US dataswitch to UK
Judicial Law Clerks
preparing briefs, legal memoranda or statements of issues involved in cases, attending court sessions to hear oral arguments or recording necessary case information and entering information into computerized court calendar. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: researching laws, court decisions, documents, opinions, briefs or other information related to cases before the court. The tasks, though, are not you.
It would be a lie to soften that; conferring with judges concerning legal questions is what this work rebuilds around. Your move starts there.
Your week, as this page understands it
Assist judges in court or by conducting research or preparing legal documents. The job title says “judicial law clerks”. The real job is the part underneath: conferring with judges concerning legal questions. 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 judicial law clerks is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with judges concerning legal questions, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 69%
- changing shape
- 6%
- staying human
- 26%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 58 out of 100 (52–64 allowing for uncertainty): partial exposure, across 18 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 judicial law clerks 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
11 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.
Preparing briefs, legal memoranda or statements of issues involved in cases, including appropriate suggestions or recommendations
This is reading one thing and writing another: briefs, legal memoranda or statements of issues in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Prepare briefs, legal memoranda, or statements of issues involved in cases, including appropriate suggestions or recommendations.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Drafting a memo on the issues in a case is writing work software does well, with checking.
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 3/4.
Researching laws, court decisions, documents, opinions, briefs or other information related to cases before the court
This is reading one thing and writing another: laws, court decisions, documents, opinions in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Research laws, court decisions, documents, opinions, briefs, or other information related to cases before the court.” (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: Case law and statutes are published in full, so searching and summarizing them suits software 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.
Reviewing complaints, petitions, motions or pleadings that have been filed to determine issues involved or basis
This is reading one thing and writing another: complaints, petitions, motions or pleadings in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Review complaints, petitions, motions, or pleadings that have been filed to determine issues involved or basis for relief.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Reading filings to identify the issues raised is document analysis software handles well.
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 3/4.
Drafting or proofreading judicial opinions
This is reading one thing and writing another: judicial opinions in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Draft or proofread judicial opinions, decisions, or citations.” (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: Opinion drafting follows established forms and cited sources, though the judge decides and signs.
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.
Keeping abreast of changes in the law and informing judges when cases are affected by such changes
This is reading one thing and writing another: abreast of changes in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Keep abreast of changes in the law and inform judges when cases are affected by such changes.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (66–74 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: Tracking published changes in the law and flagging affected cases is exactly what search tools are for.
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.
Changing shape
1 taskTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Communicating with counsel regarding case management or procedural requirements
The software now makes the first pass at counsel regarding case management, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Communicate with counsel regarding case management or procedural requirements.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Routine messages to counsel about deadlines and procedure are easy to draft.
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 1/4 · how much data exists 3/4.
Staying human
6 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.
Conferring with judges concerning legal questions
The value here is that a specific person handles judges concerning legal questions and stands behind it. That is earned, not computed.
importance 5 · CoreSource: “Confer with judges concerning legal questions, construction of documents, or granting of orders.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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: Talking legal questions through with a judge is a working conversation between two people.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Attending court sessions to hear oral arguments or recording necessary case information
This work happens in the physical world: court sessions, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Attend court sessions to hear oral arguments or record necessary case information.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Sitting in court to hear argument and note what happened means being in the courtroom.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Participating in conferences or discussions between trial attorneys and judges
This work happens in the physical world: conferences, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Participate in conferences or discussions between trial attorneys and judges.” (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; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Sitting in on discussions between judges and trial attorneys is a live, in-person role.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Performing courtroom duties, including calling calendars, administering oaths and swearing in jury panels and witnesses
This work happens in the physical world: courtroom duties, including calling calendars, in a real place. Software cannot follow it there.
importance 2 · SupplementalSource: “Perform courtroom duties, including calling calendars, administering oaths, and swearing in jury panels and witnesses.” (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; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Calling the calendar and swearing in juries is done aloud in the courtroom by a court officer.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 3/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Show the other 8 tasks
Entering information into computerized court calendar
shifting to AIThis is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Enter information into computerized court calendar, filing, or case management systems.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Entering case details into the court’s system is routine data work software does accurately.
The five ratings: output a model can produce 4/4 · 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.
Coordinating judges' meeting and appointment schedules
shifting to AIThis is reading one thing and writing another: judges' in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Coordinate judges' meeting and appointment schedules.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 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: Arranging meetings and appointments around a judge’s calendar is standard scheduling work.
The five ratings: output a model can produce 4/4 · 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.
Reviewing dockets of pending litigation to ensure adequate progress
shifting to AIThis is reading one thing and writing another: dockets of pending litigation in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review dockets of pending litigation to ensure adequate progress.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Checking dockets for cases that have stalled is tracking work software does reliably.
