COOKED
The work — threading yarn, setting machine gauges and patterns, watching for broken ends, clearing jams, logging output — resists language AI but has been the primary target of mill automation for forty years, and employment (13,030 nationally) reflects that. Modern computerized knitting and weaving machines already load patterns digitally, detect thread breaks with optical sensors, and stop themselves; the residual human role is loading, repair, and exception handling on the floor. No license, no client relationship, and pattern-setting decisions are made by designers and engineers upstream, so the only real moat is that a robot still can't cheaply re-thread a loom.
Part 2020 shock, part continued decline in the years since.
Median pay $29,980 → $39,530 +5.5% 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
-11.2%
Percentage only. The projection counts a different population from the 13,030 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 -11.2% 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.
FooterLeggerLooperRibberWeaverCreelerKnitterLatcherSmasherOperatorThreaderCrocheterLacemakerTag MakerHose MakerLoom FixerRug HookerRug WeaverSmash HandWeb WeaverBelt WeaverLace WeaverLoop PullerRib Knitter
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Re-threading a broken warp end on a running loom, pulling a jam out of a knitting head without dropping stitches, and re-tying beams by hand are still hand-eye jobs a machine can't cheaply do — but pattern loading, tension setting, needle-break detection and stop-motion already run off the machine's own controller, so roughly half your shift is monitoring work the equipment does for itself, which puts it at 10 rather than 14+.
Hands-on in uncontrolled environments You are on your feet on a mill floor for the whole shift among lint, 85+ dB noise and heat, reaching into moving needle beds and creels and doffing rolls that weigh more than you want to lift — 13 rather than 17 because the environment is at least a fixed indoor plant with the machines in known positions, not a construction site or a customer's roof.
No licence, no signature requirement There is no state license, no board, and no certificate anyone checks before you tend a Santoni or a Picanol; if a beam runs off-quality the mill eats the scrap and the QC department writes it up, so nothing legal has to stay in a human's name for the line to run.
Executes defined procedures on defined inputs You decide when a fabric flaw is bad enough to stop the machine, when tension drift means a mechanic rather than an adjustment, and how long to run before doffing — real calls, but they're bounded by the style sheet, the standard operating procedure and the loom's own alarm thresholds, with the designer and process engineer owning anything ambiguous, so 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 (10/20 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 31 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.
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 43/100 — EXPOSED.
Re-threading a broken warp end on a running loom, doffing, and clearing lint/jams in dusty mill air remains the specific manipulation task no commercial robot cell has priced out; if remaining US mills continue shifting toward technical textiles (aramid, carbon fiber, medical narrow fabrics) where lot sizes are small and changeovers frequent, the day becomes mostly setup and repair rather than tending, raising the physical share
Genuine two-tier split: sensor-driven stop/restart is already automated, leaving diagnosis of recurring defects (tension mapping, needle/sinker wear, yarn lot variation) as the residual. Where a mill runs 20+ machines per tender, the job converges on troubleshooting technician work — visible in job postings retitled 'knitting technician' with mechanic-level pay
Defense-related sourcing (Berry Amendment) and medical/PPE textile certification already require documented in-process inspection; if AS9100 or FDA QSR-style audit rules for implantable/surgical textiles are read to require a named human operator sign-off on each lot's loom setup record, a traceable human signature becomes mandatory. Narrow, and only for the fraction of mills in those markets
On short-run technical textile jobs where a single defective roll of ballistic or medical fabric is scrapped at high cost, the decision to stop the machine and scrap versus run is pushed to the operator rather than an upstream engineer; formalized as documented stop-work authority in a quality system, this is a real consequential call
The limit. No plausible route to trust premium — buyers of woven and knit goods specify fiber, construction and price, and cannot identify the operator. Employment has fallen for forty years through capital substitution, not language AI, so nothing on this list reverses the headcount trend; the levers describe a smaller, more technical residual job, not a larger one.
| Dalton, GA | 1,220 | $39,840 +1% |
| Atlanta-Sandy Springs-Roswell, GA | 820 | $44,790 +13% |
| Greenville-Anderson-Greer, SC | 530 | $39,760 +1% |
| Los Angeles-Long Beach-Anaheim, CA | 530 | $44,530 +13% |
| Charlotte-Concord-Gastonia, NC-SC | 490 | $39,530 +0% |
| Greensboro-High Point, NC | 460 | $36,730 -7% |
| Hickory-Lenoir-Morganton, NC | 430 | $36,200 -8% |
| Providence-Warwick, RI-MA | 300 | $39,110 -1% |
| Boston-Cambridge-Newton, MA-NH | 90 | $47,960 +21% |
| Chicago-Naperville-Elgin, IL-IN | 50 | $47,710 +21% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 130 | $45,580 +15% |
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 31. 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.