EXPOSED
Almost nothing here is text or screen work, so language AI barely touches the core job: sorting soiled loads, loading washers and tumblers, spot-treating stains, operating presses, and folding and bagging finished garments. The real threat is conventional industrial automation — continuous batch washers, automated feeders and folders in high-volume institutional laundries — which has been quietly shrinking headcount per pound for decades, and AI-assisted vision sorting accelerates that. Limp-fabric handling remains genuinely hard for robots, so the small-shop and retail dry-clean tier persists longer than the hospital/hotel linen plants.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $24,220 → $34,890 +15.2% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+5.4% 202,600 → 213,500 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +5.4% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~31,900 openings a year on average, including replacing people who leave.
DyerDrierMaterSizerFolderHangerHatterLeggerListerMarkerMolderPufferPullerShakerSorterWasherCleanerSpotterSprayerStamperWringerAssorterButtonerDampener
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Feeling a lapel to judge whether a stain is protein or oil-based, pinning a torn seam before it goes through a press, and untangling a mixed load of bras, bedsheets and coveralls are physical judgement tasks no language model performs — the 14 rather than 18 reflects that institutional flatwork (sheets, towels, napkins) is already fed and folded by machine in the big plants.
Hands-on in uncontrolled environments You are standing eight hours in 90-degree air near steam presses and tumblers, lifting 40-pound wet loads, handling solvent-soaked garments and shaking out limp fabric by hand — it sits at 15 rather than 19 because the floor itself is an indoor, fixed plant, not a customer's home or an unpredictable outdoor site.
No licence, no signature requirement No licence, no certificate, no exam gates this work; even the perchloroethylene rules under EPA's NESHAP land on the shop owner's permit, not on the person running the machine, which is why this is a 1 and not a 5.
Executes defined procedures on defined inputs Calls like which solvent for silk versus wool, water temperature, and when a garment is too damaged to process follow the care label and posted shop procedure, with anything expensive or disputed escalated to the manager — real but narrow discretion, hence 4.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (14/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (1/20) is whether the law requires a licensed human to sign. Trust premium (5/20) is whether buyers specifically pay for a person. Judgment and accountability (4/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 10 of this occupation's 39 points (26%).
Embodiment (15/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 53/100, still EXPOSED.
Task-mix shift within the retail tier: as tunnel washers, automated feeders and cross-folders absorb flat institutional linen and basic wash-dry-fold, what remains in the surviving shop is unknown-fiber identification, stain chemistry choice (solvent vs. wet-clean vs. enzyme) on one-off garments, and reassembly/pressing of structured tailoring. Watch for job postings shifting from 'presser/folder' to 'spotter' and 'finisher' titles as the routine tier leaves.
Growth of named-artisan restoration lines already visible in sneaker, handbag and vintage-garment care, plus bridal gown preservation sold with a specific technician's inspection report. Buyers of a $4,000 gown clean are demonstrably paying for a human's eye, not throughput. Recognisable if retail dry-clean marketing shifts from turnaround time to named craftsperson and pre-clean garment consultation.
Restoration and insurance-claim contents cleaning (post-fire/flood textiles) routes damage decisions through a certified technician whose written assessment the carrier relies on — IICRC certification is the existing vehicle. If a larger share of shop revenue comes from insurance restoration work where the technician's salvage/total-loss call is the payout basis, ownership of consequential ambiguous calls rises.
Deformable-object manipulation stays the bottleneck: if the shop's mix moves toward beaded/sequined gowns, leather and suede, tailored wool, drapery and upholstery — items no current feeder or folder can grip, and that require hand-blocking on a form — the physical-irreducibility share of the day rises even as pounds-per-worker falls elsewhere.
Healthcare linen is the only real route: HLAC accreditation and AAMI ST65 already require documented hygienically clean processing, and a state hospital-licensing or CMS condition-of-participation rule naming a trained laundry hygiene supervisor personally responsible for wash-formula validation and load release would create a signer. Nothing comparable exists for retail dry cleaning, and none is proposed.
The limit. Even with every lever, this stays a low-shield occupation: no license gates garment cleaning, and the displacement pressure is conventional mechanical automation in institutional plants rather than anything a countersignature rule would touch. Headcount can fall sharply while the surviving retail/restoration niche scores higher — the score rising is not the workforce surviving.
| New York-Newark-Jersey City, NY-NJ | 14,040 | $35,910 +3% |
| Los Angeles-Long Beach-Anaheim, CA | 9,230 | $37,330 +7% |
| Chicago-Naperville-Elgin, IL-IN | 6,330 | $35,740 +2% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 4,460 | $33,330 -4% |
| Dallas-Fort Worth-Arlington, TX | 4,360 | $32,420 -7% |
| Houston-Pasadena-The Woodlands, TX | 4,340 | $29,260 -16% |
| Las Vegas-Henderson-North Las Vegas, NV | 3,710 | $36,560 +5% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 3,500 | $36,300 +4% |
| San Jose-Sunnyvale-Santa Clara, CA | 810 | $44,410 +27% |
| Seattle-Tacoma-Bellevue, WA | 2,140 | $43,610 +25% |
| San Francisco-Oakland-Fremont, CA | 2,040 | $43,310 +24% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 39. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.