Research & Data

Video Editors: $30,647/yr in AI-Addressable Work (2026)

Jun 21, 2026

How do you put a defensible dollar figure on "AI for film and video editors"? Not with a guess. We take the role's real task list, weight it by how the work-year is actually spent, price it at a loaded wage, and discount by what AI genuinely touches in measured usage.

Headline: a film and video editor carries about 587 AI-addressable hours a year. At a loaded rate of $52.21/hour that is $30,647 of gross value; after a stated $12,000/year tooling budget, the Year-1 net is $18,647 per full-time employee.

Those numbers are a planning estimate built from defaults, not a quote. The three inputs — task hours, wage, and AI-addressable share — come from sealed public datasets; the three assumptions — a 2,080-hour work year, a 1.3× labor-loading multiplier, and the tooling budget — are stated in the open and adjustable in the calculator at the foot of this page. Change them and every figure recomputes.

Most of the upside here traces to one task — Organize and string together raw footage into a continuous whole according to scripts…. The Economic Index puts its AI-addressable share at 59%, so it contributes 81 hours and $4,213 on its own; the table further down shows where the rest comes from.

Who this is for

Agency owners, marketing and content leaders, and anyone building the case for an AI assistant aimed at film and video editors. If you need a number you can defend in a budget meeting, with a citation behind every cell, this is built for you.

How automatable is film and video editor work, really?

The Anthropic Economic Index puts this occupation's observed AI-exposure at 22.3% — the share of film and video editor task interactions in real Claude.ai usage that fell into an automation or augmentation pattern. Read it as "this is how people are already using AI here," not "this much of the job is automatable."

At the task level the picture is sharper. O*NET lists 22 distinct work tasks for this role. Of those, 4 have their own task-specific usage measurement in the Anthropic Economic Index; the remainder fall back to the occupation-level exposure above, and every row in the table below is labelled with which source it used (aei_task for a task's own data, aei_occ for the occupation fallback). We never silently mix the two.

For scale: BLS counts 28,860 people employed in this occupation nationally, at a mean wage of $83,530 a year. That wage is the spine of the dollar figures here.

Inside the film and video editor's task ledger

Each row is one ONET task. Importance and Relevance are sealed ONET ratings; modeled hours allocates a 2,080-hour year across tasks in proportion to Importance×Relevance; AI-addressable share is the Anthropic Economic Index usage figure; hours saved and gross value follow from them. The table shows the 14 highest-value addressable tasks.

O*NET task	Importance (1–5)	Relevance	Modeled hrs/yr	AI-addressable share	Source	Hrs saved/yr	Gross value/yr
Organize and string together raw footage into a continuous whole according to…	4.66	94.6%	137	59%	`aei_task`	81	$4,213
Program computerized graphic effects.	4.11	69.5%	89	61.4%	`aei_task`	54	$2,840
Study scripts to become familiar with production concepts and requirements.	3.96	80.6%	99	50%	`aei_task`	50	$2,590
Review footage sequence by sequence to become familiar with it before assembling…	4.55	100%	141	22.3%	`aei_occ`	32	$1,645
Review assembled films or edited videotapes on screens or monitors to determine…	4.37	95.4%	129	22.3%	`aei_occ`	29	$1,509
Select and combine the most effective shots of each scene to form a logical and…	4.58	87%	124	22.3%	`aei_occ`	28	$1,446
Set up and operate computer editing systems, electronic titling systems, video…	4.53	87.8%	123	22.3%	`aei_occ`	28	$1,436
Confer with producers and directors concerning layout or editing approaches…	3.82	65.8%	78	34.7%	`aei_task`	27	$1,415
Verify key numbers and time codes on materials.	4.16	87.6%	113	22.3%	`aei_occ`	25	$1,321
Cut shot sequences to different angles at specific points in scenes, making each…	4.37	82.4%	112	22.3%	`aei_occ`	25	$1,305
Edit films and videotapes to insert music, dialogue, and sound effects, to…	4.6	73.6%	105	22.3%	`aei_occ`	24	$1,227
Manipulate plot, score, sound, and graphics to make the parts into a continuous…	4.14	78.3%	101	22.3%	`aei_occ`	23	$1,175
Trim film segments to specified lengths and reassemble segments in sequences…	4.39	72.2%	98	22.3%	`aei_occ`	22	$1,149
Determine the specific audio and visual effects and music necessary to complete…	4.36	65.1%	88	22.3%	`aei_occ`	20	$1,029

Reading one row: the top task above is modeled at 137 hours/year; the Economic Index puts its AI-addressable share at 59%, so 81 hours are addressable, worth $4,213 at the loaded rate. Nothing is rounded up: hours saved is hours × share, full stop.

