COOKED verdict contested
The modal parking attendant collects fees, issues and validates tickets, monitors lot occupancy and directs drivers to open spaces — and every one of those tasks is already handled at scale by pay-on-foot kiosks, license-plate recognition, gateless app-based parking, and occupancy sensors. What survives is the physical tier: valet drivers who actually move customer vehicles through tight garages, handle keys, and deal with the guest who blocked the ramp or scraped a bumper. There is no license, no signature requirement, and almost no ambiguous decision-making to anchor the role, so the shrinkage comes from parking-tech capex rather than from language models.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $25,140 → $35,150 +11.9% 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
+3% 135,700 → 139,800 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3% 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.
~18,500 openings a year on average, including replacing people who leave.
HikerValetCar HopAttendantCar HikerAuto HikerCar ChaserCar HopperCar JockeyCar ParkerCar RunnerLot PorterAuto ParkerCar HostlerCar ShaggerRamp JockeyValet DriverValet ParkerValet RunnerLot AttendantService ValetTruck SpotterUtility ClerkNight Attendant
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier At 9 the score splits the job in two: fee collection, ticket validation, occupancy counts and space-directing are already gone wherever a garage has installed LPR and a pay-on-foot kiosk, but valet parking — reversing a stranger's manual-transmission truck down a 12% ramp into a stacked stall, then retrieving it in four minutes — has no deployed substitute, which is why this isn't a 4.
Hands-on in uncontrolled environments 14 reflects that the surviving work is entirely outdoors or in unventilated decks: standing shifts in rain and January cold, walking rows to chalk tires or check permits, and driving unfamiliar vehicles with unfamiliar clutch feel and blind spots through structures never designed for a car you didn't practice in.
No licence, no signature requirement 1 is near-floor because a driver's license is the only credential and it isn't parking-specific — damage claims land on the garage operator's garage-keeper's liability policy, not on you, and no statute requires an attendant's signature on anything.
Executes defined procedures on defined inputs 3 matches a duty set governed by posted rate boards and operator procedure — validate or don't, tow or call the manager, refuse the oversized vehicle — with the genuinely ambiguous calls (accepting a car with existing damage, disputing a lost-ticket charge) escalated to a supervisor.
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 (9/20 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 (5/20) 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 9 of this occupation's 32 points (28%).
Embodiment (14/20) 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
Postal Service Mail Carriers EXPOSED
Passenger Attendants 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 44/100 — EXPOSED.
Task-mix collapse into valet-only work: if fee collection and enforcement fully migrate to LPR/app systems (already standard in Chicago, LA, and most airport garages), the residual headcount is drivers moving customer cars in tight legacy garages with manual keys, curbside handoffs, EV charging cable management, and damage walk-arounds — physical work that autonomous valet parking (Mercedes/Bosch INTELLIGENT PARK PILOT, certified only in a single Stuttgart garage) cannot do in mixed unmapped facilities
Hotel and hospital valet as a paid amenity line item: if brand standards (Marriott/Hyatt luxury tiers, hospital patient-experience scoring tied to CMS HCAHPS-style surveys) continue to specify a live attendant at the door for arrival greeting and mobility assistance, the human is what the fee buys rather than the car movement
Garage-keeper liability and bonding: some municipal valet ordinances (e.g. Los Angeles valet permit rules, Miami Beach valet licensing) require a permitted operator, insurance certificate, and named attendants; if cities tighten these into individual attendant permits with named responsibility for vehicle custody, a thin personal-accountability layer attaches to key handling
Damage and incident calls: if operator insurers require an on-site human to document pre-existing damage, decide whether a vehicle is safe to move (low clearance, modified suspension, dead EV), and manage ramp blockages during evacuation, the surviving role owns small consequential calls under ambiguity
The limit. Even with every lever, this stays low — the ceiling is a much smaller valet-only occupation, not a protected one. Headcount loss is driven by kiosk and LPR capex already deployed, and nothing here reverses that; the levers change what the remaining jobs look like, not how many there are.
| New York-Newark-Jersey City, NY-NJ | 14,220 | $35,920 +2% |
| Los Angeles-Long Beach-Anaheim, CA | 11,070 | $37,280 +6% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 8,550 | $29,650 -16% |
| Dallas-Fort Worth-Arlington, TX | 5,490 | $31,770 -10% |
| Chicago-Naperville-Elgin, IL-IN | 4,450 | $37,810 +8% |
| Atlanta-Sandy Springs-Roswell, GA | 3,810 | $28,200 -20% |
| San Francisco-Oakland-Fremont, CA | 3,470 | $44,480 +27% |
| Houston-Pasadena-The Woodlands, TX | 3,400 | $31,120 -11% |
| Colorado Springs, CO | 150 | $47,320 +35% |
| San Jose-Sunnyvale-Santa Clara, CA | 650 | $45,030 +28% |
| San Francisco-Oakland-Fremont, CA | 3,470 | $44,480 +27% |
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 32. 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.