Audio and Video Technicians
In short: today's best AI models could do about 48% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 56% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 44% of the time, stays with people.
Set up, maintain, and dismantle audio and video equipment, such as microphones, sound speakers, connecting wires and cables, sound and mixing boards, video cameras, video monitors and servers, and related electronic equipment for live or recorded events, such as concerts, meetings, conventions, presentations, podcasts, news conferences, and sporting events.
70,230 people do this job in the US. $58,100 a year.
Reviewed on October 1, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on9 h
- A person checks AI's work10 h
- Desk work AI can't do yet3 h
- Stays with people18 h
Audio and Video Technicians, now: of a 40-hour week, AI could take on 9 hours, 10 hours become checking its work, 3 hours are desk work it can't do yet, and 18 hours stay with people.
The largest pieces: Record and edit audio (about 3 h); Decide formats and approaches (about 2 h, a person checks); Mix sound feeds (about 2 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 33%
- 23%
- 5%
- 10%
- 29%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 48% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 21%. Today's estimate: 48%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.35% went to this job's tasks in May 2026, the 75th largest of the 447 jobs that showed up at all; 3 of its 29 tasks showed up. Within each of Anthropic's methods, never across a change: 0.14% to 0.062% (released February and March 2025); 0.062% to 0.1% (August 2025 to February 2026); 0.34% to 0.35% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.14% | 122 of 708 | 10 of 29 |
| Released March 2025 | 0.062% | 234 of 703 | 9 of 29 |
| The week of Aug 4, 2025 (released September 2025) | 0.062% | 209 of 655 | 6 of 29 |
| The week of Nov 13, 2025 (released January 2026) | 0.093% | 162 of 672 | 10 of 29 |
| The week of Feb 5, 2026 (released March 2026) | 0.1% | 158 of 677 | 11 of 29 |
| April 2026 (released June 2026) | 0.34% | 73 of 434 | 3 of 29 |
| May 2026 (released June 2026) | 0.35% | 75 of 447 | 3 of 29 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 43% of this job's working time as physical work. For 4% of the working time, a robot has been shown doing most of the task somewhere: 4% in a purpose-built cell. 0.04 of 3, the 604th highest of the 923 jobs.
- Not physical 57%
- No robot shown 40%
- In a purpose-built cell 4%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
43% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Direct assistants during production, the in-person work, 8%
- Fix media system problems, the hands-on work, 6%
- Record and edit audio, the hands-on work, 6%
- Find settings and props, the conversation, 5%
- Build and position sets and lighting, the hands-on work, 5%
- Install and operate recording equipment, the hands-on work, 5%
What becomes checking the machine
28% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 28% is desk work that is small if wrong, which can be handed over with lighter checks.
- Decide formats and approaches, costly if wrong, 5%
- Mix sound feeds, costly if wrong, 4%
- Switch video sources, costly if wrong, 4%
- Control event lights and sound, costly if wrong, 3%
- Fix media system problems, costly if wrong, 3%
- Install and operate recording equipment, costly if wrong, 2%
What to learn
- Briefing a model and checking its drafts. About 33% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- The hands-on craft. About 29% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Running AI inside the business systems. About 23% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
- The conversations. About 15% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 29 tasks from .
