US dataswitch to UK
Film and Video Editors
organizing and string together raw footage into a continuous whole according to scripts or the instructions of directors and producers, cutting shot sequences to different angles at specific points in scenes and determining the specific audio and visual effects and music necessary to complete films. 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: reviewing footage sequence by sequence to become familiar with it before assembling it into a final product. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, manipulating plot, score, sound and graphics to make the parts into a continuous whole, stays yours. The tools change, the responsibility doesn't.
Your week, as this page understands it
Edit moving images on film, video, or other media. May work with a producer or director to organize images for final production. May edit or synchronize soundtracks with images. The job title says “film” or “video editors”: officially one job, two names. The real job is the part underneath: manipulating plot, score, sound and graphics to make the parts into a continuous whole. 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 film and video editors is not one task. It is 22 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is manipulating plot, score, sound and graphics to make the parts into a continuous whole, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 30%
- changing shape
- 44%
- staying human
- 26%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 52 out of 100 (45–58 allowing for uncertainty): partial exposure, across 22 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 film and video editors 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.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- 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
6 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.
Reviewing footage sequence by sequence to become familiar with it before assembling it into a final product
This is reading one thing and writing another: footage sequence in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Review footage sequence by sequence to become familiar with it before assembling it into a final product.” (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: Watching through and logging footage is exactly the kind of review software can now do quickly.
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 key numbers and time codes on materials
This is reading one thing and writing another: key numbers in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Verify key numbers and time codes on materials.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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 timecodes and key numbers is exact, rule-based verification that software does faster and more accurately.
The five ratings: output a model can produce 4/4 · 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.
Studying scripts to become familiar with production concepts and requirements
This is reading one thing and writing another: scripts in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Study scripts to become familiar with production concepts and requirements.” (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: Reading a script and pulling out what the production needs is straightforward text work.
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.
Trimming film segments to specified lengths and reassemble segments in sequences that present stories with maximum effect
This is reading one thing and writing another: film segments in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Trim film segments to specified lengths and reassemble segments in sequences that present stories with maximum effect.” (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: Trimming clips to length and reordering them is mechanical work software does accurately.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
9 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.
Organizing and string together raw footage into a continuous whole according to scripts or the instructions of directors and producers
The software now makes the first pass at together raw footage, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Organize and string together raw footage into a continuous whole according to scripts or the instructions of directors and producers.” (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 assemble footage to a script, but shaping it into something that holds together still needs an editor.
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.
Reviewing assembled films or edited videotapes on screens or monitors to determine if corrections
The software now makes the first pass at assembled films, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Review assembled films or edited videotapes on screens or monitors to determine if corrections are necessary.” (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 flag technical faults reliably, but deciding whether a cut works still needs a viewer with taste.
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.
Selecting and combining the most effective shots of each scene to form a logical and smoothly running story
The software now makes the first pass at the most effective shots of each scene, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Select and combine the most effective shots of each scene to form a logical and smoothly running story.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Picking the shot that makes a scene work is a craft judgment that is hard to write down.
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 2/4.
Staying human
7 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.
Manipulating plot, score, sound and graphics to make the parts into a continuous whole
The value here is that a specific person handles plot, score, sound and graphics and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Manipulate plot, score, sound, and graphics to make the parts into a continuous whole, working closely with people in audio, visual, music, optical, or special effects departments.” (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: Pulling picture, sound, music and effects together depends on close back-and-forth with the other departments.
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.
Supervising and coordinating activities of workers engaged in film editing
The value here is that a specific person handles activities of workers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Supervise and coordinate activities of workers engaged in film editing, assembling, and recording activities.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 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: Running an edit team depends on day-to-day working relationships with the people doing the work.
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 2/4.
Recording needed sounds or obtaining them from sound effects libraries
This work happens in the physical world: needed sounds, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Record needed sounds or obtain them from sound effects libraries.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 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: Libraries are easy to search, but recording new sounds means going out with a microphone.
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 0/4 · how much data exists 3/4.
Show the other 12 tasks
Marking frames where a particular shot or piece of sound is to begin or end
shifting to AIThis is reading one thing and writing another: frames in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Mark frames where a particular shot or piece of sound is to begin or end.” (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: Marking where a shot or sound starts and ends is precise, rule-based work 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 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Programing computerized graphic effects
shifting to AIThis is reading one thing and writing another: computerized graphic effects in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Program computerized graphic effects.” (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: Programming graphic effects is code and parameters, which software generates 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.
Editing films and videotapes to insert music
changing shapeThe software now makes the first pass at films, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Edit films and videotapes to insert music, dialogue, and sound effects, to arrange films into sequences, and to correct errors, using editing equipment.” (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: Editing tools automate a lot of the mechanics, but the choices about music, dialogue and timing remain judgment calls.
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.
Setting up and operating computer editing systems
changing shapeThe software now makes the first pass at and operating computer editing systems, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Set up and operate computer editing systems, electronic titling systems, video switching equipment, and digital video effects units to produce a final product.” (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: Modern editing, titling and effects systems are heavily automated, though some hardware still needs hands on it.
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 0/4 · how much data exists 3/4.
