US dataswitch to UK
Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic
measuring and examining extruded products to locate defects and to check for conformance, installing dies, testing physical properties of products with testing devices and loading machine hoppers with mixed materials. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: cleaning work areas is work software can't reach.
What shifts is maintaining an inventory of materials. This page scores what today's tools actually do, not headlines.
Your week, as this page understands it
Set up, operate, or tend machines to extrude or draw thermoplastic or metal materials into tubes, rods, hoses, wire, bars, or structural shapes. The job title says “extruding”, “drawing machine setters”, “operators”, “tenders”, “metal” or “plastic”: officially one job, several names. The real job is the part underneath: cleaning work areas. 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 extruding and drawing machine setters, operators, and tenders, metal and plastic is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is cleaning work areas, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 6%
- staying human
- 94%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 6 out of 100 (4–10 allowing for uncertainty): minimal exposure, across 16 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 extruding and drawing machine setters, operators, and tenders, metal and plastic 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.
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
0 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.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Changing shape
1 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.
Maintaining an inventory of materials
The software now makes the first pass at an inventory of materials, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Maintain an inventory of materials.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial 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: Tracking what materials are in stock is ordinary record work that software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
15 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.
Cleaning work areas
This work happens in the physical world: work areas, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Clean work areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning a work area is physical work in that space.
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.
Determining setup procedures and selecting machine dies and parts
This work happens in the physical world: setup procedures, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Determine setup procedures and select machine dies and parts, according to specifications.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Half of this is reading the job spec, but choosing and fetching the right dies depends on plant-specific know-how.
The five ratings: output a model can produce 1/4 · 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.
Measuring and examining extruded products to locate defects and to check for conformance
This work happens in the physical world: extruded products, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Measure and examine extruded products to locate defects and to check for conformance to specifications, adjusting controls as necessary to alter products.” (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: Someone has to be at the machine handling the product and turning the controls.
The five ratings: output a model can produce 1/4 · 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.
Starting machines and setting controls to regulate vacuum
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Start machines and set controls to regulate vacuum, air pressure, sizing rings, and temperature, and to synchronize speed of extrusion.” (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: Starting the machine and setting its controls means standing at it.
The five ratings: output a model can produce 1/4 · 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.
Reeling extruded products into rolls of specified lengths and weights
This work happens in the physical world: extruded products, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Reel extruded products into rolls of specified lengths and weights.” (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: Winding product onto rolls is hands-on work at 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 0/4 · how much data exists 1/4.
Installing dies, machine screws and sizing rings on machines that extrude thermoplastic or metal materials
This work happens in the physical world: dies, machine screws and sizing rings, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Install dies, machine screws, and sizing rings on machines that extrude thermoplastic or metal materials.” (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: Fitting dies, screws and rings onto a machine is physical work with tools.
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.
Changing dies on extruding machines
This work happens in the physical world: dies, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Change dies on extruding machines, according to production line changes.” (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: Swapping dies between production runs is done by hand at the machine.
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.
Troubleshooting, maintaining and making minor repairs to equipment
This work happens in the physical world: minor repairs, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Troubleshoot, maintain, and make minor repairs to equipment.” (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: Software can suggest likely causes, but the actual fixing needs hands and tools on the equipment.
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.
Loading machine hoppers with mixed materials
This work happens in the physical world: machine hoppers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Load machine hoppers with mixed materials, using augers, or stuff rolls of plastic dough into machine cylinders.” (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: Loading hoppers and cylinders is lifting and pouring 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.
Show the other 6 tasks
Selecting nozzles, spacers and wiring guides, according to diameters and lengths of rods
staying humanThis work happens in the physical world: nozzles, spacers and wiring guides, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Select nozzles, spacers, and wire guides, according to diameters and lengths of rods.” (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: Matching part sizes to a chart is easy to look up, but the parts still have to be fetched and fitted.
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.
