EXPOSED
The reporting half of this job — hold and drop reconciliation, table game performance analytics, labor scheduling, comp tracking, AML/Title 31 report drafting — is already being absorbed by casino management systems and player-tracking analytics that surface anomalies faster than a human reading pit sheets. What survives is floor presence: settling disputed payouts in front of an angry patron, backing up a dealer during a suspected advantage-play incident, deciding when to bar someone, and standing personally in front of a state gaming board as a licensed key employee. Modal worker here runs a pit or a floor at a mid-size property, not a corporate analytics shop, so the embodied and regulatory pieces carry real weight.
Dipped in 2020, then grew past where it started.
Median pay $74,970 → $93,220 -0.5% 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
+1.2% 5,100 → 5,200 on the projections basis
Growing, and only partly exposed
The BLS expects +1.2% more of these jobs by 2034, and at 61/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~600 openings a year on average, including replacing people who leave.
Pit ManagerCage ManagerDice ManagerKeno ManagerSlot ManagerBingo ManagerCraps ManagerPoker ManagerShift ManagerCasino ManagerGaming ManagerRacing ManagerGaming DirectorOn-Duty ManagerPai Gow ManagerBaccarat ManagerGambling ManagerBlackjack ManagerCard Room ManagerGambling DirectorCage Shift ManagerPoker Room ManagerSlot Shift ManagerTable Games Manager
Holding it up: judgment & accountability . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier An 11 reflects the split: drop counts, hold percentage reporting, comp issuance thresholds, and shift scheduling are already CMS/Bally-Konami functions, while walking a pit to verify a chip fill, reading a table for card-counting or past-posting, and talking a patron down from a disputed toke are still done by a person on the carpet — enough surviving physical-social work to clear the automatable band but not enough to protect the desk half.
Hands-on in uncontrolled environments A 13 is earned by the fact that the job is measured in floor hours, not office hours — you are physically at the pit or cage for fills and credits, on camera with surveillance during an incident, escorting a self-excluded patron out, and covering a gaming floor of thousands of square feet across a graveyard shift — but it stays at 13 rather than higher because the environment is climate-controlled, mapped, and blanketed with cameras, unlike a rig floor or a job site.
Licensed human required and personally liable A 12 rests on the key-employee or gaming-manager licence issued by the state board (Nevada Gaming Commission, NJ DGE, or tribal equivalent), which is held in your name, requires personal financial disclosure, and can be suspended for a Title 31 SAR you failed to file or a minor found at a table on your shift — real personal exposure, but the licence certifies suitability rather than conferring the kind of independent professional privilege a CPA or physician holds.
Exists to be accountable for ambiguous calls A 14 is right because the calls that land on you have no procedure that resolves them: whether an advantage player is cheating or merely skilled, whether to void a hand and refund or pay a disputed jackpot, whether an intoxicated patron's losses become a regulatory problem, and whether a pattern of structured buy-ins is a Title 31 filing — each decided in minutes, on camera, with the board reviewing it later.
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 (11/20 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 (12/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (14/20) 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 37 of this occupation's 61 points (61%).
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 gambling managers 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 76/100 — SAFE.
If tribal/state gaming boards formalize disciplinary exposure for floor decisions — barring an advantage player, voiding a disputed hand, overriding a system payout hold — through published board hearing records where the individual supervisor, not the operator, is the respondent, the role's ownership of ambiguous calls becomes explicit rather than customary. Nevada Gaming Control Board patron-dispute adjudications (NRS 463.362) already require a designated employee's on-scene determination.
Genuine two-tier job: as casino management systems absorb hold/drop reconciliation, comp tracking, labor scheduling, and Title 31 report drafting, what remains is the escalation tier — disputed payout adjudication, suspected collusion or advantage play in real time, dealer backup, barring decisions. No new law needed; the score rises mechanically as the clerical tier disappears and the residual day is contested judgment under an angry patron's eyes.
Gaming boards already license pit/floor supervisors as 'key employees' (NV Reg 3, NJ CCC), but the shield deepens if a state regulation or FinCEN consent order names a specific on-property licensed individual as personally accountable for SAR-C filing decisions and Title 31 suspicious-activity determinations — the pattern FinCEN used in its individual assessments (e.g., the Tinian Dynasty and Sands-era compliance-officer actions). A rule stating that an automated system's AML alert cannot be dispositioned without a named licensed employee's signature would convert the analytics tool into a decision-support tool with a human on the hook.
Responsible-gaming statutes that require a licensed employee — not a system — to execute self-exclusion enforcement, on-floor intervention, and minor-removal decisions, with license revocation as the penalty. UK Gambling Commission's affordability/interaction rules and several state RG mandates already push toward documented human intervention; a US state codifying 'licensed supervisor must personally verify and log each exclusion contact' is the checkable version.
Only route is duty reassignment rather than technology: if properties consolidate surveillance-room review into shared regional centers and push the physical response function down to floor management, the remaining role becomes more purely on-premises presence and physical incident handling. Watch for casino operating agreements or union contracts (Culinary 226, Teamsters at Atlantic City properties) that reclassify pit supervisors as first-responders for gaming incidents.
The limit. No credible trust_premium lever. Patrons do not select a property because a human runs the pit, and gaming revenue is indifferent to who dispositions the hold report — the only human-preference signal is VIP/high-limit host relationships, which is a different SOC. Ceiling is roughly the mid-70s: the licensure and judgment dimensions can carry this occupation, but the 5,030-worker headcount is itself the exposure, since analytics consolidation reduces the number of supervisors needed per floor even where each surviving one is harder to replace.
| Las Vegas-Henderson-North Las Vegas, NV | 560 | $94,580 +1% |
| Chicago-Naperville-Elgin, IL-IN | 260 | $80,170 -14% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 140 | $96,180 +3% |
| Riverside-San Bernardino-Ontario, CA | 130 | $92,780 +0% |
| Atlantic City-Hammonton, NJ | 120 | $101,470 +9% |
| Phoenix-Mesa-Chandler, AZ | 120 | $83,620 -10% |
| Reno, NV | 120 | $60,890 -35% |
| Detroit-Warren-Dearborn, MI | 100 | $101,930 +9% |
| Seattle-Tacoma-Bellevue, WA | 50 | $125,790 +35% |
| San Francisco-Oakland-Fremont, CA | 40 | $124,840 +34% |
| Gulfport-Biloxi, MS | 60 | $121,420 +30% |
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 61. 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.