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.
Verifying that all files, complaints or other papers are available and in the proper order
shifting to AIThis is reading one thing and writing another: all files, complaints or other papers are available in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Verify that all files, complaints, or other papers are available and in the proper order.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Checking a file is complete and in order is a checklist task, though paper files need handling.
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.
Preparing periodic reports on court proceedings
shifting to AIThis is reading one thing and writing another: periodic reports in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Prepare periodic reports on court proceedings, as required.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Periodic reports on court activity are compiled from case records, which software summarizes 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 3/4.
Responding to questions from judicial officers or court staff on general legal issues
shifting to AIThis is reading one thing and writing another: questions in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Respond to questions from judicial officers or court staff on general legal issues.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 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: General legal questions have documented answers, so drafting a reply is well within software’s reach.
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 1/4 · how much data exists 4/4.
Maintaining judges' law libraries by assembling or updating appropriate documents
staying humanThis work happens in the physical world: judges' law libraries, in a real place. Software cannot follow it there.
importance 2 · SupplementalSource: “Maintain judges' law libraries by assembling or updating appropriate documents.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 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: Deciding what to add to the library is simple; shelving and updating the volumes is physical.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Supervising law students, volunteers or other personnel assigned to the court
staying humanThe value here is that a specific person handles law students, volunteers or other personnel and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Supervise law students, volunteers, or other personnel assigned to the court.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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: Overseeing students and volunteers depends on watching and guiding them day to day.
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 2/4.
What this job pays, and how many people do it
- Median pay
- $64,920a 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
- 13,290in 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: laws, court decisions, documents, opinions in, a record out. The rows above are exactly that shape: researching laws, court decisions, documents, opinions and preparing briefs, legal memoranda or statements of issues involved in cases. What it cannot do is be trusted in person, which is what judges concerning legal questions 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
The exposed part of your job is the biggest part, and I am not going to dress that up: researching laws, court decisions, documents, opinions, briefs or other information related to cases before the court is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 69% of this job's task weight sits in rows the software is already learning, 6% in rows that change shape rather than disappear, and 26% in rows it is nowhere near. That is the position, measured across 18 scored tasks. It is not a forecast about you.
What you have that the software does not is conferring with judges concerning legal questions, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of laws, court decisions, documents, opinions you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of laws, court decisions, documents, opinions, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is researching laws, court decisions, documents, opinions, briefs or other information related to cases before the court” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of conferring with judges concerning legal questions you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 judicial law clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was judges, magistrate judges, and magistrates: only about 5% of its durable work is work you already do and the 2.4× pay gap is the market pricing a barrier. Your own job splits about 69/31: 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 “confer with judges concerning legal questions, construction of documents, or granting of…”, 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.
Judges, Magistrate Judges, and Magistrates
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already respond to questions from judicial officers or court staff on general legal issues, and their equivalent is to supervise other judges, court officers, and the court's administrative staff. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $153,990 against your $64,920 is 2.37× (OEWS May 2025 (both)), and you would be crossing it holding about 5% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Arbitrators, Mediators, and Conciliators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already research laws, court decisions, documents, opinions, briefs, or other information related to cases…, and their equivalent is to prepare written opinions or decisions regarding cases. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Court Reporters and Simultaneous Captioners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare periodic reports on court proceedings, as required, and their equivalent is to log and store exhibits from court proceedings. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% 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: 69% of its task weight, across 18 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: conferring with judges concerning legal questions is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
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 Legal associate professionals 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 Legal associate professionals. 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 judicial law clerks, 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 69% 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 judicial law clerks. 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 judicial law clerks 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 Judicial Law Clerks?
- Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Judicial Law Clerks (United States, SOC 23-1012), 69% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 58 out of 100 (range 52–64, 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 “Judicial Law Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Enter information into computerized court calendar, filing, or case management systems” (93/100, very high); “Research laws, court decisions, documents, opinions, briefs, or other information related to cases before the court” (83/100, very high); “Coordinate judges' meeting and appointment schedules” (79/100, 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 “Judicial Law Clerks” stay human?
- About 26% 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: “Perform courtroom duties, including calling calendars, administering oaths, and swearing in jury panels and witnesses” (0/100, minimal); “Participate in conferences or discussions between trial attorneys and judges” (9/100, minimal); “Attend court sessions to hear oral arguments or record necessary case information” (14/100, 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 “Judicial Law Clerks” do about AI?
- Start from the ledger rather than the headline: 69% of this job's weighted core work is exposed, and roughly 26% 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 Judicial Law Clerks 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 18 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.