AI exposure: Cooks, Fast Food
Prepare and cook food in a fast food restaurant with a limited menu. Duties of these cooks are limited to preparation of a few basic items and normally involve operating large-volume single-purpose cooking equipment.
Reading this score
computedCooks, Fast Food is among the least exposed occupations measured, at 6.6% of weighted task load, rank 856 of 923. 90.6% of this job is work current AI systems cannot produce at all. That is not a statement about skill or value. It is a statement about what these systems can and cannot do.
What holds the line here is embodiment. Across this occupation's 20 tasks it averages 2.73 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Order and take delivery of supplies, at 80.0%. The least is Mix ingredients, at 0.0%. A gap of 80.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within food preparation and serving occupations, this one is less exposed than the median of 18.2% across the group's 16 roles, with 13 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.
What would move this score. Of 20 tasks, 2 are currently banded exposed, 0 assisted and 18 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 0.47 | 0-4 |
| Embodiment | 2.73 | 0-3 |
| Presence | 0.75 | 0-3 |
| Accountability | 0.62 | 0-3 |
| Context | 0.93 | 0-3 |
| Verification cost | 0.78 | 0-3 |
Task by task
20 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Order and take delivery of supplies. | 80.0% | 20.0% | 0.0% | 3.74 | exposed |
| Take food and drink orders and receive payment from customers. | 67.1% | 20.4% | 12.5% | 4.63 | exposed |
| Verify that prepared food meets requirements for quality and quantity. | 13.3% | 11.7% | 75.0% | 4.58 | untouched |
| Schedule activities and equipment use with managers, using information about daily menus to help coordinate cooking times. | 10.0% | 15.0% | 75.0% | 3.72 | untouched |
| Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. | 0.0% | 0.0% | 100.0% | 4.74 | untouched |
| Maintain sanitation, health, and safety standards in work areas. | 0.0% | 0.0% | 100.0% | 4.70 | untouched |
| Clean food preparation areas, cooking surfaces, and utensils. | 0.0% | 0.0% | 100.0% | 4.68 | untouched |
| Operate large-volume cooking equipment, such as grills, deep-fat fryers, or griddles. | 0.0% | 0.0% | 100.0% | 4.65 | untouched |
| Cook the exact number of items ordered by each customer, working on several different orders simultaneously. | 0.0% | 0.0% | 100.0% | 4.59 | untouched |
| Prepare specialty foods, such as pizzas, fish and chips, sandwiches, or tacos, following specific methods that usually require short preparation time. | 0.0% | 0.0% | 100.0% | 4.59 | untouched |
| Serve orders to customers at windows, counters, or tables. | 0.0% | 0.0% | 100.0% | 4.55 | untouched |
| Wash, cut, and prepare foods designated for cooking. | 0.0% | 0.0% | 100.0% | 4.50 | untouched |
| Clean, stock, and restock workstations and display cases. | 0.0% | 0.0% | 100.0% | 4.45 | untouched |
| Cook and package batches of food, such as hamburgers or fried chicken, prepared to order or kept warm until sold. | 0.0% | 0.0% | 100.0% | 4.35 | untouched |
| Measure ingredients required for specific food items. | 0.0% | 0.0% | 100.0% | 4.32 | untouched |
| Pre-cook items, such as bacon, to prepare them for later use. | 0.0% | 0.0% | 100.0% | 4.11 | untouched |
| Take out garbage. | 0.0% | 0.0% | 100.0% | 3.90 | untouched |
| Prepare and serve beverages, such as coffee or fountain drinks. | 0.0% | 0.0% | 100.0% | 4.56 | untouched |
| Prepare dough, following recipe. | 0.0% | 0.0% | 100.0% | 4.07 | untouched |
| Mix ingredients, such as pancake or waffle batters. | 0.0% | 0.0% | 100.0% | 3.57 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.