SAFE
The core of this job is physically supervising, calming, feeding, toileting, and redirecting a room of three- and four-year-olds — none of which is screen work and none of which current robotics touches. AI can draft lesson plans, generate activity ideas, write parent newsletters, and organize developmental observation notes, which trims paperwork hours but not staffing. State child-care licensing sets adult-to-child ratios and minimum credentials (CDA or associate/bachelor's depending on state), so a warm body with a background check is legally mandatory regardless of software.
Dipped in 2020, then grew past where it started.
Median pay $30,520 → $38,140 -0.0% 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
+4.1%
Percentage only. The projection counts a different population from the 478,780 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Hard to automate, and growing
The work resists current AI and the BLS projects +4.1% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.
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.
~65,500 openings a year on average, including replacing people who leave.
TeacherGroup TeacherStart TeacherInfant TeacherDaycare TeacherNursery TeacherToddler TeacherDay Care TeacherChildcare TeacherPreschool TeacherMontessori TeacherPre-School TeacherAfter School TeacherNursery School TeacherEarly Childhood TeacherPrekindergarten TeacherEarly Childhood EducatorChild Development TeacherMontessori ParaprofessionalChild Care Assistant TeacherHome-Based Preschool TeacherMontessori Preschool TeacherSubstitute Preschool TeacherChildhood Development Teacher
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Zipping coats, wiping noses, breaking up a block-tower dispute, and reading a picture book to twelve kids who are all touching each other are the actual hours of the day; what AI can take is the lesson-plan template, the weekly parent email, and the typing-up of anecdotal records — maybe 4-6 hours a week out of a 40-hour job, which is why this sits at 16 and not 20.
Hands-on in uncontrolled environments Diapering and toilet-accident cleanup, lifting a thrashing 35-pound child away from a hazard, outdoor playground supervision in weather, floor-level circle time, and knowing by touch that a forehead is too warm — all in a room where the environment is defined by unpredictable small humans, which is as uncontrolled as workplaces get short of disaster response.
Certification preferred, not legally required State child-care licensing requires a credential (CDA, associate, or in some states bachelor's), a background check, and mandated-reporter training, and you can be personally named in a licensing violation — but it is a facility license and a director's license on the line, not an individually revocable practice license like an RN or attorney, which caps this at 9.
Meaningful discretion You decide in real time whether a bruise pattern triggers a CPS call, whether a bite is developmental or a pattern to document, and whether to flag a speech delay to parents who don't want to hear it — genuinely consequential calls, but framed by state licensing rules, curriculum frameworks, and a director you can escalate to, which keeps it at 12 rather than the high teens.
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 (16/20 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 (9/20) 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 (12/20) 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 38 of this occupation's 73 points (52%).
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.
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 85/100, still SAFE.
State licensing rules that tighten ratio and credential floors — e.g. states adopting NAEYC-aligned 1:8 ratios for four-year-olds, or moving from CDA-acceptable to mandatory associate degree for lead teachers (as DC's 2016 rule did) — plus universal pre-K statutes (NY, CA TK, NM) that write certified-lead-teacher-per-classroom into funded program rules. Also mandatory-reporter status for suspected abuse, which attaches personal duty to a named adult.
Task-mix shift as AI absorbs the paperwork tier (lesson plans, observation write-ups, portfolio assembly, parent communications, state QRIS documentation), leaving a day that is almost entirely in-body supervision, conflict mediation, and family relationship work. The residual job is the tier machines do not touch.
Formal assignment of developmental screening and referral duty to the lead teacher under IDEA Part B/C Child Find — where the classroom teacher signs the screening instrument (ASQ-3, DIAL-4) that triggers evaluation, and owns suspension/expulsion decisions under state early-childhood expulsion-ban laws that require documented behavioral intervention before removal.
Parent demand shifting further toward low-screen, high-adult-contact models — Waldorf/Montessori/forest-school enrollment growth and state rules capping screen time in licensed care (several states limit screen media for under-2s and cap it for preschoolers) make human presence the explicit product being purchased.
The limit. Already 73 with embodiment at 19 and trust at 17; those are near max. The binding constraint on this occupation is wages and turnover, not displacement — the register does not score that, so a high score here should not be read as a good job.
| New York-Newark-Jersey City, NY-NJ | 28,140 | $50,230 +32% |
| Los Angeles-Long Beach-Anaheim, CA | 18,430 | $46,600 +22% |
| Chicago-Naperville-Elgin, IL-IN | 17,180 | $43,050 +13% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 15,260 | $36,750 -4% |
| Boston-Cambridge-Newton, MA-NH | 14,500 | $46,030 +21% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 11,910 | $44,660 +17% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 11,650 | $36,380 -5% |
| Dallas-Fort Worth-Arlington, TX | 11,100 | $35,190 -8% |
| Vineland, NJ | 330 | $63,340 +66% |
| Albany, GA | 400 | $62,680 +64% |
| Atlantic City-Hammonton, NJ | 460 | $59,320 +56% |
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 73. 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.