EXPOSED
This catch-all bucket covers hand finishing, sample and pattern work, trimming, fitting, mending, and specialty assembly of fabric goods — manual dexterity tasks that language models cannot touch and current robotics still handles badly because limp fabric is hard to grip and predict. The real exposure is not chatbots but the older pressures: purpose-built sewing and cutting automation, digital pattern generation and grading software, and continued offshoring of the work entirely. Scored for the modal worker doing repetitive bench-level fabric handling in a shop or small plant, not the elite sample-room technician.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $27,970 → $37,280 +6.6% 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
-9.4% 14,700 → 13,300 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -9.4% 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.
~1,700 openings a year on average, including replacing people who leave.
CurerDewerFixerFlyerInkerMaterPilerPiperRoverSizerTawerBlowerBowkerBufferBurlerBurrerDouperDraperDungerDusterFolderFullerGasserGigger
The BLS uses Textile, Apparel, and Furnishings Workers, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Aligning nap on a curved seam, easing a sleeve head, re-stitching a hidden defect, hand-tacking linings and pinning a garment on a form all require continuous tactile correction of a material that deforms under its own weight, which is why 14 rather than 18 — the pattern grading, marker making and nesting that used to be part of the bench job has already moved to CAD.
Hands-on in uncontrolled environments The whole day is standing or leaning at a bench with shears, awl, steam iron, tape and needle on goods that arrive wrinkled, off-grain and dimensionally inconsistent, but it sits at 15 not 19 because the shop floor is a lit, indoor, largely predictable space rather than a job site or someone's roof.
No licence, no signature requirement There is no licence, no state board and no certification that a customer or an OSHA inspector asks to see; a shop can put a new hire on hand finishing the same week, so the 1 reflects only the fact that the work is done by an employed adult at all.
Executes defined procedures on defined inputs Calls are real but bounded: accept or reject a piece against a spec sheet, decide whether a flaw can be mended or must be scrapped, pick a thread and stitch length for the material — decisions a lead or QC checks the same shift, which is why 5 rather than 9.
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 (4/20) is whether buyers specifically pay for a person. Judgment and accountability (5/20) 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 51/100, still EXPOSED.
Task-mix shift: purpose-built automation (Softwear Automation's Sewbot lines, automated cutting tables) absorbs straight-seam and repetitive bench work first, leaving the residual role as sample-room and prototype fabric handling, one-off repair, and fit correction on non-standard bodies and materials — work with no repeatable geometry for a machine to learn. This tier genuinely exists in the occupation (sample technician vs. bench finisher), so as the routine tier leaves, the surviving job scores higher even with no capability change.
Buyer-side demand for verified hand-made provenance: enforcement of FTC 'Made in USA' labeling rules and growth of certification schemes (e.g. tailoring/bespoke marks, Homegrown/handmade certification used by heritage brands) that let a maker charge specifically because a person did the finishing. Visible now in bespoke tailoring, upholstery restoration, and repair-service growth driven by EU right-to-repair and textile durability rules; if brand repair programs (Patagonia Worn Wear, Eileen Fisher Renew style) expand under EPR mandates, mending becomes a paid human-branded service rather than an invisible cost line.
If extended producer responsibility laws for textiles (California SB 707, EU EPR) push repair-and-resale volume into domestic shops, the finisher owns the call on whether a garment or furnishing is salvageable, what substrate and thread to use, and whether the repair meets resale-grade standards — a consequential, ambiguous judgment currently made by no one because the item was simply discarded.
Only narrow route: flammability and safety compliance sign-off on children's sleepwear, mattresses, and upholstered furniture (16 CFR 1615/1616, 1633, California TB 117-2013) where a named person attests to construction conformity. If CPSC or a state fire marshal required an identified finisher/inspector attestation per lot rather than a firm-level certificate, a thin personal-accountability shield appears. No such rule is pending; treat as speculative.
The limit. The binding constraint is offshoring, not AI. Every lever above can fire and the US headcount still falls if the work moves to Vietnam or Bangladesh — trust-premium and repair-mandate gains apply to a small domestic craft tier, not the modal bench worker, and this SOC bucket is only 13,530 people already.
| Los Angeles-Long Beach-Anaheim, CA | 3,050 | $36,540 -2% |
| Dalton, GA | 890 | $32,220 -14% |
| New York-Newark-Jersey City, NY-NJ | 610 | $38,830 +4% |
| Charlotte-Concord-Gastonia, NC-SC | 360 | $44,910 +20% |
| Atlanta-Sandy Springs-Roswell, GA | 310 | $36,510 -2% |
| Dallas-Fort Worth-Arlington, TX | 250 | $35,770 -4% |
| Hickory-Lenoir-Morganton, NC | 230 | $37,370 +0% |
| Greensboro-High Point, NC | 220 | $35,380 -5% |
| Oklahoma City, OK | 50 | $62,650 +68% |
| Portland-South Portland, ME | 90 | $61,070 +64% |
| Santa Rosa-Petaluma, CA | 40 | $50,440 +35% |
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.