US dataswitch to UK
Political Scientists
teaching political science, serving on committees and forecasting political, economic and social trends. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: maintaining current knowledge of government policy decisions. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, teaching political science, stays yours. The tools change hands, the accountability doesn't.
Your week, as this page understands it
Study the origin, development, and operation of political systems. May study topics, such as public opinion, political decisionmaking, and ideology. May analyze the structure and operation of governments, as well as various political entities. May conduct public opinion surveys, analyze election results, or analyze public documents. The job title says “political scientists”. The real job is the part underneath: teaching political science. 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 political scientists 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 teaching political science, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 34%
- changing shape
- 31%
- staying human
- 35%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 47 out of 100 (40–54 allowing for uncertainty): partial 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 political scientists 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
5 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Maintaining current knowledge of government policy decisions
This is reading one thing and writing another: current knowledge of government policy decisions in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Maintain current knowledge of government policy decisions.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking what governments have decided is reading and summarising public documents, which software does very well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Collecting, analyzing and interpreting data
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Collect, analyze, and interpret data, such as election results and public opinion surveys, reporting on findings, recommendations, and conclusions.” (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: Election results and survey data are numbers on a computer, and analysing them is a well-documented job for software.
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.
Interpreting and analyzing policies, public issues, legislation or the operations of governments, businesses and organizations
It is the same call made over and over on policies, public issues, with a right answer to check it against. That is what a model is trained on.
importance 4 · CoreSource: “Interpret and analyze policies, public issues, legislation, or the operations of governments, businesses, and organizations.” (O*NET task statement)
How this row was scored
Exposure score: 65 out of 100 (58–72 allowing for uncertainty): high 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 legislation and public record are all online, but judging what a policy really means is contested and needs argument.
The five ratings: output a model can produce 2/4 · 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 4/4.
Evaluating programs and policies and making related recommendations to institutions and organizations
This is reading one thing and writing another: programs in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Evaluate programs and policies, and make related recommendations to institutions and organizations.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Evaluating a programme against its data and writing recommendations is desk work software drafts well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Changing shape
4 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.
Developing and testing theories, using information from interviews, newspapers, periodicals, case law, historical papers, polls or statistical sources
The software now makes the first pass at theories, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Develop and test theories, using information from interviews, newspapers, periodicals, case law, historical papers, polls, or statistical sources.” (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: Software can test theories against data, but framing an original argument and running interviews still needs the researcher.
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.
Disseminating research results through academic publications
The software now makes the first pass at research results, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Disseminate research results through academic publications, written reports, or public presentations.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning findings into papers, reports and slides is writing work software drafts well, though conferences are attended in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Identifying issues for research and analysis
The software now makes the first pass at issues, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Identify issues for research and analysis.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 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: Choosing what is worth researching depends on judgement about which questions matter now.
The five ratings: output a model can produce 2/4 · 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.
Staying human
5 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.
Teaching political science
This work happens in the physical world: political science, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Teach political science.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low 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: Teaching materials can be drafted by software, but the classroom hours are spent with students in the room.
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 2/4 · how much data exists 3/4.
Advising political science students
The value here is that a specific person handles political science students and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Advise political science students.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Good advice depends on knowing the individual student and their situation over time.
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 3/4 · how much data exists 2/4.
Serving on committees
This work happens in the physical world: committees, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Serve on committees.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Committee work is people sitting in a room reaching agreement.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 1/4.
Show the other 4 tasks
Writing drafts of legislative proposals
shifting to AIThis is reading one thing and writing another: drafts of legislative proposals in, a record out. That is the shape today's tools are built for.
importance 2 · SupplementalSource: “Write drafts of legislative proposals, and prepare speeches, correspondence, and policy papers for governmental use.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (65–79 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drafting bills, speeches and policy papers follows well-documented forms, and software produces strong first drafts.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Forecasting political, economic and social trends
changing shapeThe software now makes the first pass at political, economic and social trends, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Forecast political, economic, and social trends.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 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: Software can extrapolate from data, but political forecasts hinge on judgement about events that have not happened.
The five ratings: output a model can produce 2/4 · 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.
Consulting with and advising government officials
staying humanThe value here is that a specific person handles and advising government officials and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Consult with and advise government officials, civic bodies, research agencies, the media, political parties, and others concerned with political issues.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Officials take advice from people they know and trust, in conversation.
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 3/4 · how much data exists 2/4.
Providing media commentary or criticism related to public policy and political issues and events
staying humanThis work happens in the physical world: media commentary, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Provide media commentary or criticism related to public policy and political issues and events.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low 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: Commentary needs a recognised person live on air or in print under their own name.
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 2/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $142,080a 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
- 5,540in 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: current knowledge of government policy decisions in, a record out. The rows above are exactly that shape: maintaining current knowledge of government policy decisions and collecting, analyzing and interpreting data. What it cannot do is be there in the room, and that is still where political science gets done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Your week is splitting in two, and which half fills it is the whole question. Maintaining current knowledge of government policy decisions is going; teaching political science is not.
So, given all that: 34% of this job's task weight sits in rows the software is already learning, 31% in rows that change shape rather than disappear, and 35% in rows it is nowhere near. That is the position, measured across 14 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (teaching political science) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles teaching political science, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to political scientists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was communications teachers, postsecondary: only about 3% of its durable work is work you already do and it pays 44.7% less. Your own job splits about 34/66: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “teach political science”, and let the exposed end go.
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.
Communications Teachers, Postsecondary
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide media commentary or criticism related to public policy and political issues and…, and their equivalent is to prepare and deliver lectures to undergraduate or graduate students on topics. 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: $78,580 against your $142,080, 44.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Economists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already forecast political, economic, and social trends, and their equivalent is to develop economic guidelines and standards, and prepare points of view used in forecasting…. 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. It is a pay cut, in those words: $124,720 against your $142,080, 12.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Library Science Teachers, Postsecondary
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already serve on committees, and their equivalent is to serve on academic or administrative committees that deal with institutional policies, departmental matters…. Across both published task lists that is about 0% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 0% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $80,340 against your $142,080, 43.5% 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: 34% of its task weight, across 14 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Social and humanities scientists 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 Social and humanities scientists. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for political scientists, 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 34% 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 political scientists. 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 political scientists 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 Political Scientists?
- Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Political Scientists (United States, SOC 19-3094), 34% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 47 out of 100 (range 40–54, band: partial). 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 “Political Scientists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Maintain current knowledge of government policy decisions” (83/100, very high); “Collect, analyze, and interpret data, such as election results and public opinion surveys, reporting on findings, recommendations, and conclusions” (75/100, high); “Write drafts of legislative proposals, and prepare speeches, correspondence, and policy papers for governmental use” (72/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Political Scientists” stay human?
- About 35% 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: “Serve on committees” (9/100, minimal); “Provide media commentary or criticism related to public policy and political issues and events” (20/100, low); “Teach political science” (20/100, low). 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 “Political Scientists” do about AI?
- Start from the ledger rather than the headline: 34% of this job's weighted core work is exposed, and roughly 35% 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 Political Scientists 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.