,110
Casino Hotels · National industry 3,930 $36,880
Health Care and Social Assistance · Sector 3,530 $38,390
Manufacturing · Sector 2,410 $31,780
Administrative and Support and Waste Management and Remediation Services · Sector 1,240 $31,200
Retail Trade · Sector 1,230 $28,220
Other Services (except Public Administration) · Sector 870 $34,130
Fitness and Recreational Sports Centers · National industry 470 $29,340
Theater Companies and Dinner Theaters · National industry 360 $32,670
Temporary Help Services · National industry 340 $33,770

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 24.04× 357,110
Accommodation and Food Services · Sector 10.26× 404,440
Casino Hotels · National industry 4.21× 3,930
Theater Companies and Dinner Theaters · National industry 1.8× 360
Arts, Entertainment, and Recreation · Sector 1.62× 11,830
Fitness and Recreational Sports Centers · National industry 0.27× 470
Manufacturing · Sector 0.07× 2,410
Other Services (except Public Administration) · Sector 0.07× 870

Part of the Hospitality, Events, & Tourism career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop sits at the 57th percentile of AI task-overlap and the 0th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop Dining Room and Cafeteria Attendants and Bartender Helpers Locker Room, Coatroom, and Dressing Room Attendants Food Service Managers First-Line Supervisors of Food Preparation and Serving Workers Demonstrators and Product Promoters AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

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 Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop — 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.

Zoom out

On the global GenAI exposure gradient this work sits around the 37th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop show 57th-percentile AI task overlap — and about 107,700 annual U.S. openings

  • Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop rank in the 57th 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 107,700 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 (-1.5%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $30,380, across about 427,150 U.S. workers.BLS OEWS (May 2024)
  • Of the AI use actually observed for this work, 57% 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
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop show 57th-percentile AI task overlap — and about 107,700 annual U.S. openings

• Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop rank in the 57th 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 107,700 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 (-1.5%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $30,380, across about 427,150 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 57% 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 — "Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop". https://singulariki.com/roles/role-35-9031-00
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.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop." 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-35-9031-00

APA

Singulariki. (2026). Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-35-9031-00

BibTeX 
@misc{singulariki-role-35-9031-00,
  title  = {Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop},
  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-35-9031-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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