EXPOSED
The modal worker here runs a floor — a pool deck, ropes course, ski area, campground, theme park zone, or fitness center — assigning staff, watching for unsafe behavior, handling angry guests, and stepping in when a kid gets hurt or a ride stops. AI already eats the paperwork half of the job: shift scheduling, labor-cost reports, incident write-ups, training checklists, attendance forecasting. What it cannot do is stand in a wet, loud, crowded venue and decide in ten seconds whether to close the attraction, and be the person the parent yells at afterward.
Headcount grew steadily across the period.
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.
BLS projection, 2024–2034
+6.3%
Percentage only. The projection counts a different population from the 103,190 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +6.3% more of these jobs by 2034, and at 60/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~13,400 openings a year on average, including replacing people who leave.
Head UsherCaddymasterHead ButlerCaddy MasterSalon LeaderShop ManagerSalon ManagerService ChiefSalon DirectorCheckroom ChiefClub SupervisorService CaptainShop SupervisorSpa CoordinatorCaddy SupervisorRides SupervisorShift SupervisorArcade SupervisorCaddie SupervisorSalon CoordinatorHair Salon ManagerAquatics SupervisorChief Airport GuideGolf Course Manager
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Scheduling, payroll coding, incident-report drafting, and headcount forecasting are already software problems, but the other half of the day — walking a pool deck to catch a lifeguard whose scanning has gone slack, pulling a new hire aside mid-shift to re-teach a harness check, deciding staffing when three people call out on a 95-degree Saturday — needs a person present, which puts it at the top of mixed rather than into resistant.
Hands-on in uncontrolled environments The job is done standing in the environment being supervised: wet tile, ski slopes in weather, ropes-course platforms, camp grounds after dark, crowded midways — you do rescues and first aid yourself, physically clear a stuck ride queue, and cover a guard chair or belay station when short-staffed, which is uncontrolled-environment work rather than a facility walk-through.
Certification preferred, not legally required Employers typically want lifeguard/CPR/AED, Ellis or Red Cross instructor cards, ride-operator or challenge-course certifications, and in some states a camp director credential — real gates, but they are renewable certifications, not a state licence that makes you personally sueable, so negligence claims land on the operator's insurer and the general manager rather than your name.
Meaningful discretion Closing a waterslide on a thunderstorm read, ejecting an intoxicated guest, calling EMS versus icing an injury, deciding whether a guard's lapse is a coaching moment or a termination, and choosing to evacuate a zone with 400 people in it are all yours in real time with a policy manual that cannot cover the specific call — the ceiling is the escalation path to a GM and corporate risk on the biggest incidents.
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 (6/20) is whether the law requires a licensed human to sign. Trust premium (12/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) 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 31 of this occupation's 60 points (52%).
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.
No occupation passed every test: close enough to first-line supervisors of entertainment and recreation workers, except gambling services on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
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 74/100 — SAFE.
Insurer-driven rather than statutory: carriers writing amusement, camp, and ski liability (e.g. via ACCT ropes-course standards, ACA camp accreditation, NSAA ski area guidelines) requiring documented supervision ratios and a named certified supervisor on the incident report as a condition of coverage — already a live practice in ropes-course and camp accreditation, and expandable to trampoline parks and inflatables where injury litigation is heavy.
Task-mix shift with a genuine two-tier structure: once scheduling, labor forecasting, attendance prediction, and incident-report drafting are fully handled by workforce-management software, the residual role is almost entirely the close/don't-close call, discipline and termination decisions, and being the accountable name on the incident file. The score rises because the denominator shrinks, not because anything new is added.
State amusement-ride and aquatic-facility codes increasingly name a specific credentialed individual as the on-site authority — e.g. state pool codes requiring a Certified Pool Operator or AFO physically present during operating hours, and NARSO/ASTM F24-aligned state ride rules requiring a named 'qualified person' to authorize a ride's return to service after a stoppage. If more states move that authorization from 'the operator' to a personally licensed, personally liable named supervisor whose signature is required before reopening, this rises. Watch also for ASTM F24 or state fair boards adding an explicit prohibition on automated/remote sign-off for return-to-service.
Same two-tier shift as above, but note the ceiling: the remaining tier is physically resistant, not cognitively resistant, so the gain here is smaller than in judgment. Conditional on employers not simply deleting the supervisor layer and pushing the judgment call up to a general manager covering multiple venues — a consolidation pattern already visible at chain fitness and trampoline-park operators.
Little upward room — already 16. Could rise only if venue design trends toward more unpredictable physical environments (adventure parks, water attractions, obstacle courses) at the expense of controlled indoor attractions, which is a real but slow shift in the recreation capex mix.
The limit. Trust premium has no plausible route: guests do not choose a pool or trampoline park because a human supervises the deck, and there is no purchasing channel through which that preference could be expressed or priced. The dominant downside risk is not AI capability but span-of-control consolidation — one supervisor per three sites with camera analytics — which cuts headcount without needing any dimension score to fall.
| Los Angeles-Long Beach-Anaheim, CA | 5,060 | $51,870 +7% |
| New York-Newark-Jersey City, NY-NJ | 4,550 | $54,530 +12% |
| Chicago-Naperville-Elgin, IL-IN | 3,530 | $48,390 +0% |
| Dallas-Fort Worth-Arlington, TX | 2,400 | $46,650 -4% |
| Denver-Aurora-Centennial, CO | 2,090 | $57,660 +19% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,060 | $49,300 +2% |
| Boston-Cambridge-Newton, MA-NH | 2,020 | $58,000 +19% |
| Atlanta-Sandy Springs-Roswell, GA | 1,940 | $45,530 -6% |
| Kennewick-Richland, WA | 50 | $69,820 +44% |
| Seattle-Tacoma-Bellevue, WA | 1,180 | $68,020 +40% |
| San Francisco-Oakland-Fremont, CA | 1,740 | $65,820 +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 60. 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.