Dishwashers
Food Preparation & ServiceAI exposure
- Data source: BLSPublished: 2026-08
Low· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.13
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 97% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Exposed tasks only| Task | Claude.aiRaw / share % |
|---|---|
Clean or prepare various foods for cooking or serving.35-9021 | 0.0020100.0 |
| Not observed on any surface — 12 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. | |
Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand. | —0 |
Place clean dishes, utensils, or cooking equipment in storage areas. | —0 |
Maintain kitchen work areas, equipment, or utensils in clean and orderly condition. | —0 |
Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars. | —0 |
Sweep or scrub floors. | —0 |
Clean garbage cans with water or steam. | —0 |
Sort and remove trash, placing it in designated pickup areas. | —0 |
Transfer supplies or equipment between storage and work areas, by hand or using hand trucks. | —0 |
Receive and store supplies. | —0 |
Set up banquet tables. | —0 |
Prepare and package individual place settings. | —0 |
Load or unload trucks that deliver or pick up food or supplies. | —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 |
|---|---|
Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand.O*NET Task ID 11186 | 0.0 |
Place clean dishes, utensils, or cooking equipment in storage areas.O*NET Task ID 11187 | 0.0 |
Maintain kitchen work areas, equipment, or utensils in clean and orderly condition.O*NET Task ID 11188 | 0.0 |
Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars.O*NET Task ID 11189 | 0.0 |
Sweep or scrub floors.O*NET Task ID 11190 | 0.0 |
Clean garbage cans with water or steam.O*NET Task ID 11191 | 0.0 |
Sort and remove trash, placing it in designated pickup areas.O*NET Task ID 11192 | 0.0 |
Clean or prepare various foods for cooking or serving.O*NET Task ID 11193 | 0.0 |
Set up banquet tables.O*NET Task ID 11194 | 0.0 |
Transfer supplies or equipment between storage and work areas, by hand or using hand trucks.O*NET Task ID 11195 | 0.0 |
Receive and store supplies.O*NET Task ID 11196 | 0.0 |
Prepare and package individual place settings.O*NET Task ID 11197 | 0.0 |
Load or unload trucks that deliver or pick up food or supplies.O*NET Task ID 11198 | 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