Will AI replace Cleaners of Vehicles and Equipment?
Estimated risk score: 12 out of 100
Very low replacement risk
Estimate from published task research, not reviewed by a person · how we estimate
Within about 19 points for 4 in 5 of the jobs we checked (0 to 31).
For the occupation as a whole, from ratings of its 21 O*NET tasks published in 2023 and calibrated to our hand scores. A specialty inside it can sit higher or lower.
O*NET-SOC 53-7061.00 · Transportation and Material Moving
Tasks rated in 2023 as ones AI could do or speed up
Tasks a GPT-4-class model could do, or do in half the time or less: 1 of its 21 rated tasks. In O*NET’s words.
- Maintain inventories of supplies.
Tasks rated in 2023 as not sped up by AI
Tasks such a model was rated as not speeding up or taking over: 19 of its 21 rated tasks; the first 6 are shown. In O*NET’s words.
- Apply paints, dyes, polishes, reconditioners, waxes, or masking materials to vehicles to preserve, protect, or restore color or condition.
- Clean and polish vehicle windows.
- Connect hoses or lines to pumps or other equipment.
- Drive vehicles to or from workshops or customers' workplaces or homes.
- Mix cleaning solutions, abrasive compositions, or other compounds, according to formulas.
- Pre-soak or rinse machine parts, equipment, or vehicles by immersing objects in cleaning solutions or water, manually or using hoists.
What the job is
Wash or otherwise clean vehicles, machinery, and other equipment. Use such materials as water, cleaning agents, brushes, cloths, and hoses.
Also called: Car Washer, Reconditioner, Automotive Detailer (Auto Detailer), Talcer, Scraper, Lot Tech, Plateman, Car Dryer.
Closest jobs we score by hand
In the same field (Transportation and Material Moving). Different jobs, each scored by a person.
Source. This site includes information from the O*NET 30.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not approved, endorsed, or tested these modifications. (O*NET, CC BY 4.0.) Task ratings: Eloundou, Manning, Mishkin and Rock (2023), “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, data used under the MIT licence (licence texts). Our change: we averaged the ratings over this occupation’s tasks and calibrated them to our 0–100 scale. It is not a prediction about any one person and not career advice. Disagree? Write to hello@yourstopwashit.com. How we estimate