COOKED
The core of this job — taking wagers, writing and verifying tickets, calculating payouts, posting odds and results, running slips between counter and window — is transaction processing that mobile sportsbook apps and self-service kiosks already do at lower cost. Physical presence at the window and on the floor gives some cushion, and every worker needs a state gaming license, but that license is a background check, not a professional liability shield. Mobile betting legalization has already moved most handle off the counter, and the counter is where this occupation lives.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $24,750 → $34,980 +13.1% 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
-6.1% 8,200 → 7,700 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -6.1% 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.
~1,200 openings a year on average, including replacing people who leave.
BookieCallerRunnerBet TakerBookmakerBingo ClerkKeno RunnerKeno WriterBingo CallerBingo WorkerFloor RunnerSheet WriterSports ClerkBetting ClerkBingo FloaterCasino RunnerCasino WorkerSports RunnerKeno AttendantMutuel CashierRacebook WriterCasino AttendantRace Book WriterPari Mutuel Clerk
Holding it up: embodiment . Weakest point: judgment & accountability .
Core tasks are already automatable Keying a bet into the terminal, printing and verifying the ticket, calculating parlay payouts off a stored odds table, and posting line moves to the board are all steps a DraftKings app executes in under two seconds without a human — the only reason a 4 isn't a 0 is that a shift still involves physically handling cash drawers, resolving jammed printers, and dealing with the customer who lost a ticket.
Some physical or field component You are standing at a window or walking a floor in a casino for eight hours handling cash, chips, and paper slips, but it's a climate-controlled, camera-covered, fixed-layout room with no lifting, weather, or equipment risk — that's why this sits at 8 and not in the teens with a linesman or an HVAC tech.
Certification preferred, not legally required Every state requires a gaming registration or key/non-key license and Title 31 currency-reporting training, which is more than nothing, but that license certifies you passed a criminal background check and know when to file a CTR — the book's odds, limits, and any voided wager sit with the sportsbook operator and its licensed director, not with you at the window.
Executes defined procedures on defined inputs Your discretionary calls are bounded: confirm the bet before printing, refuse a wager past post time, refuse an underage or self-excluded patron, escalate anything over the house limit or any disputed ticket to a supervisor — the ambiguous decisions (moving a line, accepting sharp action, settling a contested grade) belong to the book manager or risk desk.
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 (4/20 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 (6/20) 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 15 of this occupation's 27 points (56%).
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.
Waiters and Waitresses EXPOSED
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 41/100 — EXPOSED.
Task-mix shift as kiosks and apps absorb straightforward single-game tickets, leaving the staffed window handling only disputes: voided/graded-wrong tickets, mis-posted lines, expired winning tickets, parlay reconstruction, and large cash payouts requiring tax withholding paperwork. This tier exists today but is thin — the occupation has a genuine judgment tier, just a small one, so the rise is capped.
State gaming regulations that name a licensed individual as the required human for specific counter transactions — e.g. Nevada/New Jersey rules or FinCEN Title 31 practice requiring an employee to personally verify ID and complete a CTR/SAR-triggering multiple-transaction log for cash wagers over $10,000, and to personally enforce self-exclusion list matches before a ticket is issued. If a regulator writes 'a licensed writer shall verify' rather than 'the licensee shall verify', the counter role acquires a named-human step that kiosks cannot complete.
Responsible-gaming mandates of the kind in Massachusetts and Ontario, plus proposed federal SAFE Bet Act provisions, that require a trained human to intervene on visible signs of problem gambling and to refuse or limit a wager — pushing discretionary refusal, exclusion-list edge cases, and suspicious-betting-pattern escalation onto the counter worker rather than software.
Cash-heavy floors where the same worker is the physical fallback for kiosk jams, bill-validator faults, ticket-printer failures and armored-carrier drops; if properties keep large cash cages rather than moving to cashless wallets (a live fight, with several tribal and Nevada operators resisting full cashless), the physical custody-of-cash component holds.
The limit. Even with every lever, this tops out well short of safe: mobile handle has already removed the volume that justified headcount, so licensing and responsible-gaming duties may protect the existence of a role at each property without protecting 8,950 jobs. Trust premium is omitted deliberately — bettors do not choose a sportsbook because a person writes the ticket, and the VIP host relationship business is a different occupation (gaming managers/hosts), not the writer or runner.
| Las Vegas-Henderson-North Las Vegas, NV | 1,190 | $38,390 +10% |
| San Francisco-Oakland-Fremont, CA | 240 | $36,360 +4% |
| Omaha, NE-IA | 190 | $30,610 -12% |
| Phoenix-Mesa-Chandler, AZ | 190 | $37,000 +6% |
| Boston-Cambridge-Newton, MA-NH | 140 | $47,720 +36% |
| Missoula, MT | 120 | $22,580 -35% |
| Reno, NV | 110 | $33,780 -3% |
| Cincinnati, OH-KY-IN | 90 | $34,980 +0% |
| New York-Newark-Jersey City, NY-NJ | 90 | $57,880 +65% |
| San Diego-Chula Vista-Carlsbad, CA | 60 | $52,140 +49% |
| Boston-Cambridge-Newton, MA-NH | 140 | $47,720 +36% |
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.