US dataswitch to UK
Pourers and Casters, Metal
pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings, collecting samples and turning valves to circulate water through cores. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings is work software can't reach.
What shifts is the routine end of the work: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Operate hand-controlled mechanisms to pour and regulate the flow of molten metal into molds to produce castings or ingots. The job title says “pourers”, “casters” or “metal”: officially one job, several names. The real job is the part underneath: pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings. 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 pourers and casters, metal 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 pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 0%
- staying human
- 100%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 0 out of 100 (0–4 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 pourers and casters, metal 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
0 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
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.
Staying human
16 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings
This work happens in the physical world: the flow of molten metal, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Pour and regulate the flow of molten metal into molds and forms to produce ingots or other castings, using ladles or hand-controlled mechanisms.” (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: Pouring molten metal into a mold is dangerous physical work done at the furnace.
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.
Skimming slag or removing excess metal from ingots or equipment
This work happens in the physical world: slag, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Skim slag or remove excess metal from ingots or equipment, using hand tools, strainers, rakes, or burners, collecting scrap for recycling.” (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: Skimming slag with rakes and burners is manual work over hot metal.
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.
Collecting samples or signal workers to sample metal for analysis
This work happens in the physical world: samples, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Collect samples, or signal workers to sample metal for analysis.” (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: Taking a sample of molten metal, or signaling someone to, happens on the furnace 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 1/4.
Loading specified amounts of metal and flux into furnaces or clay crucibles
This work happens in the physical world: specified amounts of metal, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Load specified amounts of metal and flux into furnaces or clay crucibles.” (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 metal and flux into a furnace is physical handling of heavy material.
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.
Reading temperature gauges and observing color changes
This work happens in the physical world: temperature gauges, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Read temperature gauges and observe color changes, adjusting furnace flames, torches, or electrical heating units as necessary to melt metal to 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: Judging metal readiness by watching its color and adjusting the flame happens at the furnace with eyes on the melt.
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.
Positioning equipment, ladles, grinding wheels, pouring nozzles or crucibles or signal other workers to position equipment
This work happens in the physical world: equipment, ladles, grinding wheels, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Position equipment such as ladles, grinding wheels, pouring nozzles, or crucibles, or signal other workers to position 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: Moving ladles, crucibles and nozzles into place is heavy physical positioning 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.
Examining molds to ensure they are clean
This work happens in the physical world: molds, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Examine molds to ensure they are clean, smooth, and properly coated.” (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: Examining a mold for cleanliness and coating means looking at and touching the actual mold.
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.
Removing solidified steel or slag from pouring nozzles
This work happens in the physical world: solidified steel, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Remove solidified steel or slag from pouring nozzles, using long bars or oxygen burners.” (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: Clearing set steel from a pouring nozzle needs bars or a burner in someone’s 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 1/4.
Removing metal ingots or cores from molds
This work happens in the physical world: metal ingots, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Remove metal ingots or cores from molds, using hand tools, cranes, and chain hoists.” (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: Getting ingots and cores out of molds with cranes and hand tools is heavy manual work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Repairing and maintaining metal forms and equipment
This work happens in the physical world: metal forms, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Repair and maintain metal forms and equipment, using hand tools, sledges, and bars.” (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: Repairing molds and equipment with sledges and bars is hands on maintenance 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.
Show the other 6 tasks
Turning valves to circulate water through cores
staying humanThis work happens in the physical world: valves, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Turn valves to circulate water through cores, or spray water on filled molds to cool and solidify metal.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Turning water valves and spraying molds to cool metal is done by hand at the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Adding metal to molds to compensate for shrinkage
staying humanThis work happens in the physical world: metal, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Add metal to molds to compensate for shrinkage.” (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: Topping up a mold as the metal shrinks is a timed physical action at the pour.
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.
Pulling levers to lift ladle stoppers and to allow molten steel to flow into ingot molds to specified heights
staying humanThis work happens in the physical world: levers, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Pull levers to lift ladle stoppers and to allow molten steel to flow into ingot molds to specified heights.” (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: Pulling levers to release molten steel into molds is a physical operation at the ladle.
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.
Assembling and embeding cores in casting frames
staying humanThis work happens in the physical world: cores, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Assemble and embed cores in casting frames, using hand tools and equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting cores into casting frames by hand is physical assembly at the molding station.
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.
Transporting metal ingots to storage areas
staying humanThis work happens in the physical world: metal ingots, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Transport metal ingots to storage areas, using forklifts.” (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: Driving a forklift to move ingots into storage is physical work in the plant.
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.
Stenciling identifying information on ingots and pigs
staying humanThis work happens in the physical world: information, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Stencil identifying information on ingots and pigs, using special hand tools.” (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: Stenciling markings onto ingots with hand tools means physically marking each piece.
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
- $51,810a 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
- 4,560in 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. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on the flow of molten metal, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings and skimming slag or removing excess metal from ingots or equipment. The parts that are changing are the paperwork and the tools around the job, not the middle of it, 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: pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings 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, 0% in rows that change shape rather than disappear, and 100% 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. The routine end of the work 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 the routine work, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether the flow of molten metal is in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings. 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 pourers and casters, metal (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was metal-refining furnace operators and tenders: only about 3% of its durable work is work you already do. And on the numbers you do not need one. This job scores 0/100 here, with only 0% of the task list in the top band, and “pour and regulate the flow of molten metal into molds and forms…” 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.
Metal-Refining Furnace Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already pour and regulate the flow of molten metal into molds and forms to…, and their equivalent is to drain, transfer, or remove molten metal from furnaces, and place it into molds…. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Foundry Mold and Coremakers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already pour and regulate the flow of molten metal into molds and forms to…, and their equivalent is to pour molten metal into molds, manually or with crane ladles. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already load specified amounts of metal and flux into furnaces or clay crucibles, and their equivalent is to calculate amounts of materials to be loaded into furnaces, adjusting amounts as necessary…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
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 pouring and regulating the flow of molten metal into molds and forms to produce ingots or other castings 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 Metal making and treating 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
In UK official statistics this job is counted as Metal making and treating process operatives. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
Why there is no community here
Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.
So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.
Noted, and thank you. We’ll email you if a Space for pourers / casters / metal 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 Pourers and Casters, Metal?
- Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Pourers and Casters, Metal (United States, SOC 51-4052), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 0 out of 100 (range 0–4, 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 “Pourers and Casters, Metal” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Collect samples, or signal workers to sample metal for analysis” (0/100, minimal); “Pour and regulate the flow of molten metal into molds and forms to produce ingots or other castings, using ladles or hand-controlled mechanisms” (0/100, minimal); “Read temperature gauges and observe color changes, adjusting furnace flames, torches, or electrical heating units as necessary to melt metal to specifications” (0/100, 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 “Pourers and Casters, Metal” stay human?
- About 100% 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: “Repair and maintain metal forms and equipment, using hand tools, sledges, and bars” (0/100, minimal); “Stencil identifying information on ingots and pigs, using special hand tools” (0/100, minimal); “Transport metal ingots to storage areas, using forklifts” (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 “Pourers and Casters, Metal” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% 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 Pourers and Casters, Metal 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
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.