US dataswitch to UK
Astronomers
analyzing research data to determine its significance, supervising students' research on celestial and astronomical phenomena and teaching astronomy or astrophysics. 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: analyzing research data to determine its significance. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, collaborating with other astronomers to carry out research projects, stays yours. The tools change hands, the accountability doesn't.
Your week, as this page understands it
Observe, research, and interpret astronomical phenomena to increase basic knowledge or apply such information to practical problems. The job title says “astronomers”. The real job is the part underneath: collaborating with other astronomers to carry out research projects. 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 astronomers is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is collaborating with other astronomers to carry out research projects, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 17%
- changing shape
- 19%
- staying human
- 64%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 38 out of 100 (32–45 allowing for uncertainty): low exposure, across 17 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 astronomers 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
3 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.
Analyzing research data to determine its significance
It is the same call made over and over on research data, with a right answer to check it against. That is what a model is trained on.
importance 5 · CoreSource: “Analyze research data to determine its significance, using computers.” (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: Software already handles much of the number crunching, but judging what a result means still takes an astronomer.
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.
Developing and modifying astronomy-related programs for public presentation
This is reading one thing and writing another: astronomy-related programs in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop and modify astronomy-related programs for public presentation.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Public astronomy programs draw on well-documented material that AI writes well with light editing.
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 4/4.
Calculating orbits and determining sizes
This is reading one thing and writing another: orbits in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Calculate orbits and determine sizes, shapes, brightness, and motions of different celestial bodies.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very 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: Orbits, sizes and brightness follow known equations that computers solve routinely.
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.
Changing shape
3 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 theories based on personal observations or on observations and theories of other astronomers
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 theories based on personal observations or on observations and theories of other astronomers.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (36–44 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Building genuinely new theory from observations is where machines offer starting points at best.
The five ratings: output a model can produce 1/4 · 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.
Reviewing scientific proposals and researching papers
The software now makes the first pass at scientific proposals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Review scientific proposals and research papers.” (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: A machine can summarize a paper's claims, but judging whether the science holds up is expert work.
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.
Developing instrumentation and software for astronomical observation and analysis
The software now makes the first pass at instrumentation, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Develop instrumentation and software for astronomical observation and analysis.” (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: Analysis software can largely be drafted by machine, but building instruments needs engineers on site.
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
11 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.
Presenting research findings at scientific conferences and in papers written for scientific journals
The ratings behind this row put research findings well outside what today's tools can do on their own.
importance 5 · CoreSource: “Present research findings at scientific conferences and in papers written for scientific journals.” (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: Papers draft quickly with help, though the science and the conference talk still come from 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 1/4 · how much data exists 3/4.
Studying celestial phenomena, using a variety of ground-based and space-borne telescopes and scientific instruments
The ratings behind this row put celestial phenomena well outside what today's tools can do on their own.
importance 5 · CoreSource: “Study celestial phenomena, using a variety of ground-based and space-borne telescopes and scientific instruments.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (26–34 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Telescope observations create new data from the sky, which no model can generate for you.
The five ratings: output a model can produce 1/4 · 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.
Collaborating with other astronomers to carry out research projects
The value here is that a specific person handles other astronomers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Collaborate with other astronomers to carry out research projects.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 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: Research collaborations run on shared judgment and give-and-take between colleagues.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Mentoring graduate students and junior colleagues
The value here is that a specific person handles graduate students and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Mentor graduate students and junior colleagues.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (14–22 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Mentoring works because a specific person invests in someone's development over time.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Show the other 7 tasks
Raising funds for scientific research
staying humanThe value here is that a specific person handles funds and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Raise funds for scientific research.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Proposal text drafts easily, yet funding follows a researcher's track record and relationships.
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 2/4 · how much data exists 2/4.
Teaching astronomy or astrophysics
staying humanThe value here is that a specific person handles astronomy and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Teach astronomy or astrophysics.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Astronomy material is abundant online, but a class still runs on a teacher in the room.
