COOKED verdict contested
The core of this job is watching banks of camera feeds for cheating, card counting, chip-passing, and dealer error, then writing incident reports — which is precisely the workload computer vision plus chip-and-bet-tracking systems now handle continuously and without fatigue. Casinos are already deploying RFID chips, automated bet recognition, and facial recognition against exclusion lists, which converts the officer from a watcher into an alert-triager. What survives is the licensed human who reviews flagged clips, coordinates with floor staff and gaming regulators, and testifies to what happened; that tier is much smaller than the current headcount.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $34,190 → $43,370 +1.5% 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
+0.3% 10,300 → 10,300 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +0.3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~1,300 openings a year on average, including replacing people who leave.
Gambling MonitorGaming InspectorSecurity OfficerSurveillance AgentCasino InvestigatorGaming InvestigatorSpecial InvestigatorSurveillance MonitorSurveillance OfficerSurveillance ObserverSurveillance OperatorArmed Security OfficerSurveillance InspectorCasino Security OfficerSurveillance TechnicianCasino Enforcement AgentInvestigative SpecialistVideo Surveillance AgentSurveillance InvestigatorCasino Surveillance OfficerGaming Surveillance OfficerSurveillance System MonitorGaming Surveillance ObserverSecurity Bike Patrol Officer
Holding it up: liability shield . Weakest point: trust premium .
Core tasks are already automatable A shift spent panning PTZ cameras over pit games, logging hand-by-hand play on a suspected counter, and typing up an incident narrative is exactly what RFID chip trays, automated bet-recognition, and face-match against exclusion lists now do frame-by-frame, which is why this sits at 6 rather than in the mixed band — even the report drafting starts from machine-timestamped clip metadata.
Some physical or field component Most of the shift is in a windowless surveillance room at a monitor wall, but the 7 reflects the real floor work: walking the pit to verify a dealer's mucking procedure, escorting a detained patron to a holding room, pulling a physical drop box or dice from a game for evidence, and appearing at a hearing.
Certification preferred, not legally required State gaming boards do license you — a Nevada or New Jersey gaming registration you can lose over a bad call — but that card gates employment rather than making you personally liable the way a PE stamp does; the casino's compliance officer and the licensed operator carry the Title 31 SAR and regulatory exposure, which is why this is 9 and not 14.
Meaningful discretion Calling a chip-pass versus a legitimate toke, or deciding whether a counter gets read the trespass notice, is a genuine judgment call with money and a patron's liberty attached — but it is made inside Title 31 thresholds, internal control procedures filed with the regulator, and a surveillance manual that scripts most escalations, so the discretion is real but bounded at 8.
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 (6/20 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 (9/20) 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 (8/20) 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 20 of this occupation's 33 points (61%).
Embodiment (7/20) 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 gambling surveillance officers and gambling investigators 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 48/100 — EXPOSED.
As routine feed-watching is automated, the remaining role owns the contested calls: voiding a disputed jackpot, ejecting a suspected advantage player, referring to Gaming Control Board agents. If facial-recognition and biometric-consent litigation (Illinois BIPA-style suits, state AG scrutiny) makes casinos require a named human decision-maker on every biometric match before action, ownership of the call is formalized
Task-mix shift to the judgment tier: if automated bet/chip tracking absorbs continuous monitoring, what remains is evidence packaging for regulators, court and grand-jury testimony, collusion investigations across sessions, and internal-theft cases involving staff — work that requires interviewing and chain-of-custody discipline AI cannot supply. This raises the score for the surviving smaller headcount, not the occupation's size
State gaming boards conditioning use of automated surveillance analytics on a licensed surveillance officer's signed review — e.g. Nevada Gaming Commission Reg 5 minimum internal control standards (MICS) or NJ DGE technology-approval rules being amended to require a badged human to review and attest to any AI-flagged incident before an exclusion, jackpot denial, or player ejection is actioned; tribal compacts and NIGC MICS could impose the same countersign
A court or gaming-board evidentiary rule holding that automated surveillance output is inadmissible in license-revocation or criminal proceedings unless a licensed officer authenticated it contemporaneously, mirroring how breath-test and body-cam evidence require a certified operator
The limit. There is no realistic route to a trust premium: gamblers and casino patrons do not choose venues because humans watch the cameras, and the buyer is the operator, whose incentive is headcount reduction. Embodiment is also near its ceiling — the work is a chair in a dark room; even the floor-coordination portion is radio and walking, not manipulation. Every plausible lever protects a smaller surviving tier, not the current 9,520 jobs.
| Las Vegas-Henderson-North Las Vegas, NV | 660 | $47,420 +9% |
| Chicago-Naperville-Elgin, IL-IN | 300 | $46,100 +6% |
| Riverside-San Bernardino-Ontario, CA | 290 | $44,660 +3% |
| Tulsa, OK | 270 | $34,590 -20% |
| Atlantic City-Hammonton, NJ | 240 | $47,710 +10% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 230 | $45,490 +5% |
| Seattle-Tacoma-Bellevue, WA | 230 | $47,570 +10% |
| San Juan-Bayamon-Caguas, PR | 200 | $23,000 -47% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 70 | $57,890 +33% |
| Indianapolis-Carmel-Greenwood, IN | 80 | $57,580 +33% |
| New York-Newark-Jersey City, NY-NJ | 90 | $55,910 +29% |
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 33. 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.