Cutting shot sequences to different angles at specific points in scenes
changing shapeThe software now makes the first pass at shot sequences, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Cut shot sequences to different angles at specific points in scenes, making each individual cut as fluid and seamless as possible.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Making a cut feel seamless depends on a feel for rhythm that is not written down anywhere.
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 2/4.
Determining the specific audio and visual effects and music necessary to complete films
changing shapeThe software now makes the first pass at the specific audio, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Determine the specific audio and visual effects and music necessary to complete films.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Deciding which effects and music a film needs is a creative call that varies with every project.
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 2/4.
Developing post-production models for films
changing shapeThe software now makes the first pass at post-production models, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Develop post-production models for films.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Post-production models depend on the specific film and studio, so software can only offer starting points.
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 2/4.
Piecing sounds together to develop film soundtracks
changing shapeThe software now makes the first pass at sounds together, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Piece sounds together to develop film soundtracks.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Software can layer sound, but building a soundtrack that fits the picture is still a craft judgment.
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 2/4.
Collaborating with music editors to select appropriate passages of music and develop production scores
staying humanThe value here is that a specific person handles music editors and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Collaborate with music editors to select appropriate passages of music and develop production scores.” (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: Choosing music passages happens in conversation with a music editor who knows the film.
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.
Discussing the sound requirements of pictures with sound effects editors
staying humanThe value here is that a specific person handles the sound requirements of pictures and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Discuss the sound requirements of pictures with sound effects editors.” (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: Working out sound requirements happens through conversation with the sound editor.
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.
Conferring with producers and directors concerning layout or editing approaches needed to increase dramatic or entertainment value of productions
staying humanThe value here is that a specific person handles producers and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Confer with producers and directors concerning layout or editing approaches needed to increase dramatic or entertainment value of productions.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 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: Agreeing an editing approach with producers and directors is a discussion between people who know the project.
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 2/4.
Conducting film screenings for directors and members of production staffs
staying humanThis work happens in the physical world: film screenings, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Conduct film screenings for directors and members of production staffs.” (O*NET task statement)
How this row was scored
Exposure score: 4 out of 100 (0–8 allowing for uncertainty): minimal exposure, high 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 screening for the director means everyone in a room watching together.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 1/4.
What this job pays, and how many people do it
- Median pay
- $75,420a 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
- 25,610in 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: footage sequence in, a record out. The rows above are exactly that shape: reviewing footage sequence by sequence to become familiar with it before assembling it into a final product and verifying key numbers and time codes on materials. What it cannot do is be trusted in person, which is what plot, score, sound and graphics 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
Your week is splitting in two, and which half fills it is the whole question. Reviewing footage sequence by sequence to become familiar with it before assembling it into a final product is going; manipulating plot, score, sound and graphics to make the parts into a continuous whole is not.
So, given all that: 30% of this job's task weight sits in rows the software is already learning, 44% in rows that change shape rather than disappear, and 26% in rows it is nowhere near. That is the position, measured across 22 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 (manipulating plot, score, sound and graphics to make the parts into a continuous whole) 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 manipulating plot, score, sound and graphics to make the parts into a continuous whole, 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 film and video editors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was sound engineering technicians: only about 5% of its durable work is work you already do. Your own job splits about 30/70: 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 “organize and string together raw footage into a continuous whole according to…”, 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.
Sound Engineering Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already record needed sounds or obtain them from sound effects libraries, and their equivalent is to record speech, music, and other sounds on recording media, using recording equipment. 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.
News Analysts, Reporters, and Journalists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already trim film segments to specified lengths and reassemble segments in sequences that present…, and their equivalent is to present news stories, and introduce in-depth videotaped segments or live transmissions from on-the-scene…. 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. It is a pay cut, in those words: $62,200 against your $75,420, 17.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Technical Writers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already study scripts to become familiar with production concepts and requirements, and their equivalent is to interview production and engineering personnel and read journals and other material to become…. 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. I will not move you off one melting floe onto another: 69% of its own task list already scores in the top exposure band (69/100 in this release), so the same software is eating it.
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: 30% of its task weight, across 22 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.
If you run a team doing this job
If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are reviewing footage sequence by sequence to become familiar with it before assembling it into a final product, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Arts officers, producers and directors 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 Arts officers, producers and directors. 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 film / video editors, 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 30% 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 film / video editors. 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 film / video editors launches. Nothing else.
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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 Film and Video Editors?
- Not as a job, but it is already doing parts of the work. Across the 22 official task statements scored for Film and Video Editors (United States, SOC 27-4032), 30% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 52 out of 100 (range 45–58, 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 “Film and Video Editors” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Study scripts to become familiar with production concepts and requirements” (75/100, high); “Mark frames where a particular shot or piece of sound is to begin or end” (75/100, high); “Program computerized graphic effects” (75/100, 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 “Film and Video Editors” 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: “Conduct film screenings for directors and members of production staffs” (4/100, minimal); “Supervise and coordinate activities of workers engaged in film editing, assembling, and recording activities” (17/100, minimal); “Confer with producers and directors concerning layout or editing approaches needed to increase dramatic or entertainment value of productions” (17/100, 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 “Film and Video Editors” do about AI?
- Start from the ledger rather than the headline: 30% 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 Film and Video Editors 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 22 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.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- 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.