COOKED
The work is hands-on — loading fabric into jets and becks, mixing dye and bleach recipes, watching temperature and pH, pulling shade samples — so language AI is not the direct threat. The threat is that these are exactly the repetitive, spec-driven tasks that modern computer-controlled dyeing lines, automated dispensing systems, and spectrophotometric color matching already absorb, and that US mill employment has been shrinking for decades regardless. What survives is troubleshooting off-shade lots, machine changeovers, and rescue work on damaged goods — a smaller, more skilled tier than the median tender occupies today.
This fall is concentrated in 2020 and has not recovered since.
Median pay $29,460 → $38,180 +3.7% 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
-10.1%
Percentage only. The projection counts a different population from the 5,310 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 -10.1% 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.
~700 openings a year on average, including replacing people who leave.
AgerDyerJiggerWasherBlenderColorerTreaterBlancherBleacherJig HandRetannerRug DyerShrinkerWhitenerBeam DyerKier HandWool DyerYarn DyerChain DyerCloth DyerDye FeederDye WorkerJig WorkerPiece Dyer
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Automated color kitchens dispense dyes to the gram and spectrophotometers grade shade against standard without a human eye, but somebody still has to sling wet rope into the jet, thread the beck, clear a crease mark, and decide whether an off-shade lot gets a shading addition or a strip-and-redye — that split between the absorbed half and the stubborn half is what puts it at 12 instead of 6 or 16.
Hands-on in uncontrolled environments You work a wet floor at 200°F+ with steam, caustic, peroxide and reactive dye powder, hauling and doffing heavy saturated goods and reaching into machines during changeovers — 13 rather than 18 because it's a fixed indoor mill bay with the machine in one place, not a roof, trench, or roadside.
No licence, no signature requirement No state license gates this job; you may hold OSHA HazCom or respirator-fit training and handle regulated chemicals, but the mill's EHS manager and the plant permit carry the legal exposure, so the 1 reflects that nothing about your credential legally has to be a person.
Executes defined procedures on defined inputs Recipe cards, formula sheets and time-temperature-pH programs set the call for you, and shade approval routes through the lab or a customer standard — the 5 credits the real discretion you do exercise on when to add, when to hold, and when to stop the machine before a lot is ruined, but those are recoverable production calls, not irreversible ones.
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 (12/20 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 (2/20) 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 8 of this occupation's 33 points (24%).
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.
Furniture Finishers 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 46/100 — EXPOSED.
Task-mix shift: as automated dispensing and spectrophotometric matching absorb first-run recipe execution, the surviving role becomes off-shade rescue, restrike/stripping decisions on damaged lots, and changeover between substrates (recycled poly, hemp blends, bast fibers) where dye uptake is erratic and no stable recipe library exists. Growth in recycled/mixed-feedstock fiber mandates (EU ESPR digital product passport, textile recycling scale-up) would enlarge exactly this unpredictable tier.
Reshored small-lot and sample dyeing (DoD Berry Amendment textile procurement, near-shoring of fast-turn apparel) concentrates remaining US work in low-volume beck/jig and sample-lab dyeing, where lot sizes never justify robotic loading and the operator physically threads, ropes, and inspects wet goods.
Effluent and chemical compliance shifting from the plant to a named individual: an EPA pretreatment permit or state POTW discharge permit condition naming a certified wastewater/chemical operator who must personally sign off on bleach and dyebath discharge batches, plus RSL/chemical-restriction attestations under a Zero Discharge of Hazardous Chemicals (ZDHC) or state PFAS-in-textiles law (NY, CA AB 1817) requiring a signed process record. This is a small, real route — mostly it would attach to a plant chemist, not the tender, unless the certification lands on the line operator.
Formal shade-approval authority: a customer or brand quality contract making a named operator the person who releases or rejects a lot to shade standard (rather than a lab tech or QC manager), with scrap/reprocess cost owned at the machine. Some union and mill quality systems already vest 'first-piece approval' at the operator; making it explicit and auditable raises it.
The limit. No realistic route on trust_premium — buyers of dyed fabric specify color, fastness, and hand, and cannot perceive whether a human tended the machine. The one narrow exception is craft/natural-indigo and small-batch artisan dyeing sold on provenance, which is a different occupation with a few hundred workers, not this SOC. Even with every lever above, the ceiling is a low-50s score covering a workforce substantially smaller than 5,310, since US mill employment decline is driven by offshoring and consolidation, not by AI, and no lever here touches that.
| Los Angeles-Long Beach-Anaheim, CA | 760 | $36,670 -4% |
| Greenville-Anderson-Greer, SC | 250 | $46,210 +21% |
| Providence-Warwick, RI-MA | 250 | $45,930 +20% |
| Greensboro-High Point, NC | 230 | $39,600 +4% |
| Charlotte-Concord-Gastonia, NC-SC | 200 | $37,960 -1% |
| New York-Newark-Jersey City, NY-NJ | 170 | $39,030 +2% |
| Dalton, GA | 150 | $44,940 +18% |
| Atlanta-Sandy Springs-Roswell, GA | 120 | $45,100 +18% |
| Greenville-Anderson-Greer, SC | 250 | $46,210 +21% |
| Providence-Warwick, RI-MA | 250 | $45,930 +20% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 80 | $45,680 +20% |
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 33. 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.