Parking Attendants
Parking Attendants — AI exposure, safer roles, and a pivot plan.
Also known as: Car Hop · Attendant · Car Hiker · Auto Hiker · Car Chaser · Car Hopper
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
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Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips.66
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Provide customer assistance and information, such as giving directions or handling wheelchairs.56
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Explain and calculate parking charges, collect fees from customers, and respond to customer complaints.55
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Park and retrieve automobiles for customers in parking lots, storage garages, or new car lots.52
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
The current data release does not distinguish durable tasks for this role.
Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 46 puts Parking Attendants in the second quartile of analyzed occupations. In practice, exposure this level is about the mix: 1 of 15 analyzed tasks lean automatable, 14 augmentable, and 0 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.
One next move: adopt AI deliberately on the augmentable tasks and build visible evidence of the durable ones.
Lower-exposure adjacent roles
No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.
Labor-market context
- $35,150median wage
- 137,880employed
- 18,500annual openings
- +3.0%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Your week probably doesn’t match the average
This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.
Personalize my result — $9Related roles
Adjacent by skills or family — no exposure claim implied.
FAQ — Parking Attendants
- What does a score of 46 mean for a Parking Attendants?
- It means that, weighted across the 15 tasks we analyzed for this role, the task mix sits at 46 on a 0–100 exposure scale — in the second quartile of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
- Which tasks in this role are most exposed to AI?
- The highest-exposure tasks are: Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips. Exposure is scored per task from structured data, not generated by a language model.
- Which parts of this job are most durable?
- The current data release does not distinguish durable tasks for this role.
- Is this score personalized to me?
- No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.
Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 15 of 15 tasks carry source-level provenance · methodology