The savings calculation, unrounded

No black box. Here is every step:

Loaded hourly cost = (mean annual wage $83,530 ÷ 2,080 hours) × 1.3 loading = $52.21/hour. The 1.3× covers benefits, payroll tax, and overhead on top of base pay.
Addressable hours saved = the sum of (task hours × AI-addressable share) across the role's addressable tasks = 587 hours/year.
Gross annual value = 587 hours × $52.21 = $30,647/year.
Net Year-1 ROI = $30,647 gross − $12,000 stated tooling budget = $18,647 per FTE.

The break-even point is worth stating plainly: this role's AI-addressable work is worth $30,647 a year at the loaded rate, so any tooling spend below $30,647 per FTE is net-positive on hours alone — before any quality, speed, or capacity upside.

How this was built — and what it can't tell you

The single most important caveat: the Anthropic Economic Index measures observed Claude.ai usage patterns, not a theoretical "this much of the job can be automated." A high share means practitioners are already routing that task to AI; a low share can mean the task is hard to automate or simply that few people have tried. Treat these as a grounded default, then replace them with your own automatable share in the calculator — that is exactly what it is for.

The hour-allocation heuristic. O*NET does not publish hours per task, so we allocate the work year in proportion to each task's Importance×Relevance. It is a transparent, defensible split, not a stopwatch study; if you know your team spends disproportionate time on one task, the calculator lets you see the table and reason about it.

Why Importance×Relevance? O*NET rates each task on how important it is to the role and how relevant it is to a typical worker (the share who actually perform it). Multiplying the two ranks tasks by real time-pull — a high-importance task nearly everyone does outranks a niche one — which is precisely the weighting you want when dividing a fixed work-year. It is the most defensible allocation available short of a per-employer time study, and any row you disagree with is editable in the calculator below.

The wage is a national mean. BLS OEWS reports a $83,530 mean across all employers nationally (median $70,980). Your local, loaded cost may differ; set your own wage to localize the dollars.

What this is. A sourced, reproducible first estimate to start a buying conversation — not a guarantee of savings. The value of the method is that every input is sealed and checkable, so a skeptic can audit it rather than argue with a vendor's slide.

The sealed data behind every figure

O*NET 30_3 — task statements and Importance/Relevance ratings. This page includes information from O*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. License: CC BY 4.0. Sealed snapshot 251d3df7766aa152, evidence 9e12c3890449ec21.
BLS OEWS May 2024 — occupational mean wage and employment. Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2024. License: Public Domain (17 U.S.C. §105 — U.S. Government work). Sealed snapshot d032d178d7a95cdc, evidence 1237fd6700a000e9.
Anthropic Economic Index — observed AI task/occupation exposure (Claude.ai usage). Source: Anthropic Economic Index (https://huggingface.co/datasets/Anthropic/EconomicIndex), released under CC-BY. Reflects observed Claude.ai usage patterns, not a measure of theoretical automatability. Pinned to commit db51ecb12920, sealed snapshot c6870bb780772e4f, evidence 66b4254a97b1e852.

Every numeral on this page is reproducible from those three sealed snapshots by re-running our open model — there is no hand-entered or estimated figure in the tables or the math.

FAQ

Is "22.3% AI exposure" the share of the job AI will replace?
No. It is the share of measured Claude.ai task interactions for this occupation that showed an automation or augmentation pattern — an observed-usage signal, not a replacement forecast.

Where does the $83,530 wage come from?
BLS Occupational Employment and Wage Statistics, May 2024 — the national mean annual wage for this occupation, used verbatim from the sealed snapshot.

How do you get 587 hours saved?
For each addressable task we multiply its modeled annual hours by its AI-addressable share, then sum. Modeled hours allocate a 2,080-hour year by each task's O*NET Importance×Relevance.

Can I change the assumptions?
Yes — the calculator below this article lets you set the wage, the work-year hours, the labor-loading multiplier, the tooling budget, and each task's automatable share. The net ROI updates live.

Why these three data sources?
O*NET gives the tasks, BLS gives the labor cost, and the Anthropic Economic Index grounds "how much is AI-addressable" in real usage rather than a guess. Each is public and pinned to a sealed snapshot.

What this looks like in production

The math above is the business case; the next step is watching it run. USTA builds the agentic workflows that actually do this film and video editor work — drafting, routing, reconciling, and updating the systems of record — so the addressable hours above convert into capacity you keep instead of headcount you chase.

See how AI agents handle film and video editors → — or bring this page's numbers to a scoping call and we will pressure-test them against your actual task mix.

More automation-ROI breakdowns

Same sealed O*NET + BLS + Anthropic Economic Index method, other roles:

Run your own numbers

The interactive calculator below loads this role's sealed task table. Adjust the wage, hours, loading, tooling budget, or any task's automatable share, and watch the net Year-1 ROI move. The defaults are the sourced figures above; the controls are yours.

Loading the interactive ROI calculator…