SAFE
The job is watching water and slopes with your own eyes and then physically entering them — swimming a limp swimmer to the deck, applying rescue breaths, sledding an injured skier down a run, setting avalanche charges. AI vision systems (drowning-detection cameras, avalanche forecasting models) are genuinely encroaching on the surveillance half of the work, but they can only page a human to do the extraction, and health codes plus insurers require certified bodies on duty in fixed ratios. The real pressure on this occupation is budget and seasonality, not software.
Dipped in 2020, then grew past where it started.
Median pay $23,420 → $33,580 +14.7% 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
+5.8% 149,700 → 158,400 on the projections basis
Hard to automate, and growing
The work resists current AI and the BLS projects +5.8% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.
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.
~42,700 openings a year on average, including replacing people who leave.
RangerLifeguardLife GuardBus MonitorPark RangerPool MonitorRescue WorkerSki PatrollerPool AttendantPool LifeguardBeach AttendantBeach LifeguardOcean LifeguardPool SupervisorRecreation AideGamewell OperatorAquatics LifeguardPlayground MonitorAquatics SpecialistCertified LifeguardAquatics CoordinatorSki Patrol ParamedicMarine Safety OfficerCertified Ski Patroller
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation A drowning-detection camera can flag a swimmer on the bottom, but nothing else in the shift — the 20-yard approach swim, the spinal board extraction with a two-person roll, the toboggan belay down a mogul field, the chairlift evacuation rope work — has a digital substitute, which is why this sits at 17 rather than in the mixed band.
Hands-on in uncontrolled environments You work in moving water, on ice, in whiteouts and 95-degree pool decks, and the environment is the hazard you are managing: 19 reflects that the rescue itself happens in the uncontrolled medium, with the only reason it isn't 20 being the fraction of hours spent scanning from a stand or checking chemical logs.
Certification preferred, not legally required Lifeguard/CPR/AED and OEC or EMT-B certifications are legally required to occupy the post — health codes set guard-to-bather ratios — but they are short-course certificates renewed every 1-2 years, not a state licence with a personal practice you can lose and be sued over, so the patrol or aquatic operator absorbs the negligence claim, putting this at 8 rather than an RN's 14.
Meaningful discretion Deciding to close a run for instability, calling for a helicopter versus a toboggan, or choosing to hold C-spine on a skier who insists he's fine are calls made in seconds with no supervisor present and real death exposure, but they run through protocol trees — OEC algorithms, avalanche hazard scales, EAP triggers — which keeps it at 13 instead of the 16+ of someone setting the protocol.
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 (17/20 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 (8/20) is whether the law requires a licensed human to sign. Trust premium (11/20) 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 32 of this occupation's 68 points (47%).
Embodiment (19/20) 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 83/100, still SAFE.
Insurer requirements: aquatic-facility and ski-resort liability carriers (e.g., programs underwriting municipal pools, or the ski industry's captive insurers) conditioning coverage on documented certified-guard headcount and named patroller-in-charge for avalanche route decisions, so a named certified human signs the daily mitigation/opening log. Avalanche control blasting is already federally/state permitted to named licensed blasters — extending explicit personal sign-off to run-opening decisions would harden this.
Same two-tier shift: the surveillance tier is the automatable one; extraction, packaging, sled evacuation, and on-slope patient care are not. Score is near ceiling already, so movement is small.
State/county health codes and pool codes (e.g., the CDC Model Aquatic Health Code, adopted piecemeal by states) being amended to fix minimum certified-lifeguard-per-bather ratios that explicitly cannot be reduced by installing drowning-detection camera systems — i.e., language stating AI surveillance supplements but does not substitute for staffed positions. Several jurisdictions have debated the reverse (allowing staffing credits for tech); a codified no-substitution rule would raise this materially.
Task-mix shift: if drowning-detection cameras and avalanche forecast models absorb continuous scanning, the residual role concentrates on the ambiguous calls — whether to close a run after a forecast disagrees with observed instability, triage among multiple casualties, whether to spinal-immobilize. Formal recognition of this via patrol-director sign-off requirements on open/close decisions (as in some resort avalanche safety plans) raises the score.
Parent- and school-driven demand: swim programs and camps advertising staffed guard ratios rather than camera coverage after a publicized camera-miss drowning. Weak lever — buyers here are municipalities and resorts buying compliance, not families buying humans.
The limit. task_resistance and embodiment are already near maximum; the realistic headroom is almost entirely in liability_shield, which is low only because certification requirements attach to staffing ratios rather than to a personally liable signature. The binding threat is budget cuts and seasonal contraction, which no dimension here measures.
| Los Angeles-Long Beach-Anaheim, CA | 11,640 | $43,800 +30% |
| New York-Newark-Jersey City, NY-NJ | 9,610 | $36,600 +9% |
| Chicago-Naperville-Elgin, IL-IN | 4,960 | $33,750 +1% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4,600 | $34,710 +3% |
| Dallas-Fort Worth-Arlington, TX | 3,930 | $29,770 -11% |
| Seattle-Tacoma-Bellevue, WA | 3,060 | $39,240 +17% |
| Orlando-Kissimmee-Sanford, FL | 2,980 | $30,510 -9% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,980 | $32,220 -4% |
| Kahului-Wailuku, HI | 110 | $64,030 +91% |
| Bozeman, MT | 100 | $44,610 +33% |
| Los Angeles-Long Beach-Anaheim, CA | 11,640 | $43,800 +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 68. 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.