COOKED verdict contested
The core of this job — visual defect checks, dimensional gauging, sorting by grade, weighing, logging results into quality systems — is exactly what machine vision, in-line sensors, and automated gauging already do faster and more consistently, and the factory floor is the most controlled, automation-friendly physical environment that exists. Physical handling of parts and fixtures still needs hands, which is the main thing holding the number up. The surviving tier is narrow: NDT-certified technicians, calibration and metrology specialists, and inspectors who own nonconformance dispositions, supplier audits, and root-cause investigations rather than pass/fail calls.
Roughly flat across the period, with year-to-year wobble.
Median pay $39,140 → $48,570 -0.7% 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
0% 598,000 → 598,100 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects 0% 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.
~69,900 openings a year on average, including replacing people who leave.
SizerTimerCoilerCullerGaugerPairerPasserScalerSorterTasterTesterCheckerClasserPatcherSamplerSpotterWeigherAssorterCollatorMeasurerRegraderRejectorSelectorEstimator
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier At 7 rather than 4, the pass/fail visual scan, caliper and micrometer readings, and grade sorting are already being done by in-line cameras and laser gauges on the same lines you work, but first-article layouts, fixturing odd geometries, and chasing an intermittent defect back to a machine still take a person walking the floor — that residue is what keeps it out of the bottom band.
Some physical or field component An 11 reflects that you are physically lifting, rotating, and mounting parts, running CMM setups, pulling samples from tanks and totes, and standing at a line all shift — but it happens inside a lit, climate-managed plant with fixed stations and known part families, not on a roof or in a trench, so it stops well short of the uncontrolled-environment band.
No licence, no signature requirement A 4 is right because most inspection jobs post as high-school-plus-training with no state licence: your sign-off on an inspection record is countersigned into the QMS and the legal exposure lands on the company's quality manager and PE-stamped drawings, and even ASNT Level II NDT or ASQ CQI credentials are employer- or program-issued preferences, not a personal liability that follows you.
Meaningful discretion A 7 acknowledges genuine calls — accept-with-deviation, borderline cosmetic defects, when to stop the line, whether a sample is representative — but those decisions are bounded by written specs, control limits, and AQL sampling tables, and anything expensive escalates to an MRB or engineering disposition rather than resting on you.
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 (7/20 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 (4/20) is whether the law requires a licensed human to sign. Trust premium (4/20) is whether buyers specifically pay for a person. Judgment and accountability (7/20) 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 15 of this occupation's 33 points (45%).
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 49/100 — EXPOSED.
Genuine two-tier structure: as in-line vision and CMM automation absorb pass/fail gauging, the residual job becomes nonconformance disposition, MRB participation, gage R&R and measurement-systems analysis, supplier corrective-action audits, and root-cause investigation — tasks that require reconciling conflicting evidence and negotiating with suppliers. Watch for BLS/employer job postings shifting from 'quality inspector' to 'quality engineer/technician' with CQI/CQE credential requirements. Composition effect, not capability gain: the count falls while the surviving role's resistance rises.
Certification regimes that require a named, personally-accountable human signature on inspection records: ASNT/NAS-410 Level II/III NDT certification already requires a certified individual to sign radiographic/UT interpretations, and FAA 14 CFR Part 43/145 requires an authorized person to sign airworthiness inspection releases. If FDA (21 CFR 820 QSR harmonization to ISO 13485) or FAA explicitly bar unreviewed automated vision results as the sole record of conformance — i.e. require a certified inspector to countersign machine-vision dispositions — this rises materially for the aerospace/medical/pressure-vessel subset, less for general production.
Formal ownership of dispositions under AS9100/IATF 16949 Material Review Board authority, and named-auditor accountability under supplier audit schemes. If OEM supplier-quality contracts or a post-incident regulatory response (e.g. following an aerospace or medical-device recall) name an individual inspector as the accountable signatory for use-as-is/rework/scrap decisions and root-cause closure, this rises for that subset.
Field and in-situ inspection work that resists fixturing: weld and pipeline NDT in refineries, bridge and tank inspection, in-service turbine borescope work, agricultural grading at variable receiving points. If demand shifts toward these unstructured settings — e.g. sustained infrastructure inspection spend under IIJA bridge/pipeline programs — the embodiment-heavy share of the occupation grows even as factory-floor roles shrink.
The limit. Trust premium is omitted: buyers of inspection are OEMs and regulators who want defensible records, not human hands, and no realistic market pays extra for a human eye over a calibrated sensor. The levers above apply almost entirely to the certified NDT/metrology/supplier-audit minority; for the majority doing visual sorting, gauging, and weighing on a line, no credible route raises any dimension, and the honest ceiling for the occupation as a whole stays well below 50.
| Chicago-Naperville-Elgin, IL-IN | 23,080 | $48,230 -1% |
| Los Angeles-Long Beach-Anaheim, CA | 22,610 | $51,560 +6% |
| Houston-Pasadena-The Woodlands, TX | 16,520 | $46,630 -4% |
| Dallas-Fort Worth-Arlington, TX | 16,440 | $46,210 -5% |
| New York-Newark-Jersey City, NY-NJ | 16,370 | $50,160 +3% |
| Detroit-Warren-Dearborn, MI | 10,610 | $45,400 -7% |
| Atlanta-Sandy Springs-Roswell, GA | 9,260 | $46,870 -4% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 8,520 | $51,990 +7% |
| Lexington Park, MD | 100 | $92,000 +89% |
| Seattle-Tacoma-Bellevue, WA | 8,190 | $76,800 +58% |
| Savannah, GA | 1,110 | $72,800 +50% |
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 33. 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.