US dataswitch to UK
Outdoor Power Equipment and Other Small Engine Mechanics
recording repairs made, time spent and parts, adjusting points, valves, carburetors, distributors and spark plug gaps and repairing or replacing defective parts. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: dismantling engines, using hand tools and examining parts for defects is work software can't reach.
What shifts is recording repairs made, time spent and parts. This page scores what today's tools actually do, not headlines.
Your week, as this page understands it
Diagnose, adjust, repair, or overhaul small engines used to power lawn mowers, chain saws, recreational sporting equipment, and related equipment. The job title says “outdoor power equipment” or “other small engine mechanics”: officially one job, two names. The real job is the part underneath: dismantling engines, using hand tools and examining parts for defects. 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 outdoor power equipment and other small engine mechanics is not one task. It is 14 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is dismantling engines, using hand tools and examining parts for defects, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 8%
- changing shape
- 0%
- staying human
- 92%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 11 out of 100 (9–15 allowing for uncertainty): minimal exposure, across 14 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 outdoor power equipment and other small engine mechanics 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
1 taskTasks 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.
Recording repairs made, time spent and parts
This is reading one thing and writing another: repairs made, time spent and parts in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Record repairs made, time spent, and parts used.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up what was repaired, how long it took and which parts were used is straightforward record-keeping at a screen.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
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
13 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.
Testing and inspecting engines to determine malfunctions
This work happens in the physical world: engines, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Test and inspect engines to determine malfunctions, to locate missing and broken parts, and to verify repairs, using diagnostic instruments.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Software can read fault codes, but someone still has to hook the tools up and run the engine.
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.
Dismantling engines, using hand tools and examining parts for defects
This work happens in the physical world: engines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Dismantle engines, using hand tools, and examine parts for defects.” (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 an engine apart with hand tools is pure hands-on 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 gasoline engines used to power equipment
This work happens in the physical world: gasoline engines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Repair and maintain gasoline engines used to power equipment such as portable saws, lawn mowers, generators, and compressors.” (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: Fixing a running engine means physically working on 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 2/4.
Adjusting points, valves, carburetors, distributors and spark plug gaps, using feeler gauges
This work happens in the physical world: points, valves, carburetors, distributors and spark plug gaps, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Adjust points, valves, carburetors, distributors, and spark plug gaps, using feeler gauges.” (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 gaps with feeler gauges is done by hand on the engine 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 2/4.
Repairing or replacing defective parts
This work happens in the physical world: defective parts, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Repair or replace defective parts such as magnetos, water pumps, gears, pistons, and carburetors, using 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: Swapping out broken parts requires hands and tools on 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 2/4.
Performing routine maintenance, cleaning and oiling parts, honing cylinders and tuning ignition systems
This work happens in the physical world: routine maintenance, cleaning and oiling parts, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Perform routine maintenance such as cleaning and oiling parts, honing cylinders, and tuning ignition systems.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cleaning, oiling and tuning are physical jobs done on 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 3/4.
Reassembling engines after repair or maintenance work is complete
This work happens in the physical world: engines after repair, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Reassemble engines after repair or maintenance work is complete.” (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: Putting an engine back together is entirely 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.
Replacing motors
This work happens in the physical world: motors, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Replace motors.” (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: Lifting out an old motor and fitting a new one is 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 2/4.
Obtaining problem descriptions from customers
The ratings behind this row put problem descriptions well outside what today's tools can do on their own.
importance 4 · CoreSource: “Obtain problem descriptions from customers, and prepare cost estimates for repairs.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 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.
The rating behind it: Pricing a repair from parts and labor data is easy to draft, but the customer conversation happens at the counter.
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 1/4 · how much data exists 3/4.
Show the other 4 tasks
Selling parts and equipment
staying humanThis work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Sell parts and equipment.” (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: Looking up and pricing parts is easy to automate, but handing them over at the counter is not.
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.
Showing customers how to maintain equipment
staying humanThis work happens in the physical world: customers how, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Show customers how to maintain equipment.” (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: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: A written or video guide is easy to produce, but showing someone on their own machine means being there.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Grinding ream, rebore and re-tap parts to obtain specified clearances, using grinders, lathes, taps, reamers, boring machines and micrometers
staying humanThis work happens in the physical world: ream, rebore and re-tap parts, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Grind, ream, rebore, and re-tap parts to obtain specified clearances, using grinders, lathes, taps, reamers, boring machines, and micrometers.” (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: Machining parts to fine tolerances is hands-on work at the bench.
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 engines from equipment and positioning and bolt engines to repair stands
staying humanThis work happens in the physical world: engines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Remove engines from equipment, and position and bolt engines to repair stands.” (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: Lifting an engine onto a stand 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.
What this job pays, and how many people do it
- Median pay
- $47,880a 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
- 36,060in 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: repairs made, time spent and parts in, a record out. The rows above are exactly that shape: recording repairs made, time spent and parts. What it cannot do is be there in the room, and that is still where engines 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: dismantling engines, using hand tools and examining parts for defects is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 8% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 92% in rows it is nowhere near. That is the position, measured across 14 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. Recording repairs made, time spent and parts 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 repairs made, time spent and parts, 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 engines 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 testing and inspecting engines to determine malfunctions. 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 outdoor power equipment and other small engine mechanics (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was motorcycle mechanics: only about 11% of its durable work is work you already do. And on the numbers you do not need one. This job scores 11/100 here, with only 8% of the task list in the top band, and “test and inspect engines to determine malfunctions, to locate missing and broken…” 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.
Motorcycle Mechanics
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already repair or replace defective parts, and their equivalent is to dismantle engines and repair or replace defective parts. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Helpers--Installation, Maintenance, and Repair Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already reassemble engines after repair or maintenance work is complete, and their equivalent is to disassemble broken or defective equipment to facilitate repair and reassemble equipment when repairs…. 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. It is a pay cut, in those words: $39,630 against your $47,880, 17.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Industrial Machinery Mechanics
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already reassemble engines after repair or maintenance work is complete, and their equivalent is to reassemble equipment after completion of inspections, testing, or repairs. 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.
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: 8% of its task weight, across 14 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 dismantling engines, using hand tools and examining parts for defects 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 Vehicle technicians, mechanics and electricians 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 Vehicle technicians, mechanics and electricians, Tyre, exhaust and windscreen fitters, Electricians and electrical fitters and Vehicle body builders and repairers. 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
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for outdoor power equipment / other small engine mechanics, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 8% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for outdoor power equipment / other small engine mechanics. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for outdoor power equipment / other small engine mechanics 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 Outdoor Power Equipment and Other Small Engine Mechanics?
- Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Outdoor Power Equipment and Other Small Engine Mechanics (United States, SOC 49-3053), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 11 out of 100 (range 9–15, 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 “Outdoor Power Equipment and Other Small Engine Mechanics” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Record repairs made, time spent, and parts used” (75/100, high); “Obtain problem descriptions from customers, and prepare cost estimates for repairs” (37/100, low); “Sell parts and equipment” (24/100, low). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Outdoor Power Equipment and Other Small Engine Mechanics” 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: “Remove engines from equipment, and position and bolt engines to repair stands” (0/100, minimal); “Grind, ream, rebore, and re-tap parts to obtain specified clearances, using grinders, lathes, taps, reamers, boring machines, and micrometers” (0/100, minimal); “Replace motors” (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 “Outdoor Power Equipment and Other Small Engine Mechanics” do about AI?
- Start from the ledger rather than the headline: 8% 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 Outdoor Power Equipment and Other Small Engine Mechanics 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 14 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.