EXPOSED
Almost nothing in this job is text or screen work: you hand out towels and lockers, hang and retrieve coats, stock supplies, wipe down benches and showers, and keep an eye on who belongs in the room. Language AI can't touch that, and today's robotics can't navigate a wet locker room or find a specific garment on a crowded rack. The real threat isn't AI — it's self-service RFID lockers, automated coat-check carousels, and clubs and venues cutting the position entirely to save labor cost.
Roughly flat across the period, with year-to-year wobble.
Median pay $25,110 → $36,300 +15.7% 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% 15,600 → 16,600 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +6.4% 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.
~4,200 openings a year on average, including replacing people who leave.
AttendantDoorkeeperHat CheckerHousekeeperShoe ShinerBath StewardClub StewardCoat CheckerJockey ValetKey AttendantMarina PorterSpa AssociateSpa AttendantArea AttendantBath AttendantRoom AttendantBathhouse KeeperColors CustodianLadies AttendantLocker AttendantRest Room MatronShower AttendantLocker Room ClerkHot Room Attendant
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Retrieving one member's suit from a 400-hanger rack, fitting a wet towel bin, and spotting a non-member wandering the sauna are all tasks no current system does — but issuing lockers, tracking claim tickets, and taking payment are already handled by RFID wristbands and automated carousels in newer venues, which is what pulls this to 13 rather than the high teens.
Hands-on in uncontrolled environments You are on your feet for a full shift in steam, standing water, and tile, lifting laundry bags, restocking amenities on high shelves, and reaching into stalls and lockers — an unstructured, wet, humid space with people undressing in it, which is about as far from a controlled workcell as service work gets; it stops short of 20 only because the space is indoors, mapped, and never involves vehicles or weather.
No licence, no signature requirement No state licenses coatroom or locker room attendants, no exam or continuing education exists, and when a coat goes missing the venue's posted liability limit or its insurer pays — not you, so there is no statutory body that would have to be rewritten before the job is replaced by a carousel.
Executes defined procedures on defined inputs The decisions are bounded and scripted: match ticket to garment, refer lost-property claims to a manager, call security rather than confront someone yourself, follow the venue's cleaning schedule — nothing you decide in a shift carries consequences that outlast it.
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 (13/20 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 (7/20) is whether buyers specifically pay for a person. Judgment and accountability (3/20) 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 11 of this occupation's 40 points (28%).
Embodiment (16/20) 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.
Orderlies EXPOSED
Maids and Housekeeping Cleaners EXPOSED
Cooks, Restaurant 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 57/100, still EXPOSED.
High-end venues (private golf clubs, luxury hotel spas, theater subscription tiers) marketing attended coat-check and locker service as a named amenity, the way concierge and valet parking survived automation; visible signal would be club dues structures or spa pricing that itemize attended locker room service, or hotel brand standards (e.g., Forbes Travel Guide five-star inspection criteria) requiring a staffed attendant in the spa locker area
Formal designation of locker room attendants as safeguarding/observation staff under youth-athletics and gym abuse-prevention rules — e.g., state laws following the USA Gymnastics/Nassar reforms and SafeSport requirements that adult supervision be present in changing areas used by minors, making the attendant the person accountable for who enters and for reporting
Task-mix shift if RFID self-service lockers and coat carousels absorb the handout/retrieval tier: the remaining role becomes exception handling — lost property claims, intoxicated or distressed patrons, minors, disability assistance, incident documentation. Watch for job postings retitled to 'locker room supervisor' or 'spa attendant/host' with lost-and-found and incident-report duties
Nothing raises this by policy, but wet-floor slip liability and locker-room privacy rules that bar cameras and mobile robots from changing areas keep the work physically human-only; a state health-club code explicitly prohibiting recording devices or autonomous machines in changing rooms would harden it
A narrow route only: a state or municipal bailment/lost-property ordinance (or venue insurer requirement) conditioning a venue's liability cap for checked property on a named human attendant logging and signing for items, as some bailment case law already distinguishes attended from unattended checkrooms
The limit. Even with every lever, this stays a low-scoring occupation. There is no license, no personal liability, and the displacement pressure is a capital-for-labor swap by venue operators, not model capability — so improvements in AI barely move it and cost-cutting moves it a lot. Realistic ceiling is roughly the mid-50s, and it would be concentrated in luxury and youth-supervision settings rather than across the 15,560 workers.
| New York-Newark-Jersey City, NY-NJ | 1,980 | $37,420 +3% |
| Los Angeles-Long Beach-Anaheim, CA | 1,350 | $47,650 +31% |
| Chicago-Naperville-Elgin, IL-IN | 770 | $38,990 +7% |
| Detroit-Warren-Dearborn, MI | 620 | $29,890 -18% |
| Phoenix-Mesa-Chandler, AZ | 480 | $38,570 +6% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 460 | $36,110 -1% |
| Las Vegas-Henderson-North Las Vegas, NV | 440 | $36,820 +1% |
| Riverside-San Bernardino-Ontario, CA | 380 | $42,710 +18% |
| Kahului-Wailuku, HI | 70 | $55,830 +54% |
| Salinas, CA | 50 | $54,880 +51% |
| Urban Honolulu, HI | 80 | $50,140 +38% |
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 40. 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.