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needsahuman.

Will AI replace audio and video technicians?

A little.

Setup, rigging, and live fixes in real venues keep most of this work with people, while editing and file prep move into software. This job scores 70 out of 100 on (higher is safer). Today AI could do about 2% of the work by itself, people do 55% with AI’s help, and 43% still needs a person.

Updated 3 October 2026 27-4011 3417 2026-Q4
Arts, Design, Entertainment, Sports, and MediaAudio and Video Technicians27-4011 · 2026-Q4
2% AI does it55% AI helps43% needs a human
Your job's name, lit by the work that still needs a human.Needs a human 43%AI helps 55%AI does it 2%

AI does it: AI can do the task largely by itself. AI helps: a person still does it, faster with AI. Needs a human: AI can do little of it yet.

Why this work keeps a person in the room

Ask whether AI will replace video technicians and the answer starts with where the work happens. Audio and video technicians set up and operate sound, projection, and recording gear for live events, meetings, classes, and broadcasts. That means hauling cases, running cable, mounting cameras, placing microphones, and checking a signal path end to end before anyone walks in. Software can plan a layout. It cannot tape down a cable run or re-seat a loose connector behind a rack.

The second reason is failure under time pressure. A large part of the job is diagnosing equipment problems and fixing them while the room is full: a dead feed, feedback in the house system, a laptop that will not hand off to the projector. These fixes are physical and improvised, and they happen in minutes. A model can suggest likely causes. Someone still has to crawl behind the stage and swap the cable.

The flip side is real. Plenty of this job is file work: digitizing and compressing audio and video, trimming and assembling recordings, labeling and archiving media, monitoring levels during capture. Those tasks have been moving into software for years, and generative tools have sped that up. Our Can AI do it? score for this occupation, which estimates the share of task time AI can handle today, sits at 31 out of 100. The headline Still needs a human figure is 70 out of 100 (higher is safer).

What AI handles, what it assists, and what stays with people

Start with the tasks AI can take end to end: 2% of task time on our split. These are the desk-bound pieces, such as converting and compressing recorded audio and video into delivery formats, and producing captions, transcripts, and rough cuts from raw footage. The work still gets checked, but the first pass no longer needs a technician’s hands on a timeline.

Next come the tasks where AI assists a person rather than finishing the job: 55% of task time. Mixing and balancing multiple sound inputs is one. Noise reduction, auto-leveling, and speaker tracking now do a lot of the grunt work, while the technician decides what the room should sound and look like. Documenting setups and keeping equipment records is another: tools draft and sort, people confirm what is actually in the case.

Then there is the work that stays with people: 43% of task time. Installing and striking equipment in a venue sits here, along with live troubleshooting and the coordination side of the role, such as directing assistants and showing clients or staff how to use a system without breaking it. None of that is a file. It is a room, a schedule, and other humans.

What the evidence shows so far

No study has yet tested AI against audio and video technicians on their own tasks under real conditions. Our evidence grade for Is it better than a person? reads D, which is the grade we use when there is no direct head-to-head measurement. For that reason we publish no parity number for this job, and you should treat any outside claim of one with care.

What would settle it is specific: a benchmark that scores recorded mixes and edits from AI systems against work by qualified technicians, judged blind; a timed test of fault diagnosis on real AV hardware; and field data from venues on how many crew hours an event takes with current tools compared with a few years ago. Until something like that exists, the honest reading is that the file-side tasks are measurably assisted and the on-site tasks are untested because no system does them. Our quality parity method explains how the grades work, and the full approach sits on the methodology page.

The labor market numbers are steadier ground. The Bureau of Labor Statistics counted about 70,230 audio and video technicians in the US with median pay near $58,100 a year, and projects employment growth of 3.9% from 2025 to 2035 (BLS, 2025). That is modest growth, not contraction.

When the picture could shift

Most likely between 2037 and 2054 (8 in 10 of our scenarios). The replacement-year method explains what that window is measuring and why it is a range rather than a date.

