EXPOSED
Pinning a hem on a moving body, reading how a shoulder seam falls, and coaxing bias-cut silk through a machine are dexterity problems robotics has not solved — limp-fabric manipulation remains one of the hardest open problems in automation. What AI does erode is the front end: design sketching, pattern drafting and grading, measurement-to-size prediction, and the consultation/mood-board work that used to justify a bespoke premium. The real headwind for this occupation is not AI but cheap ready-to-wear and offshore production, which has been shrinking it for decades; the surviving core is fitting, repair, and alteration on garments a customer already owns.
Part 2020 shock, part continued decline in the years since.
Median pay $31,520 → $41,640 +5.7% 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
-4.5%
Percentage only. The projection counts a different population from the 13,920 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Shrinking, but not obviously because of AI
The BLS projects -4.5% by 2034, but at 59/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
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.
~5,000 openings a year on average, including replacing people who leave.
FitterTailorFurrierQuilterAppliquerCrocheterLacemakerCoat MakerDressmakerFur TailorLaceworkerSeamstressSuit MakerUnderlinerVest MakerCoat BasterCoat CutterCoat TailorEmbroidererHat TrimmerPants MakerPurse MakerQuilt MakerShop Tailor
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Fitting a jacket on a live client, easing a sleeve head, taking in a princess seam by eye, and rebuilding a bridal bodice three days before the wedding are per-garment problems with no repeatable digital input — the drafting and grading that AI can take is a minority of the billable hours in an alterations shop, which is what holds this at 17 rather than the low teens.
Hands-on in uncontrolled environments The work is entirely hands-on-cloth in a space that changes with every customer: kneeling to chalk a trouser break, pressing with a hot iron and clapper, manipulating limp bias silk under a needle, and standing at a form for hours — 18 rather than 20 only because the shop itself is a fixed indoor bench, not a roof or a roadside.
No licence, no signature requirement No state licence, no board exam, no certification anyone asks to see; you can open a tailoring shop tomorrow with a machine and a business licence, and a ruined $4,000 gown is settled as a commercial dispute over the garment's value, not a professional negligence claim against a credentialed person.
Meaningful discretion Deciding whether a garment can be let out without shadowing the old seamline, whether to recut or ease, and how to salvage a mis-cut panel are calls with real money and irreversible scissors behind them, but they sit inside long-settled craft practice with the customer approving at each fitting — that's discretion inside a known frame, not owning an ambiguous high-stakes outcome alone.
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 (17/20 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 (13/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) 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 24 of this occupation's 59 points (41%).
Embodiment (18/20) 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.
No occupation passed every test: close enough to tailors, dressmakers, and custom sewers on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
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 72/100 — SAFE.
Publicly funded repair subsidies that pay a registered human repairer: France's Bonus Réparation Textile (launched Oct 2023, Refashion-administered, €7–25 per repair paid only to labelled repairers) is the live template. If EU ecodesign/right-to-repair rules for textiles extend it, or a US state adopts a garment repair credit, paid demand routes specifically to a registered human shop. Separately, if 'Made in Italy' / couture-style rules tighten (Chambre Syndicale haute couture requires an atelier with a minimum number of full-time hand workers), brand-level premiums for verifiably human atelier labour deepen.
Genuine two-tier structure: the drafting/grading/measurement-prediction tier is being absorbed by CAD and body-scan sizing, leaving fitting on a live body, limp-fabric handling, and repair diagnosis. As the front end is automated away, the residual job is almost entirely the tier robotics has not solved, so measured resistance can hold or rise even as headcount falls.
NFPA 1851 already requires that repairs/alterations to firefighter turnout gear be done by the manufacturer or a verified Independent Service Provider, and the repair must not void the garment's certification. If more PPE and safety-apparel standards (flame-resistant workwear under NFPA 2113, ballistic vest carriers, medical drapes) adopt the same 'certified repair technician only, repair log signed' model, a slice of alteration work becomes a named-person-signs job rather than an unlicensed craft. Watch also for state cosmetology-style licensure proposals for bridal/formalwear shops after high-profile ruined-gown suits — no such bill exists now.
Task-mix shift concentrating the role on irreplaceable-garment calls: museum/archival costume conservation and heirloom restoration, where the cutter decides what is reversible and personally signs a treatment record (AIC conservation-practice norms). If insurers of high-value wardrobes and bridal contracts start requiring documented pre-alteration assessment before payout, the fit decision becomes an owned, recorded call rather than a shop-floor judgement.
The limit. Embodiment is already at 18 and has no headroom. The binding constraint on this occupation is volume, not capability: cheap ready-to-wear has shrunk it for decades and every lever above raises resistance per surviving job without adding jobs. A high score here can coexist with continued contraction.
| Los Angeles-Long Beach-Anaheim, CA | 1,150 | $40,080 -4% |
| New York-Newark-Jersey City, NY-NJ | 1,100 | $63,050 +51% |
| Houston-Pasadena-The Woodlands, TX | 420 | $36,260 -13% |
| El Paso, TX | 280 | — |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 270 | $57,020 +37% |
| Chicago-Naperville-Elgin, IL-IN | 240 | $45,630 +10% |
| Dallas-Fort Worth-Arlington, TX | 240 | $38,080 -9% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 230 | $55,000 +32% |
| New York-Newark-Jersey City, NY-NJ | 1,100 | $63,050 +51% |
| San Jose-Sunnyvale-Santa Clara, CA | 80 | $59,020 +42% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 270 | $57,020 +37% |
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 59. 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.