EXPOSED
This catch-all bucket covers venue and attraction floor staff — taking tickets, staging guests, operating rides and games, monitoring crowds, resetting equipment, answering questions on the floor. Almost none of that is text or screen work AI can do, so language models barely touch it; the real threat is self-service kiosks, mobile ticketing, QR scanning and automated ride controls that have already eliminated the transactional half of the job. What remains is physical presence in a crowded, unpredictable space: nobody has a robot that clears a stuck ride, calms a panicked child, or spots the guest who's had too much to drink.
Dipped in 2020, then grew past where it started.
Median pay $26,460 → $32,640 -1.3% 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
+3.6%
Percentage only. The projection counts a different population from the 7,060 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3.6% 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.
~2,200 openings a year on average, including replacing people who leave.
ShillGamblerChip MuckerChip PersonJockey ValetGame AttendantFloor AttendantCardroom AttendantGame Room AttendantCardroom Plastic Card Grader
The BLS uses Entertainment Attendants and Related Workers, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Ticket scanning, queue metering and cash handling are already gone to turnstiles and phone barcodes, but the other half of the shift — physically resetting bowling pins that jam, walking a lost kid to guest services, sweeping a midway for spilled drinks, reading a crowd that's getting rowdy — has no digital substitute, which lands it mid-band rather than lower.
Hands-on in uncontrolled environments The work is done standing on a game floor, ride platform or arcade midway with moving machinery, wet surfaces and thousands of strangers per shift, and the unpredictability — a guest who faints in line, a stuck harness, a fight breaking out — is exactly what keeps this at 15 instead of a controlled-environment 8.
No licence, no signature requirement No state licence, no exam, no certification gates the job; the ride certificate or amusement inspection sits with the operator company and the state inspector, and an injured guest sues the venue's insurer, not the attendant who was standing at the gate.
Executes defined procedures on defined inputs Decisions are bounded by posted rules and an on-shift supervisor: height and rider restrictions, prize redemption tables, when to hit the emergency stop, when to call security — real calls, but the escalation path is one radio click away, which is why this is a 5 and not a 10.
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 (12/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (7/20) is whether buyers specifically pay for a person. Judgment and accountability (5/20) 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 41 points (34%).
Embodiment (15/20) 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 57/100, still EXPOSED.
Same two-tier collapse: kiosks and mobile scanning already took the transactional tier, so what's measured as remaining work is de-escalation, medical first response and incident handling. Wider adoption of mandated crowd-manager training (Massachusetts' crowd manager rule after station nightclub fire; NFPA 101 crowd manager requirements) shifts the measured task mix toward the tier no system performs.
State ride-operator certification with named personal accountability — NAARSO/AIMS operator credentials becoming a licensure prerequisite in state ride codes rather than a voluntary industry cert, with the certified operator's signature required on daily pre-opening inspection logs. Post-incident, this is already the document regulators subpoena; making the signature statutorily personal is the change to watch for after a high-profile fatality.
Task-mix shift: with ticketing and access control fully automated, the residual role is the go/no-go call — refusing a rider on height, intoxication or medical grounds, and pulling an attraction from service. If venue insurers (the amusement lines at Allianz, Beazley) condition coverage on a documented human stop-ride authority rather than automated sensor trip alone, the remaining job is defined by that authority.
Crowd density and unscripted human behavior in venues stays the binding constraint: a stuck restraint, a fallen guest, a fight in a queue line all require a body that can improvise. Scores rise only if state amusement-ride codes (e.g., NJ, PA, CA DOSH ride safety units) formalize minimum staffed-attendant ratios per ride or per occupancy load, as several already do for water attractions — turning presence from a staffing choice into a code requirement.
The limit. Trust premium has no realistic route — guests do not choose a venue because a human takes the ticket, and this is a low-wage, high-turnover bucket where operators cut staffing wherever code permits. Even with every lever, headcount pressure is a staffing-ratio question, not a skill-protection question: the job survives per venue but not per guest served.
| Minneapolis-St. Paul-Bloomington, MN-WI | 450 | $32,150 -2% |
| Nashville-Davidson--Murfreesboro--Franklin, TN | 440 | $28,620 -12% |
| Dallas-Fort Worth-Arlington, TX | 390 | $25,980 -20% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 380 | $35,120 +8% |
| Atlanta-Sandy Springs-Roswell, GA | 320 | $25,460 -22% |
| Las Vegas-Henderson-North Las Vegas, NV | 280 | $48,560 +49% |
| New York-Newark-Jersey City, NY-NJ | 270 | $44,430 +36% |
| San Diego-Chula Vista-Carlsbad, CA | 190 | $39,040 +20% |
| Las Vegas-Henderson-North Las Vegas, NV | 280 | $48,560 +49% |
| New York-Newark-Jersey City, NY-NJ | 270 | $44,430 +36% |
| San Francisco-Oakland-Fremont, CA | 60 | $43,570 +33% |
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 41. 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.