Two things could pull it earlier. First, venue automation: more rooms built around fixed, networked systems with preset scenes, auto-tracking cameras, and remote operation, so one person covers several spaces instead of one. Second, cost. Software and cloud tooling for capture, cleanup, and delivery is cheap next to crew hours, and that gap pushes clients to accept a simpler setup with fewer people on site.

Two things hold it back. The physical share of this job lands in our dexterous humanoid tier, which means the hands-on parts would need general-purpose robots that can rig, cable, and reach awkward spaces. Nothing on the market does that work reliably. The other brake is risk: a live keynote, a court recording, or a graduation has no second take, so clients keep a technician in the room for the same reason they keep a spare cable.

How to stay needed in AV work

Lean into the parts of the job that happen in a venue. On-site installation and strike, live fault-finding during an event, and teaching clients and junior crew how to run a system are all work that software cannot finish. Being the person who can be called at 7 a.m. for an 8 a.m. start is a position AI does not compete for.

Two skills raise your floor. One is networked AV: IP video routing, audio over Ethernet, control systems, and basic network troubleshooting, since more rooms are now IT projects with speakers attached. The other is coordination, meaning scoping a job, briefing a client, and running a crew. Both push you toward the parts of the role that are scored as needing a person.

What to do: pick one networked AV certification and one event where you own the room end to end, then build your resume around those two things.

Nearby jobs are worth comparing, because the task mixes differ more than the titles suggest: sound engineering technicians, broadcast technicians, and lighting technicians. You can put any two of them side by side on our job comparison tool, see the wider group on the media and communication equipment workers family page, or read the sector view for arts and entertainment. If you want the broader pattern, our list of jobs that mostly need a person shows what the hands-on roles have in common.

Frequently asked questions

Will AI take over videography jobs?

Not as whole jobs, but the editing and delivery steps are changing fast. Tools now cut rough assemblies, clean audio, add captions, and reformat files. What they do not do is show up at a venue, place cameras for a specific room, or fix a dropped feed mid-event. The task list above shows which parts of the work sit with people and which have moved into software.

Which parts of AV work are most exposed to AI?

The file-based tasks. Converting and compressing media, transcription and captioning, basic editing, noise cleanup, and sorting archives are all handled well by current tools. Monitoring levels and mixing are assisted rather than replaced. Setup, strike, cable management, live troubleshooting, and training other people remain hands-on. The breakdown on this page groups every task by that status.

Is audio and video technician a good career right now?

The federal numbers are steady. The Bureau of Labor Statistics counted roughly 70,230 of these jobs in the US, with median annual pay around $58,100 and projected growth of 3.9% from 2025 to 2035 (BLS, 2025). Pay varies a lot by venue type and union status. Technicians who handle networked systems and client-facing work tend to have the most options.

Could AI tools mean fewer entry-level AV jobs?

That is the more likely pressure. Junior roles often start with the tasks AI now does first: logging footage, basic edits, file conversion, captions. If those hours shrink, there are fewer easy ways in. The practical response is to get on-site experience early, since setup, rigging, and live problem-solving are still learned by doing them in real rooms.

Can a robot set up AV equipment?

Not in any general way today. Rigging a truss, dressing cable, mounting a camera, and reaching behind a rack need hands, balance, and judgment about a specific space. Our robotics tier for this job reflects that: the physical tasks would require a dexterous humanoid machine, and no such system is commercially available for venue work. Fixed automation covers only preset rooms.

What should an AV technician learn to work alongside AI tools?

Two areas pay off. First, networked AV: IP video, audio over Ethernet, control systems, and basic network fault-finding, because more installs are IT work with speakers attached. Second, the production software workflow, so you can supervise AI-assisted edits, captions, and cleanup rather than compete with them. Client communication and crew coordination round it out.

