客房送餐员
食品准备与服务AI暴露度
- 数据来源: BLS发布时间: 2026-08
中等· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: 2026-03
0.000
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
0.28
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 73%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
仅显示有暴露的工作| Task | Claude.aiRaw / share % |
|---|---|
Take food orders and relay orders to kitchens or serving counters so they can be filled.35-3041 | 0.0068100.0 |
| Not observed on any surface — 13 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. | |
Monitor food distribution, ensuring that meals are delivered to the correct recipients and that guidelines, such as those for special diets, are followed. | —0 |
Clean or sterilize dishes, kitchen utensils, equipment, or facilities. | —0 |
Examine trays to ensure that they contain required items. | —0 |
Place food servings on plates or trays according to orders or instructions. | —0 |
Load trays with accessories such as eating utensils, napkins, or condiments. | —0 |
Stock service stations with items such as ice, napkins, or straws. | —0 |
Remove trays and stack dishes for return to kitchen after meals are finished. | —0 |
Prepare food items, such as sandwiches, salads, soups, or beverages. | —0 |
Monitor food preparation or serving techniques to ensure that proper procedures are followed. | —0 |
Carry food, silverware, or linen on trays or use carts to carry trays. | —0 |
Determine where patients or patrons would like to eat their meals and help them get situated. | —0 |
Record amounts and types of special food items served to customers. | —0 |
Total checks, present them to customers, and accept payment for services. | —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 |
|---|---|
Record amounts and types of special food items served to customers.O*NET Task ID 11161 | 1.0 |
Examine trays to ensure that they contain required items.O*NET Task ID 11151 | 0.5 |
Total checks, present them to customers, and accept payment for services.O*NET Task ID 11162 | 0.5 |
Monitor food distribution, ensuring that meals are delivered to the correct recipients and that guidelines, such as those for special diets, are followed.O*NET Task ID 11149 | 0.0 |
Clean or sterilize dishes, kitchen utensils, equipment, or facilities.O*NET Task ID 11150 | 0.0 |
Place food servings on plates or trays according to orders or instructions.O*NET Task ID 11152 | 0.0 |
Load trays with accessories, such as eating utensils, napkins, or condiments.O*NET Task ID 11153 | 0.0 |
Take food orders and relay orders to kitchens or serving counters so they can be filled.O*NET Task ID 11154 | 0.0 |
Stock service stations with items, such as ice, napkins, or straws.O*NET Task ID 11155 | 0.0 |
Remove trays and stack dishes for return to kitchen after meals are finished.O*NET Task ID 11156 | 0.0 |
Prepare food items, such as sandwiches, salads, soups, or beverages.O*NET Task ID 11157 | 0.0 |
Monitor food preparation or serving techniques to ensure that proper procedures are followed.O*NET Task ID 11158 | 0.0 |
Carry food, silverware, or linen on trays or use carts to carry trays.O*NET Task ID 11159 | 0.0 |
Determine where patients or patrons would like to eat their meals and help them get situated.O*NET Task ID 11160 | 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