US dataswitch to UK
Cooks, Restaurant
ensuring food is stored and cooked at correct temperature by regulating temperature of ovens, baking roast, broil and steam meats, fish, vegetables and other foods and weighing, measuring. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: portioning, arranging and garnish food and serving food to waiters or patrons is work software can't reach.
What shifts is keeping records and accounts: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Prepare, season, and cook dishes such as soups, meats, vegetables, or desserts in restaurants. May order supplies, keep records and accounts, price items on menu, or plan menu. The job title says “cooks” or “restaurant”: officially one job, two names. The real job is the part underneath: portioning, arranging and garnish food and serving food to waiters or patrons. 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, restaurant is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is portioning, arranging and garnish food and serving food to waiters or patrons, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 4%
- changing shape
- 7%
- staying human
- 89%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 9 out of 100 (7–14 allowing for uncertainty): minimal exposure, across 20 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, restaurant 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.
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.
- 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
2 tasksTasks 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.
Planning and pricing menu items
This is reading one thing and writing another: menu items in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Plan and price menu items.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Costing and pricing dishes is a calculation software does, though judging what customers will pay is local knowledge.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Keeping records and accounts
This is reading one thing and writing another: records in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Keep records and accounts.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high 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: Keeping kitchen records and accounts is straightforward bookkeeping that software handles.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
2 tasksTasks 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.
Estimating expected food consumption, requisition or purchasing supplies or procure food from storage
The software now makes the first pass at expected food consumption, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Estimate expected food consumption, requisition or purchase supplies, or procure food from storage.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Forecasting how much food will be needed and ordering it is planning software does well, though stock is fetched by hand.
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 0/4 · how much data exists 3/4.
Consulting with supervisory staff to plan menus
The software now makes the first pass at supervisory staff, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Consult with supervisory staff to plan menus, taking into consideration factors such as costs and special event needs.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Menu planning around costs and events is desk work software helps with, though what sells here is local knowledge.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Staying human
16 tasksTasks 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.
Portioning, arranging and garnish food and serving food to waiters or patrons
This work happens in the physical world: garnish food, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Portion, arrange, and garnish food, and serve food to waiters or patrons.” (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: Portioning, arranging and garnishing plates is done by hand at the pass.
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.
Ensuring freshness of food and ingredients by checking
This work happens in the physical world: freshness of food, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Ensure freshness of food and ingredients by checking for quality, keeping track of old and new items, and rotating stock.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Stock records can be handled by software, but judging freshness relies on handling, smelling and looking at food.
The five ratings: output a model can produce 2/4 · 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.
Seasoning and cooking food according to recipes or personal judgment and experience
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Season and cook food according to recipes or personal judgment and experience.” (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: Cooking and seasoning to taste is physical work at the stove guided by experience.
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.
Ensuring food is stored and cooked at correct temperature by regulating temperature of ovens
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Ensure food is stored and cooked at correct temperature by regulating temperature of ovens, broilers, grills, and roasters.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 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: Equipment holds temperatures automatically, but setting and watching ovens and grills through service happens at the equipment.
The five ratings: output a model can produce 2/4 · 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 3/4.
Turning or stirring foods to ensure even cooking
This work happens in the physical world: foods, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Turn or stir foods to ensure even 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: work that happens in the physical world.
The rating behind it: Turning and stirring food is manual work at the stove.
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.
Inspecting and cleaning food preparation areas
This work happens in the physical world: food preparation areas, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Inspect and clean food preparation areas, such as equipment, work surfaces, and serving areas, to ensure safe and sanitary food-handling practices.” (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: Inspecting and cleaning surfaces, equipment and serving areas 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 2/4.
Show the other 10 tasks
Coordinating and supervising work of kitchen staff
staying humanThis work happens in the physical world: work of kitchen staff, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Coordinate and supervise work of kitchen staff.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Running a kitchen team through service depends on being on the line with them.
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 3/4 · how much data exists 1/4.
Observing and testing foods to determine if they have been cooked sufficiently
staying humanThis work happens in the physical world: foods, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Observe and test foods to determine if they have been cooked sufficiently, using methods such as tasting, smelling, or piercing them with 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: work that happens in the physical world.
The rating behind it: Judging doneness by tasting, smelling and piercing food relies on senses used 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 0/4.
Weighing, measuring and mixing ingredients according to recipes or personal judgment, using various kitchen utensils and equipment
staying humanThis work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Weigh, measure, and mix ingredients according to recipes or personal judgment, using various kitchen utensils and equipment.” (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: Weighing, measuring and mixing ingredients is hands-on preparation.
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.
