EXPOSED
Washing, peeling, slicing, portioning, and assembling salads and sandwiches are manual tasks that language AI cannot touch — this job is protected by hands, not by knowledge work. The real pressure comes from purpose-built kitchen automation (automated fry stations, robotic wok and bowl lines, pre-cut and pre-portioned supplier products) plus menu simplification, which shrinks headcount per kitchen rather than replacing the worker's skill. No license, no signature, minimal customer relationship, and little discretion means there is no regulatory or trust moat to slow that squeeze.
Roughly flat across the period, with year-to-year wobble.
Median pay $24,800 → $35,320 +13.9% 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
-3.4% 902,700 → 871,800 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -3.4% 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.
~148,000 openings a year on average, including replacing people who leave.
CookParerCarverPeelerSlicerCatererStewardCook AideDiet AideLine CookDishwasherMeat ClerkPie CutterFood ServerLine ServerPantry CookPizza MakerSalad MakerCoffee MakerDietary AideFood HandlerKitchen CrewCoffee BrewerKitchen Clerk
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Deboning a chicken, trimming a case of romaine, and hand-portioning steam-table pans are irregular-object manipulations no software has solved, and the 14 rather than 18 reflects that the volume tasks — dicing onions, shredding cheese, portioning sauce — are already offloaded to supplier pre-cut bags and to buffalo choppers and Hobart slicers in-house.
Hands-on in uncontrolled environments You are on your feet the full shift moving between a 38°F walk-in and a hot line, lifting 50-lb produce cases, handling wet floors and open knives, with the 17 rather than 20 owing to the kitchen being an indoor, fixed layout rather than a fully unpredictable outdoor site.
No licence, no signature requirement The only credential in most jurisdictions is a food handler card obtained from a two-hour online course, and when a health inspector writes up a temperature violation or a norovirus case is traced back, the citation lands on the establishment's permit and the certified food protection manager, not on you.
Executes defined procedures on defined inputs The day runs off a prep list, standardized recipe cards, spec'd portion scoops, and HACCP hold temperatures with logged checks; the discretion left is calling a case of tomatoes bad or flexing prep quantities against an unexpectedly slow lunch.
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 (14/20 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 (4/20) 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 10 of this occupation's 41 points (24%).
Embodiment (17/20) 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
Maids and Housekeeping Cleaners 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 52/100, still EXPOSED.
Task-mix shift: if automated lines take routine slicing and portioning, the remaining human work concentrates on receiving inspection, spoilage and temperature-abuse calls, cross-contact decisions, and HACCP corrective actions — consequential calls under ambiguity that carry the store's health-inspection score. This tier genuinely exists today but is thinly recognized in the role.
Same two-tier shift: once fry stations, wok lines, and bowl assembly are automated, what remains is exception handling, machine cleaning and sanitation validation, and non-standard order remediation — none of which current robotic cells do. Rises only in occupations that survive the headcount cut, not for the cut positions.
If FDA Food Code adoption in more states moves beyond one Person-in-Charge toward requiring a certified food protection manager physically present per prep shift (already law in NYC, Illinois, and Texas manager-certification rules), a named certified human becomes a staffing floor rather than a one-per-store box-tick. Allergen-specific certification mandates (post-Natasha's-Law-style bills introduced in several US states) would compound this.
If the menu mix shifts further toward whole-ingredient, variable-input prep (whole fish butchery, irregular produce, allergen-segregated prep lines) that defeats fixed-geometry robotic cells, the unpredictable-environment share of the day rises. Watch for chains reversing pre-cut supplier reliance on cost/freshness grounds, as some grocery-adjacent kitchens have done.
The limit. There is no realistic route to a meaningful trust premium: buyers of prepped salad and sandwich components do not know or pay for who cut them, and back-of-house work is invisible at the point of sale. Note also that every lever above raises per-worker resistance while the automation and menu-simplification squeeze reduces the number of workers — the occupation's score can rise while employment falls.
| New York-Newark-Jersey City, NY-NJ | 63,530 | $35,670 +1% |
| Los Angeles-Long Beach-Anaheim, CA | 44,930 | $39,770 +13% |
| Chicago-Naperville-Elgin, IL-IN | 27,100 | $35,410 +0% |
| Dallas-Fort Worth-Arlington, TX | 21,700 | $30,740 -13% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 19,630 | $36,090 +2% |
| Atlanta-Sandy Springs-Roswell, GA | 17,060 | $36,090 +2% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 16,810 | $35,100 -1% |
| Houston-Pasadena-The Woodlands, TX | 16,240 | $35,020 -1% |
| Kahului-Wailuku, HI | 720 | $46,680 +32% |
| San Jose-Sunnyvale-Santa Clara, CA | 5,930 | $45,580 +29% |
| Seattle-Tacoma-Bellevue, WA | 11,060 | $43,830 +24% |
CBC reports a restaurant in the Okanagan region of British Columbia has installed an AI-powered robotic cooking system to prepare dishes such as fried rice in its kitchen.
The New York Post reports McDonald's is testing humanoid robots dressed in staff uniforms in restaurant roles.
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