EXPOSED
Nothing a presser does — feeding garments onto a buck, positioning sleeves and collars, operating steam presses and form finishers, inspecting for scorch and shine — is text or screen work, so language AI barely touches this job directly. The real pressure is mechanical and economic: automated form finishers, tunnel finishers, and conveyor systems already absorb high-volume pressing, and apparel production keeps moving offshore. Limp-fabric handling remains genuinely hard for robots, which is why finishing tricky fabrics and garment shapes still needs hands, but that niche is small and shrinking.
This fall is concentrated in 2020 and has not recovered since.
Median pay $24,190 → $35,060 +15.9% 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
-13.5% 28,400 → 24,600 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -13.5% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
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.
~2,800 openings a year on average, including replacing people who leave.
IronerBlockerBoarderCreaserManglerPresserSleeverSteamerFur IronerHat IronerBulk FolderFlat IronerHand IronerHat BlockerHat PresserHat SteamerTie PresserBrim PresserCoat PresserFlap PresserForm PresserHand PresserLinen FolderSeam Presser
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier A 13 reflects the split inside the job: tunnel finishers and automated form finishers already handle shirts, trousers and knits in volume without a human at the buck, but tailored jackets, pleats, linings, silk and beaded pieces still need someone to smooth limp fabric by hand and judge steam and pressure per garment — that residual handwork keeps it out of the 0-6 band, while the volume work already gone keeps it out of 14+.
Hands-on in uncontrolled environments At 14 the score recognises you stand at a hot press for a full shift — lifting garments onto the buck, working foot pedals and hand controls, absorbing steam, humidity and 100°F+ air, and dealing with fabric that changes shape every time you touch it — an uncontrolled physical setting, though it's a fixed station rather than a job that sends you into fields, roofs or crawlspaces.
No licence, no signature requirement A 1 is simply accurate: there is no state licence, no certification body and no exam standing between a person and a pressing job — a plant can put someone on a press the day they're hired, and no legal requirement forces an employer to keep a credentialed human in that spot.
Executes defined procedures on defined inputs A 3 fits because the calls you make — steam duration, correct temperature for acetate versus wool, whether to re-press a wrinkle — are real skill but run against known fabric specifications and care labels, and a scorched garment costs the shop a replacement, not a lawsuit or a safety investigation.
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 (13/20 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 (4/20) is whether buyers specifically pay for a person. Judgment and accountability (3/20) 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 8 of this occupation's 35 points (23%).
Embodiment (14/20) 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.
Cooks, Restaurant EXPOSED
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 48/100, still EXPOSED.
Task-mix shift as tunnel and form finishers absorb the flat, high-volume tier (shirts, pants, sheets), leaving the residual job as hand-finishing of structured tailoring, pleats, linings, beading, silk/velvet/wool crepe and vintage or repaired garments where heat and moisture settings are judgment calls per item. This is a genuine two-tier occupation, but the judgment tier is a small share of headcount, so the score rises for survivors while employment falls.
Explicit, priced 'hand-finished' or 'hand-pressed' designation — already used in bespoke tailoring (Savile Row, Neapolitan makers), high-end drycleaners charging separately for hand-finish on couture, and textile conservation work at museums and for insurance restoration claims after fire/water loss. If restoration contractors or luxury resale platforms (The RealReal, Sotheby's fashion) start specifying human hand-finishing in their condition/service standards, the premium becomes contractual rather than marketing.
If reshoring of small-batch apparel (e.g. USMCA-driven near-shoring, on-demand/made-to-measure lines like the ones Stitch Fix and Amazon Custom piloted) puts finishing back in US shops on short runs and varied garment shapes, deformable-fabric grasping stays the binding constraint — no fixed-buck automation pays off below a run-length threshold. Watch for capital spend on flexible cells versus continued reliance on hand-fed bucks.
If pressing on high-value garments is folded into a documented restoration or conservation workflow — where the finisher decides steam versus dry heat, whether a fabric can take pressure at all, and signs off on an irreversible treatment against an insurer's or museum's condition report — the role owns a consequential, non-recoverable call. This exists today at AIC-affiliated textile conservation labs and in some restoration-dryclean contracts; it would need to spread into commercial finishing to move the score.
The limit. Even with every lever, this is a shrinking 26k-worker occupation and the ceiling is roughly the high 40s. There is no plausible route to a liability shield: no state licenses pressers, no board countersignature exists or is being proposed, and no bill anywhere treats garment finishing as a regulated act. The trust-premium and accountability gains attach to a few thousand luxury, bespoke, and conservation positions, not to production finishing rooms; raising the register score for that niche does not slow displacement in the volume tier, where tunnel finishers and offshoring are the operative forces and neither responds to institutional protection.
| New York-Newark-Jersey City, NY-NJ | 1,990 | $37,920 +8% |
| Los Angeles-Long Beach-Anaheim, CA | 1,420 | $39,300 +12% |
| Dallas-Fort Worth-Arlington, TX | 1,050 | $31,790 -9% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 1,040 | $32,480 -7% |
| Houston-Pasadena-The Woodlands, TX | 740 | $29,680 -15% |
| Las Vegas-Henderson-North Las Vegas, NV | 730 | $35,560 +1% |
| San Francisco-Oakland-Fremont, CA | 600 | $43,790 +25% |
| Riverside-San Bernardino-Ontario, CA | 490 | $37,820 +8% |
| San Jose-Sunnyvale-Santa Clara, CA | 110 | $44,440 +27% |
| San Francisco-Oakland-Fremont, CA | 600 | $43,790 +25% |
| Portland-Vancouver-Hillsboro, OR-WA | 130 | $39,510 +13% |
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 35. 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.