SAFE
The core job is physically supervising and socializing twenty-plus five-year-olds — toileting accidents, tying shoes, breaking up shoving matches, reading body language for a child who is hungry or scared — and none of that is screen work. AI already writes lesson plans, generates differentiated worksheets, drafts parent newsletters, and scores early-literacy assessments, which trims prep hours but not contact hours. Public-school employment requires a state teaching license and the teacher is legally the responsible adult for child safety, so the role can't be unbundled to software.
Most of this decline happened after 2021 — it is not the pandemic dip.
Median pay $56,850 → $62,680 -11.8% 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
-1.6% 117,200 → 115,200 on the projections basis
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -1.6% 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.
~12,800 openings a year on average, including replacing people who leave.
TeacherEducatorInstructorArt TeacherKinder TeacherClassroom TeacherMontessori TeacherEarly Childhood TeacherPhysical Fitness TeacherBilingual Education TeacherPrivate Kindergarten TeacherBilingual Kindergarten TeacherKindergarten Classroom TeacherTitle One Kindergarten TeacherTransitional Kindergarten TeacherPhysical Education Teacher (PE Teacher)Long Term Substitute Kindergarten Teacher
Holding it up: embodiment . Weakest point: judgment & accountability .
Tasks largely resist digitisation Teaching a five-year-old to hold a pencil, sit on a carpet square, take turns, and sound out CVC words happens through physical modeling, hand-over-hand correction, and 180 days of repetition with a body in the room — the 15 rather than 19 reflects that lesson planning, worksheet differentiation, DIBELS/running-record scoring, and report-card comment generation are genuinely being handed to software.
Hands-on in uncontrolled environments You are on your knees at child height most of the day, walking a line of twenty to the cafeteria and back, managing recess in the cold, cleaning up vomit and bathroom accidents, and physically separating children who are biting — a classroom of five-year-olds is an uncontrolled environment even inside four walls, which is what puts this at 17.
Licensed human required and personally liable A state teaching license with early-childhood endorsement is a hard condition of public-school employment, and you are the named mandated reporter under state child-abuse statutes and the responsible adult for headcount during fire drills and dismissal — the 14 rather than 18 is because the district and principal absorb most negligence exposure rather than you personally carrying malpractice risk like a physician.
Meaningful discretion You decide daily whether a child's behavior is developmental or a referral for evaluation, whether a bruise gets reported, and whether to hold a child at the reading level or push them — real discretion, but a 12 rather than 16 because state standards, district pacing guides, and IEP/RTI teams constrain and share the biggest calls.
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 (14/20) 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 43 of this occupation's 75 points (57%).
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.
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 86/100, still SAFE.
Formal codification of the kindergarten teacher as the accountable decision-maker in early-intervention referrals — IDEA Child Find and MTSS/RTI processes increasingly require a named general-education teacher to own the referral call and defend it in an eligibility meeting; if districts adopt AI screening tools, board policy that a licensed teacher must override or ratify the algorithm's retention/referral recommendation puts the consequential call formally on the teacher
Growth in parent demand for explicitly screen-free early education (Waldorf, Montessori, and the 'phone-free/screen-free childhood' movement) plus district policies banning screen time in K classrooms — this converts human-only instruction into a marketed, paid-for feature rather than a default
Task-mix shift as AI absorbs lesson planning, differentiation, assessment scoring and parent communication, leaving the residual job almost entirely as supervision, socialization, and behavioral triage — the tier machines cannot reach. This raises resistance without any new law, though it may also shift hours-per-pupil and thus headcount
State child-care/education codes tightening adult-to-child ratio and 'responsible licensed adult' requirements for K classrooms, plus mandated-reporter duties expanding to require a named licensed teacher to review and sign any AI-flagged behavioral or developmental concern before it enters a student record; several states already legislate K class-size caps and some (e.g., Tennessee, Texas) have moved on AI-use disclosure rules in schools
The limit. Already 75/100; the realistic ceiling is high-80s. The binding risk for this occupation is not capability but budget and enrollment — declining K enrollment, universal-pre-K reshuffling, and ratio deregulation cut headcount without any AI doing the job. A high score here does not mean a stable number of positions.
| New York-Newark-Jersey City, NY-NJ | 8,180 | $77,400 +23% |
| Chicago-Naperville-Elgin, IL-IN | 3,350 | $70,250 +12% |
| Atlanta-Sandy Springs-Roswell, GA | 2,650 | $75,690 +21% |
| San Juan-Bayamon-Caguas, PR | 2,610 | $61,760 -1% |
| Seattle-Tacoma-Bellevue, WA | 2,530 | $101,580 +62% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 2,420 | $57,300 -9% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,400 | $77,200 +23% |
| Phoenix-Mesa-Chandler, AZ | 2,310 | $56,920 -9% |
| El Centro, CA | 50 | $111,880 +78% |
| Sacramento-Roseville-Folsom, CA | 740 | $106,000 +69% |
| Modesto, CA | 160 | $103,790 +66% |
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 75. 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.