非レストラン食品サーバー
食品調理・サービスAI露出度
- データ出典: BLS公表時点: 2026-08
中程度· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.000
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.28
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは73%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
| Task | Claude.aiRaw / share % |
|---|---|
Record amounts and types of special food items served to customers.35-3041 | 0.00000.0 |
| 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 |
Take food orders and relay orders to kitchens or serving counters so they can be filled. | —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 |
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 |
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