EXPOSED
Almost nothing in this job is text or screen work: clearing and wiping tables, hauling bus tubs, restocking glassware and garnish trays, refilling water, mopping spills, and setting up cafeteria lines are physical tasks in crowded, unpredictable rooms that current robotics handles poorly. The real threat isn't language AI — it's business-model change: self-bussing fast-casual layouts, tray-return systems, kiosk ordering, and tray-delivery robots that let one attendant cover more floor. There is no license, no signature, and little discretion, so the only moat is embodiment, and that moat protects the task more than the headcount.
Dipped in 2020, then grew past where it started.
Median pay $23,470 → $33,980 +15.8% 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
+6.3% 527,400 → 560,600 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +6.3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~99,600 openings a year on average, including replacing people who leave.
BusboyBusserIcemanBarbackBarmaidBus BoyBar BackBus PersonCafe HelperFloorpersonFood RunnerLine ServerTray SetterKitchen AideBar AttendantWater CarrierCafeteria AideClub AttendantFood ExpeditorKitchen HelperLunchroom AideSilver StewardSilver WrapperBeverage Server
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Stacking a bus tub of mixed china and stemware, spot-wiping a booth between seatings, and cutting fruit garnish during a rush are all tasks that require judging where things are and how much force to use in a room full of moving people — that's a 15 rather than an 18 because the ordering, tab-splitting, and tray-return steps around you have already been offloaded to kiosks and drop stations.
Hands-on in uncontrolled environments You are on your feet a full shift on wet tile, reaching over occupied tables, carrying loaded trays through service doors, and handling broken glass and hot dish-machine racks — a 17 not 20 only because it's indoors on a known floor plan rather than outdoors or on unmapped sites.
No licence, no signature requirement No credential stands between you and replacement: at most a food handler card that costs an afternoon and a small fee, no scope of practice, and no state statute that says the bussing must be done by a licensed person, so the 1 reflects the ServSafe-style card existing at all.
Executes defined procedures on defined inputs Bus in this order, sanitizer at this concentration, wet-floor sign here, discard anything left uncovered — the calls you make are which table to hit first and when to flag a spill, which is real but bounded, and no decision of yours ends up in an incident report signed in your name.
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 (15/20 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 (5/20) 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 9 of this occupation's 41 points (22%).
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.
Maids and Housekeeping Cleaners EXPOSED
Cooks, Restaurant EXPOSED
Butchers and Meat Cutters 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 53/100, still EXPOSED.
If the tipped-service segment where guests visibly reward human floor presence expands — e.g. state elimination of the tip credit (DC Initiative 82, Chicago's One Fair Wage ordinance) pushing full-service restaurants toward higher-check, higher-touch formats where table maintenance by a person is part of the sold experience rather than a cost line
Food-safety rules that name a trained person for sanitation tasks: if state adoption of FDA Food Code provisions on certified food protection managers is extended so that surface-sanitizing, allergen cleaning between seatings, and glassware handling must be performed or verified by a ServSafe-certified employee — not a machine cycle — the busser role acquires a documented, auditable sign-off
If dram-shop liability enforcement pushes bars to make barbacks and floor staff part of the documented over-service and ID-check chain (state responsible-beverage-service mandates, e.g. Texas TABC or Utah training requirements applied to all bar personnel, not just servers), the role starts owning intervention calls on visibly intoxicated guests
Nothing raises the physical difficulty itself, but the score can hold at its ceiling if restaurant layouts stay dense and unpredictable — e.g. if the fine-dining and full-service segment grows relative to fast-casual self-bussing, or if local fire/ADA aisle-width rules and crowded urban footprints keep tray-delivery robots (Bear Robotics/Pudu deployments) confined to wide-aisle chains. Watch for hotel and casino union contracts (UNITE HERE Local 11 in California) that cap robot use or require staffing ratios per covered section.
Genuine two-tier shift is weak here: when tray return and self-bussing remove the routine clearing tier, what remains is more physical exception work (spills, broken glass, unstable guests) but not a protected judgment tier — headcount falls rather than the remaining job hardening
The limit. Realistic ceiling is low-to-mid 50s. Embodiment is already near max and cannot rise further; the threat is headcount compression through layout and business-model change, which no dimension on this register captures. Liability and judgment gains would attach mainly to the bar-adjacent and hotel-union slices of the occupation, not to cafeteria attendants.
| New York-Newark-Jersey City, NY-NJ | 37,600 | $36,980 +9% |
| Los Angeles-Long Beach-Anaheim, CA | 24,860 | $35,800 +5% |
| Dallas-Fort Worth-Arlington, TX | 18,600 | $21,050 -38% |
| Chicago-Naperville-Elgin, IL-IN | 18,100 | $31,200 -8% |
| Houston-Pasadena-The Woodlands, TX | 16,740 | $21,850 -36% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 16,490 | $34,710 +2% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 13,800 | $37,260 +10% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 10,990 | $32,220 -5% |
| Kahului-Wailuku, HI | 840 | $61,230 +80% |
| Urban Honolulu, HI | 2,500 | $50,200 +48% |
| Seattle-Tacoma-Bellevue, WA | 3,630 | $45,120 +33% |
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 41. 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.