Share and intensity of work current AI systems can materially affect.
SOC 35-2021
Food Preparation Workers AI displacement risk
Washing, peeling, cutting, portioning, and sanitizing across variable ingredients and kitchens is physical work robots handle only in narrow, high-volume formats. Temperature logging and prep-list software assist; hands do the prep.
Likely potential for exposed tasks to move to software after workflow integration.
Automation targets uniform tasks like frying and dispensing, while prep work's variability — odd-shaped produce, changing menus, shared equipment — keeps it manual. Chronic kitchen staffing shortages reinforce the point.
Distribution
Where Food Preparation Workers 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 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 35-2021. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $35,320 (May 2025, US national). The latest BLS row matched SOC 35-2021.
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 Food Preparation Workers
SOC 35-2021 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 Food Preparation Workers
The current evidence import matched 30 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 tasks30
SOC35-2021
- Core task / ID 18728
Clean and sanitize work areas, equipment, utensils, dishes, or silverware.
- Core task / ID 2204
Assist cooks and kitchen staff with various tasks as needed, and provide cooks with needed items.
- Core task / ID 18730
Take and record temperature of food and food storage areas, such as refrigerators and freezers.
- Core task / ID 2210
Carry food supplies, equipment, and utensils to and from storage and work areas.
- Core task / ID 2208
Remove trash and clean kitchen garbage containers.
- Core task / ID 2197
Store food in designated containers and storage areas to prevent spoilage.
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
Wash, peel, and cut ingredients
Exposure 28, automation 20%, augmentation 14%.
O*NET evidence: Wash, peel, and cut various foods, such as fruits and vegetables, to prepare for cookin... (ID 18731)
Portion and assemble food items
Exposure 34, automation 26%, augmentation 18%.
O*NET evidence: Portion and wrap food, or place it directly on plates for service to patrons. (ID 2200)
Sanitize work areas and equipment
Exposure 26, automation 16%, augmentation 20%.
O*NET evidence: Clean and sanitize work areas, equipment, utensils, dishes, or silverware. (ID 18728)
Record temperatures and supplies
Exposure 52, automation 32%, augmentation 44%.
TaskExposureAutomationAugmentation
Wash, peel, and cut ingredients2820%14%
Portion and assemble food items3426%18%
Sanitize work areas and equipment2616%20%
Record temperatures and supplies5232%44%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateLine Cook
Training horizon: 1-3 months. Skill overlap 78. Wage preservation signal 108.
- Learn station cooking
- Master timing across orders
- Practice plating standards
role redesign
ModerateKitchen Supervisor
Training horizon: 2-5 months. Skill overlap 68. Wage preservation signal 128.
- Own prep schedules
- Track food safety logs
- Train new kitchen staff
Comparison guides
Compare the next move before you commit
1-3 months
Food Preparation Workers to Line Cook
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Food Preparation Workers into Line Cook.
2-5 months
Food Preparation Workers to Kitchen Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Food Preparation Workers into Kitchen Supervisor.
What the AI risk score means for Food Preparation Workers
The displacement pressure score for Food Preparation Workers 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. Record temperatures and supplies carries 32% automation pressure, while Record temperatures and supplies carries 44% 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: $35,320 (May 2025, US national). Employment context: Large kitchen support workforce with persistent demand. Typical education: No formal educational credential.
Wage vulnerability is 76, while transition feasibility is 60. 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
- Prep robotics serve narrow formats
- Kitchen staffing shortages persist
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Food Preparation Workers, 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.
Food handling and knife skills
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.
Speed under pressure
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.
Reliability
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 Line Cook, such as learn station cooking.
- By 90 days, compare internal openings and external postings for Line Cook or Kitchen Supervisor and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Food Preparation Workers
Will AI replace Food Preparation Workers?
Washing, peeling, cutting, portioning, and sanitizing across variable ingredients and kitchens is physical work robots handle only in narrow, high-volume formats. Temperature logging and prep-list software assist; hands do the prep. 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 Food Preparation Workers work are most exposed to AI?
Record temperatures and supplies and Portion and assemble food items show the strongest automation pressure in this model. Record temperatures and supplies and Sanitize work areas and equipment are better treated as AI-augmented work.
What should Food Preparation Workers learn next?
Start with Food handling and knife skills, Food safety, Speed under pressure. The most practical adjacent paths in this model are Line Cook and Kitchen Supervisor.
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
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureO*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