Each ridge is a slice of the job's task time.Needs a human 43%AI helps 55%AI does it 2%
The job’s mark

No two jobs leave the same print

Every job gets its own fingerprint, drawn from its code. The amber ridges are the share of task time that still needs a person. Below them, the same ridges are written out in ones and zeros: slate for the work AI helps with, white for the work AI can do.

Audio and Video Technicians, O*NET-SOC 27-4011. 43% of the job’s task time still needs a human, so 43 of every 100 ridges are amber; slate is what AI helps with, white what AI can do.

What AI can and cannot do

The tasks that make up the job, from , and where AI stands on each today: , (a person does it, with AI speeding it up) or . 43% of the still needs a human.

Each block is one task; its height is its share of working time.Needs a human 43%AI helps 55%AI does it 2%
The job's task list: the parts AI can do are blacked out.Needs a human 43%AI helps 55%AI does it 2%
Notify supervisors when major equipment repairs are needed.AI helps
Diagnose and resolve media system problems.Needs a human
Direct and coordinate activities of assistants and other personnel during production.Needs a human
Compress, digitize, duplicate, and store audio and video data.AI helps
Install, adjust, and operate electronic equipment to record, edit, and transmit radio and television programs, motion pictures, video conferencing, or multimedia presentations.Needs a human
Control the lights and sound of events, such as live concerts, before and after performances, and during intermissions.Needs a human
Switch sources of video input from one camera or studio to another, from film to live programming, or from network to local programming.AI helps
Record and edit audio material, such as movie soundtracks, using audio recording and editing equipment.AI helps
Perform minor repairs and routine cleaning of audio and video equipment.Needs a human
Design layouts of audio and video equipment and perform upgrades and maintenance.Needs a human
Conduct training sessions on selection, use, and design of audio-visual materials and on operation of presentation equipment.Needs a human
Monitor incoming and outgoing pictures and sound feeds to ensure quality and notify directors of any possible problems.AI helps
Mix and regulate sound inputs and feeds or coordinate audio feeds with television pictures.AI helps
Construct and position properties, sets, lighting equipment, and other equipment.Needs a human
Reserve audio-visual equipment and facilities, such as meeting rooms.AI helps
Determine formats, approaches, content, levels, and mediums to effectively meet objectives within budgetary constraints, using research, knowledge, and training.AI helps
Edit videotapes by erasing and removing portions of programs and adding video or sound as required.AI helps
Obtain, set up, and load videotapes for scheduled productions or broadcasts.Needs a human
Produce rough and finished graphics and graphic designs.AI helps
Locate and secure settings, properties, effects, and other production necessities.Needs a human
Meet with directors and senior members of camera crews to discuss assignments and determine filming sequences, camera movements, and picture composition.Needs a human
Maintain inventories of audio and videotapes and related supplies.Needs a human
Obtain and preview musical performance programs prior to events to become familiar with the order and approximate times of pieces.AI helps
Perform narration of productions or present announcements.AI helps
Plan and develop pre-production ideas into outlines, scripts, story boards, and graphics, using own ideas or specifications of assignments.AI helps
Organize and maintain compliance, license, and warranty information related to audio and video facilities.AI helps
Inform users of audio and videotaping service policies and procedures.AI helps
Analyze and maintain data logs for audio-visual activities.AI helps
Develop manuals, texts, workbooks, or related materials for use in conjunction with production materials or for training.AI does it

Is it better than a person? The evidence

No direct test against people in this job yet. Every study is , and vendor studies are labelled as such.

When could it be replaced?

When AI could largely do this job: 2037–2054

Most likely between 2037 and 2054 (8 in 10 of our scenarios). A range from our of how fast AI improves, how fast employers take it up and what holds it back, not a forecast that the job ends. “” has a strict meaning here. Today’s answer is at the top of the page; this is how it could change.

The sand is the human working years left, measured in the same 40-year glass for every job, so a safe trade starts nearly full and an exposed job with a thin layer.

The sand is the human working years left, in the same 40-year glass for every job.Years still needing a humanYears run out

How this job could shift, year by year

Where the job could sit on our scale each year to 2060, across the ten behind its .

