Parking Attendants
In short: today's best AI models could do about 10% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 90% of the time, stays with people.
Park vehicles or issue tickets for customers in a parking lot or garage. May park or tend vehicles in environments such as a car dealership or rental car facility. May collect fee.
137,880 people do this job in the US. $35,150 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work4 h
- Desk work AI can't do yet0 h
- Stays with people36 h
Parking Attendants, now: of a 40-hour week, AI could take on 0 hours, 4 hours become checking its work, 0 hours are desk work it can't do yet, and 36 hours stay with people.
The largest pieces: Handle cash (about 4 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 0%
- 10%
- 1%
- 14%
- 75%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 10% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 1%. With Claude Mythos Preview (early), measured in April 2026, 10%. Today's estimate: 10%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 15 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0056% to 0.0046% (released February and March 2025); 0.015% to 0.0089% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0056% | 592 of 708 | 2 of 15 |
| Released March 2025 | 0.0046% | 592 of 703 | 2 of 15 |
| The week of Aug 4, 2025 (released September 2025) | 0.015% | 400 of 655 | 1 of 15 |
| The week of Nov 13, 2025 (released January 2026) | 0.0091% | 492 of 672 | 1 of 15 |
| The week of Feb 5, 2026 (released March 2026) | 0.0089% | 510 of 677 | 1 of 15 |
| April 2026 (released June 2026) | none | n/a | 0 of 15 |
| May 2026 (released June 2026) | none | n/a | 0 of 15 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 73% of this job's working time as physical work. For 73% of the working time, a robot has been shown doing most of the task somewhere: 42% in a purpose-built cell, 30% in a structured facility. 1.03 of 3, the 104th highest of the 923 jobs.
- Not physical 27%
- In a purpose-built cell 42%
- In a structured facility 30%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
90% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Keep the lot clean and orderly, the hands-on work, 22%
- Park and retrieve cars, the hands-on work, 11%
- Handle cash, the hands-on work, 10%
- Patrol the lot, the hands-on work, 7%
- Inspect vehicles for damage, the hands-on work, 6%
- Retrieve vehicles, the hands-on work, 6%
What becomes checking the machine
10% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it.
- Handle cash, costly if wrong, 10%
What to learn
- The hands-on craft. About 75% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- The conversations. About 16% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 15 tasks from .
| Task | ||||
|---|---|---|---|---|
| Keep parking areas clean and orderly to ensure that space usage is maximized.Tidying the parking area.Done daily by most who do it. Robots: in a purpose-built cell, Stan (Stanley Robotics) (sold). Anthropic's rating. | 22% | 25 min13 min to 50 min | Hands-on 100% | Stays with people |
| Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips.Change made, the drawer balanced and recorded.Done daily by most who do it. Robots: in a structured facility, Parking BOXX Big Pay BOXX pay station (sold). Anthropic's rating. | 19% | 17 min8 min to 38 min | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Park and retrieve automobiles for customers in parking lots, storage garages, or new car lots.Driving customers' cars in the lot or garage.Done hourly or more by most who do it. Robots: in a purpose-built cell, ParkPlus AGV automated parking system, Brickell House (deployed). Anthropic's rating. | 11% | 8 min4 min to 17 min | Hands-on 100% | Stays with people |
| Patrol parking areas to prevent vehicle damage and vehicle or property thefts.Walking the area to prevent damage and theft.Done daily by most who do it.Robots: in a structured facility, Knightscope K5 Autonomous Security Robot (Knightscope, Inc.) (sold). Anthropic's rating. | 7% | 25 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Inspect vehicles to detect any damage.Looking the car over.Done hourly or more by most who do it. Robots: in a structured facility, UVeye automated vehicle inspection (Atlas/Artemis/Helios) at Hertz airports (deployed). Anthropic's rating. | 6% | 4 min2 min to 13 min | Hands-on 100% | Stays with people |
| Take numbered tags from customers, locate vehicles, and deliver vehicles, or provide customers with instructions for locating vehicles.Finding and driving up the car, and handing it over.Done hourly or more by most who do it. Robots: in a purpose-built cell, Stan (Stanley Robotics) at Lyon-Saint Exupery P5+ (deployed). Anthropic's rating. | 6% | 4 min3 min to 8 min | Hands-on 70%, in person 30% | Stays with people |
| Explain and calculate parking charges, collect fees from customers, and respond to customer complaints.Fees explained and taken, complaints heard.Done daily by most who do it. | 5% | 5 min3 min to 13 min | In person 50%, hands-on 50% | Stays with people |
| Issue ticket stubs or place numbered tags on windshields, log tags or attach tag to customers' keys, and give customers matching tags for locating parked vehicles.Tags placed and logged, and the stub handed over.Done hourly or more by most who do it. Robots: in a structured facility, Robotic Parking Systems (RPS) automated garage, Entry/Exit Terminal (deployed). Anthropic's rating. | 4% | 3 min2 min to 4 min | Hands-on 70%, in person 30% | Stays with people |
| Call emergency responders or the proper authorities and provide motorist assistance, such as giving directions or helping jump start a stalled vehicle.A call to responders, and help such as a jump start.Done more than monthly by most who do it. Robots: in a structured facility, LG Airport Guide Robot (CLOi GuideBot lineage) (deployed). Anthropic's rating. | 4% | 17 min8 min to 38 min | Hands-on 40%, conversation 30%, in person 30% | Stays with people |
| Provide customer assistance and information, such as giving directions or handling wheelchairs.Directions and help with wheelchairs.Done daily by most who do it. | 4% | 4 min2 min to 13 min | In person 70%, hands-on 30% | Stays with people |
| Greet customers and open their car doors.Greeting and opening doors.Done hourly or more by most who do it. Robots: in a purpose-built cell, Mornine humanoid robot (AiMOGA Robotics) (deployed). Anthropic's rating. | 3% | 2 min1 min to 3 min | In person 100% | Stays with people |
| Perform maintenance on cars in storage to protect tires, batteries, or exteriors from deterioration.Tires, batteries and exteriors looked after.Done daily by most who do it.Robots: in a purpose-built cell, In-bay automatic car wash systems (touchless and soft-touch) (National Carwash Solutions / Reliable Plus) (sold). Anthropic's rating. | 3% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Parking Attendants is within reach of AI now?
- An estimated 10% of the working time is within reach of AI models now and 10% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Parking Attendants come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Handle cash (50% of the task's time now).
- Which tasks of Parking Attendants stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Keep the lot clean and orderly; Park and retrieve cars; and Patrol the lot. Together such tasks are about 81% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Parking Attendants learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; judgment where errors are severe; and the conversations. By the end of 2028 on the long-run pace at four in five, about 90% of the working time stays with people, 10% becomes checking a model's work and 0% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Parking Attendants?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Parking Attendants?
- Anthropic's robot exposure rating (September 2026) finds 73% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 73% of the working time, 30% in a structured facility. Its index is 1.03 of 3. One example: Parking BOXX Big Pay BOXX pay station for "handle cash". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 15 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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