EXPOSED
Almost nothing in this job is text or screen work: stripping beds, scrubbing bathrooms, vacuuming around furniture, restocking carts, and handling guest requests are physical tasks in cluttered, variable spaces that current robotics cannot do end-to-end. The real exposure is not the AI doing the cleaning but AI-driven scheduling, room-assignment optimization, and demand forecasting squeezing headcount per room and shortening allotted minutes per unit. There is no license and little discretion, so the protection here is purely physical, and private-home cleaners hold more of a trust relationship than hotel-floor staff, who are largely interchangeable to the buyer.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $24,850 → $35,510 +14.3% 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
+0.4%
Percentage only. The projection counts a different population from the 860,670 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +0.4% more of these jobs by 2034, and at 54/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~193,500 openings a year on average, including replacing people who leave.
MaidButlerCleanerBed MakerCharwomanHousemaidRoom MaidWard MaidHotel MaidMotel MaidChambermaidHall WorkerHousekeeperHousepersonLounge MaidParlor MaidShower MaidHouse WorkerHousecleanerLinen FolderLinen KeeperLinen WorkerRoom CleanerCabin Cleaner
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation An 18 reflects that essentially every duty — pulling soiled linens off a mattress, scrubbing tub grout, moving nightstands to vacuum behind them, wiping baseboards, hauling trash to a chute — has no text or data analogue at all, and the handful of digitizable pieces (marking a room clean in the PMS tablet, logging a lost-and-found item) take minutes out of an eight-hour shift.
Hands-on in uncontrolled environments A 19 is warranted because the work happens in rooms no two of which are laid out the same after a guest has used them: unpredictable spills, furniture shoved around, cords, pets and children in private homes, wet tile, and constant kneeling, reaching over beds, and pushing a loaded cart down uneven corridors — conditions no fixed robotic footprint or mapped environment covers.
No licence, no signature requirement A 1 is right because nothing about entering a stranger's bedroom and handling their possessions requires a state credential; the only formal gates are OSHA hazard-communication training on chemical labels and a background check the employer runs, and if a guest's property goes missing the hotel's insurer and management absorb the claim, not you.
Executes defined procedures on defined inputs A 5 matches a job run on written room-cleaning sequences, brand standards for towel folds and amenity placement, and a supervisor's inspection checklist; the discretion you do hold — flagging a bed bug, deciding a stain needs the carpet team, choosing not to disturb a DND room — is real but escalates upward rather than being your call to close.
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 (18/20 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 (11/20) 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 17 of this occupation's 54 points (31%).
Embodiment (19/20) 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.
No occupation passed every test: close enough to maids and housekeeping cleaners on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
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 66/100, still EXPOSED.
Expansion of state hotel-housekeeping workload laws (e.g., NV SB 4-style panic-button and room-quota rules, Seattle I-124, LA's Hotel Worker Protection Ordinance capping square footage per shift and mandating overtime pay above it) that make the buyer pay explicitly for a named, assigned human per floor rather than a pooled algorithmic dispatch; plus continued growth of vetted-named-cleaner platforms in private homes where repeat-client matching is the product
Task-mix shift is limited but real where robotic floor scrubbers absorb the open-floor vacuuming tier (Tennant/Brain, Avidbots deployments in hotels and airports), leaving the residual job concentrated in edge-cleaning, bathrooms, linen handling and guest interaction — the tier robots handle worst. This raises resistance of what remains while reducing headcount
Formalizing housekeepers as the reporting node for guest-safety events — human-trafficking indicator training mandated for lodging staff (already law in CA AB 2034/ECPAT-style statutes and several state hotel licensing rules), infection-control sign-off after isolation-room turnover in healthcare/assisted-living cleaning, and documented chain-of-custody for lost-and-found and controlled items. If the room turnover record becomes an attestable document the cleaner signs, the role owns a consequential call
Certification-gated cleaning niches becoming mandatory rather than optional: EPA RRP lead-safe certification for pre-1978 dwellings already requires a certified individual, and state-level requirements for terminal-clean or biohazard/crime-scene remediation credentials (e.g., trauma-scene practitioner licensing in CA and FL) put a named certified person on the record. Broadening a healthcare-environmental-services credential requirement (AHE CHEST-style) into CMS conditions of participation would attach personal accountability to hospital cleaning
The limit. Even with every lever, the binding constraint is headcount, not capability: AI squeezes minutes-per-room and staffing ratios without touching the physical task, so the score can rise while employment falls. Trust premium is structurally capped in hotels, where the buyer never meets the cleaner; the private-home segment is where it lives, and it is the smaller share of these 860k jobs.
| New York-Newark-Jersey City, NY-NJ | 40,030 | $44,010 +24% |
| Los Angeles-Long Beach-Anaheim, CA | 34,440 | $43,530 +23% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 22,370 | $34,360 -3% |
| Las Vegas-Henderson-North Las Vegas, NV | 20,800 | $48,140 +36% |
| Chicago-Naperville-Elgin, IL-IN | 18,260 | $37,310 +5% |
| Orlando-Kissimmee-Sanford, FL | 17,810 | $35,150 -1% |
| Dallas-Fort Worth-Arlington, TX | 17,480 | $31,670 -11% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 15,860 | $37,840 +7% |
| Urban Honolulu, HI | 6,540 | $53,850 +52% |
| Kahului-Wailuku, HI | 2,260 | $53,030 +49% |
| San Jose-Sunnyvale-Santa Clara, CA | 5,620 | $48,200 +36% |
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 54. 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.