Food Batchmakers
Food Preparation & ServiceAI exposure
- Data source: BLSPublished: 2026-08
Moderate· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.15
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 92% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Formulate or modify recipes for specific kinds of food products.51-3092 | 0.00000.0 | —0 |
Grade food products according to government regulations or according to type, color, bouquet, and moisture content. | —0 | 0.00000.0 |
| Not observed on any surface — 23 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle. | —0 | —0 |
Observe gauges and thermometers to determine if the mixing chamber temperature is within specified limits, and turn valves to control the temperature. | —0 | —0 |
Clean and sterilize vats and factory processing areas. | —0 | —0 |
Press switches and turn knobs to start, adjust, and regulate equipment, such as beaters, extruders, discharge pipes, and salt pumps. | —0 | —0 |
Observe and listen to equipment to detect possible malfunctions, such as leaks or plugging, and report malfunctions or undesirable tastes to supervisors. | —0 | —0 |
Set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients in the manufacturing of food products, according to formulas or recipes. | —0 | —0 |
Mix or blend ingredients, according to recipes, using a paddle or an agitator, or by controlling vats that heat and mix ingredients. | —0 | —0 |
Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. | —0 | —0 |
Select and measure or weigh ingredients, using English or metric measures and balance scales. | —0 | —0 |
Turn valve controls to start equipment and to adjust operation to maintain product quality. | —0 | —0 |
Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients. | —0 | —0 |
Give directions to other workers who are assisting in the batchmaking process. | —0 | —0 |
Examine, feel, and taste product samples during production to evaluate quality, color, texture, flavor, and bouquet, and document the results. | —0 | —0 |
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. | —0 | —0 |
Fill processing or cooking containers, such as kettles, rotating cookers, pressure cookers, or vats, with ingredients, by opening valves, by starting pumps or injectors, or by hand. | —0 | —0 |
Homogenize or pasteurize material to prevent separation or to obtain prescribed butterfat content, using a homogenizing device. | —0 | —0 |
Inspect vats after cleaning to ensure that fermentable residue has been removed. | —0 | —0 |
Test food product samples for moisture content, acidity level, specific gravity, or butter-fat content, and continue processing until desired levels are reached. | —0 | —0 |
Inspect and pack the final product. | —0 | —0 |
Cool food product batches on slabs or in water-cooled kettles. | —0 | —0 |
Operate refining machines to reduce the particle size of cooked batches. | —0 | —0 |
Place products on carts or conveyors to transfer them to the next stage of processing. | —0 | —0 |
Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle.O*NET Task ID 4919 | 1.0 |
Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients.O*NET Task ID 4929 | 1.0 |
Formulate or modify recipes for specific kinds of food products.O*NET Task ID 4936 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page