EXPOSED
Nothing about guiding limp fabric under a needle, matching plaids, or easing a sleeve into an armhole is text or screen work, so language AI barely touches the core task — the threat here is mechanical and geographic, not conversational. Sewbots and automated cut-and-sew cells handle flat, simple items (towels, straight seams, some t-shirt panels) but still fail on stretchy, draping, three-dimensional assembly, which is why the work persists at all. Offshoring and shrinking domestic apparel plants have already cut this occupation hard; there is no license, no signature, and almost no buyer paying for the specific operator's name.
Part 2020 shock, part continued decline in the years since.
Median pay $26,420 → $36,670 +11.0% 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
-10.8%
Percentage only. The projection counts a different population from the 104,880 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -10.8% 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.
~13,000 openings a year on average, including replacing people who leave.
FacerLinerPiperSewerYokerBanderBasterBinderCufferHemmerMenderPadderPurlerSeamerSergerTackerTuckerWelterBraiderFagoterFlosserKnotterLapelerPlaiter
Holding it up: task resistance . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier A 13 rather than 17 reflects the split inside the trade: Softwear-style sewbots and automated hemming, pocket-setting, and buttonhole units already take the flat, repeatable operations, while set-in sleeves, bias-cut linings, knit necklines, and anything that has to be eased or stretched under the presser foot still need a hand on the fabric.
Hands-on in uncontrolled environments Standing or seated at a machine feeding limp goods with your fingers, changing needles and bobbins, clipping threads, and pressing seams is entirely physical, but it happens at a fixed station in a lit plant with predictable materials — not on a roof or in a crawlspace — which is what holds it at 13 instead of the high teens.
No licence, no signature requirement There is no license, no certification exam, and no state board for machine operators; a plant can hire you on Monday and put you on a single-needle lockstitch by Tuesday, and any defect claim on a garment lands on the manufacturer's QC and the brand, never your name.
Executes defined procedures on defined inputs You decide when a seam is puckering, when tension needs adjusting, and when a piece goes in the reject bin, but those calls run against a spec sheet, a sewn sample, and a supervisor's sign-off, so the discretion is real-time and narrow rather than consequential.
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 (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 9 of this occupation's 35 points (26%).
Embodiment (13/20) 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 51/100, still EXPOSED.
If sewbot deployment stalls at flat goods (as SoftWear Automation's Atlanta cells have, largely limited to towels, mats and simple tees) while domestic reshoring pulls back the hard stuff — tailored jackets, bras, swimwear, stretch knits, upholstery on curved frames — the surviving job is exactly the 3D limp-material manipulation robots cannot grip. Watch DoD Berry Amendment contracts and reshored technical/outdoor apparel: those lines are assembly no gripper handles.
Genuine two-tier occupation: straight-seam and panel-joining work is the automatable tier; sample-room sewing, first-article construction for a pattern maker, and fix/rework of defective garments are the judgment tier. If plants keep only sample rooms and repair stations domestically, the remaining role is diagnose-and-solve on one-off garments, not repetition.
Narrow but real: named-maker premiums already exist in bespoke tailoring, alterations at bridal shops, and repair services under brand programs (Patagonia Worn Wear, Nudie repair shops, Eileen Fisher Renew). If EU-style right-to-repair/textile durability rules (the ESPR textile delegated act, and France's repair bonus for clothing, already paying subsidies since 2023) spread to US state law, garment repair becomes a paid service where the customer hands over their own garment to a specific person. That is a different buyer relationship than piece-rate assembly.
If safety-critical sewn goods work concentrates domestically — parachutes, personal flotation, airbags, ballistic vests, medical soft goods — operators work under FAA/NFPA/military inspection regimes where a specific stitch line is traceable to a specific operator and lot. Parachute rigging already requires an FAA-certificated rigger to sign work; sewn assembly adjacent to that regime carries documented accountability that piecework apparel never had.
No realistic route for the occupation at large. FAA parachute rigger certification (14 CFR 65 subpart F) is the one licensed sewing-adjacent credential with personal liability, but it covers a few thousand people and is a separate occupation, not a rule that could plausibly extend to garment machine operators.
The limit. The binding constraint is not AI capability, it is that the domestic employment base has already been offshored — headcount fell from over 500,000 in the 1990s to ~105,000. Levers here reshape what the remaining job is rather than protect the number of jobs; a smaller, higher-judgment occupation can score higher on this register while still shrinking.
| Los Angeles-Long Beach-Anaheim, CA | 9,310 | $37,130 +1% |
| New York-Newark-Jersey City, NY-NJ | 6,700 | $37,780 +3% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 3,680 | $30,620 -16% |
| Chicago-Naperville-Elgin, IL-IN | 3,450 | $36,220 -1% |
| Dallas-Fort Worth-Arlington, TX | 2,420 | $35,260 -4% |
| Mayaguez, PR | 2,370 | $21,840 -40% |
| Hickory-Lenoir-Morganton, NC | 1,860 | $46,540 +27% |
| Charlotte-Concord-Gastonia, NC-SC | 1,640 | $34,940 -5% |
| Bend, OR | 50 | $50,830 +39% |
| Glens Falls, NY | 40 | $50,000 +36% |
| Appleton, WI | 110 | $48,460 +32% |
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.