Food Batchmakers
In short: today's best AI models could do about 15% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 17% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 83% of the time, stays with people.
Set up and operate equipment that mixes or blends ingredients used in the manufacturing of food products. Includes candy makers and cheese makers.
174,520 people do this job in the US. $42,290 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work6 h
- Desk work AI can't do yet1 h
- Stays with people33 h
Food Batchmakers, now: of a 40-hour week, AI could take on 0 hours, 6 hours become checking its work, 1 hours are desk work it can't do yet, and 33 hours stay with people.
The largest pieces: Formulate recipes (about 3 h, a person checks); Record batch data (about 1 h, a person checks); Determine mixing sequences (about 1 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 13%
- 4%
- 0%
- 2%
- 81%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 15% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 1%. With Claude Mythos Preview (early), measured in April 2026, 10%. Today's estimate: 15%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 25 tasks showed up. Within each of Anthropic's methods, never across a change: 0.13% to 0.056% (released February and March 2025); 0.017% to 0.012% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.13% | 133 of 708 | 5 of 25 |
| Released March 2025 | 0.056% | 249 of 703 | 4 of 25 |
| The week of Aug 4, 2025 (released September 2025) | 0.017% | 389 of 655 | 2 of 25 |
| The week of Nov 13, 2025 (released January 2026) | 0.011% | 466 of 672 | 3 of 25 |
| The week of Feb 5, 2026 (released March 2026) | 0.012% | 465 of 677 | 2 of 25 |
| April 2026 (released June 2026) | none | n/a | 0 of 25 |
| May 2026 (released June 2026) | none | n/a | 0 of 25 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 89% of this job's working time as physical work. For 89% of the working time, a robot has been shown doing most of the task somewhere: 65% in a purpose-built cell, 24% in a structured facility. 1.13 of 3, the 74th highest of the 923 jobs.
- Not physical 11%
- In a purpose-built cell 65%
- In a structured facility 24%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
83% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Follow recipes, the hands-on work, 11%
- Set up and tend processing equipment, the hands-on work, 8%
- Clean and sterilize vats, the hands-on work, 6%
- Formulate recipes, the hands-on work, 6%
- Mix and blend ingredients, the hands-on work, 5%
- Inspect and pack the product, the hands-on work, 5%
What becomes checking the machine
17% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it.
- Formulate recipes, costly if wrong, 8%
- Record batch data, costly if wrong, 3%
- Determine mixing sequences, costly if wrong, 3%
- Adjust cooking from sample results, costly if wrong, 1%
- Sample and evaluate product, costly if wrong, 0.6%
- Grade food products, costly if wrong, 0.5%
What to learn
- The hands-on craft. About 81% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 13% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 25 tasks from .
| Task | ||||
|---|---|---|---|---|
| Formulate or modify recipes for specific kinds of food products.Working out the recipe on paper and trying batches.Done daily by most who do it. | 14% | 3.3 h50 min to 1.3 work days | Documents 60%, hands-on 40% | 48%Desk part half by now |
| Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color.Producing the product to the recipe by hand and machine.Done hourly or more by most who do it. Robots: in a purpose-built cell, Sterling Systems & Controls food ingredient batching / batching control systems (deployed). Anthropic's rating. | 11% | 50 min25 min to 2.5 h | Hands-on 100% | Stays with people |
| Set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients in the manufacturing of food products, according to formulas or recipes.Setting up and tending cookers and mixers to the recipe.Done hourly or more by most who do it. Robots: in a purpose-built cell, Peerless horizontal mixers with automated ingredient dosing and auto dough discharge (Coperion); Exact Mixing continuous mixing systems (Reading Bakery Systems) (Coperion/Peerless; Reading Bakery Systems) (deployed). Anthropic's rating. | 8% | 38 min17 min to 1.3 h | Hands-on 100% | Stays with people |
| Clean and sterilize vats and factory processing areas.Cleaning and sterilizing the vats and the area.Done daily by most who do it. Robots: in a structured facility, CIP systems (e.g., Sani-Matic, Ecolab-monitored dairy CIP) (deployed). Anthropic's rating. | 6% | 50 min25 min to 2.5 h | Hands-on 100% | Stays with people |
| Mix or blend ingredients, according to recipes, using a paddle or an agitator, or by controlling vats that heat and mix ingredients.Mixing by paddle or agitator or at the vat controls.Done hourly or more by most who do it. Robots: in a purpose-built cell, Rockwell Automation FactoryTalk Batch / PlantPAx batch process control in bakeries and food plants (deployed). Anthropic's rating. | 5% | 25 min13 min to 50 min | Hands-on 100% | Stays with people |
