SAFE
The job is diapering, feeding, lifting, comforting, breaking up toddler conflicts, and keeping eyes on children who move unpredictably — none of which robotics can do and none of which parents would delegate to a machine. The automatable slice is thin: daily reports to parents, activity planning, attendance and billing paperwork, developmental checklists. Individual licensure is weak (CPR/first aid, background checks, state ratio rules protect the headcount more than the credential), and pay stays low, so the real threat to this job is wages and turnover, not AI.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $24,230 → $34,980 +15.5% 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
-2.9%
Percentage only. The projection counts a different population from the 518,910 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -2.9% by 2034. Whatever is shrinking this occupation, the evidence does not point to automation — demand, demographics, offshoring and industry decline all shrink jobs that no machine could do.
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.
~160,200 openings a year on average, including replacing people who leave.
NannySitterAttendantCaregiverGovernessNursemaidBabysitterBaby SitterParent AideDaycare AideTravel NannyCare ProviderChild MonitorChild's NurseDay Care AideHouse ManagerBaby AttendantCare AttendantChildcare AideDaycare WorkerFamily ManagerInfant TeacherNursery HelperNursery Worker
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Changing a soiled diaper, spoon-feeding a resistant one-year-old, and physically separating two biting toddlers are the bulk of the shift, and the only slices software touches are the daily sheet, the supply order, and the state-required developmental checklist — enough to shave minutes, not tasks.
Hands-on in uncontrolled environments You are on the floor, on your knees, carrying a 30-pound child on one hip while scanning a playground where nothing stays where you put it, and the environment changes every time a child vomits, bolts for the gate, or has a seizure.
Certification preferred, not legally required CPR/first-aid cards, a background check, and 12–24 hours of annual training are real requirements but they attach to the center's license, not to you — the director and the operator answer to the state licensing inspector, and your personal credential can be replaced in a two-week onboarding.
Meaningful discretion You decide in the moment whether a fever goes to the office, whether a bruise gets documented as a mandated-reporter concern, and when to let a conflict resolve itself — real calls, but ratios, nap schedules, illness-exclusion policies, and incident-report forms are written down and a director is down the hall for anything serious.
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 (17/20 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 (7/20) is whether the law requires a licensed human to sign. Trust premium (17/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) 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 34 of this occupation's 71 points (48%).
Embodiment (20/20) 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.
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 88/100, still SAFE.
Strengthened mandated-reporter statutes that name individual childcare staff (not just directors) as personally liable for failure to report suspected abuse or for injury reporting, as several states have moved toward post-scandal. This makes the worker the accountable observer of ambiguous signals — bruising, behavior change, custody disputes — rather than a logger of them.
Task-mix shift as report-writing, activity planning, attendance, billing and developmental checklists are absorbed by center management software with AI drafting (Brightwheel, Procare features already shipping). The residual job is pure unpredictable-body supervision and behavioral judgment — a thin routine tier, so the shift is small but real.
State licensing rules that attach the credential to the individual rather than the facility — e.g., states following the DC/Head Start route of requiring lead classroom staff to hold a CDA or associate degree, plus staff-level (not center-level) named responsibility on the licensing certificate. If a named credentialed adult must be documented as present per ratio and can personally lose that credential for a supervision lapse, the headcount becomes legally, not just operationally, mandatory.
Insurer-driven requirement: childcare liability carriers (a visibly tightening market — several carriers have exited or repriced center coverage) conditioning policies on documented human sightline supervision ratios and named supervisory staff per room, with camera/AI monitoring explicitly disallowed as a ratio substitute.
Inclusion mandates: state QRIS or IDEA-adjacent rules requiring centers to serve children with disabilities and behavioral needs, with named staff responsible for executing individualized behavior/feeding/medical plans (allergy epinephrine, seizure protocols, IFSP goals). Ownership of a consequential judgment call under ambiguity moves from the director to the room.
The limit. Trust premium is already near-max and has nowhere useful to go: parents already refuse machine caregivers, so no plausible change raises it. The binding constraint on this occupation is not AI at all — it is wages, turnover, and the childcare funding cliff. Credential-tightening levers that raise liability_shield can simultaneously shrink headcount by pricing out existing workers; a higher score here does not mean more jobs.
| New York-Newark-Jersey City, NY-NJ | 48,780 | $37,870 +8% |
| Chicago-Naperville-Elgin, IL-IN | 17,540 | $36,610 +5% |
| Los Angeles-Long Beach-Anaheim, CA | 16,530 | $39,890 +14% |
| Atlanta-Sandy Springs-Roswell, GA | 13,220 | $28,840 -18% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 11,650 | $37,610 +8% |
| Boston-Cambridge-Newton, MA-NH | 10,440 | $41,590 +19% |
| Houston-Pasadena-The Woodlands, TX | 8,690 | $29,530 -16% |
| Dallas-Fort Worth-Arlington, TX | 8,660 | $31,110 -11% |
| North Port-Bradenton-Sarasota, FL | 1,530 | $47,300 +35% |
| San Jose-Sunnyvale-Santa Clara, CA | 3,090 | $45,720 +31% |
| San Francisco-Oakland-Fremont, CA | 8,260 | $44,540 +27% |
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 71. 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.