Will AI replace Cooks, Fast Food?

Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Cooks, Fast Food (United States, SOC 35-2011), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Cooks, Fast Food” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Schedule activities and equipment use with managers, using information about daily menus to help coordinate cooking times” (48/100, partial); “Order and take delivery of supplies” (32/100, low); “Take food and drink orders and receive payment from customers” (18/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Cooks, Fast Food” stay human?

About 95% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Pre-cook items, such as bacon, to prepare them for later use” (0/100, minimal); “Mix ingredients, such as pancake or waffle batters” (0/100, minimal); “Prepare and serve beverages, such as coffee or fountain drinks” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Cooks, Fast Food” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 95% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Cooks, Fast Food calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Cooks, Fast Food

ordering and taking delivery of supplies, cleaning food preparation areas, cooking surfaces and utensils and serving orders to customers at windows. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: cooking the exact number of items ordered by each customer is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 5%
staying human: 95%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 5 out of 100 (4–10 allowing for uncertainty): minimal exposure, across 19 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for cooks, fast food is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

What shifts is scheduling activities and equipment use with managers. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

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. The job title says “cooks” or “fast food”: officially one job, two names. The real job is the part underneath: cooking the exact number of items ordered by each customer. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But cooks, fast food is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is cooking the exact number of items ordered by each customer, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 5%
staying human: 95%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

0 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Changing shape

1 task

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Scheduling activities and equipment use with managers
The software now makes the first pass at activities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Schedule activities and equipment use with managers, using information about daily menus to help coordinate cooking times.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working out cooking times and equipment slots from the day's menu is scheduling that software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

18 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Cooking the exact number of items ordered by each customer
This work happens in the physical world: the exact number of items, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Cook the exact number of items ordered by each customer, working on several different orders simultaneously.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cooking several customers' orders at once is hands-on work at the grill.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Operating large-volume cooking equipment, such as grills, deep-fat fryers or griddles
This work happens in the physical world: large-volume cooking equipment, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Operate large-volume cooking equipment, such as grills, deep-fat fryers, or griddles.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Operating grills and fryers means standing at the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cleaning food preparation areas, cooking surfaces and utensils
This work happens in the physical world: food preparation areas, cooking surfaces and utensils, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Clean food preparation areas, cooking surfaces, and utensils.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cleaning surfaces and utensils is physical work in the kitchen.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing specialty foods, such as pizzas, fish and chips, sandwiches or tacos
This work happens in the physical world: specialty foods, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Prepare specialty foods, such as pizzas, fish and chips, sandwiches, or tacos, following specific methods that usually require short preparation time.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Making pizzas, sandwiches or tacos means assembling real food by hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reading food order slips or receiving verbal instructions as to food
This work happens in the physical world: food order slips, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Read food order slips or receive verbal instructions as to food required by patron, and prepare and cook food according to instructions.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Reading the ticket is trivial, but cooking the food to it is entirely hands-on.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Cleaning, stocking and restock workstations and displaying cases
This work happens in the physical world: restock workstations, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Clean, stock, and restock workstations and display cases.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Stocking and cleaning workstations means moving real items around.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Maintaining sanitation, health and safety standards in work areas
This work happens in the physical world: sanitation, health and safety standards, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Maintain sanitation, health, and safety standards in work areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Keeping the work area to hygiene standards is done physically in that area.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Verifying that prepared food meets requirements for quality and quantity
This work happens in the physical world: prepared food meets requirements, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Verify that prepared food meets requirements for quality and quantity.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking that food looks and weighs right means someone looking at the actual food.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cooking and packaging batches of food
This work happens in the physical world: batches of food, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Cook and package batches of food, such as hamburgers or fried chicken, prepared to order or kept warm until sold.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cooking and packing batches of food is manual kitchen work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 9 tasks