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 2/4 · how much data exists 4/4.
Conducting question-and-answer presentations on astronomy topics with public audiences
staying humanThe value here is that a specific person handles question-and-answer presentations and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Conduct question-and-answer presentations on astronomy topics with public audiences.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Answers are easy to draft, but a public audience turns up to question a real astronomer.
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 2/4 · how much data exists 4/4.
Measuring radio, infrared, gamma and x-ray emissions from extraterrestrial sources
staying humanThe ratings behind this row put radio, infrared, gamma and x-ray emissions well outside what today's tools can do on their own.
importance 4 · CoreSource: “Measure radio, infrared, gamma, and x-ray emissions from extraterrestrial sources.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (26–34 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Taking these measurements requires real instruments pointed at real sources.
The five ratings: output a model can produce 1/4 · 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.
Supervising students' research on celestial and astronomical phenomena
staying humanThe value here is that a specific person handles students' research and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Supervise students' research on celestial and astronomical phenomena.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 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: Guiding a student's own research depends on knowing that student and their project closely.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Serving on professional panels and committees
staying humanThe value here is that a specific person handles professional panels and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Serve on professional panels and committees.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Panel and committee work depends on standing, judgment and discussion among named people.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 1/4.
Directing the operations of a planetarium
staying humanThis work happens in the physical world: the operations of a planetarium, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Direct the operations of a planetarium.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 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: Running a planetarium means managing staff, shows and a physical building.
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 2/4.
What this job pays, and how many people do it
- Median pay
- $128,820a 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
- 2,120in 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 research data, where the right answer is already known. The rows above are exactly that shape: analyzing research data to determine its significance and developing and modifying astronomy-related programs for public presentation. What it cannot do is be trusted in person, which is what other astronomers run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: collaborating with other astronomers to carry out research projects is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 17% of this job's task weight sits in rows the software is already learning, 19% in rows that change shape rather than disappear, and 64% in rows it is nowhere near. That is the position, measured across 17 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. Analyzing research data to determine its significance 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 research data, 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 other astronomers 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 developing theories based on personal observations or on observations and theories of other astronomers. 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 astronomers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was physicists: only about 13% of its durable work is work you already do. And on the numbers you do not need one. This job scores 38/100 here, with only 17% of the task list in the top band, and “present research findings at scientific conferences and in papers written for scientific…” 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.
Physicists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present research findings at scientific conferences and in papers written for scientific journals, and their equivalent is to report experimental results by writing papers for scientific journals or by presenting information…. Across both published task lists that is about 13% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Sociologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present research findings at scientific conferences and in papers written for scientific journals, and their equivalent is to present research findings at professional meetings. 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: $106,030 against your $128,820, 17.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Social Science Research Assistants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present research findings at scientific conferences and in papers written for scientific journals, and their equivalent is to present research findings to groups of people. 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. I will not move you off one melting floe onto another: 72% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. It is a pay cut, in those words: $61,990 against your $128,820, 51.9% 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: 17% of its task weight, across 17 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 collaborating with other astronomers to carry out research projects 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 Physical 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 Physical 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 astronomers, 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 17% 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 astronomers. 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 astronomers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Astronomers?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Astronomers (United States, SOC 19-2011), 17% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 38 out of 100 (range 32–45, band: low). 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 “Astronomers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Calculate orbits and determine sizes, shapes, brightness, and motions of different celestial bodies” (83/100, very high); “Develop and modify astronomy-related programs for public presentation” (70/100, high); “Analyze research data to determine its significance, using computers” (65/100, 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 “Astronomers” stay human?
- About 64% 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: “Direct the operations of a planetarium” (11/100, minimal); “Serve on professional panels and committees” (13/100, minimal); “Mentor graduate students and junior colleagues” (18/100, 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 “Astronomers” do about AI?
- Start from the ledger rather than the headline: 17% of this job's weighted core work is exposed, and roughly 64% 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 Astronomers 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 17 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.