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Time Machine

Laundry and Dry-Cleaning Workers

Scrub through 185years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
187519001925195019752000now
2026
Known today as Laundry and Dry-Cleaning Workers (BLS SOC 51-6011)
Latest actual · 2024
195K
OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.
Latest actual · 2024
$33,800
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Hand laundry (washboard, boiling copper, flat iron)

    The hand laundry of the Chinese immigrant era ran entirely on manual craft: the operator soaked and scrubbed fabric on a washboard, boiled whites in a copper pot over an open fire, wrung each item by hand, and finished with a flat iron heated on the stove. The operator needed to know fabric types, water temperature for each, and the sequence of starch, iron, and fold that produced a crisp collar or a presentable shirt. There were no machines to consult. Speed and knowledge lived in the worker's hands and memory.

    Work toolChanging equipment
  • Steam-powered laundry machinery (rotary washers, mangles, tunnel finishers)

    The steam laundry of the 1880s-1930s organized laundry work as a factory: a boiler drove rotary drum washers, a mangle pressed flat goods between steam-heated rollers, and a tunnel finisher blew hot air through hanging garments. The workforce was divided into specialized stations, each requiring speed rather than judgment. Workers loaded, unloaded, sorted, and folded at an assembly-line pace. The craft knowledge of the hand laundry largely disappeared from individual workers; it was encoded into machine settings and the foreman's instructions.

    Effect on the work

    Steam laundries employed a predominantly female workforce at wages roughly half those of male industrial workers. Women's entry into laundry factory work was documented as one of the earliest large-scale industrial employments of women outside textiles. By the 1920s, African-American women had largely replaced immigrant European women as steam-laundry workers as better-paying industrial jobs opened to white women.

    Work toolChanging equipment
  • Petroleum and chlorinated solvents in dry cleaning (Stoddard 1924, perc from 1933)

    The dry-cleaning trade, which had used kerosene and gasoline since Jean Baptiste Jolly opened the first dry-cleaning service in Paris in 1845, graduated through a sequence of solvents as fire safety became non-negotiable. William Joseph Stoddard, a US dry cleaner, developed a safer petroleum-based solvent in 1924 that bore his name. Then in 1930 chemists at Dow Chemical recommended perchloroethylene (perc, PCE) as a non-flammable alternative; by 1933 it was considered the ideal dry-cleaning solvent. Perc offered excellent cleaning power, compatibility with most fabrics, and no fire risk. Dry-cleaning machine manufacturers developed closed-loop systems specifically for perc by the late 1930s. By the 1950s, perc had become the nearly universal solvent in US dry-cleaning plants. The switch from petroleum solvents to perc transformed the dry-cleaning worker's job: the solvent was now recovered and recycled inside the machine, and the operator's primary skill shifted from managing fire risk toward managing machine cycles, fabric sorting, and the hand-spotting board.

    Work toolChanging equipment
  • Automatic home washers and coin-operated laundromats (Bendix 1937, mass adoption 1950s)

    Bendix introduced the first automatic home washer in 1937; General Electric and Maytag brought consumer models to the mass market in the late 1940s. By 1960, 55% of US households owned a washing machine. This was the single most destructive technology event in the history of commercial laundry employment: the household segment of the business simply moved home. Commercial laundries that had served residential customers collapsed or pivoted to clients the home machine could not serve: hospitals, hotels, restaurants, and uniform-rental services. Employment in the commercial laundry trade fell throughout the 1950s-1970s. The workers who kept their jobs were those serving institutional clients with genuinely industrial-scale linen volumes.

    Effect on the work

    Commercial laundry employment declined from an estimated peak of 400,000-450,000 in the 1940s to roughly 230,000 by the early 2000s, a decline driven almost entirely by home appliance penetration among residential customers. The trade stabilized when institutional linen services became the dominant client base.

