EXPOSED
The core of this job — scanning tickets, checking seat numbers, directing people to sections — is happening in a physical venue with unpredictable crowds, which language models cannot touch, but self-scan turnstiles and phone-based e-tickets have already eaten the ticket-taking half. What persists is the crowd-facing half: spotting an intoxicated patron, walking a wheelchair user to accessible seating, running an aisle during an evacuation, and being the face guests ask when something goes wrong. Displacement pressure here comes from cheap hardware and venue staffing cuts, not from AI reasoning.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $23,500 → $32,910 +12.0% 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% 121,700 → 123,100 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +1.2% 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.
~30,800 openings a year on average, including replacing people who leave.
UsherDocentDoormanGreeterSpot ManDoorpersonGate ClerkDoor TenderGate TenderMuseum HostSpot WorkerDoor CaptainTicket TakerConcessionistTheater UsherDoor AttendantGate AttendantMuseum HostessEvent AttendantLobby AttendantLodge AttendantOfficial GreeterTicket AttendantTicket Collector
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Your ticket-taking half is already gone at any venue that installed self-scan gates and phone e-tickets, but nothing digital walks a lost patron down a dark stair aisle mid-performance, holds a late seating until intermission, or reads a crowd surge at the exit doors — that split of duties, roughly half absorbed by hardware and half untouched, is exactly what a 12 describes rather than a 16.
Hands-on in uncontrolled environments You work standing for a four-hour event in a concourse or raked seating bowl, on stairs, often in the dark, in weather at gates and outdoor amphitheaters, moving through crowds you don't control — that's uncontrolled-environment work, and it sits at 15 rather than 19 only because the venue itself is a fixed, mapped, lit-when-needed space, not a construction site or a roadside.
No licence, no signature requirement There is no state licence to take tickets or seat guests; where NFPA 101 and state fire codes require trained crowd managers, the training is a few hours provided by the venue and the legal exposure for an injury or a failed evacuation lands on the operator's insurer, not on your name — hence 1 and not 5.
Executes defined procedures on defined inputs Most calls you make are already written down: check the stub, seat only at scene breaks, no re-entry without a hand stamp, radio security for anyone intoxicated or fighting rather than intervening yourself — the genuinely ambiguous decisions, like whether to start clearing an aisle before the house manager says so, get escalated, and that thin margin of real discretion is what keeps this at 6 instead of 3.
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 (1/20) is whether the law requires a licensed human to sign. Trust premium (8/20) is whether buyers specifically pay for a person. Judgment and accountability (6/20) 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 42 points (36%).
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.
Maids and Housekeeping Cleaners EXPOSED
Bartenders SAFE
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 58/100, still EXPOSED.
Venue occupancy/egress codes that specify minimum trained crowd-manager ratios per occupant load — NFPA 101's crowd manager requirement (1 per 250 occupants, tightened after Astroworld) being adopted and enforced in more state fire codes, plus insurer conditions on event cancellation/liability policies requiring certified crowd managers on every aisle. This makes a trained human body legally countable rather than optional.
Task-mix shift: once scanning is fully self-serve, what remains is the discretionary tier — ejection decisions, intoxication refusals, ADA accommodation calls, evacuation routing. If venues formalize this by designating ushers as documented incident-report authors whose written account is the record used in injury litigation (as some arena operators already do post-Astroworld), the role owns consequential calls.
Same two-tier shift: the routine half is largely gone already, so the measured residual is judgment-and-crowd work. Formal certification (IAVM/Crowd Manager, ALICE-style active-threat training) written into venue contracts would lock the remaining tier as non-automatable.
Narrow route only: premium-seating and hospitality tiers where a named seat host is part of the ticket price, plus ADA guest-services roles where disability advocacy groups push back on kiosk-only accommodation intake. This does not extend to general admission.
The limit. The binding threat is venue labor-cost cutting, not model capability, so capability-side gains are limited. Even with full NFPA crowd-manager adoption, headcount can be met with fewer, better-trained staff — the occupation's score can rise while its employment falls.
| New York-Newark-Jersey City, NY-NJ | 7,290 | $38,310 +16% |
| Los Angeles-Long Beach-Anaheim, CA | 6,950 | $37,870 +15% |
| Chicago-Naperville-Elgin, IL-IN | 4,490 | $35,000 +6% |
| Dallas-Fort Worth-Arlington, TX | 4,020 | $27,230 -17% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 3,150 | $29,750 -10% |
| Seattle-Tacoma-Bellevue, WA | 2,960 | $44,250 +34% |
| Las Vegas-Henderson-North Las Vegas, NV | 2,840 | $30,110 -9% |
| Phoenix-Mesa-Chandler, AZ | 2,790 | $32,510 -1% |
| San Jose-Sunnyvale-Santa Clara, CA | 960 | $44,760 +36% |
| Seattle-Tacoma-Bellevue, WA | 2,960 | $44,250 +34% |
| San Francisco-Oakland-Fremont, CA | 1,880 | $42,860 +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 42. 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.