COOKED
The core work — exchanging cash for chips or tokens, redeeming TITO tickets, counting drawers, processing jackpot payouts and filling out CTR paperwork — has already been largely absorbed by ticket-in/ticket-out kiosks, cashless wallets, and automated bill validators, which is why this occupation has been shrinking for two decades. Physical presence on the floor and state gaming-board licensing provide a thin buffer, but the license is a background-check registration, not a professional liability shield. What survives is exception handling: disputed payouts, suspicious-activity judgment calls, and the guest-facing moment when a machine eats someone's money.
Roughly flat across the period, with year-to-year wobble.
Median pay $25,690 → $36,220 +12.8% 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
-6.4% 22,600 → 21,100 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -6.4% 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.
~4,000 openings a year on average, including replacing people who leave.
CashierCage CashierBingo CashierBooth CashierBooth MonitorCasino BankerChange PersonFloor CashierMutuel TellerVault CashierCasino CashierSlot AttendantSlot TechnicianChange AttendantSlot Floor PersonCarousel AttendantLottery Sales ClerkCasino Gaming WorkerSlot Floor AttendantPlayer Services Cashier
Holding it up: embodiment . Weakest point: judgment & accountability .
Core tasks are already automatable A TITO kiosk already does the ticket redemption, the bill validator already does the change, and the cage's drawer count runs off a computerized cash-tracking system — the residual 5 points come from hopper jams, hand-pay verification, and walking a W-2G over to a winner, not from anything that requires a person at the window.
Some physical or field component You are standing for eight hours on a casino floor or locked in a cage, hauling coin bags and chip racks and keying open a drop box — real physical work, but it happens in a climate-controlled, camera-covered building on a fixed route, which is what separates an 8 from the 14+ of someone working an unpredictable outdoor site.
Certification preferred, not legally required A state gaming board key-employee or non-key registration means fingerprints, a criminal-history check, and the power to revoke your badge — it gates who may stand at the cage, but no one sues you personally for a miscounted fill; the casino's compliance officer and Title 31 program owner absorb that.
Executes defined procedures on defined inputs Nearly everything is written down: CTR at $10,000 in cash-in or cash-out, MTL logging above $3,000, jackpots over $1,200 to the slot attendant, disputes escalated to a shift supervisor with surveillance pulling the tape — your call is whether to escalate, not what the answer is.
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 (5/20 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 (5/20) is whether the law requires a licensed human to sign. Trust premium (5/20) is whether buyers specifically pay for a person. Judgment and accountability (4/20) 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 14 of this occupation's 27 points (52%).
Embodiment (8/20) 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.
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 40/100 — EXPOSED.
Self-exclusion and responsible-gaming enforcement rules (as in the UK Gambling Commission's LCCP and several US state compacts) that require a human cage attendant to check a patron against an exclusion list before large redemptions, with license sanctions on the individual for a missed match.
Task-mix shift: this occupation genuinely has two tiers. As kiosks and cashless wallets absorb all routine redemption, the residual role is exception-only — disputed payouts, malfunction adjudication, AML judgment, hand-pay verification. If headcount continues shrinking while exception volume stays constant, the surviving positions score higher on task resistance even with no new law. Watch for job postings retitled toward 'cage supervisor' or 'AML analyst' functions.
State gaming regulations or FinCEN Title 31 guidance requiring a licensed, individually-named casino cage employee to verify identity and personally attest to Currency Transaction Reports and Suspicious Activity Reports above the $10,000 threshold — i.e. an attestation that cannot be executed by a kiosk or cashless wallet system. Nevada Gaming Commission Reg 6A already imposes cage-level recordkeeping duties; extending personal attestation and naming individual accountability for structuring detection would convert a background-check registration into something closer to a signing duty.
Formal delegation of hand-pay and dispute-resolution authority to the cage, with documented individual sign-off, as gaming boards tighten dispute-record requirements after cashless-wallet errors. Nevada and New Jersey both require patron dispute reporting to the board; rules naming the deciding employee would raise this.
The limit. Even with every lever, this occupation is capped low. The headcount trend is the dominant fact: kiosks removed the transaction, not just the labor, and liability levers protect a shrinking number of cage supervisors rather than change persons. There is no realistic route to a trust premium — patrons do not pay extra for a human to hand them chips, and cashless adoption is driven by casino operators who capture the savings. Embodiment cannot rise; the floor is a predictable indoor environment.
| Las Vegas-Henderson-North Las Vegas, NV | 2,320 | $33,030 -9% |
| Riverside-San Bernardino-Ontario, CA | 1,310 | $36,630 +1% |
| San Diego-Chula Vista-Carlsbad, CA | 650 | $38,020 +5% |
| Los Angeles-Long Beach-Anaheim, CA | 580 | $39,920 +10% |
| Sacramento-Roseville-Folsom, CA | 530 | $38,060 +5% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 400 | $37,620 +4% |
| New Orleans-Metairie, LA | 400 | $23,280 -36% |
| Reno, NV | 360 | $28,670 -21% |
| Tucson, AZ | 150 | $62,500 +73% |
| New York-Newark-Jersey City, NY-NJ | 240 | $58,790 +62% |
| San Jose-Sunnyvale-Santa Clara, CA | 70 | $48,950 +35% |
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 27. 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.