COOKED
The core of this job was already gutted before AI arrived: digital cinema servers ingest DCPs, validate KDM keys, and run automated playlists that trigger lights, curtains, and sound cues without a human in the booth. What remains is hands-on — lamp and laser module swaps, lens and port glass cleaning, projector alignment, and troubleshooting a dead show mid-feature — which is why this isn't a zero, plus a small repertory/70mm niche where film handling is a genuine craft. At 1,480 US jobs nationally, the occupation is already the residue of a much larger one.
This fall is concentrated in 2020 and has not recovered since.
Median pay $25,150 → $38,270 +21.7% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
-3.7%
Percentage only. The projection counts a different population from the 1,480 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -3.7% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~500 openings a year on average, including replacing people who leave.
ProjectorBooth UsherProjectionistStereopticianBooth OperatorMovie OperatorCinema OperatorCinematographerFilm SpecialistMachine OperatorEquipment OperatorProjector OperatorMovie ProjectionistCinema ProjectionistDigital ProjectionistProjection TechnicianMovie Machine OperatorFilm Projector OperatorMotion Picture OperatorMoving Picture OperatorProjector Booth OperatorTechnical Projection GuideMotion Picture Projectionist
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 8 reflects the split day: ingesting a DCP, applying the KDM, building the playlist, and hitting show start are already scripted or scheduled by the TMS across every screen in the building, but nobody has automated pulling a spent xenon bulb, re-dousing and aligning a lamphouse, swapping a laser light engine, or diagnosing why a server dropped audio sync three reels into a sold-out show — roughly half the week resists, which is why this sits above the fully-automatable band but nowhere near 14.
Hands-on in uncontrolled environments The booth is indoor and climate-controlled, which caps this below the true field trades, but you are on ladders and catwalks, handling a pressurized xenon lamp that can shatter, reaching into a hot lamphouse, cleaning port glass and lenses at height, threading 70mm on platters, and crawling behind screens for speaker work — physical work in a space that was never designed for safe access, hence 13 rather than a desk-bound 4.
No licence, no signature requirement There is no projectionist licence in almost any US jurisdiction anymore — the old municipal operator permits that once required a written exam died with nitrate film, and the theatre's general manager, not you, signs for occupancy and fire code, so a 1 rather than a 0 only acknowledges that some venues still ask for basic electrical or high-voltage safety training.
Executes defined procedures on defined inputs Almost everything is a documented procedure — lamp hours logged and replaced on schedule, KDM windows checked against the booking, fault codes traced through the manufacturer's manual — and the one genuinely discretionary call, stopping a show versus limping through a flicker, gets escalated to the house manager who owns the refund decision, keeping this at 3.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (8/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (1/20) is whether the law requires a licensed human to sign. Trust premium (3/20) is whether buyers specifically pay for a person. Judgment and accountability (3/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 7 of this occupation's 28 points (25%).
Embodiment (13/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to motion picture projectionists on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 42/100 — EXPOSED.
Continued shift of the remaining headcount toward field service on laser light engines and 70mm/IMAX film platters — work that requires physical presence in a specific booth, ladder access to a ceiling-mounted projector, hazardous-energy lockout on high-voltage laser modules, and no two auditoriums geometrically alike. If the surviving jobs consolidate into circuit-level roving technician roles (as AMC and Cinemark have largely done), the measured job becomes almost entirely unpredictable-environment physical repair.
Task-mix shift within a genuine two-tier job: automated playlist building, KDM ingest and show scheduling are already gone, leaving diagnosis of intermittent faults (color drift, ghosting, sync loss, xenon/laser derate) where the symptom is reported by a manager in non-technical terms and the cause is mechanical. The occupation has a routine tier that is already automated and a judgment tier that is not, so the residual measured job scores higher than the historical one.
Growth of the premium-format repertory circuit where the human projectionist is named in programming copy and marketing — Nolan-driven 70mm runs, Alamo Drafthouse and Film Forum-style archival series, and studio-mandated film-handling conditions for loaned archival prints. If distributors and archives (e.g. Academy Film Archive, BFI-style loan terms) continue to require a named, qualified human print handler as a condition of lending nitrate or rare 35/70mm elements, a small real premium attaches to a person.
Reinstatement or enforcement of municipal/state projectionist licensing tied to high-power laser illumination — laser projectors above certain classes already trigger FDA/CDRH variance requirements and some jurisdictions require a trained operator on premises for Class 4 laser installations. If a state occupational-safety rule named a certified operator as personally responsible for laser interlock compliance in cinemas, this moves off the floor.
The limit. Even with every lever, this stays a small, low-ceiling occupation: 1,480 workers is the residue after the automation event already happened, and the levers protect the residue rather than regrow the job. The trust premium and any licensing route apply to a few hundred repertory and premium-format positions, not the whole SOC.
| New York-Newark-Jersey City, NY-NJ | 230 | $82,270 +115% |
| Los Angeles-Long Beach-Anaheim, CA | 100 | $92,180 +141% |
| Los Angeles-Long Beach-Anaheim, CA | 100 | $92,180 +141% |
| New York-Newark-Jersey City, NY-NJ | 230 | $82,270 +115% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 28. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.