    Work toolChanging equipment
  • EPA NESHAP perc emissions standards (1993 rule, 2006 tightening, 2024 TSCA phase-out)

    The EPA promulgated the National Emission Standards for Hazardous Air Pollutants (NESHAP) for dry-cleaning facilities on September 22, 1993. The rule required dry cleaners using perc to install solvent recovery and emission controls, reducing PCE releases substantially. Amendments in 2006 tightened the standards further. For the dry-cleaning worker, the NESHAP transformed compliance work into a core job duty: monitoring machine performance data, logging solvent inventory, scheduling inspections, and disposing of still residue as hazardous waste. On December 18, 2024, EPA finalized a TSCA rule prohibiting perc use in dry cleaning, with a 10-year phase-out. This is the most consequential regulatory change in the trade since 1993 and will force operators toward silicone (GreenEarth D5), liquid CO2, and professional wet-cleaning systems over the coming decade.

    Work toolChanging equipment
  • GreenEarth silicone solvent and professional wet cleaning (perc alternatives, 1999-present)

    GreenEarth Cleaning commercialized decamethylcyclopentasiloxane (D5 silicone) as a dry-cleaning solvent in the late 1990s. A 2002 independent study found the GreenEarth system as effective as perc with no environmental concerns. The California Air Resources Board evaluated D5 as a perc alternative during its 2008 perc ban deliberations. In 2014, ASTM International made GreenEarth silicone the first new dry-cleaning solvent added to its fabric-care labeling standard since fluorocarbon in 1996. Professional wet cleaning, which uses computerized washers, conditioners, and tensioning finishers to clean fabrics previously considered dry-clean-only, expanded in parallel. For the laundry and dry-cleaning worker, these alternatives introduced new machine parameters and chemical protocols, but the core judgment skills (fabric identification, stain chemistry, pressing) remained essentially unchanged.

    Work toolChanging equipment
  • POS order-management software, RFID garment tracking, and laundry operations platforms

    Cloud-based point-of-sale platforms (CleanCloud, Starchup) arrived in the 2010s, automating order intake via barcode scanning, garment tagging, customer SMS updates, and delivery-route optimization. RFID chips embedded in hotel and hospital linen enabled individual-item tracking across hundreds of wash cycles. By 2020, IoT sensors on commercial washers and dryers fed real-time data to operations dashboards (Spindle platform) that flagged equipment anomalies and optimized load throughput. For the laundry worker, these tools elevated the data-monitoring dimension of the job: reading alerts, acting on cycle reports, and logging compliance data became part of the daily routine alongside the physical work of loading, sorting, and pressing.

    Work toolChanging equipment
  • Commercial folding robots and AI fabric-scanning systems (Dyna Robotics DYNA-1, 2025)

    Dyna Robotics unveiled the DYNA-1 commercial laundry folding robot in April 2025, a stationary dual-arm system capable of autonomous 24-hour folding of napkins, towels, and standard garments at 60% of human throughput with a 99.4% success rate on commercial linen batches. AI-powered fabric-scanning systems for garment intake also entered commercial trial in 2025-2026, scanning barcodes, reading care labels, and routing items to the correct cleaning process. For bulk-linen operations (hotels, healthcare), these robots and scanners will reduce the number of workers needed per unit of throughput. Spotting, specialty pressing, and customer-facing judgment remain in human hands: a robot folds identically shaped towels reliably but still fails on tailored garments, beaded fabrics, and draped clothing.

    Effect on the work

    Commercial linen folding is the highest-volume single task in the trade and the one that robotic automation addresses most directly. Deployment at scale would reduce the folding-station headcount in large linen operations; the timing depends on capital costs relative to minimum-wage levels, which vary significantly by state.

