Share and intensity of work current AI systems can materially affect.
SOC 35-2012
Cooks, Institution and Cafeteria AI displacement risk
Institution and cafeteria cooks produce large-batch meals for schools, hospitals, and care facilities. Automated kettles and production-planning software smooth the volume work, but dietary restrictions, allergen control, and texture-modified diets for medical patients require cooks who understand what is at stake in every tray.
Likely potential for exposed tasks to move to software after workflow integration.
Distinct from restaurant line and fast-food cooks: the differentiator is accountability for restricted diets at scale. Equipment automates stirring and portioning; allergen safety and special-diet judgment stay with the cook.
Distribution
Where Cooks, Institution and Cafeteria sits across 620 tracked roles
Displacement pressure 30 — higher than 48% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
17 O*NET task statements matched to SOC 35-2012. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $37,450 (May 2025, US national). The latest BLS row matched SOC 35-2012.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Cooks, Institution and Cafeteria
SOC 35-2012 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 30/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Cooks, Institution and Cafeteria
The current evidence import matched 17 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks17
SOC35-2012
- Core task / ID 21148
Monitor and record food temperatures to ensure food safety.
- Core task / ID 9488
Cook foodstuffs according to menus, special dietary or nutritional restrictions, or numbers of portions to be served.
- Core task / ID 21149
Rotate and store food supplies.
- Core task / ID 9491
Wash pots, pans, dishes, utensils, or other cooking equipment.
- Core task / ID 9487
Apportion and serve food to facility residents, employees, or patrons.
- Core task / ID 9486
Clean and inspect galley equipment, kitchen appliances, and work areas to ensure cleanliness and functional operation.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Cook foodstuffs per menus and dietary restrictions
Exposure 32, automation 15%, augmentation 40%.
O*NET evidence: Cook foodstuffs according to menus, special dietary or nutritional restrictions, or num... (ID 9488)
Monitor and record food temperatures
Exposure 48, automation 26%, augmentation 50%.
O*NET evidence: Monitor and record food temperatures to ensure food safety. (ID 21148)
Apportion and serve food to residents
Exposure 26, automation 12%, augmentation 34%.
O*NET evidence: Apportion and serve food to facility residents, employees, or patrons. (ID 9487)
Clean and inspect kitchen equipment
Exposure 24, automation 11%, augmentation 36%.
O*NET evidence: Clean and inspect galley equipment, kitchen appliances, and work areas to ensure cleanl... (ID 9486)
TaskExposureAutomationAugmentation
Cook foodstuffs per menus and dietary restrictions3215%40%
Monitor and record food temperatures4826%50%
Apportion and serve food to residents2612%34%
Clean and inspect kitchen equipment2411%36%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateFood Service Supervisor
Training horizon: 6-12 months. Skill overlap 68. Wage preservation signal 116.
- Lead kitchen crews
- Own production schedules
- Manage sanitation compliance
credentialed transition
ModerateDietary Cook Specialist
Training horizon: 6-12 months. Skill overlap 60. Wage preservation signal 110.
- Earn dietary-manager certification
- Master therapeutic diets
- Coordinate with dietitians
Comparison guides
Compare the next move before you commit
6-12 months
Cooks, Institution and Cafeteria to Food Service Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Cooks, Institution and Cafeteria into Food Service Supervisor.
6-12 months
Cooks, Institution and Cafeteria to Dietary Cook Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Cooks, Institution and Cafeteria into Dietary Cook Specialist.
What the AI risk score means for Cooks, Institution and Cafeteria
The displacement pressure score for Cooks, Institution and Cafeteria is 30. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Monitor and record food temperatures carries 26% automation pressure, while Monitor and record food temperatures carries 50% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $37,450 (May 2025, US national). Employment context: Institutional volume cooking with dietary-accountability anchor. Typical education: High school plus on-the-job training.
Wage vulnerability is 68, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Batch equipment automates volume work
- Allergen control stays accountable
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Cooks, Institution and Cafeteria, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Institutional cooking
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Food safety
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Volume production
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Dietary compliance
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Food Service Supervisor, such as lead kitchen crews.
- By 90 days, compare internal openings and external postings for Food Service Supervisor or Dietary Cook Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Cooks, Institution and Cafeteria
Will AI replace Cooks, Institution and Cafeteria?
Institution and cafeteria cooks produce large-batch meals for schools, hospitals, and care facilities. Automated kettles and production-planning software smooth the volume work, but dietary restrictions, allergen control, and texture-modified diets for medical patients require cooks who understand what is at stake in every tray. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Cooks, Institution and Cafeteria work are most exposed to AI?
Monitor and record food temperatures and Cook foodstuffs per menus and dietary restrictions show the strongest automation pressure in this model. Monitor and record food temperatures and Cook foodstuffs per menus and dietary restrictions are better treated as AI-augmented work.
What should Cooks, Institution and Cafeteria learn next?
Start with Institutional cooking, Food safety, Volume production. The most practical adjacent paths in this model are Food Service Supervisor and Dietary Cook Specialist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics