عمال غرف الطعام
إعداد الطعام والخدمةالتعرّض للذكاء الاصطناعي
- مصدر البيانات: BLSتاريخ النشر: 2026-08
منخفض· نسبي
منخفضأربع فئات نسبيةمرتفع جدًاقيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)
831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه
- مصدر البيانات: Anthropicتاريخ النشر: 2026-03
0.000
0.000نطاق القيم المعروضة هنا0.745المقياس والأساس والمصدر
- مصدر البيانات: ILOتاريخ النشر: 2025
0.24
0.09نطاق القيم المعروضة هنا0.70قيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر
وحدة التصنيف ISCO-08 — تأخذ كل المهن ذات الرمز نفسه هذه القيمة
محسوب من قِبل هذا الموقع وليس منشورًا من منظمة العمل الدولية: من بين 1,012 مهنة يربطها هذا الموقع بمجموعة بيانات المنظمة، تبلغ نسبة التي تساوي هذه القيمة أو تتجاوزها 79%.
ما نوع القيمة التي ينشرها هذا المصدر
فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.
Task-level exposure
المهام ذات التعرض فقط| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Locate items requested by customers.35-9011 | 0.0200100.0 | 0.0100100.0 |
| Not observed on any surface — 22 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Wipe tables or seats with dampened cloths or replace dirty tablecloths. | —0 | —0 |
Set tables with clean linens, condiments, or other supplies. | —0 | —0 |
Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning. | —0 | —0 |
Clean up spilled food or drink or broken dishes and remove empty bottles and trash. | —0 | —0 |
Perform serving, cleaning, or stocking duties in establishments, such as cafeterias or dining rooms, to facilitate customer service. | —0 | —0 |
Maintain adequate supplies of items, such as clean linens, silverware, glassware, dishes, or trays. | —0 | —0 |
Serve ice water, coffee, rolls, or butter to patrons. | —0 | —0 |
Fill beverage or ice dispensers. | —0 | —0 |
Stock cabinets or serving areas with condiments and refill condiment containers. | —0 | —0 |
Carry food, dishes, trays, or silverware from kitchens or supply departments to serving counters. | —0 | —0 |
Serve food to customers when waiters or waitresses need assistance. | —0 | —0 |
Clean and polish counters, shelves, walls, furniture, or equipment in food service areas or other areas of restaurants and mop or vacuum floors. | —0 | —0 |
Carry trays from food counters to tables for cafeteria patrons. | —0 | —0 |
Run cash registers. | —0 | —0 |
Greet and seat customers. | —0 | —0 |
Replenish supplies of food or equipment at steam tables or service bars. | —0 | —0 |
Wash glasses or other serving equipment at bars. | —0 | —0 |
Garnish foods and position them on tables to make them visible and accessible. | —0 | —0 |
Carry linens to or from laundry areas. | —0 | —0 |
Stock refrigerating units with wines or bottled beer or replace empty beer kegs. | —0 | —0 |
Mix and prepare flavors for mixed drinks. | —0 | —0 |
Slice and pit fruit used to garnish drinks. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Wipe tables or seats with dampened cloths or replace dirty tablecloths.O*NET Task ID 11163 | 0.0 |
Set tables with clean linens, condiments, or other supplies.O*NET Task ID 11164 | 0.0 |
Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning.O*NET Task ID 11165 | 0.0 |
Clean up spilled food or drink or broken dishes and remove empty bottles and trash.O*NET Task ID 11166 | 0.0 |
Perform serving, cleaning, or stocking duties in establishments, such as cafeterias or dining rooms, to facilitate customer service.O*NET Task ID 11167 | 0.0 |
Maintain adequate supplies of items, such as clean linens, silverware, glassware, dishes, or trays.O*NET Task ID 11168 | 0.0 |
Serve ice water, coffee, rolls, or butter to patrons.O*NET Task ID 11169 | 0.0 |
Fill beverage or ice dispensers.O*NET Task ID 11170 | 0.0 |
Stock cabinets or serving areas with condiments and refill condiment containers.O*NET Task ID 11171 | 0.0 |
Locate items requested by customers.O*NET Task ID 11172 | 0.0 |
Carry food, dishes, trays, or silverware from kitchens or supply departments to serving counters.O*NET Task ID 11173 | 0.0 |
Serve food to customers when waiters or waitresses need assistance.O*NET Task ID 11174 | 0.0 |
Clean and polish counters, shelves, walls, furniture, or equipment in food service areas or other areas of restaurants and mop or vacuum floors.O*NET Task ID 11175 | 0.0 |
Carry trays from food counters to tables for cafeteria patrons.O*NET Task ID 11176 | 0.0 |
Replenish supplies of food or equipment at steam tables or service bars.O*NET Task ID 11177 | 0.0 |
Run cash registers.O*NET Task ID 11178 | 0.0 |
Wash glasses or other serving equipment at bars.O*NET Task ID 11179 | 0.0 |
Garnish foods and position them on tables to make them visible and accessible.O*NET Task ID 11180 | 0.0 |
Carry linens to or from laundry areas.O*NET Task ID 11181 | 0.0 |
Stock refrigerating units with wines or bottled beer or replace empty beer kegs.O*NET Task ID 11182 | 0.0 |
Mix and prepare flavors for mixed drinks.O*NET Task ID 11183 | 0.0 |
Slice and pit fruit used to garnish drinks.O*NET Task ID 11184 | 0.0 |
Stock vending machines with food.O*NET Task ID 11185 | 0.0 |
Greet and seat customers.O*NET Task ID 23929 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page