EXPOSED
The work is hands-on — loading stock, setting blades and guides, clearing jams, gauging cuts with calipers, feeding foam, glass, stone, rubber or food product through a machine — and language AI does none of that. The real threat isn't chatbots, it's CNC and automated-feed equipment plus vision-based inspection, which have been quietly eating this occupation for decades and continue to; one operator now tends what three used to. Judgment exists (reading grain, adjusting for material variation, catching an off-spec run) but it is narrow, unlicensed, and increasingly encoded in the machine's controller.
Part 2020 shock, part continued decline in the years since.
Median pay $35,600 → $46,570 +4.7% 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
-2.3% 49,000 → 47,900 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -2.3% 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.
~5,300 openings a year on average, including replacing people who leave.
FoxerBeaterCarverCutterHasherLooperMoonerNickerPeelerPinkerRipperSawyerScorerSkiverChipperChopperClickerClipperGrooverPresserRounderSaddlerShearerSheeter
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Setting blade gap, dressing a saw, shimming a guide and clearing a jam on a fouled feed still take hands on the machine, but the cut program itself, the material feed and the dimensional check are all things CNC controllers and vision gauges already do unattended — that split of unautomated setup against automated cutting is what puts it at 11 instead of 15.
Hands-on in uncontrolled environments You work at the machine in the plant — lifting foam buns, stone slabs, coils or carcass primals onto a table, standing in noise, dust and coolant mist, reaching into guarded areas to clear stock — and the material varies enough run to run that it isn't a fixed, tidy cell, which is why this sits at 14 rather than a controlled-line 9.
No licence, no signature requirement No state licence exists to operate a slicer or waterjet; a forklift card, a lockout/tagout sign-off or HACCP training in a meat plant is employer paperwork, not a credential that makes you personally answerable for a bad cut — the company's OSHA exposure and the customer's scrap claim land on the plant, not on you.
Executes defined procedures on defined inputs Your calls are real but bounded — bump the feed rate for a harder slab, reject a run that drifted past the print tolerance, stop for a dulling blade — all against a spec sheet and a QC gauge, with anything beyond that escalated to the setup lead or engineering, so it lands at 5 rather than in discretion territory.
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 (11/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (3/20) 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 10 of this occupation's 35 points (29%).
Embodiment (14/20) 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 50/100, still EXPOSED.
Genuine two-tier structure: the routine tier (repeat-run tending, load/unload, dimensional checks) is what CNC and vision inspection take first, leaving setup, first-article qualification, blade/tooling selection, and jam-cause diagnosis. Where the job title survives as 'setter' rather than 'tender' — visible already in job postings that merge this SOC with CNC programming and require G-code editing — the remaining work is the judgment tier.
In food cutting and slicing, FSMA preventive-controls rules already require a Preventive Controls Qualified Individual to validate and sign off on critical control points; if slicing thickness or metal-detection/vision reject steps are formally designated CCPs at more plants, the operator running that station becomes the documented monitor of record with signature and corrective-action duty.
Material classes that defeat automated feed remain the residual work: irregular natural stone slabs, book-matched veneer, whole-carcass meat and fish primal cutting, and reclaimed/recycled feedstock of unknown geometry. If demand shifts toward these (e.g. natural-stone countertop volume, or whole-animal butchery in mid-size plants where robotic primal cutters like Scott/JBT systems remain uneconomic below a throughput floor), the surviving jobs are disproportionately the ones requiring hands on unpredictable stock.
OSHA machine-guarding and lockout/tagout (29 CFR 1910.147) already require an authorized employee to perform energy isolation before clearing jams or changing blades; a rule or state-plan interpretation making the authorized-employee designation a named, trained, documented individual for collaborative/robotic cutting cells — analogous to ANSI/RIA R15.06 safeguarding sign-off — would put a specific human's name on the release-to-run.
Aerospace and medical-device supply chains under AS9100/ISO 13485 require named first-article inspection and nonconforming-material disposition authority; if more cutting operations serve those chains, the operator who stops a run and dispositions scrap owns a traceable, auditable call rather than an informal one.
The limit. No realistic route to a trust premium — buyers purchase cut parts to spec and cannot tell, and do not ask, whether a human ran the saw. Any gains here are ceiling-limited: the underlying force is decades of steady CNC and automated-feed substitution reducing headcount per plant, and none of these levers slow that; they raise the score of the shrinking remainder.
| Dallas-Fort Worth-Arlington, TX | 1,500 | $44,360 -5% |
| New York-Newark-Jersey City, NY-NJ | 1,490 | $48,990 +5% |
| Los Angeles-Long Beach-Anaheim, CA | 1,460 | $44,870 -4% |
| Chicago-Naperville-Elgin, IL-IN | 1,230 | $49,730 +7% |
| Charlotte-Concord-Gastonia, NC-SC | 940 | $44,790 -4% |
| Cleveland, OH | 860 | $53,370 +15% |
| Houston-Pasadena-The Woodlands, TX | 720 | $37,840 -19% |
| Cincinnati, OH-KY-IN | 680 | $47,890 +3% |
| Wilmington, NC | 100 | $63,550 +36% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 530 | $62,620 +34% |
| St. Cloud, MN | 140 | $58,450 +26% |
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 35. 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.