Will AI replace Food Preparation Workers?
Most of the work in Food Preparation Workers still leans on things AI struggles with — research rates its theoretical AI reach at only ~4%, and real-world use lower still.
O*NET-SOC 35-2021
How your 23 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~4%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~4%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 23 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- Inform supervisors when equipment is not working properly and when food and supplies are getting low, and order needed items.
- Clean and sanitize work areas, equipment, utensils, dishes, or silverware.
- Store food in designated containers and storage areas to prevent spoilage.
- Portion and wrap food, or place it directly on plates for service to patrons.
- Take and record temperature of food and food storage areas, such as refrigerators and freezers.
- Prepare a variety of foods, such as meats, vegetables, or desserts, according to customers' orders or supervisors' instructions, following approved procedures.
- Place food trays over food warmers for immediate service, or store them in refrigerated storage cabinets.
- Package take-out foods or serve food to customers.
- Stock cupboards and refrigerators, and tend salad bars and buffet meals.
- Wash, peel, and cut various foods, such as fruits and vegetables, to prepare for cooking or serving.
- Carry food supplies, equipment, and utensils to and from storage and work areas.
- Distribute food to waiters and waitresses to serve to customers.
- Cut, slice or grind meat, poultry, and seafood to prepare for cooking.
- Remove trash and clean kitchen garbage containers.
- Receive and store food supplies, equipment, and utensils in refrigerators, cupboards, and other storage areas.
- Weigh or measure ingredients.
- Assist cooks and kitchen staff with various tasks as needed, and provide cooks with needed items.
- Add cutlery, napkins, food, and other items to trays on assembly lines in hospitals, cafeterias, airline kitchens, and similar establishments.
- Use manual or electric appliances to clean, peel, slice, and trim foods.
- Scrape leftovers from dishes into garbage containers.
- Load dishes, glasses, and tableware into dishwashing machines.
- Make special dressings and sauces as condiments for sandwiches.
- Mix ingredients for green salads, molded fruit salads, vegetable salads, and pasta salads.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.