| Inspect and pack the final product.Looking over and packing the finished product.Done hourly or more by most who do it.Robots: in a purpose-built cell, Schubert lightline Pickerline (T4 delta pick-and-place robots + integrated 3D scanner) (Gerhard Schubert GmbH) (deployed). Anthropic's rating. | 5% | 38 min17 min to 1.3 h | Hands-on 100% | Stays with people |
| Modify cooking and forming operations based on the results of sampling processes, adjusting time cycles and ingredients to achieve desired qualities, such as firmness or texture.Changing times and ingredients at the equipment after sampling.Done hourly or more by most who do it. Robots: in a structured facility, Tetra Pak Cheese Vat OST SH5 with coagulation sensor (sold). Anthropic's rating. | 4% | 33 min17 min to 1.3 h | Hands-on 70%, documents 30% | 30%Desk part half by now |
| Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients.Working out the sequence from temperature and solubility, at the vat.Done hourly or more by most who do it. | 4% | 25 min8 min to 50 min | Documents 70%, hands-on 30% | 70%Desk part half by now |
| Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products.Working the product by hand or machine.Done hourly or more by most who do it.Robots: in a purpose-built cell, Baker Perkins Accurist dividers and Conical Rounder (sold). Anthropic's rating. | 4% | 29 min13 min to 1.1 h | Hands-on 100% | Stays with people |
| Test food product samples for moisture content, acidity level, specific gravity, or butter-fat content, and continue processing until desired levels are reached.Testing moisture, acidity or fat and processing on until right.Done hourly or more by most who do it.Robots: in a purpose-built cell, On-line/in-line pH measurement in dairy processing (process analytical technology) (peer-reviewed review, MDPI Dairy) (sold). Anthropic's rating. | 4% | 29 min13 min to 50 min | Hands-on 100% | Stays with people |
| Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle.Ingredients, temperatures and test results entered for each batch.Done hourly or more by most who do it. | 3% | 17 min8 min to 33 min | Systems 100% | 100%Half by now |
| Select and measure or weigh ingredients, using English or metric measures and balance scales.Weighing out ingredients on the scales.Done hourly or more by most who do it. Robots: in a purpose-built cell, AZO COMPONENTER batching system (sold). Anthropic's rating. | 3% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Food Cooking Machine Operators and Tenders7% now, 7% by 2028
- Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders8% now, 8% by 2028
- Mixing and Blending Machine Setters, Operators, and Tenders3% now, 3% by 2028
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders5% now, 5% by 2028
- Bakers19% now, 20% by 2028
- Graders and Sorters, Agricultural Products7% now, 7% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Food Batchmakers is within reach of AI now?
- An estimated 15% of the working time is within reach of AI models now and 17% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Food Batchmakers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Formulate recipes (48% of the task's time now, 60% by the end of 2028); Record batch data (100% of the task's time now); and Determine mixing sequences (70% of the task's time now).
- Which tasks of Food Batchmakers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Follow recipes; Set up and tend processing equipment; and Clean and sterilize vats. Together such tasks are about 69% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Food Batchmakers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; reviewing work you did not do yourself; and briefing a model and checking its drafts. By the end of 2028 on the long-run pace at four in five, about 83% of the working time stays with people, 17% becomes checking a model's work and 0% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Food Batchmakers?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Food Batchmakers?
- Anthropic's robot exposure rating (September 2026) finds 89% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 89% of the working time, 24% in a structured facility. Its index is 1.13 of 3. One example: CIP systems (e.g., Sani-Matic, Ecolab-monitored dairy CIP) for "clean and sterilize vats". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 25 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
This job in your state or metro area
Embed these figures on your site
A small card with the share of working time within reach now and at the end of 2027, 2028 and 2030, the date and a link back. Free under CC BY 4.0; the card carries the credit.
Your own version of this job
The page above is the occupation. Your job is your title and what you actually do: give both, and they are read against these tasks in your browser, nothing sent anywhere. The headline is free: how much of your working time is within reach now and by the end of 2028, and how many of the tasks your description names.
The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.