EXPOSED
Nothing a fast food cook does is text or screen work, so language models are irrelevant here — the threat is purpose-built kitchen robotics and equipment automation aimed at exactly these tasks: fry baskets, griddle patties, drink and shake dispensing, assembly lines. The work is embodied but deliberately deskilled and standardized, which is the easiest kind of physical work to mechanize, and there's no license, no signature, and no customer paying for a relationship with the person at the fryer. What survives is the messy human middle: reading a rush, fixing a jammed line, prepping unpredictable produce, keeping a station clean and safe when equipment misbehaves.
Headcount grew steadily across the period.
Median pay $23,510 → $30,890 +5.1% 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
-13.5% 669,500 → 579,200 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -13.5% 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.
~82,100 openings a year on average, including replacing people who leave.
CookFryerFry CookLine CookGrill CookPizza ChefPizza CookPizza BakerPizza MakerFast Food CookSnack Bar CookSpecialty CookGrill Line CookFast Food WorkerFood Service CookFryline AttendantRestaurant WorkerTruck Team MemberFast Food Fry CookKitchen Team MemberPancake ProfessionalRestaurant Line CookDeep Fat Fryer OperatorHotel and Restaurant Baker
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Timed fry drops, griddle patty flips, and shake dispensing are already commercially automated (Flippy, autonomous fry stations), but the same shift also means breaking down a jammed lettuce shredder, working a lunch rush where orders arrive out of sequence, restocking a walk-in, and hand-wrapping items whose sizes and fillings change with every LTO promo — enough non-scripted physical work to sit at 11 rather than in the single digits.
Hands-on in uncontrolled environments A 14 reflects a fixed kitchen footprint — same fryers, same line, same layout — but an environment nobody would call controlled: 350°F oil, wet tile, steam, someone's elbow in your path, and equipment that fails mid-rush; robots handle the fryer well and handle spills and crowding badly.
No licence, no signature requirement A ServSafe food handler card is a few hours and a multiple-choice test in most states, and when a health inspector writes up a temperature violation the citation lands on the store's permit and the operator, not on the cook who pulled the tray — no personal license is at risk, hence 2.
Executes defined procedures on defined inputs Cook times, hold times, portion charts, and holding-cabinet timers dictate nearly every decision, and the real calls you make — whether the oil needs changing, whether that batch of nuggets sat too long, when to pre-drop before a bus pulls in — are consequential but bounded and reversible, putting this at 5 rather than 12.
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.
Postal Service Mail Carriers EXPOSED
Cooks, Restaurant 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 46/100, still EXPOSED.
Task-mix shift as fry/griddle robotics (Miso Robotics Flippy, White Castle/Jack in the Box pilots) take the repeatable stations: the remaining shift becomes exception handling — robot jam clearing, sanitation of automated equipment, non-standard produce prep, allergen-modified and custom orders that break the automated flow. Two genuine tiers exist here, and the residual tier is harder than the average of today's job.
A state or local food-safety rule requiring a certified food protection manager (ServSafe-style) physically present on every shift and personally named on the HACCP log for automated cooking equipment — extending existing FDA Food Code Person-In-Charge requirements to cover robotic fry/grill temperature verification. Some jurisdictions already require a certified manager on premises; the specific extension is personal signature on automated-equipment cook-temperature records.
If chains formalize the shift lead / 'automation attendant' role — the person who decides to shut down a miscalibrated fryer, void a batch, or switch to manual during a rush — with documented authority and accountability, the call under ambiguity becomes part of the job description rather than informal. Union contracts from the Fight for $15 lineage and California's AB 1228 Fast Food Council are venues where staffing-and-authority terms are actually being written.
If franchise menu complexity keeps expanding (limited-time offers, regional items, customization via app ordering) faster than robot retooling cycles, the physical work stays in unstructured layouts — cramped legacy kitchens not rebuilt for automation, where equipment placement varies store to store. Embodiment scores rise where the environment resists standardization rather than where the task does.
The limit. Trust premium has no realistic route: fast food's entire value proposition is speed and price uniformity, and no customer segment pays extra to know a human touched the patty. Even with every lever above, this occupation stays in the exposed band — the levers slow attrition of headcount per store and shift who survives, they do not create a protected role.
| Los Angeles-Long Beach-Anaheim, CA | 48,440 | $38,000 +23% |
| St. Louis, MO-IL | 17,700 | $30,860 +0% |
| Riverside-San Bernardino-Ontario, CA | 16,910 | $42,480 +38% |
| Charlotte-Concord-Gastonia, NC-SC | 15,830 | $27,690 -10% |
| Kansas City, MO-KS | 13,850 | $30,170 -2% |
| Raleigh-Cary, NC | 11,500 | $28,220 -9% |
| San Diego-Chula Vista-Carlsbad, CA | 10,630 | $42,450 +37% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 10,580 | $35,800 +16% |
| San Jose-Sunnyvale-Santa Clara, CA | 4,790 | $47,220 +53% |
| San Francisco-Oakland-Fremont, CA | 10,160 | $46,670 +51% |
| Napa, CA | 370 | $45,300 +47% |
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.