As of August 2026, Dining Room and Cafeteria Attendants and Bartender Helpers has an AI-exposure score of 37/100 (Low exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Dining Room and Cafeteria Attendants and Bartender Helpers

37/100
Low exposure
LowModerateElevatedHighVery High

More exposed than 5% of the roles we track. Median pay ~US$33,980. About 99,600 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Dining Room and Cafeteria Attendants and Bartender Helpers?

No exposure score can predict whether AI will replace this role. The 37/100 score means our current model estimates low task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role is in our Low exposure band, not our highest-exposure bands. The study's 16% finding should not be applied directly to this role.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

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.

Common titles
BarbackBus BoyBus PersonBusserBuffet AttendantDining Room Attendant
O*NET job-zone preparation
Job Zone 2 · Job Zone 1-2: Very Little to Some Preparation Needed Usually requires a high school diploma or GED, though some occupations may not. Some occupations may need little or no previous experience; others require several months to a year of experience.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
CoordinationService OrientationMonitoringSpeakingCustomer and Personal ServiceEnglish Language
Work context
Frequent contact with othersIndoor controlled settingDecision latitude

Source: O*NET 29.1 exact occupation - Dining Room and Cafeteria Attendants and Bartender Helpers, SOC 35-9011.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Dining Room and Cafeteria Attendants and Bartender Helpers tasks, by AI exposure

Automatable

No automatable tasks identified for this role - its individually-assessed tasks split 90% durable / 10% augmentable.

Augmentable
  • Run cash registers.
  • Greet and seat customers.
Durable
  • Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning.
  • Clean up spilled food or drink or broken dishes and remove empty bottles and trash.
16 more durable tasks locked in the report.

We analyzed all 20 Dining Room and Cafeteria Attendants and Bartender Helpers tasks - 2 augmentable and 18 durable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

This role is already among lower-exposure work. The adjacent paths below are shown for opportunity and skill transfer, not as lower-exposure alternatives.

Waiters and Waitresses
64% skills overlap; 2 points higher - similar exposure within the 5-point model resolution; Low band; ~US$35,230
View path
39
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Run cash registers.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Dining Room and Cafeteria Attendants and Bartender Helpers - median pay by US state (BLS OEWS, USD)

New York: US$36,740California: US$36,010Florida: US$33,280Texas: US$21,550

Median annual wage, in USD. US national: US$33,980. More states are being added.

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