Chefs

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Chefs (ISCO-08 3434) score an average of 0.25 on a 0–1 exposure scale — more exposed than about 47% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Planning and developing recipes and menus, estimating food and labour costs, and ordering food supplies;”

Scores 0.38 on the 2025 scale. The task of planning and developing recipes and menus, estimating food and labour costs, and ordering food supplies involves a blend of creative, analytical, and logistical skills. Generative AI can contribute significantly to the logistical and analytical components, assisting in data-driven tasks such as estimating costs and managing order supplies. Automation in similar tasks like "Developing a menu for special occasions" (0.435) suggests moderate AI support in creative tasks, while the physical execution tasks like "Finishing, portioning, decorating and aesthetically serving culinary products" (0.165) scored lower due to their hands-on nature. This task is not entirely physical; AI can help optimize and plan menus by analyzing data on dietary trends and preferences. However, the need for human creativity, particularly in menu and recipe development, retains a considerable human element. Given the task's components and the level of generative AI's current capabilities in a high-income context like Poland, an adjusted score of 0.315 reflects a balanced estimation, acknowledging substantial AI support in logistical aspects while recognizing the indispensable human role in creative and sensory tasks.

Moving fastest, 2023 → 2025

“Inspecting supplies, equipment, and work areas to ensure conformity to established standards;”

Model capability on this task changed by +0.13 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3434, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• First-Line Supervisors of Food Preparation and Serving Workers
• Chefs and Head Cooks

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Chefs sit at the 47th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Chefs rank in the 47th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Planning and developing recipes and menus, estimating food and labour costs, and ordering food supplies;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Chefs". https://singulariki.com/gradient/3434-chefs.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.