| Task | ||||
|---|---|---|---|---|
| Record and edit audio material, such as movie soundtracks, using audio recording and editing equipment.Recording sessions at the equipment, then editing at the workstation.Done more than yearly by most who do it. | 15% | 3.3 h50 min to 1.3 work days | Documents 60%, hands-on 40% | 48%Desk part half by now |
| Diagnose and resolve media system problems.Hands on the equipment with checks through the system.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 9% | 1.7 h25 min to 4.2 h | Hands-on 70%, systems 30% | 29%Desk part half by now |
| Direct and coordinate activities of assistants and other personnel during production.Leading the crew on the floor during the production.Done more than monthly by most who do it. | 8% | 1.3 h25 min to 3.3 h | In person 100% | Stays with people |
| Install, adjust, and operate electronic equipment to record, edit, and transmit radio and television programs, motion pictures, video conferencing, or multimedia presentations.Setting up and running the gear, with some control through the system.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 7% | 2.5 h50 min to 6.7 h | Hands-on 70%, systems 30% | 29%Desk part half by now |
| Plan and develop pre-production ideas into outlines, scripts, story boards, and graphics, using own ideas or specifications of assignments.Writing outlines, scripts and storyboards.Done more than yearly by most who do it. | 7% | 6.7 h1.7 h to 2.5 work days | Documents 100% | 55%Half by now |
| Locate and secure settings, properties, effects, and other production necessities.Calls to find locations and props, and the legwork to secure them.Done more than monthly by most who do it.Robots: in a purpose-built cell, Amazon Sparrow (deployed). Anthropic's rating. | 6% | 2.5 h50 min to 6.7 h | Conversation 40%, hands-on 40%, documents 20% | 19%Desk part half by now |
| Determine formats, approaches, content, levels, and mediums to effectively meet objectives within budgetary constraints, using research, knowledge, and training.Planning the production's format and content at the desk.Done more than weekly by most who do it. | 5% | 5 h1.7 h to 1.7 work days | Documents 100% | 80%Half by now |
| Construct and position properties, sets, lighting equipment, and other equipment.Building and placing sets and lights by hand.Done more than weekly by most who do it.Robots: no robot shown. Anthropic's rating. | 5% | 3.3 h50 min to 1.3 work days | Hands-on 100% | Stays with people |
| Mix and regulate sound inputs and feeds or coordinate audio feeds with television pictures.At the mixing board through the program.Done more than weekly by most who do it. | 4% | 1.3 h25 min to 3.3 h | Systems 100% | 99%Half by now |
| Switch sources of video input from one camera or studio to another, from film to live programming, or from network to local programming.At the switcher console during the program.Done more than monthly by most who do it. | 4% | 50 min25 min to 2.5 h | Systems 100% | 100%Half by now |
| Design layouts of audio and video equipment and perform upgrades and maintenance.Layouts drawn, then the equipment installed and upgraded by hand.Done more than monthly by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 5 h1.7 h to 1.7 work days | Documents 50%, hands-on 50% | 40%Desk part half by now |
| Control the lights and sound of events, such as live concerts, before and after performances, and during intermissions.At the lighting and sound boards during the event.Done more than weekly by most who do it. | 3% | 1.7 h50 min to 3.3 h | Systems 100% | 100%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Broadcast Technicians31% now, 32% by 2028
- Sound Engineering Technicians32% now, 39% by 2028
- Camera Operators, Television, Video, and Film27% now, 32% by 2028
- Lighting Technicians12% now, 12% by 2028
- Electrical and Electronic Engineering Technologists and Technicians39% now, 48% by 2028
- Audiovisual Equipment Installers and Repairers7% now, 7% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Audio and Video Technicians is within reach of AI now?
- An estimated 48% of the working time is within reach of AI models now and 56% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Audio and Video Technicians come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Record and edit audio (48% of the task's time now, 60% by the end of 2028); Decide formats and approaches (80% of the task's time now, 100% by the end of 2028); and Mix sound feeds (99% of the task's time now).
- Which tasks of Audio and Video Technicians stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Direct assistants during production; Build and position sets and lighting; and Narrate and present announcements. Together such tasks are about 16% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Audio and Video Technicians learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are briefing a model and checking its drafts; reviewing work you did not do yourself; and the hands-on craft. By the end of 2028 on the long-run pace at four in five, about 43% of the working time stays with people, 28% becomes checking a model's work and 28% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Audio and Video Technicians?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.35% went to this job's tasks, the 75th largest share of the 447 jobs that showed up; 3 of its 29 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 2%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Audio and Video Technicians?
- Anthropic's robot exposure rating (September 2026) finds 43% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 4% of the working time. Its index is 0.04 of 3. One example: Amazon Sparrow for "find settings and props". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 29 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 1, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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