Will AI replace Cooks, Private Household?

Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Cooks, Private Household (United States, SOC 35-2013), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 18 out of 100 (range 14–24, 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, Private Household” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Keep records pertaining to menus, finances, and other business-related issues” (75/100, high); “Plan menus according to employers' needs and diet restrictions” (57/100, partial); “Shop for or order food and kitchen supplies and equipment” (34/100, low). 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, Private Household” stay human?

About 82% 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: “Travel with employers to vacation homes to provide meal preparation at those locations” (0/100, minimal); “Serve meals and snacks to employing families and their guests” (0/100, minimal); “Stock, organize, and clean kitchens and cooking utensils” (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, Private Household” do about AI?

Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 82% 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, Private Household 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 13 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, Private Household

peeling, washing, preparing meals in private homes according to employers' recipes or tastes and keeping records pertaining. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries 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: 9%
changing shape: 9%
staying human: 82%

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

Whole-job exposure score 18 out of 100 (14–24 allowing for uncertainty): minimal exposure, across 13 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, private household 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 2026-08-05. Read the full method.

What shifts is keeping records pertaining: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Prepare meals in private homes. Includes personal chefs. The job title says “cooks” or “private household”: officially one job, two names. The real job is the part underneath: peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries. 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, private household is not one task. It is 13 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 9%
changing shape: 9%
staying human: 82%

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 2026-08-05. 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.

1 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

1 task

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.

Keeping records pertaining
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 · Core
Source: “Keep records pertaining to menus, finances, and other business-related issues.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high 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: Menu logs and household spending records are ordinary bookkeeping that software handles well.
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 3/4.

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.

Planning menus according to employers' needs and diet restrictions
The software now makes the first pass at menus, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Plan menus according to employers' needs and diet restrictions.” (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: Software can build a week of menus around allergies and diets, though it will not know the family’s private likes and dislikes.
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

11 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.

Peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries
This work happens in the physical world: vegetables, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Peel, wash, trim, and cook vegetables and meats, and bake breads 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: Peeling, trimming, cooking and baking are all done with hands and heat in a 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.
Cooling, packaging, labelling and freeze foods for later consumption and providing instructions for reheating
This work happens in the physical world: freeze foods, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Cool, package, label, and freeze foods for later consumption and provide instructions for reheating.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium 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: Reheating instructions and labels are easy to write, but chilling, packing and freezing the food is physical kitchen work.
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 3/4.
Shopping for or ordering food and kitchen supplies and equipment
This work happens in the physical world: or ordering food, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Shop for or order food and kitchen supplies and equipment.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low 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: Ordering supplies online is easily automated, but walking a market and choosing fresh produce is not.
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 0/4 · how much data exists 2/4.
Stocking, organizing and cleaning kitchens and cooking utensils
This work happens in the physical world: kitchens, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Stock, organize, and clean kitchens and cooking 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: Stocking shelves and cleaning pans and worktops 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 1/4.
Preparing meals in private homes according to employers' recipes or tastes
This work happens in the physical world: meals, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Prepare meals in private homes according to employers' recipes or tastes, handling all meals for the family and possibly for other household staff.” (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 the family’s meals in their kitchen is hands-on work that has to happen there.
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.
Specializing in preparing fancy dishes or food for special diets
This work happens in the physical world: preparing fancy dishes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Specialize in preparing fancy dishes or food for special diets.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Special-diet rules are well documented, but the actual cooking of the dish is done by hand.
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.
Directing the operation and organization of kitchens and all food-related activities
This work happens in the physical world: the operation, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Direct the operation and organization of kitchens and all food-related activities, including the presentation and serving of food.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium 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: Service timings can be planned in advance, but running the kitchen during a meal means being there directing people.
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 2/4 · how much data exists 2/4.
Creating and exploring new cuisines
This work happens in the physical world: new cuisines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Create and explore new cuisines.” (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: New dish ideas can be generated easily, but developing and tasting them happens at the stove.
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.

Show the other 3 tasks

Planning and preparing food
staying human
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Plan and prepare food for parties, holiday meals, luncheons, special functions, and other social events.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Planning a function menu can be drafted digitally, but the food itself is cooked by hand.
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 1/4 · how much data exists 2/4.
Serving meals and snacks to employing families and their guests
staying human
This work happens in the physical world: meals, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Serve meals and snacks to employing families and their guests.” (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: Carrying plates to the table and serving guests requires a person in the room.
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.
Travelling with employers to vacation homes to provide meal preparation at those locations
staying human
This work happens in the physical world: employers, in a real place. Software cannot follow it there.
importance 2 · Supplemental
Source: “Travel with employers to vacation homes to provide meal preparation at those locations.” (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: Travelling with the family to cook at another house is presence-based 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: $47,940a 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: 1,100in 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: records in, a record out. The rows above are exactly that shape: keeping records pertaining and planning menus according to employers' needs and diet restrictions. What it cannot do is be there in the room, and that is still where vegetables get 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: peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 9% of this job's task weight sits in rows the software is already learning, 9% in rows that change shape rather than disappear, and 82% in rows it is nowhere near. That is the position, measured across 13 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 pertaining 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 records, 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 freeze foods are 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 planning menus according to employers' needs and diet restrictions. 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, private household (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was dishwashers: only about 13% of its durable work is work you already do and it pays 27.4% less. And on the numbers you do not need one. This job scores 18/100 here, with only 9% of the task list in the top band, and “peel, wash, trim, and cook vegetables and meats, and bake breads and…” 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.

Dishwashers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already stock, organize, and clean kitchens and cooking utensils, and their equivalent is to maintain kitchen work areas, equipment, or utensils in clean and orderly condition. 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. It is a pay cut, in those words: $34,810 against your $47,940, 27.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Cooks, Restaurant
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already peel, wash, trim, and cook vegetables and meats, and bake breads and pastries, and their equivalent is to wash, peel, cut, and seed fruits and vegetables to prepare them for consumption. 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. It is a pay cut, in those words: $37,390 against your $47,940, 22.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Dietitians and Nutritionists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already plan menus according to employers' needs and diet restrictions, and their equivalent is to assess nutritional needs, diet restrictions, and current health plans to develop and implement…. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step.
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: 9% of its task weight, across 13 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 peeling, washing, trimming and cooking vegetables and meats and bake breads and pastries 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:
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

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for cooks / private household, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 9% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for cooks / private household. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

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

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No Space for cooks / private household 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.

What a Space actually is, in full

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

Your email

We use your email address for one thing: to tell you if a Space for cooks / private household launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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, Private Household?: Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Cooks, Private Household (United States, SOC 35-2013), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 18 out of 100 (range 14–24, 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, Private Household” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Keep records pertaining to menus, finances, and other business-related issues” (75/100, high); “Plan menus according to employers' needs and diet restrictions” (57/100, partial); “Shop for or order food and kitchen supplies and equipment” (34/100, low). 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, Private Household” stay human?: About 82% 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: “Travel with employers to vacation homes to provide meal preparation at those locations” (0/100, minimal); “Serve meals and snacks to employing families and their guests” (0/100, minimal); “Stock, organize, and clean kitchens and cooking utensils” (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, Private Household” do about AI?: Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 82% 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, Private Household 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 13 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.
1 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 2026-08-05.
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.