COOKED
The core task — visually inspecting produce, eggs, or meat on a line and pulling out defects by size, color, ripeness, or blemish — is the textbook case for optical sorting machines, and machine vision plus air-jet and robotic ejectors already do it faster and more consistently in large packinghouses. Physical presence on a wet, cold, fast-moving line is a real moat against pure software, but here the competing technology isn't a chatbot, it's a camera rig that's been commercially mature for two decades and keeps getting cheaper. What survives is hand-grading of delicate or irregular commodities, machine tending and calibration, and USDA-standard grade calls where a human still signs the certificate.
This fall is concentrated in 2020 and has not recovered since.
Median pay $25,670 → $35,730 +11.4% 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
-5.4%
Percentage only. The projection counts a different population from the 25,180 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 -5.4% 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.
~5,100 openings a year on average, including replacing people who leave.
GraderSorterEgg GraderEgg SorterEgg TesterEgg TrayerEgg WorkerNut CullerNut GraderNut PackerNut PickerNut SorterOnion TierRing FacerBean SorterBulb GraderCarrot TierCelery TierChick SexerClam SorterEgg CandlerFood TasterHide GraderLeaf Sorter
Holding it up: embodiment . Weakest point: liability shield .
Core tasks are already automatable Judging a tomato by color break or an egg by candling shadow is exactly what a hyperspectral camera and weight cell do at 20 fruits per second, and the USDA grade standards you apply are written as numeric tolerances — defect counts, diameter ranges, brix — which is a threshold rule a sorting machine executes without fatigue, leaving only the 5 points for hand-handling of soft berries, stone fruit, and odd-shaped commodities that bruise in a chute.
Some physical or field component You stand at a wet, 38-degree line pulling culls at speed, reaching into bins and flipping fruit to check the blossom end, which is why this isn't a 2 — but the work happens inside a packinghouse with fixed lighting, a fixed belt height, and product presented to you, so it's nowhere near the 16-plus of a field crew or a livestock handler working uncontrolled ground.
No licence, no signature requirement There is no licence to grade produce; USDA-licensed graders under the Agricultural Marketing Act are a separate, much smaller federal classification, and the packinghouse sorter works under a plant's own quality program with the shipper, not the sorter, answering to the buyer for a mis-graded load.
Executes defined procedures on defined inputs Your calls are bounded by a posted grade sheet and physical sizing rings — U.S. No. 1 versus No. 2 on a specified percentage of surface blemish — and a borderline lot gets kicked to the line lead or QC supervisor rather than decided by you, though setting the ripeness cutoff on the first pass of a new block earns the points above zero.
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 (5/20 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 (2/20) is whether buyers specifically pay for a person. Judgment and accountability (4/20) 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 24 points (29%).
Embodiment (12/20) 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.
Cooks, Restaurant EXPOSED
Food Preparation Workers EXPOSED
Dishwashers 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 38/100 — EXPOSED.
Two-tier shift: as optical sorters absorb size/color/blemish culling, the surviving day is calibration of sorter thresholds against USDA grade standards, lot sampling and certificate work, and hand-grading of soft or irregular commodities (table grapes, stone fruit, tobacco leaf) that machines still bruise. Watch for job postings retitled toward 'grading technician / QA sampler'
USDA AMS official grading certificates (7 CFR Part 51/70) still require a licensed federal or federal-state licensed grader to sign; if AMS formally rules that camera-derived grade data cannot substitute for a licensed grader's certification on export lots or on shell-egg grading (where an AMS grader's presence is already tied to the USDA grade shield), the signing role becomes a legally protected niche
Food-safety recall exposure: if FSMA 204 traceability enforcement (compliance date now 2026-2028) makes lot-level accept/reject calls the documented decision point for a recall, the person making borderline lot dispositions owns a consequential call under ambiguity rather than just pulling culls
Little upward route in large packinghouses, but growth in field-pack and small-lot organic/direct-market operations where capex on a $300k optical line never pencils out keeps hand grading on wet, irregular, variable-throughput lines
The limit. Ceiling is low. The displacing technology is a mature camera rig, not a model, so capability arguments do not help; and buyers of graded produce pay for the USDA grade shield, not for a human eye, so there is no realistic trust-premium route. Even with every lever, this is a much smaller occupation doing a narrower certifying job.
| Yakima, WA | 1,250 | $34,750 -3% |
| Fresno, CA | 950 | $35,010 -2% |
| Bakersfield-Delano, CA | 720 | $35,310 -1% |
| Visalia, CA | 540 | $34,960 -2% |
| Salinas, CA | 520 | $37,340 +5% |
| Wenatchee-East Wenatchee, WA | 460 | $35,080 -2% |
| Fayetteville-Springdale-Rogers, AR | 420 | $36,000 +1% |
| Kennewick-Richland, WA | 390 | $35,720 +0% |
| Grand Forks, ND-MN | 40 | $48,700 +36% |
| San Francisco-Oakland-Fremont, CA | 80 | $46,350 +30% |
| Sacramento-Roseville-Folsom, CA | 210 | $44,510 +25% |
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 24. 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.