EXPOSED
The information half of this job — taking reservations, quoting wait times, managing the waitlist, texting guests when a table opens, tracking table status — is already handled by OpenTable, Yelp Waitlist, and QR-code check-in, and that displacement is happening now. The greeting, escorting, menu handoff, and on-the-floor reading of a crowded dining room still require a body in the room, and at $14/hour the payback on replacing that body with a robot is nonexistent. What shrinks is headcount per shift, as one host covers what two used to and the tablet absorbs the rest.
Roughly flat across the period, with year-to-year wobble.
Median pay $23,090 → $31,200 +8.1% 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
-1.5% 429,900 → 423,500 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -1.5% 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.
~107,700 openings a year on average, including replacing people who leave.
HostSeaterGreeterHostessBar HostParty HostBar HostessTearoom HostParty HostessBreakfast HostBuffet HostessGeneral TellerFront Desk HostRestaurant HostTearoom HostessDining Room HostHost CoordinatorParlor ChaperoneDining CoordinatorFront Desk HostessRestaurant HostessDining Room HostessHospitality CoordinatorMaitre D' (Maitre d'hotel)
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Reservation intake, wait-time quoting, and waitlist paging are already fully productised, but seating a party of six with a stroller in a room where two tables are half-bussed and a server section is drowning is still a live judgment made by walking the floor — the split between those two halves is what puts this at 10 rather than 4.
Hands-on in uncontrolled environments You are on your feet the whole shift in an uncontrolled space — squeezing between occupied chairs, carrying highchairs and menus, wiping and resetting tables during a rush, working around spills, door traffic, and guests who move unpredictably — which is the same physical unpredictability that keeps 14 out of reach of any current machine.
No licence, no signature requirement No state licence, no certification, no exam gates this job; a food-handler card at most in some jurisdictions, and nothing you do at the podium creates personal legal exposure that a manager doesn't own — hence 0.
Executes defined procedures on defined inputs Seating rotation, party-size rules, and quoted wait times run off manager-set procedure and the table chart; the discretion you do exercise — bumping a party, holding a booth, calming someone at 40 minutes — is real but reversible and low-stakes, which is a 5 and not a 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 (10/20 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 (0/20) is whether the law requires a licensed human to sign. Trust premium (8/20) 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 13 of this occupation's 37 points (35%).
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.
Waiters and Waitresses EXPOSED
Cooks, Short Order 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 48/100, still EXPOSED.
Task-mix shift: once reservations, wait-time quotes, waitlist texting and table-status tracking are fully absorbed by OpenTable/SevenRooms, the residual role is the judgment tier — VIP and regular recognition, seating-chart triage during a rush, absorbing complaints before they reach a manager, pacing seats to kitchen capacity. The job that remains is harder to automate than the average of the job today, even as headcount falls.
Fine-dining and hospitality-brand segments that explicitly market a human greeting — e.g. Michelin/Forbes Travel Guide service standards that score 'personal greeting by name within 30 seconds' and penalize kiosk check-in — expanding as a differentiator as mid-market restaurants go tablet-only. If graded human-greeting standards become a marketing requirement in a larger share of covers, this rises modestly.
Formal absorption of front-of-house duties currently held by managers: ID/age verification at bar entry, refusal of service, occupancy and fire-code headcount at the door, and allergen intake questions logged at seating. Where state alcohol boards or FDA Food Code allergen-disclosure adoption push the point of first contact to the host stand, the role owns consequential calls.
The limit. Realistic ceiling is low-to-mid 40s. There is no licensure route — no jurisdiction licenses hosts, so liability_shield stays at 0 and the largest available lever is closed. The trust premium is real but confined to a small high-end segment; for the diner-and-chain majority it is not recoverable. Even where scores rise, they rise for a shrinking number of positions per shift.
| New York-Newark-Jersey City, NY-NJ | 18,530 | $36,500 +17% |
| Los Angeles-Long Beach-Anaheim, CA | 15,340 | $36,920 +18% |
| Chicago-Naperville-Elgin, IL-IN | 13,390 | $31,720 +2% |
| Atlanta-Sandy Springs-Roswell, GA | 11,940 | $28,560 -8% |
| Boston-Cambridge-Newton, MA-NH | 10,580 | $35,140 +13% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 10,440 | $32,280 +3% |
| Dallas-Fort Worth-Arlington, TX | 10,140 | $28,890 -7% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 9,270 | $36,360 +17% |
| Urban Honolulu, HI | 1,740 | $47,110 +51% |
| Kahului-Wailuku, HI | 490 | $45,240 +45% |
| Portland-Vancouver-Hillsboro, OR-WA | 3,390 | $43,410 +39% |
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 37. 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.