EXPOSED
Fueling aircraft, hooking up lav and potable water carts, de-icing, cabin cleaning, towing and marshalling on an active ramp in weather is physical work that language models cannot touch and today's robotics cannot do at cost. The exposure is not AI writing your job away — it's automated fueling systems, autonomous tugs and baggage vehicles, and AI-driven turnaround scheduling that squeezes headcount per flight. Pay and staffing are set by carriers and ground-handling contractors, and the role carries little licensure or client relationship to defend it.
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
+5.1% 28,000 → 29,500 on the projections basis
Growing, and only partly exposed
The BLS expects +5.1% more of these jobs by 2034, and at 53/100 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.
~4,300 openings a year on average, including replacing people who leave.
Ramp AgentAircraft DeicerAircraft FuelerFuel TechnicianAircraft CleanerAirline RefuelerAircraft DetailerAircraft RefuelerAirplane RefuelerLavatory TechnicianGround Operations AgentLine Service TechnicianExterior Aircraft CleanerAircraft Service AttendantAircraft Technical CleanerAirfield Maintenance Equipment Operator
Holding it up: embodiment . Weakest point: trust premium .
Tasks largely resist digitisation Dragging a fuel hose to a wing point, bonding the aircraft, hooking a lav cart to a service panel and driving a de-ice bucket around a tail cone are physical sequences no software performs; the 16 rather than 19 reflects that autonomous tugs and pit-fueling automation already exist in trials at large hubs and turnaround scheduling is being taken over by software.
Hands-on in uncontrolled environments You work outdoors on live ramps in jet blast, wing ice, -10°F and 105°F, climbing de-ice buckets and crawling under fuselages around moving GSE — that is as uncontrolled as a worksite gets, and only the fact that it's a fenced, marked, procedurally governed apron keeps it off a 20.
Certification preferred, not legally required There's no state licence to be an aircraft service attendant; you hold a SIDA badge, an airport driver's permit and carrier-specific fueling/de-icing training under 14 CFR 139.321, which employers can issue and revoke themselves — that's a 6, not a 2, because a fuel-quality or de-ice-holdover mistake is traceable to the individual signature on the service log.
Meaningful discretion You call holdover time from the de-icing tables, decide whether contamination requires a second Type IV application and refuse to fuel when you see water in the sump — real calls with real consequences, but bounded by carrier procedures, checklists and a supervisor or captain who signs off, which caps it at 8.
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 (16/20 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 (4/20) is whether buyers specifically pay for a person. Judgment and accountability (8/20) 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 18 of this occupation's 53 points (34%).
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.
No occupation passed every test: close enough to aircraft service attendants 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 70/100 — SAFE.
Airport authority or insurer conditions that autonomous tugs/de-icers may operate airside only under a licensed human safety operator with recorded responsibility — the pattern already applied to autonomous ramp vehicles in trials at Heathrow, Changi and by Aurrigo/TLD, where a human remote supervisor is contractually accountable per movement.
Same two-tier shift raises the difficulty floor of what remains: lav/potable servicing on mixed legacy fleets, damage inspection, winter-ops improvisation in wind and slush are the tasks robotics cost curves fail on longest. Score rises only as headcount falls, so it is not protective of the number of jobs.
An FAA rule or airline/insurer requirement that a named, certificated ground-handling technician personally sign the fueling and de-icing release for each departure — analogous to the de-icing holdover-time attestation already required under FAR 121.629 and to fuel-quality sign-offs in airline GOMs. If an FAA rulemaking converted ground handling from carrier-delegated training to individual certification (repeatedly recommended by NTSB after ramp fuel-contamination and de-icing events), a human signature becomes non-removable from the turnaround even where the pumping and spraying is automated.
Task-mix shift: if automated fuel loading and autonomous baggage tractors absorb the routine tier, the residual role is exception handling — contaminated fuel calls, holdover-time judgment in changing precipitation, FOD and wing-contamination checks, aborting a pushback. Union contracts (IAM/Teamsters ground agreements) that name a designated turnaround coordinator per gate with authority to stop a departure would formalize that ownership.
The limit. Trust premium has no realistic route — passengers never see or select the attendant, and the buyer is a carrier procuring on cost per turn. Even with full liability and judgment gains the occupation stays exposed on headcount, because the levers protect the role's necessity, not its staffing level.
| New York-Newark-Jersey City, NY-NJ | 1,910 | $45,250 +12% |
| Boston-Cambridge-Newton, MA-NH | 1,390 | $44,190 +9% |
| Dallas-Fort Worth-Arlington, TX | 1,280 | $42,830 +6% |
| Los Angeles-Long Beach-Anaheim, CA | 1,060 | $47,200 +17% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 1,040 | $41,910 +4% |
| Houston-Pasadena-The Woodlands, TX | 830 | $36,480 -10% |
| Denver-Aurora-Centennial, CO | 700 | $46,350 +15% |
| Chicago-Naperville-Elgin, IL-IN | 640 | $39,070 -3% |
| Bozeman, MT | 30 | $57,780 +43% |
| Memphis, TN-MS-AR | 440 | $56,620 +40% |
| Baltimore-Columbia-Towson, MD | 100 | $53,890 +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 53. 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.