COOKED
The core of this job — loading blanks into presses, offbearing finished parts onto conveyors, stacking, and visually spotting obvious defects — happens in a fixed, controlled plant environment with predictable part geometry, which is exactly where pick-and-place robots, cobot machine tenders, and vision inspection systems already work at production cost. The moat is not AI capability but capital budgets and short production runs: small shops with frequent changeovers keep humans because the payback math doesn't close. Nothing about the role requires licensure, a customer relationship, or ownership of ambiguous decisions, so there is no non-physical shield.
Most of this decline happened after 2021 — it is not the pandemic dip.
Median pay $31,180 → $41,220 +5.8% 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
-13% 46,500 → 40,400 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -13% 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.
~4,700 openings a year on average, including replacing people who leave.
DrierLayerTakerDofferDumperFeederFlumerFolderGuiderHackerJoggerLoaderNeckerOpenerPlacerRackerRodmanSawyerTabberTaggerTailerCatcherChipmanCrimper
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier Feeding blanks and clearing finished parts is repetitive material handling on a fixed cycle, which robotic arms already do at production cost, so the 8 reflects only the surviving human-held work — jam clearing, unjamming a misfed sheet, re-orienting an odd blank, and handling changeover-heavy short runs where fixturing a robot costs more than the part.
Some physical or field component You are on your feet on a plant floor lifting, stacking, and reaching into machine envelopes all shift, which puts this above any desk role, but the environment is a known bay with fixed machine positions, guarding, and pallet locations rather than a construction site or a customer's basement — hence 10, not the high teens.
No licence, no signature requirement No licence, certificate, or apprenticeship gates this work; you're hired on general safety orientation and lockout/tagout training, and any injury or defect claim runs to the employer under OSHA and workers' comp, not to you personally.
Executes defined procedures on defined inputs Your decisions are bounded by the setup sheet and the machine's cycle — feed rate, scrap the obvious short shot, hit the e-stop, call the setter — and the 3 exists only because when to stop the press and flag a run is genuinely yours to call in the moment.
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 (8/20 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 (3/20) 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 5 of this occupation's 23 points (22%).
Embodiment (10/20) 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 35/100 — EXPOSED.
Shift of remaining human-fed lines toward deformable or highly variable feedstock — poultry/meat portions, hides, textiles, sand-cast parts with flash and scale, mixed-grade recycled stock — where part geometry is not repeatable and vision-guided pick-and-place still fails on grasp. Visible already in poultry deboning and apparel cut-part handling, where USDA/industry automation pilots have repeatedly stalled on soft-tissue variability.
Task-mix shift within the role: if cobot machine tenders take the steady-state feeding on long runs, what remains for the human is changeover, fixture swap, jam clearing, first-article checking and scrap triage on short runs. This occupation does have a thin second tier (the job-shop feeder who also sets up), and in high-mix shops that tier is what is left. Watch for job postings retitled 'machine tender/setup' at the same wage band.
Formal assignment of in-process quality hold authority to the line operator under an AS9100 / IATF 16949 / ISO 13485 quality system — i.e. the feeder is the named person of record who can stop the line and quarantine a lot, with signature on the traveler. Some aerospace and medical-device suppliers already do this to satisfy customer audits; where the signature is a person rather than a station, the role owns a consequential call.
A narrow route only: if OSHA's long-pending update to the machine guarding standard (29 CFR 1910 Subpart O, on the regulatory agenda since the 2019 robotics RFI) or a state analogue requires a trained, designated human 'authorized employee' physically present for lockout/tagout and for entry into a collaborative robot's operating space during jam clearing, the presence requirement becomes a rule rather than a preference. This shields presence, not the loading task itself, and confers no license or personal liability.
The limit. No plausible route to trust_premium: the buyer of a stamped bracket or a bagged part cannot see who loaded the press and has never paid extra for it. Even with every lever above, this stays in the low 30s — the levers protect presence and hold authority in high-mix and soft-material plants, not the feeding motion, and the underlying trend is that capital cost of cobot tenders keeps falling into the short-run payback window.
| Los Angeles-Long Beach-Anaheim, CA | 1,480 | $38,600 -6% |
| Chicago-Naperville-Elgin, IL-IN | 1,410 | — |
| Dallas-Fort Worth-Arlington, TX | 1,000 | $40,400 -2% |
| Riverside-San Bernardino-Ontario, CA | 910 | $38,980 -5% |
| Portland-Vancouver-Hillsboro, OR-WA | 630 | $43,780 +6% |
| Indianapolis-Carmel-Greenwood, IN | 600 | $37,820 -8% |
| New York-Newark-Jersey City, NY-NJ | 570 | $41,690 +1% |
| Columbus, OH | 560 | $60,410 +47% |
| Kansas City, MO-KS | 540 | $77,100 +87% |
| Columbus, OH | 560 | $60,410 +47% |
| Milwaukee-Waukesha, WI | 80 | $59,870 +45% |
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 23. 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.