Replacing worn dies when products vary from specifications
staying humanThis work happens in the physical world: worn dies, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Replace worn dies when products vary from specifications.” (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: Changing a worn die is a hands-on swap at the press.
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.
Adjusting controls to draw or press metal into specified shapes and diameters
staying humanThis work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Adjust controls to draw or press metal into specified shapes and diameters.” (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: Shaping metal by working the machine controls happens at the machine itself.
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.
Operating shearing mechanisms to cut rods to specified lengths
staying humanThis work happens in the physical world: mechanisms, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Operate shearing mechanisms to cut rods to specified lengths.” (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: Cutting rods on a shearing machine is physical operation.
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.
Weighing and mixing pelletized, granular or powdered thermoplastic materials and coloring pigments
staying humanThis work happens in the physical world: pelletized, granular or powdered thermoplastic materials, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Weigh and mix pelletized, granular, or powdered thermoplastic materials and coloring pigments.” (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 and mixing raw material is physical handling on the shop floor.
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.
Testing physical properties of products with testing devices
staying humanThis work happens in the physical world: physical properties of products, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Test physical properties of products with testing devices such as acid-bath testers, burst testers, and impact testers.” (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: The samples have to be physically loaded into test rigs, even though reading the numbers is easy.
The five ratings: output a model can produce 1/4 · 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
- $47,720a 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
- 60,840in 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: an inventory of materials in, a record out. The rows above are exactly that shape: maintaining an inventory of materials. What it cannot do is be there in the room, and that is still where work areas 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: cleaning work areas is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 0% of this job's task weight sits in rows the software is already learning, 6% in rows that change shape rather than disappear, and 94% in rows it is nowhere near. That is the position, measured across 16 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. Maintaining an inventory of materials 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 an inventory of materials, 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 work areas 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 maintaining an inventory of materials. 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 extruding and drawing machine setters, operators, and tenders, metal and plastic (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was forging machine setters, operators, and tenders, metal and plastic: only about 13% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 6/100 here, with only 0% of the task list in the top band, and “clean work areas” 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.
Forging Machine Setters, Operators, and Tenders, Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already determine setup procedures and select machine dies and parts, according to specifications, and their equivalent is to confer with other workers about machine setups and operational specifications. 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. And it is a narrow door: about 8,930 of those jobs against 60,840 of yours (OEWS May 2025), 15% as many seats.
Machine Feeders and Offbearers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean work areas, and their equivalent is to clean and maintain machinery, equipment, and work areas to ensure proper functioning and…. 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: $41,220 against your $47,720, 13.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already measure and examine extruded products to locate defects and to check for conformance…, and their equivalent is to notify other workers of defects, and direct them to adjust extruding and forming…. 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. And it is a narrow door: about 12,850 of those jobs against 60,840 of yours (OEWS May 2025), 21% 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: 0% of its task weight, across 16 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 cleaning work areas 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 Plastics process operatives is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Plastics process operatives, Metal machining setters and setter-operators, Metal working machine operatives and Metal making and treating process operatives. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
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 extruding / drawing machine setters / operators / tenders / metal / plastic 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 Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic?
- Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic (United States, SOC 51-4021), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 6 out of 100 (range 4–10, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Maintain an inventory of materials” (56/100, partial); “Determine setup procedures and select machine dies and parts, according to specifications” (16/100, minimal); “Select nozzles, spacers, and wire guides, according to diameters and lengths of rods” (14/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic” stay human?
- About 94% 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: “Test physical properties of products with testing devices such as acid-bath testers, burst testers, and impact testers” (0/100, minimal); “Load machine hoppers with mixed materials, using augers, or stuff rolls of plastic dough into machine cylinders” (0/100, minimal); “Weigh and mix pelletized, granular, or powdered thermoplastic materials and coloring pigments” (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 “Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 94% 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 Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic 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 16 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.
Using these figures?
Cite this
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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.