Today
Will AI replace this job?
A little.
By 2045
70%
of our scenarios have AI largely doing this job by 2045 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)
By 2060
100%
of our scenarios have AI largely doing this job by 2060 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)

We run this job as ten scenarios spread across its replacement range. In each, the score moves towards the bottom band (Largely: AI could largely do the job) by the year that scenario reaches it, slowly at first and faster later, as adoption usually goes. Each bar splits the ten by the band they put the job in. The model stops at 2060. How the timeline works

Share of this job's scenarios in each verdict band, today to 20600%25%50%75%100%2026: 100.0% of scenarios: AI could do a little of this job (A little.)100%Today2030: 70.0% of scenarios: AI could do a little of this job (A little.)70%2030: 30.0% of scenarios: AI could partly do this job (Partly.)30%20302035: 50.0% of scenarios: AI could partly do this job (Partly.)50%2035: 40.0% of scenarios: AI could mostly do this job (Mostly.)40%2035: 10.0% of scenarios: AI could largely do this job (Largely.)10%20352040: 20.0% of scenarios: AI could partly do this job (Partly.)20%2040: 30.0% of scenarios: AI could mostly do this job (Mostly.)30%2040: 50.0% of scenarios: AI could largely do this job (Largely.)50%20402045: 30.0% of scenarios: AI could mostly do this job (Mostly.)30%2045: 70.0% of scenarios: AI could largely do this job (Largely.)70%20452050: 10.0% of scenarios: AI could mostly do this job (Mostly.)10%2050: 90.0% of scenarios: AI could largely do this job (Largely.)90%20502055: 100.0% of scenarios: AI could largely do this job (Largely.)100%20552060: 100.0% of scenarios: AI could largely do this job (Largely.)100%2060
Will AI replace the job?Largely.Mostly.Partly.A little.Nah.
Share of this job's scenarios in each band, year by year. Updated with every release.
Show the data
YearLargelyMostlyPartlyA littleNah
Today (2026)0.0%0.0%0.0%100.0%0.0%
20300.0%0.0%30.0%70.0%0.0%
203510.0%40.0%50.0%0.0%0.0%
204050.0%30.0%20.0%0.0%0.0%
204570.0%30.0%0.0%0.0%0.0%
205090.0%10.0%0.0%0.0%0.0%
2055100.0%0.0%0.0%0.0%0.0%
2060100.0%0.0%0.0%0.0%0.0%

What’s stopping AI taking over?

The things that keep this work with people, strongest first. Each is scored 0 to 100 from work context, licensing and the evidence we have.

LiabilityMistakes are rated 2.2 out of 5 for consequence and decisions 4.0 out of 5 for impact; someone has to answer for them.
Clients want a personFace-to-face contact is rated 4.8 and physical closeness 3.4 out of 5; caring for or serving people is 2.4 out of 5 in importance.
Evidence gapNo study yet compares AI with people doing this job, so employers have no proof it is good enough.
LicensingUsual entry requirement (BLS): postsecondary nondegree award, then short-term on-the-job training; 1 task statement mentions a licence or certification.
RegulationWorkers rate responsibility for others' health and safety 3.1 out of 5.
Physical work29% of the task time is physical; robots have been shown on 34% of that time.

What would it cost to hand the work to AI?

The share of the year AI could handle (634 of 2,080 hours a year), priced two ways. Both are ranges, not quotes.

AI model usage, a year
$60–$6,340
A person’s wage for the same hours
$11,590–$30,700

AI cost covers model usage only: no integration, licences, oversight or the human time still needed to review the work. Human cost is the wage for the same hours, without benefits or overheads. As of 2026-10.

Robots and humanoids

AI software can only take the work at a screen. The rest needs a robot that can do it.

29%
of the task time is physical work
Dexterous humanoid
the kind of robot the physical work would need
Not commercial: no cited robot does most of this work; humanoids are at demonstration and pilot stage.

