US dataswitch to UK
Excavating and Loading Machine and Dragline Operators, Surface Mining
moving levers, operating machinery to perform activities, measuring and verifying levels of rock or gravel and creating or maintaining inclines or ramps. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: moving levers, depress foot pedals and turn dials to operate power machinery is work software can't reach.
What shifts is becoming familiar with digging plans: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Operate or tend machinery at surface mining site, equipped with scoops, shovels, or buckets to excavate and load loose materials. The job title says “excavating”, “loading machine”, “dragline operators” or “surface mining”: officially one job, several names. The real job is the part underneath: moving levers, depress foot pedals and turn dials to operate power machinery. 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 excavating and loading machine and dragline operators, surface mining 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 moving levers, depress foot pedals and turn dials to operate power machinery, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 8%
- staying human
- 92%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 7 out of 100 (5–11 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 excavating and loading machine and dragline operators, surface mining 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.
- 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.
Becoming familiar with digging plans
The software now makes the first pass at familiar, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The digging plan and machine limits are written down and easy to summarize, but the operator has to hold them in mind while working.
The five ratings: output a model can produce 2/4 · 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.
Moving levers, depress foot pedals and turn dials to operate power machinery
This work happens in the physical world: levers, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Move levers, depress foot pedals, and turn dials to operate power machinery, such as power shovels, stripping shovels, scraper loaders, or backhoes.” (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: Working the levers and pedals of a digging 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 2/4.
Setting up or inspecting equipment prior to operation
This work happens in the physical world: or inspecting equipment prior, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Set up or inspect equipment prior to 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: Walking round the machine and checking it before a shift is hands-on.
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.
Observing hand signals, grade stakes or other markings when operating machines so that work
This work happens in the physical world: hand signals, grade stakes or other markings, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Observe hand signals, grade stakes, or other markings when operating machines so that work can be performed 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: Watching for signals and stakes while operating the machine means sitting in the cab.
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.
Receiving written or oral instructions regarding material movement or excavation
This work happens in the physical world: written, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Receive written or oral instructions regarding material movement or excavation.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low 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: Written instructions are easy to process, but spoken briefings on a noisy site go to the person in the cab.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Operating machinery to perform activities
This work happens in the physical world: machinery, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Operate machinery to perform activities such as backfilling excavations, vibrating or breaking rock or concrete, or making winter roads.” (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: Backfilling and breaking rock is machine work done on site.
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.
Moving materials over short distances
This work happens in the physical world: materials over short distances, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Move materials over short distances, such as around a construction site, factory, or warehouse.” (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 material around a site is physical machine work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Creating or maintaining inclines or ramps
This work happens in the physical world: inclines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Create or maintain inclines or ramps.” (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 and maintaining ramps is done with the machine on the ground.
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.
Lubricating, adjusting or repairing machinery and replacing parts, such as gears, bearings or bucket teeth
This work happens in the physical world: machinery, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Lubricate, adjust, or repair machinery and replace parts, such as gears, bearings, or bucket teeth.” (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: Greasing, adjusting and replacing machine parts requires hands and 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 2/4.
Handling slides, mud or pit cleanings or maintenance
This work happens in the physical world: slides, mud or pit cleanings or maintenance, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Handle slides, mud, or pit cleanings or maintenance.” (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 slides and mud from a pit is heavy physical 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
Measuring and verifying levels of rock or gravel
staying humanThis work happens in the physical world: levels of rock, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Measure and verify levels of rock or gravel, bases, or other excavated material.” (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: The calculations are simple, but the levels have to be measured out on the ground.
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.
Directing ground workers engaged in activities
staying humanThis work happens in the physical world: ground workers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Direct ground workers engaged in activities such as moving stakes or markers, or changing positions of towers.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Directing ground workers around a moving machine depends on being there to see 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 1/4 · how much data exists 2/4.
Directing workers engaged in placing blocks or outriggers to prevent capsizing of machines when lifting heavy loads
staying humanThis work happens in the physical world: workers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Direct workers engaged in placing blocks or outriggers to prevent capsizing of machines when lifting heavy loads.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Blocking a machine safely for a heavy lift depends on someone watching the ground crew in real time.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Adjusting dig face angles for varying overburden depths and setting lengths
staying humanThis work happens in the physical world: dig face angles, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Adjust dig face angles for varying overburden depths and set 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: Setting the dig face angle is done from the cab as the ground changes.
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.
Driving machines to work sites
staying humanThis work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Drive machines to work sites.” (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 the machine to where it is needed 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.
Performing manual labor to prepare or finish sites
staying humanThis work happens in the physical world: manual labor, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Perform manual labor to prepare or finish sites, such as shoveling materials by hand.” (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: Shoveling and finishing ground by hand is manual labor.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
What this job pays, and how many people do it
- Median pay
- $57,430a 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
- 34,480in 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 the same call made over and over on familiar, where the right answer is already known. The rows above are exactly that shape: becoming familiar with digging plans. What it cannot do is be there in the room, and that is still where levers 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: moving levers, depress foot pedals and turn dials to operate power machinery 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, 8% in rows that change shape rather than disappear, and 92% 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. Becoming familiar with digging plans 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 familiar, 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 or inspecting equipment prior 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 becoming familiar with digging plans. 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 excavating and loading machine and dragline operators, surface mining (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was loading and moving machine operators, underground mining: only about 10% 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 7/100 here, with only 0% of the task list in the top band, and “move levers, depress foot pedals, and turn dials to operate power machinery” 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.
Loading and Moving Machine Operators, Underground Mining
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “observe hand signals, grade stakes, or other markings when operating machines”. Across the whole of both lists that adds up to about 10% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 10% 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 5,930 of those jobs against 34,480 of yours (OEWS May 2025), 17% as many seats.
Industrial Machinery Mechanics
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already lubricate, adjust, or repair machinery and replace parts, and their equivalent is to clean, lubricate, or adjust parts, equipment, or machinery. 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.
Maintenance and Repair Workers, General
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already lubricate, adjust, or repair machinery and replace parts, and their equivalent is to clean or lubricate shafts, bearings, gears, or other parts of machinery. 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. It is a pay cut, in those words: $49,590 against your $57,430, 13.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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 moving levers, depress foot pedals and turn dials to operate power machinery 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 Mobile machine drivers and operatives n.e.c. 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 Mobile machine drivers and operatives n.e.c. and Mining and quarry workers and related 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 excavating / loading machine / dragline operators / surface mining 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 Excavating and Loading Machine and Dragline Operators, Surface Mining?
- Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Excavating and Loading Machine and Dragline Operators, Surface Mining (United States, SOC 47-5022), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 7 out of 100 (range 5–11, 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 “Excavating and Loading Machine and Dragline Operators, Surface Mining” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application” (43/100, partial); “Receive written or oral instructions regarding material movement or excavation” (24/100, low); “Measure and verify levels of rock or gravel, bases, or other excavated material” (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 “Excavating and Loading Machine and Dragline Operators, Surface Mining” stay human?
- About 92% 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: “Handle slides, mud, or pit cleanings or maintenance” (0/100, minimal); “Create or maintain inclines or ramps” (0/100, minimal); “Perform manual labor to prepare or finish sites, such as shoveling materials by hand” (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 “Excavating and Loading Machine and Dragline Operators, Surface Mining” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 92% 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 Excavating and Loading Machine and Dragline Operators, Surface Mining 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.
- 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.