US dataswitch to UK
Cooks, Institution and Cafeteria
monitoring and recording food temperatures to ensure food safety, monitoring menus and spending to ensure that meals are prepared economically and training new employees. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: compiling and maintaining records of food use and expenditures. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, cooking foodstuffs, stays yours. New tools, same person answering for it.
Your week, as this page understands it
Prepare and cook large quantities of food for institutions, such as schools, hospitals, or cafeterias. The job title says “cooks”, “institution” or “cafeteria”: officially one job, several names. The real job is the part underneath: cooking foodstuffs. 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, institution and cafeteria is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is cooking foodstuffs, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 21%
- changing shape
- 5%
- staying human
- 75%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 21 out of 100 (18–26 allowing for uncertainty): low exposure, across 17 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, institution and cafeteria 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.
- 2 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
5 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.
Requisitioning food supplies, kitchen equipment and appliances, based on estimates of future needs
This is reading one thing and writing another: food supplies, kitchen equipment and appliances in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Requisition food supplies, kitchen equipment, and appliances, based on estimates of future needs.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): 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: Working out how much to order from past usage is number work software already does 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.
Compiling and maintaining records of food use and expenditures
This is reading one thing and writing another: records of food in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Compile and maintain records of food use and expenditures.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very 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: Pulling invoices and usage into a running record is routine bookkeeping that software handles well.
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.
Planning menus
This is reading one thing and writing another: menus in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Plan menus that are varied, nutritionally balanced, and appetizing, taking advantage of foods in season and local availability.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): 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: Menu planning works from published nutrition data and price lists, though a dietitian usually approves the result.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring menus and spending to ensure that meals are prepared economically
This is reading one thing and writing another: menus in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Monitor menus and spending to ensure that meals are prepared economically.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): 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: Comparing what meals cost against the budget is straightforward number-crunching from existing records.
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.
Determining meal prices, based on calculations of ingredient prices
This is reading one thing and writing another: meal prices in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Determine meal prices, based on calculations of ingredient prices.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very 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: Pricing a meal is arithmetic on ingredient costs, which software does reliably.
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
1 taskTasks 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.
Monitoring use of government food commodities to ensure that proper procedures
The software now makes the first pass at use of government food commodities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Monitor use of government food commodities to ensure that proper procedures are followed.” (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: The rules are written down and the usage sits in records, so checking compliance is mostly desk work.
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.
Staying human
11 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.
Monitoring and recording food temperatures to ensure food safety
This work happens in the physical world: food temperatures, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Monitor and record food temperatures to ensure food safety.” (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: Readings must be taken physically from the food, though the record itself is routine paperwork.
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.
Cooking foodstuffs
This work happens in the physical world: foodstuffs, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Cook foodstuffs according to menus, special dietary or nutritional restrictions, or numbers of portions to be served.” (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 is hands-on work at the stove, and no software can handle the pans or taste the result.
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 and inspecting galley equipment
This work happens in the physical world: galley equipment, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Clean and inspect galley equipment, kitchen appliances, and work areas to ensure cleanliness and functional operation.” (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: Cleaning surfaces and checking equipment by eye and hand needs a person 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.
Cleaning, cutting and cooking meat, fish or poultry
This work happens in the physical world: meat, fish or poultry, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Clean, cut, and cook meat, fish, or poultry.” (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: Trimming and cooking meat or fish is knife-and-heat 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 2/4.
Show the other 7 tasks
Taking inventory of supplies and equipment
staying humanThis work happens in the physical world: inventory of supplies, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Take inventory of supplies and equipment.” (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: Counting what is actually on the shelves needs a person walking the store room, even if the list is digital.
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.
Directing activities of one or more workers who assist in preparing and serving meals
staying humanThis work happens in the physical world: activities of one, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Direct activities of one or more workers who assist in preparing and serving meals.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 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: Running a busy kitchen line means being there, watching the pass and telling people what to do next.
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 2/4 · how much data exists 2/4.
Training new employees
staying humanThis work happens in the physical world: new employees, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Train new employees.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 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: New cooks learn by being shown the equipment and copying someone standing next to 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 2/4 · how much data exists 2/4.
Apportioning and serving food to facility residents
staying humanThis work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Apportion and serve food to facility residents, employees, 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 and handing food to people happens at the counter, with hands.
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 pots, pans, dishes, utensils or other cooking equipment
staying humanThis work happens in the physical world: pots, pans, dishes, utensils or other cooking equipment, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Wash pots, pans, dishes, utensils, or other cooking 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: Washing up is entirely hands-on work at the sink.
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 or other pastries
staying humanThis work happens in the physical world: breads, rolls or other pastries, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Bake breads, rolls, or other 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 is hands-on work with dough and ovens.
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.
Rotating and storing food supplies
staying humanThis work happens in the physical world: food supplies, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Rotate and store food supplies.” (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: Moving and shelving stock is physical work that needs a person in the store 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 2/4.
What this job pays, and how many people do it
- Median pay
- $37,450a 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
- 441,050in 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 of food in, a record out. The rows above are exactly that shape: compiling and maintaining records of food use and expenditures and requisitioning food supplies, kitchen equipment and appliances. What it cannot do is be there in the room, and that is still where foodstuffs 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
Your week is splitting in two, and which half fills it is the whole question. Compiling and maintaining records of food use and expenditures is going; cooking foodstuffs is not.
So, given all that: 21% of this job's task weight sits in rows the software is already learning, 5% in rows that change shape rather than disappear, and 75% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (cooking foodstuffs) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles cooking foodstuffs, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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, institution and cafeteria (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 on the numbers you do not need one. This job scores 21/100 here, with only 21% of the task list in the top band, and “monitor and record food temperatures to ensure food safety” 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 wash pots, pans, dishes, utensils, or other cooking equipment, and their equivalent is to wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand. 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 bake breads, rolls, or other pastries, and their equivalent is to peel, wash, trim, and cook vegetables and meats, and bake breads and pastries. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% 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 441,050 of yours (OEWS May 2025), 0% as many seats.
Dietitians and Nutritionists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already cook foodstuffs according to menus, special dietary or nutritional restrictions, or numbers of…, 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 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $76,400 against your $37,450 is 2.04× (OEWS May 2025 (both)), and you would be crossing it holding about 8% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 77,570 of those jobs against 441,050 of yours (OEWS May 2025), 18% 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: 21% of its task weight, across 17 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for cooks / institution / cafeteria, 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 21% of the work on this page is already inside what they can do.
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 / institution / cafeteria. 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 / institution / cafeteria launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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, Institution and Cafeteria?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Cooks, Institution and Cafeteria (United States, SOC 35-2012), 21% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 21 out of 100 (range 18–26, band: low). 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, Institution and Cafeteria” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Compile and maintain records of food use and expenditures” (93/100, very high); “Determine meal prices, based on calculations of ingredient prices” (93/100, very high); “Monitor menus and spending to ensure that meals are prepared economically” (75/100, high). 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, Institution and Cafeteria” stay human?
- About 75% 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: “Rotate and store food supplies” (0/100, minimal); “Bake breads, rolls, or other pastries” (0/100, minimal); “Wash pots, pans, dishes, utensils, or other cooking equipment” (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, Institution and Cafeteria” do about AI?
- Start from the ledger rather than the headline: 21% of this job's weighted core work is exposed, and roughly 75% 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, Institution and Cafeteria 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 17 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.
- 2 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.