Laundry and Dry-Cleaning Workers
In short: today's best AI models could do about 1% 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 99% of the time, stays with people.
Operate or tend washing or dry-cleaning machines to wash or dry-clean industrial or household articles, such as cloth garments, suede, leather, furs, blankets, draperies, linens, rugs, and carpets. Includes spotters and dyers of these articles.
198,040 people do this job in the US. $34,890 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 work0 h
- Desk work AI can't do yet0 h
- Stays with people40 h
Laundry and Dry-Cleaning Workers, now: of a 40-hour week, AI could take on 0 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 40 hours stay with people.
The largest pieces: Choose spotting treatments (under an hour, 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.
- 1%
- 0%
- 0%
- 0%
- 99%
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 1% 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, 0%. With Claude Mythos Preview (early), measured in April 2026, 1%. Today's estimate: 1%.
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 32 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0029% to 0.0025% (released February and March 2025); 0.0024% to none (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0029% | 660 of 708 | 1 of 32 |
| Released March 2025 | 0.0025% | 637 of 703 | 1 of 32 |
| The week of Aug 4, 2025 (released September 2025) | 0.0024% | 615 of 655 | 1 of 32 |
| The week of Nov 13, 2025 (released January 2026) | none | n/a | 0 of 32 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 32 |
| April 2026 (released June 2026) | none | n/a | 0 of 32 |
| May 2026 (released June 2026) | none | n/a | 0 of 32 |
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 100% of this job's working time as physical work. For 75% of the working time, a robot has been shown doing most of the task somewhere: 63% in a purpose-built cell, 12% in a structured facility. 0.87 of 3, the 198th highest of the 923 jobs.
- No robot shown 25%
- In a purpose-built cell 63%
- In a structured facility 12%
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
97% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Fold and wrap finished articles, the hands-on work, 15%
- Sort articles into lots, the hands-on work, 12%
- Load the machines, the hands-on work, 8%
- Run extractors and driers, the hands-on work, 8%
- Run the washers and dry cleaners, the hands-on work, 7%
- Unload the machines, the hands-on work, 5%
What becomes checking the machine
1% 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.
- Choose spotting treatments, costly if wrong, 0.9%
What to learn
- The hands-on craft. About 99% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- 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 . 32 tasks from .
| Task | ||||
|---|---|---|---|---|
| Sort and count articles removed from dryers, and fold, wrap, or hang them.By hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, sewts.VELUM (FANUC robot cell) (deployed). Anthropic's rating. | 15% | 25 min13 min to 1.1 h | Hands-on 100% | Stays with people |
| Examine and sort into lots articles to be cleaned, according to color, fabric, dirt content, and cleaning technique required.By hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Inwatec THOR Robot Separator (JENSEN-GROUP) (sold). Anthropic's rating. | 12% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
| Load articles into washers or dry-cleaning machines, or direct other workers to perform loading.By hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Kannegiesser PowerTrans batch washer with monorail bag storage (Herbert Kannegiesser GmbH) (sold). Anthropic's rating. | 8% | 13 min4 min to 29 min | Hands-on 100% | Stays with people |
| Operate extractors and driers, or direct their operation.At the machines.Done hourly or more by most who do it. Robots: in a purpose-built cell, Continuous batch tunnel washing system (loading conveyor, batch tunnel washer, water extraction press, shuttle lifting conveyors, pass-through dryers, master control) (Girbau Industrial) (deployed). Anthropic's rating. | 8% | 17 min8 min to 38 min | Hands-on 100% | Stays with people |
| Start washers, dry cleaners, driers, or extractors, and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch, and other additives.Setting the machines and the additives.Done hourly or more by most who do it. Robots: in a structured facility, Kannegiesser SmartLaundry with PowerTrans batch washer and Process Control (Herbert Kannegiesser GmbH) (deployed). Anthropic's rating. | 7% | 13 min4 min to 25 min | Hands-on 100% | Stays with people |
| Remove items from washers or dry-cleaning machines, or direct other workers to do so.By hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Milnor M-Series automated open-pocket washer-extractors with loose-goods shuttle (Clemens Uniform installation) (Pellerin Milnor Corporation) (deployed). Anthropic's rating. | 5% | 8 min4 min to 21 min | Hands-on 100% | Stays with people |
| Pre-soak, sterilize, scrub, spot-clean, and dry contaminated or stained articles, using neutralizer solutions and portable machines.By hand and portable machine.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 4% | 50 min17 min to 1.7 h | Hands-on 100% | Stays with people |
| Clean machine filters, and lubricate equipment.By hand.Done daily by most who do it. Robots: in a purpose-built cell, Denray Automated Filter Cleaner V2 (cartridge filter cleaner) (sold). Anthropic's rating. | 3% | 17 min8 min to 50 min | Hands-on 100% | Stays with people |
| Sprinkle chemical solvents over stains, and pat areas with brushes or sponges to remove stains.By hand.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
| Wash, dry-clean, or glaze delicate articles or fur garment linings by hand, using mild detergents or dry cleaning solutions.By hand.Done daily by most who do it. | 3% | 33 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Inspect soiled articles to determine sources of stains, to locate color imperfections, and to identify items requiring special treatment.Looking each item over.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 13 min4 min to 33 min | Hands-on 100% | Stays with people |
| Operate dry-cleaning machines to clean soiled articles.At the machines.Done daily by most who do it. | 3% | 25 min13 min to 1.1 h | 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.
- Cleaners of Vehicles and Equipment1% now, 1% by 2028
- Pressers, Textile, Garment, and Related Materials2% now, 2% by 2028
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders5% now, 5% by 2028
- Sewing Machine Operators2% now, 2% by 2028
- Painting, Coating, and Decorating Workers0% now, 0% by 2028
- Textile Bleaching and Dyeing Machine Operators and Tenders11% now, 11% by 2028
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 Laundry and Dry-Cleaning Workers is within reach of AI now?
- An estimated 1% of the working time is within reach of AI models now and 1% 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 Laundry and Dry-Cleaning Workers 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: Choose spotting treatments (40% of the task's time now); and Take in and tag articles (20% of the task's time now).
- Which tasks of Laundry and Dry-Cleaning Workers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Fold and wrap finished articles; Sort articles into lots; and Load the machines. Together such tasks are about 97% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Laundry and Dry-Cleaning Workers 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; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 97% of the working time stays with people, 1% 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 Laundry and Dry-Cleaning Workers?
- 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 Laundry and Dry-Cleaning Workers?
- Anthropic's robot exposure rating (September 2026) finds 100% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 75% of the working time, 12% in a structured facility. Its index is 0.87 of 3. One example: Kannegiesser SmartLaundry with PowerTrans batch washer and Process Control for "run the washers and dry cleaners". 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 32 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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