Source: Anthropic Economic Index, 'What work can robots do?' (30 September 2026); O*NET 31.0 task weights.

Which AI skills does this job lean on?

The job’s task time split by what an AI model would need to be good at, and where models stand today.

Each star is a task, grouped by the AI skill it leans on.Needs a human 43%AI helps 55%AI does it 2%
Writing · 11.1% of time
Strong
Drafts, edits and translates most routine documents at professional quality.
Analysis · 14.8% of time
Strong
Reliable on structured data and rules; uneven on judgement calls with thin information.
Coding · 4.1% of time
Strong
Agents complete many routine software tasks end to end; larger systems still need people.
Vision and design · 14.4% of time
Good
Reads documents, images and layouts well; specialist imaging needs dedicated, approved tools.
Speech · 6.3% of time
Good
Voice agents handle routine calls and live interpreting; complex or sensitive calls still go to people.
Planning and agents · 23.8% of time
Emerging
Multi-step agents work in narrow, well-tooled workflows; open-ended coordination is unreliable.
Physical manipulation · 18.5% of time
Early
Robots handle structured, repetitive handling; general dexterity outside fixed settings is not commercial.
Care and persuasion · 7% of time
Limited
Can script, coach and advise; trust, presence and accountability still need a person.
Amber matter holds its orbit, slate circles the inner disc, white falls in.Needs a human 43%AI helps 55%AI does it 2%
How exposed is it?

Still needs a human: 70/100↑ safer

The amber matter on the outside holds its orbit: that is the task time that still needs a person. The slate matter circling the inner disc is the work AI helps with. The white matter is the share AI can do; it spirals in and is gone over the edge.

The matter follows the job’s task split: 43% needs a human, 55% AI helps, 2% AI does it. Still needs a human: 70/100 ↑ safer. Will AI replace them? A little.

What the AIs say

We asked four AI assistants the same question: will AI replace this job in the next 10 years? One word (Yes, Partly or No), then one sentence. Our own answer, from the data: Still needs a human: 70/100 ↑ safer. Will AI replace them? A little.

ChatGPTPartly

AI will automate some routine setup, monitoring, and editing tasks, but skilled video technicians will still be needed for creative judgment, troubleshooting, live production, and complex workflows.

gpt-5.5-2026-04-23 · asked 2026-10-03
ClaudePartly

AI will automate many routine technical tasks (color correction, basic editing, equipment monitoring), but video technicians will likely shift toward overseeing systems, handling creative judgment calls, and managing complex on-set problems that still require human expertise.

claude-sonnet-5 · asked 2026-10-03
GeminiPartly

While AI will automate routine tasks like color grading, editing, and troubleshooting, human technicians will still be required for complex physical setups, creative direction, and live, high-stakes adaptability.

gemini-3.8-flash · asked 2026-10-03
PerplexityPartly

AI will automate routine video-technical tasks and reduce some roles, but technicians who handle physical equipment, troubleshooting, live judgment, and client needs will remain essential.

sonar · asked 2026-10-03

Assistants answer from what they learned in training (Perplexity also searches the web), so they can be confidently wrong, and the same question can get a different answer tomorrow. Our score is built from task data and graded evidence. Answers collected through DataForSEO.

Cite this page

NeedsAHuman.com (2026). Will AI replace Audio and Video Technicians? A little. Still needs a human: 70/100, higher is safer; release 2026-Q4. https://needsahuman.com/jobs/audio-and-video-technicians/ (accessed 4 October 2026).

Scores change with each , so cite the release. The data is open under : credit NeedsAHuman.com with a link. Open data · Press

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Sources

  • Tasks and work context: 31.0, ().
  • Jobs, pay and projections: US , and 2025–35.
  • How AI is used today: ; Microsoft Research, .
  • What AI can do: our task ratings ( r1) and the quality evidence register.
  • UK names and employment: coding index and .

How each score is built: methodology. Every figure on this page: open data. Release 2026-Q4.