    Work toolChanging equipment
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
Grand View Research — US Dry-Cleaning and Laundry Services Market 2025-2030
2030
+12%
Market research firm Grand View Research projects the US dry-cleaning and laundry services market to grow at a compound annual growth rate through 2030, driven by on-demand delivery app platforms (Rinse, Cleanly, Tide Cleaners), rising demand for professional fabric care, and commercial linen services expansion in hospitality and healthcare. If the market grows at the projected rate, employment in 51-6011 could expand proportionately; actual headcount depends on whether productivity-enhancing technology (folding robots, AI sorting) absorbs a share of that demand. This projection is optimistic relative to the BLS baseline and is included as the upper end of the uncertainty cone.
BLS National Employment Matrix 2024-34
2034
+5.4%
BLS Employment Projections National Matrix 2024-34. BLS projects 51-6011 employment to grow from 202,600 (2024) to approximately 213,500 (2034), an increase of 10,900 positions and a 5.4% rate, classified as "faster than average" against the all-occupations average of approximately 4%. The BLS methodology models demand-side growth in commercial linen services (healthcare expansion, hotel recovery post-COVID, restaurant linen) as the primary tailwind, offset partially by labor-saving technology and continued consolidation of dry-cleaning storefronts. The projection does not explicitly model the 2024 TSCA perc phase-out, which could accelerate plant closures or require capital investment that suppresses employment growth.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. — "GPTs are GPTs" (2023)
2028
8%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for Production Occupations. Laundry and dry-cleaning workers score very low on LLM exposure: the dominant tasks (operating machines, spotting stains, pressing garments, sorting and folding, handling solvents) require physical presence and manual dexterity that language models cannot provide. The 8% exposure estimate reflects only the administrative and intake-documentation tasks (logging orders, entering customer data, reading machine alerts) that could be partially assisted by AI tools. This is among the lower-exposure scores in the BLS SOC 51 Production occupations major group.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onSort cleaned items by order, fold or hang each piece to customer specification, and prepare finished bundles for counter pickup or delivery route assignment.

Sort cleaned items by order, fold or hang each piece to customer specification, and prepare finished bundles for counter pickup or delivery route assignment.[6],[7]

Tools picking this up
Where your edge is

Shift toward quality-control review of robot-folded batches and handling specialty items (knit sweaters, draped garments) that require human fold judgment.

AI is sitting alongside you hereTag and ticket incoming items with barcoded garment labels, scan them into the POS or order-management system, and verify customer notes (rush, special care, alterations requested).

Tag and ticket incoming items with barcoded garment labels, scan them into the POS or order-management system, and verify customer notes (rush, special care, alterations requested).[8],[9]

Tools picking this up
Where your edge is

Learn barcode scanning and order-tracking workflows in current POS platforms; accurate intake data reduces costly mis-routes and customer complaints downstream.

AI is sitting alongside you hereOperate commercial washers, dry-cleaning machines, and extractors, setting water temperature, solvent levels, cycle times, and spin speeds according to fabric type and soil load.

Operate commercial washers, dry-cleaning machines, and extractors, setting water temperature, solvent levels, cycle times, and spin speeds according to fabric type and soil load.[1],[10]

Tools picking this up
Where your edge is

Learn to read machine-generated cycle reports and IoT sensor alerts to catch off-spec loads early, reducing rewash rates and fabric damage.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Maids and Housekeeping Cleaners

Maids and Housekeeping Cleaners share the same physical service environment (hotels, healthcare facilities) and many laundry workers already handle linen rooms; the move transfers textile-care and cleanliness standards directly.

What you'd add
  • · Hospitality room-cleaning procedures and quality inspection standards
  • · Safe handling of commercial cleaning chemicals (OSHA GHS labelling)
  • · Basic housekeeping software (property management system check-ins)
What it takesMost of your skills carry over
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The data behind this timeline

On record since1851
Latest tracked employment195,360 (US, 2024)
Latest median pay$33,800 (2024)
Outlook+12% by 2030 (Grand View Research — US Dry-Cleaning and Laundry Services Market 2025-2030)
View all 25 cited data points
YearUS employmentMedian annual paySource
186038,633n/aCENSUS-DECENNIAL
1908350,000$312ESTIMATE
1960430,000n/aESTIMATE
2003217,820$16,920BLS-OEWS
2004218,610$17,220BLS-OEWS
2005218,360$17,440BLS-OEWS
2006217,580$17,850BLS-OEWS
2007218,060$18,420BLS-OEWS
2008221,230$19,010BLS-OEWS
2009211,490$19,310BLS-OEWS
2010204,820$19,540BLS-OEWS
2011201,180$19,670BLS-OEWS
2012198,750$19,930BLS-OEWS
2013197,650$20,090BLS-OEWS
2014199,330$20,320BLS-OEWS
2015201,620$20,820BLS-OEWS
2016207,710$21,510BLS-OEWS
2017209,350$22,370BLS-OEWS
2018213,350$23,210BLS-OEWS
2019209,330$24,220BLS-OEWS
2020179,890$25,470BLS-OEWS
2021157,400$28,350BLS-OEWS
2022175,730$29,060BLS-OEWS
2023185,000$31,050BLS-OEWS
2024195,360$33,800BLS-OEWS
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