COOKED
The core loop — weigh a load, record the figure, compare it against the order or spec, flag discrepancies — is exactly what in-line scales, barcode/RFID scanners, and machine-vision inspection stations already do continuously and without transcription errors. What keeps this role alive at all is the physical part: physically pulling samples from a rail car or tote, handling odd or damaged goods, and being on the dock when the system disagrees with reality. There is no license, no signature requirement, and almost no discretion at the median, so the automation path is capital spending rather than any legal or trust barrier.
This fall is concentrated in 2020 and has not recovered since.
Median pay $35,040 → $46,380 +5.9% 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
-4.8% 49,800 → 47,400 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -4.8% 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.
ScalerCheckerCounterStackerTallierRecorderScalemanTare ManUnitizerWeighterGin ClerkHam ClerkScale AgentScale ClerkTally ClerkTare WorkerWeighmasterBean WeigherCane WeigherCase CheckerCoal WeigherFish CheckerLand CheckerLoad Checker
Holding it up: embodiment . Weakest point: trust premium .
Core tasks are already automatable Reading a scale display, keying the number into a WMS, and comparing it to a bill of lading is a data-capture loop that in-line checkweighers and scanner-gated conveyors already run at line speed, which is why this sits at 4 rather than 10 — the exceptions (retare a drum, recount a broken pallet) are minutes of an eight-hour shift, not the shift itself.
Some physical or field component A 10 reflects that the sampling and tallying happen on a dock, in a warehouse aisle, or at a rail siding — climbing onto a car, scooping grain, cutting a bale, walking a yard in weather — but it is a fixed, mapped facility with known load types, not an uncontrolled field, so a scale pit and a scanner arch cover most of the ground a person now walks.
No licence, no signature requirement Nothing here requires a licence: state weights-and-measures law licenses the sealed device and the service technician who certifies it, not the person reading it, and when a shipment is short the carrier or shipper eats it under the bill of lading — hence 2, with the point or two reflecting only that certified-scale operators in grain or scrap sometimes have to be named on a ticket.
Executes defined procedures on defined inputs Tolerances, sampling frequency, and the reject threshold are written into the spec or the QC plan before you clock in; the call you own is "flag it and tell the supervisor," which is why this is a 3 — real ambiguity gets escalated to quality, purchasing, or the plant manager rather than settled on the dock.
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 (4/20 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 (2/20) 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 7 of this occupation's 21 points (33%).
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.
Log Graders and Scalers 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 35/100 — EXPOSED.
Genuine two-tier split: if in-line scales, RFID and vision inspection absorb the routine count-and-record tier, what remains is exception work — reconciling system-versus-reality disputes on the dock, damaged/nonconforming loads, calibration drift investigation, chain-of-custody sampling that must be defensible. Watch for job postings retitled toward 'quality/metrology technician' or 'calibration and exception coordinator' with ASQ or NIST-traceable calibration credentials attached. The headcount falls sharply either way; the residual job is harder.
Legal-metrology and custody-transfer regimes are the one real route: NIST Handbook 44 / state weights-and-measures programs already require certified weighmasters in some states (e.g. California's Division of Measurement Standards licensed weighmaster certificates, which carry a personal signature on the certified weight ticket used in commerce and court). If state boards extend certified-weighmaster signature requirements to automated in-line scale output — i.e. a licensed human must attest that the unattended system's ticket is valid — the role gains a genuine signature it does not now have at the median. Similar hooks exist in USDA AMS official sampling/grading (licensed samplers under the US Grain Standards Act) and in EPA/DEA chain-of-custody sampling.
If regulated-commodity sampling (grain grading, hazardous-waste characterization under RCRA, pharmaceutical incoming-materials sampling under 21 CFR 211.84) is where surviving positions concentrate, the sampler owns a consequential accept/reject call under ambiguity with an audit trail. FDA Part 211 already names the person who approves or rejects components; that is a named accountability the median dock role lacks.
The limit. Even with a weighmaster signature requirement, the ceiling is low — the licensing exists in a minority of states, covers a minority of transactions, and one licensed signer can attest to output from many unattended stations, so the credential concentrates rather than preserves employment. There is no realistic trust-premium route: no buyer pays extra for a human to have read a scale.
| New York-Newark-Jersey City, NY-NJ | 2,240 | $47,350 +2% |
| Los Angeles-Long Beach-Anaheim, CA | 2,220 | $43,670 -6% |
| Dallas-Fort Worth-Arlington, TX | 1,670 | $52,420 +13% |
| Indianapolis-Carmel-Greenwood, IN | 1,510 | $57,510 +24% |
| Riverside-San Bernardino-Ontario, CA | 1,450 | $47,910 +3% |
| Atlanta-Sandy Springs-Roswell, GA | 1,310 | $45,780 -1% |
| Chicago-Naperville-Elgin, IL-IN | 1,070 | $52,480 +13% |
| Houston-Pasadena-The Woodlands, TX | 1,010 | $47,710 +3% |
| Akron, OH | 180 | $61,940 +34% |
| Boulder, CO | 40 | $60,940 +31% |
| Allentown-Bethlehem-Easton, PA-NJ | 250 | $60,770 +31% |
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 21. 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.