Substituting for or assisting other cooks during emergencies or rush periods
staying humanThis work happens in the physical world: or assisting other cooks during emergencies, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Substitute for or assist other cooks during emergencies or rush periods.” (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: Stepping in for other cooks during a rush means physically working the line.
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 1/4.
Baking roast, broil and steam meats, fish, vegetables and other foods
staying humanThis work happens in the physical world: roast, broil and steam meats, fish, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Bake, roast, broil, and steam meats, fish, vegetables, and other foods.” (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: Baking, roasting, broiling and steaming food is hands-on cooking.
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.
Washing peel, cut and seed fruits and vegetables to prepare them for consumption
staying humanThis work happens in the physical world: peel, cut and seed fruits and vegetables, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Wash, peel, cut, and seed fruits and vegetables to prepare them for consumption.” (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: Washing, peeling, cutting and seeding produce is manual preparation 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.
Carving and trimming meats, beef, veal, ham, pork and lamb for hot or cold service
staying humanThis work happens in the physical world: meats, beef, veal, ham, pork and lamb, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Carve and trim meats such as beef, veal, ham, pork, and lamb for hot or cold service, or for sandwiches.” (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: Carving and trimming meat is skilled knife work done 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 1/4.
Butchering and dressing animals, fowl or shellfish or cutting and bone meat prior to cooking
staying humanThis work happens in the physical world: animals, fowl or shellfish or cutting and bone, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Butcher and dress animals, fowl, or shellfish, or cut and bone meat prior to 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: work that happens in the physical world.
The rating behind it: Butchering and dressing meat, poultry and shellfish is skilled manual 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.
Baking breads, rolls, cakes and pastries
staying humanThis work happens in the physical world: breads, rolls, cakes and pastries, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Bake breads, rolls, cakes, and pastries.” (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: Baking bread, cakes and pastries is hands-on 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 2/4.
Preparing relishes and hors d'oeuvres
staying humanThis work happens in the physical world: relishes, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Prepare relishes and hors d'oeuvres.” (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: Preparing relishes and hors d'oeuvres is manual food preparation.
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
- $37,390a 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
- 1,409,890in 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: menu items in, a record out. The rows above are exactly that shape: keeping records and accounts and planning and pricing menu items. What it cannot do is be there in the room, and that is still where garnish food 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: portioning, arranging and garnish food and serving food to waiters or patrons is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 4% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 89% in rows it is nowhere near. That is the position, measured across 20 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. Keeping records and accounts 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 menu items, 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 garnish food 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 estimating expected food consumption, requisition or purchasing supplies or procure food from storage. 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, restaurant (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was food preparation workers: only about 13% of its durable work is work you already do. And on the numbers you do not need one. This job scores 9/100 here, with only 4% of the task list in the top band, and “portion, arrange, and garnish food, and serve food to waiters or patrons” 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.
Food Preparation Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already wash, peel, cut, and seed fruits and vegetables to prepare them for consumption, and their equivalent is to wash, peel, and cut various foods. 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.
Cooks, Private Household
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh, measure, and mix ingredients according to recipes or personal judgment, using various…, and their equivalent is to stock, organize, and clean kitchens and cooking utensils. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 1,100 of those jobs against 1,409,890 of yours (OEWS May 2025), 0% as many seats.
Food Service Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already observe and test foods to determine if they have been cooked sufficiently, using…, and their equivalent is to test cooked food by tasting and smelling it to ensure palatability and flavor…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 238,430 of those jobs against 1,409,890 of yours (OEWS May 2025), 17% as many seats.
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: 4% of its task weight, across 20 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 portioning, arranging and garnish food and serving food to waiters or patrons 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
In UK official statistics this job is counted as Cooks. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
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:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
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 / restaurant launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
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, Restaurant?
- Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Cooks, Restaurant (United States, SOC 35-2014), 4% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–14, 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, Restaurant” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Keep records and accounts” (93/100, very high); “Plan and price menu items” (68/100, high); “Consult with supervisory staff to plan menus, taking into consideration factors such as costs and special event needs” (57/100, partial). 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, Restaurant” stay human?
- About 89% 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: “Inspect and clean food preparation areas, such as equipment, work surfaces, and serving areas, to ensure safe and sanitary food-handling practices” (0/100, minimal); “Prepare relishes and hors d'oeuvres” (0/100, minimal); “Bake breads, rolls, cakes, and pastries” (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, Restaurant” do about AI?
- Start from the ledger rather than the headline: 4% of this job's weighted core work is exposed, and roughly 89% 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, Restaurant 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 20 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
Worth knowing about these figures
- 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.
Using these figures?
Cite this
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}
}Data as of release 2026-q4.1, published . 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.