Occupation · SOC 35-2011.00
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.
Also called: Cook · Grill Cook · Line Cook · Pizza Cook · Deep Fat Fryer Operator · Fast Food Cook · Fry Cook · Fryer · Pizza Maker · Prep Cook (Preparatory Cook) · Fast Food Fry Cook · Fast Food Worker
Job family: Food Preparation and Serving Related Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-35-2011-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
30th-percentile task overlap — yet about 82,100 openings a year (-13.5% projected, BLS), and observed AI use leans 4580% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 29th | -0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 3rd | 0.0 | |
| AI assistant applicability (Microsoft) Moderate | 65th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.8 · 65th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. | 2.2% | |
| Prepare dough, following recipe. | 0.3% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -13.5% by 2034 |
| Projected annual openings | 82,100 |
| Employment 2024 → 2034 | 669,500 → 579,200 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Fast Food Preparers · 9411 | 18% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 45.8% working with AI · 40.7% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Prepare specialty foods such as pizzas, fish and chips, sandwiches, and tacos, following specific methods that usually require short preparation time. | Directive | 2.5% |
| Wash, cut, and prepare foods designated for cooking. | Directive | 1.9% |
| Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. | Directive | 1.5% |
| Measure ingredients required for specific food items being prepared. | Directive | 1.1% |
| Prepare dough, following recipe. | Learning | 0.6% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Prepare specialty foods such as pizzas, fish and chips, sandwiches, and tacos, following specific methods that usually require short preparation time. | 100.0% | |
| Wash, cut, and prepare foods designated for cooking. | 100.0% | |
| Measure ingredients required for specific food items being prepared. | 100.0% | |
| Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. | 99.3% | |
| Prepare dough, following recipe. | 98.2% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me prepare specialty foods such as pizzas, fish and chips, sandwiches, and tacos, following specific methods that usually require short preparation time. From: Prepare specialty foods such as pizzas, fish and chips, sandwiches, and tacos, following specific methods that usually require short preparation time. · 2.5% of measured AI use · directive
Help me wash, cut, and prepare foods designated for cooking. From: Wash, cut, and prepare foods designated for cooking. · 1.9% of measured AI use · directive
Help me read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. From: Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions. · 1.5% of measured AI use · directive
Help me measure ingredients required for specific food items being prepared. From: Measure ingredients required for specific food items being prepared. · 1.1% of measured AI use · directive
All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Oral Comprehension | 3.6 | |
| Information Ordering | 3.1 | |
| Near Vision | 3.1 | |
| Speech Recognition | 3.0 | |
| Written Comprehension | 2.9 | |
| Oral Expression | 2.9 | |
| Problem Sensitivity | 2.9 | |
| Deductive Reasoning | 2.9 | |
| Selective Attention | 2.9 | |
| Manual Dexterity | 2.9 | |
| Trunk Strength | 2.9 | |
| Speech Clarity | 2.9 | |
| Time Sharing | 2.6 | |
| Arm-Hand Steadiness | 2.6 | |
| Stamina | 2.5 | |
| Written Expression | 2.4 | |
| Inductive Reasoning | 2.4 |
| Active Listening | 3.0 | |
| Speaking | 2.8 | |
| Reading Comprehension | 2.6 | |
| Critical Thinking | 2.6 | |
| Monitoring | 2.6 | |
| Active Learning | 2.4 |
| Service Orientation | 3.0 | |
| Social Perceptiveness | 2.8 | |
| Coordination | 2.8 | |
| Time Management | 2.5 | |
| Judgment and Decision Making | 2.4 | |
| Persuasion | 2.3 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
Share of people in this occupation at each level of education.
| High School Diploma | 58.0% | |
| Less than a High School Diploma | 42.0% |
The interests and personal qualities O*NET associates with people who do this work.
| Realistic | 6.3 | |
| Conventional | 4.3 | |
| Enterprising | 3.4 | |
| Social | 2.4 | |
| Artistic | 1.9 | |
| Investigative | 1.4 |
| Physical/Manual Labor | 3.6 | |
| Culinary Art | 2.2 | |
| Personal Service | 1.8 | |
| Sales | 1.6 | |
| Management/Administration | 1.4 | |
| Human Resources | 1.3 | |
| Mechanics/Electronics | 1.3 |
| Dependability | 2.1 | |
| Attention to Detail | 1.4 | |
| Stress Tolerance | 1.3 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $22,370 |
| 25th percentile | $26,540 |
| Median (50th) | $30,160 |
| 75th percentile | $35,710 |
| 90th percentile | $38,980 |
| People employed | 668,230 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Accommodation and Food Services · Sector | 647,470 | $30,140 |
| Full-Service Restaurants · National industry | 37,330 | $29,990 |
| Retail Trade · Sector | 14,500 | $30,730 |
| Arts, Entertainment, and Recreation · Sector | 2,490 | $34,320 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 1,230 | $33,280 |
| Manufacturing · Sector | 910 | $36,180 |
| Temporary Help Services · National industry | 830 | $33,280 |
| Fitness and Recreational Sports Centers · National industry | 400 | $18,860 |
| Wholesale Trade · Sector | 370 | $31,650 |
| Educational Services · Sector | 270 | $37,030 |
| Casino Hotels · National industry | 240 | $42,090 |
| Health Care and Social Assistance · Sector | 180 | $31,690 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Accommodation and Food Services · Sector | 10.5× | 647,470 |
| Full-Service Restaurants · National industry | 1.61× | 37,330 |
| Arts, Entertainment, and Recreation · Sector | 0.22× | 2,490 |
| Retail Trade · Sector | 0.21× | 14,500 |
| Casino Hotels · National industry | 0.16× | 240 |
| Fitness and Recreational Sports Centers · National industry | 0.15× | 400 |
| Temporary Help Services · National industry | 0.07× | 830 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.03× | 1,230 |
Part of the Hospitality, Events, & Tourism career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Cooks, Fast Food — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 29th percentile of 427 international occupations.
Cooks, Fast Food show 30th-percentile AI task overlap — and about 82,100 annual U.S. openings
Cooks, Fast Food show 30th-percentile AI task overlap — and about 82,100 annual U.S. openings • Cooks, Fast Food rank in the 30th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 82,100 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-13.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $30,160, across about 668,230 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 46% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Cooks, Fast Food". https://singulariki.com/roles/role-35-2011-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Cooks, Fast Food." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-35-2011-00
Singulariki. (2026). Cooks, Fast Food. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-35-2011-00
@misc{singulariki-role-35-2011-00,
title = {Cooks, Fast Food},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-35-2011-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.