Operadores de Maquinas de Cocina
Preparación de Alimentos y ServicioExposición a la IA
- Fuente de datos: BLSPublicado: 2026-08
Bajo· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: 2026-03
0.000
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
- Fuente de datos: ILOPublicado: 2025
0.15
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Grupo primario de la CIUO-08 — todas las ocupaciones con ese código reciben este valor
Calculado por este sitio, no publicado por la OIT: de las 1012 ocupaciones que este sitio vincula al conjunto de datos de la OIT, un 92% alcanza o supera este valor.
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
| Task | Claude.aiRaw / share % |
|---|---|
Measure or weigh ingredients, using scales or measuring containers.51-3093 | 0.00000.0 |
| Not observed on any surface — 16 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. | |
Record production and test data, such as processing steps, temperature and steam readings, cooking time, batches processed, and test results. | —0 |
Listen for malfunction alarms, and shut down equipment and notify supervisors when necessary. | —0 |
Collect and examine product samples during production to test them for quality, color, content, consistency, viscosity, acidity, or specific gravity. | —0 |
Observe gauges, dials, and product characteristics, and adjust controls to maintain appropriate temperature, pressure, and flow of ingredients. | —0 |
Read work orders, recipes, or formulas to determine cooking times and temperatures, and ingredient specifications. | —0 |
Clean, wash, and sterilize equipment and cooking area, using water hoses, cleaning or sterilizing solutions, or rinses. | —0 |
Set temperature, pressure, and time controls, and start conveyers, machines, or pumps. | —0 |
Tend or operate and control equipment, such as kettles, cookers, vats and tanks, and boilers, to cook ingredients or prepare products for further processing. | —0 |
Admit required amounts of water, steam, cooking oils, or compressed air into equipment, such as by opening water valves to cool mixtures to the desired consistency. | —0 |
Remove cooked material or products from equipment. | —0 |
Notify or signal other workers to operate equipment or when processing is complete. | —0 |
Turn valves or start pumps to add ingredients or drain products from equipment and to transfer products for storage, cooling, or further processing. | —0 |
Pour, dump, or load prescribed quantities of ingredients or products into cooking equipment, manually or using a hoist. | —0 |
Place products on conveyors or carts, and monitor product flow. | —0 |
Activate agitators and paddles to mix or stir ingredients, stopping machines when ingredients are thoroughly mixed. | —0 |
Operate auxiliary machines and equipment, such as grinders, canners, and molding presses, to prepare or further process products. | —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 production and test data, such as processing steps, temperature and steam readings, cooking time, batches processed, and test results.O*NET Task ID 4944 | 1.0 |
Read work orders, recipes, or formulas to determine cooking times and temperatures, and ingredient specifications.O*NET Task ID 4948 | 1.0 |
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