COOKED
The core of this job — loading uppers and soles into stitching, cementing, lasting, and heel-attaching machines, watching for defects, and adjusting feed rates — is repetitive work on a fixed bench in a controlled factory, which is exactly what purpose-built automation targets. The physical component gives real protection against language AI, but not against dedicated shoe-manufacturing machinery and offshoring, which have already cut US employment to about 3,280. Nothing about the role requires a licensed human or a customer relationship.
This fall is concentrated in 2020 and has not recovered since.
Median pay $30,570 → $35,650 -6.7% 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
-3.7%
Percentage only. The projection counts a different population from the 3,280 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 -3.7% 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.
~400 openings a year on average, including replacing people who leave.
DyerCaserFakerInkerLacerPiperSolerTreerTruerArcherBarrerCloserFlamerFudgerGougerHeelerHookerLapperLasterNailerNickerSeamerSkiverStayer
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier Feeding limp, variable leather uppers onto a last and judging when cement tack is right still defeats general-purpose robots, so this sits at 10 rather than 4 — but the stitching, sole-pressing, and heel-nailing cycles themselves are already machine-timed, and computerized stitchers and direct-injection molding lines have absorbed those steps in the plants that remain.
Some physical or field component You are standing at a machine, hand-feeding parts, clearing jams, changing needles and dies, and pulling finished pairs off a rack — real hands-on work, but inside a lit factory on a fixed bench with the same fixtures every shift, which is why this lands at 11 and not with the roofers and linemen working in weather and unpredictable spaces.
No licence, no signature requirement No state licence, no certification, no exam gates entry to running a lasting or cementing machine; a supervisor signs off on your training and the plant's OSHA machine-guarding compliance sits with the employer, so there is no personal credential standing between the work and automation.
Executes defined procedures on defined inputs Decisions are bounded to what the work order and machine setup sheet already specify — feed rate, temperature, adhesive amount, stitch length — and anything outside tolerance goes to the mechanic or lead, so the discretion is limited to pulling a bad pair off the line.
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 (1/20) is whether buyers specifically pay for a person. Judgment and accountability (2/20) 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 4 of this occupation's 25 points (16%).
Embodiment (11/20) 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 39/100 — EXPOSED.
Growth of the US bespoke/handmade footwear and resoling segment where buyers explicitly pay for 'handmade in USA' construction (e.g. Goodyear-welt makers like Rancourt, Nicks, Truman, plus the cobbler/resole trade). If surviving US employment concentrates in these shops, the remaining operators are selling human-made provenance rather than throughput — this is the only route, and it shrinks the occupation while raising the score.
Task-mix shift within surviving shops: fully automated cementing/lasting cells absorb the repetitive bench feeding, leaving hand-lasting, welt stitching on irregular lasts, orthopedic and custom-width builds, and repair work on worn shoes of unknown construction — tasks where every unit differs. Watch whether BLS/OES reclassification and shop job postings shift toward 'hand laster / welter / cobbler' content.
Concentration of remaining work in repair and custom orthopedic footwear, where the workpiece is a used, deformed shoe or a patient-specific last rather than a uniform component — unpredictable fixturing that purpose-built shoe machinery is not designed for. Also driven by any durable reshoring of small-batch production under tariff/procurement pressure (Berry Amendment military boot sourcing already requires domestic manufacture).
Only narrow route: pedorthic and orthopedic footwear fabrication falling under state licensure or ABC/BOC pedorthist certification requirements, where a credentialed person must sign off on a therapeutic device build. This would apply to a small fraction of workers who are usually classified as pedorthists rather than shoe machine operators, so the effect on this SOC code is marginal.
The limit. Even with every lever, this stays low. The binding constraint is not AI capability but 3,280 remaining US workers and continued offshoring — the routes above mostly describe a smaller, more artisanal occupation rather than a protected one. There is no plausible path to a liability shield or accountability role for factory bench operation.
| El Paso, TX | 170 | $23,070 -35% |
| New York-Newark-Jersey City, NY-NJ | 40 | $48,530 +36% |
| Los Angeles-Long Beach-Anaheim, CA | 30 | $47,730 +34% |
| New York-Newark-Jersey City, NY-NJ | 40 | $48,530 +36% |
| Los Angeles-Long Beach-Anaheim, CA | 30 | $47,730 +34% |
| El Paso, TX | 170 | $23,070 -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 25. 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.