Ordering and taking delivery of supplies
staying human
This work happens in the physical world: delivery of supplies, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Order and take delivery of supplies.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Placing the order is simple paperwork, but receiving and checking a delivery means someone at the back door.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Taking food and drink orders and receiving payment from customers
staying human
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Take food and drink orders and receive payment from customers.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Self-service screens and apps already take orders and payments, though counter service still means someone standing there.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Washing, cutting and preparing foods designated for cooking
staying human
This work happens in the physical world: foods, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Wash, cut, and prepare foods designated for cooking.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Washing, cutting and prepping food is manual kitchen work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Measuring ingredients required for specific food items
staying human
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Measure ingredients required for specific food items.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Measuring ingredients means handling them with scoops and scales.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Serving orders to customers at windows
staying human
This work happens in the physical world: orders, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Serve orders to customers at windows, counters, or tables.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Handing an order to a customer means someone physically passing it across.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Preparing dough, following recipe
staying human
This work happens in the physical world: dough, following recipe, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare dough, following recipe.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Making dough is physical work judged by feel as well as by recipe.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Preparing and serving beverages, such as coffee or fountain drinks
staying human
This work happens in the physical world: beverages, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Prepare and serve beverages, such as coffee or fountain drinks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Pouring and serving drinks means physically making and handing them over.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Mixing ingredients, such as pancake or waffle batters
staying human
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Mix ingredients, such as pancake or waffle batters.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Mixing batter is done by hand with real ingredients.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Precooking items, such as bacon, to prepare them for later
staying human
This work happens in the physical world: items, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Pre-cook items, such as bacon, to prepare them for later use.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Pre-cooking items ready for service is physical kitchen work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $30,890a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 641,070in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: activities in, a record out. The rows above are exactly that shape: scheduling activities and equipment use with managers. What it cannot do is be there in the room, and that is still where the exact number of items gets done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: cooking the exact number of items ordered by each customer is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 0% of this job's task weight sits in rows the software is already learning, 5% in rows that change shape rather than disappear, and 95% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Scheduling activities and equipment use with managers is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to activities, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether the exact number of items is in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near scheduling activities and equipment use with managers. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to cooks, fast food (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was chefs and head cooks: only about 14% of its durable work is work you already do and the 2.0× pay gap is the market pricing a barrier. And on the numbers you do not need one. This job scores 5/100 here, with only 0% of the task list in the top band, and “cook the exact number of items ordered by each customer, working on…” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Chefs and Head Cooks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean food preparation areas, cooking surfaces, and utensils, and their equivalent is to instruct cooks or other workers in the preparation, cooking, garnishing, or presentation of…. Across both published task lists that is about 14% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. The pay gap is the market pricing a barrier: $62,470 against your $30,890 is 2.02× (OEWS May 2025 (both)), and you would be crossing it holding about 14% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Look at that job’s page anyway →
Waiters and Waitresses
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already take food and drink orders and receive payment from customers, and their equivalent is to collect payments from customers. Across both published task lists that is about 13% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Food Preparation Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already measure ingredients required for specific food items, and their equivalent is to weigh or measure ingredients. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 19 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like cooking the exact number of items ordered by each customer arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Cooks is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Kitchen and catering assistants

In UK official statistics this job is counted as Cooks and Kitchen and catering assistants. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for cooks / fast food launches. Nothing else.

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No Space for cooks / fast food yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

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Collab365 launches new communities where the need is real. If one for cooks / fast food existed, with researched problems, courses and people in the same boat, would you want in?

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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Cooks, Fast Food?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Cooks, Fast Food (United States, SOC 35-2011), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Cooks, Fast Food” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Schedule activities and equipment use with managers, using information about daily menus to help coordinate cooking times” (48/100, partial); “Order and take delivery of supplies” (32/100, low); “Take food and drink orders and receive payment from customers” (18/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Cooks, Fast Food” stay human?: About 95% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Pre-cook items, such as bacon, to prepare them for later use” (0/100, minimal); “Mix ingredients, such as pancake or waffle batters” (0/100, minimal); “Prepare and serve beverages, such as coffee or fountain drinks” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Cooks, Fast Food” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 95% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Cooks, Fast Food calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.