SAFE
Roughly half of this job is content work AI already does well — lecture slides, syllabi, test-item banks, case scenarios, rubric drafts, accreditation report prose. The other half is standing on a hospital floor watching a student push a medication and deciding in real time whether it is safe, then owning the pass/fail call that determines who enters the profession. State boards of nursing and accreditors (ACEN/CCNE) require licensed RNs with graduate degrees as faculty and cap clinical student-to-instructor ratios, which is a hard regulatory floor on headcount.
Headcount grew steadily across the period.
Median pay $74,600 → $80,250 -13.9% 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
+16.8%
Percentage only. The projection counts a different population from the 77,960 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 +16.8% 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.
~8,600 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorFaculty MemberNurse EducatorNurse InstructorNursing ProfessorAdjunct InstructorNursing InstructorAssistant ProfessorAssociate ProfessorClinical InstructorNurse Aide InstructorNurse's Aides TeacherNursing Faculty MemberPractical Nursing TeacherAdjunct Nursing InstructorAdvanced Nursing ProfessorClinical Nursing ProfessorNursing Assistants TeacherClinical Nursing InstructorNursing Assistant ProfessorNursing Assistant InstructorPractical Nursing Instructor
Holding it up: trust premium . Weakest point: task resistance .
Mixed — a routine tier and a judgment tier A 13 rather than a 16 reflects that lecture prep, NCLEX-style item writing, care-plan case studies, and skills-checkoff paperwork are genuinely draftable by machine, while direct supervision of eight students on a med-surg unit — catching a wrong insulin draw before it reaches the patient, coaching a first sterile Foley insertion, running a mock code in the sim lab and debriefing it — has no digital substitute.
Hands-on in uncontrolled environments Clinical rotations put you on hospital floors, in ORs and OB suites, in long-term care, standing for eight- to twelve-hour shifts under the same infection-control, lifting, and needle-stick conditions as staff nurses, plus hands-on sim-lab work positioning manikins and demonstrating on real bodies; it lands at 14 rather than 18 because a meaningful share of the week is still classroom and office.
Licensed human required and personally liable You must hold an active RN licence (usually MSN or doctorate) to be faculty at all, your licence is on the line when a student under your supervision harms a patient, and state boards plus ACEN/CCNE dictate clinical group size caps — a 14 rather than 18 because the facility and the employing college absorb much of the malpractice exposure and you rarely sign as the patient's nurse of record.
Exists to be accountable for ambiguous calls You decide whether a student who made a medication error is remediated or failed out, whether someone is safe to progress to preceptorship, and whether to pull a student off an assignment mid-shift — gatekeeping calls with a patient-safety consequence and an appeal-and-due-process trail, sitting at 15 because program handbooks and clinical evaluation tools structure the decision even when the facts are ambiguous.
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 (13/20 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 (15/20) is whether buyers specifically pay for a person. Judgment and accountability (15/20) 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 44 of this occupation's 71 points (62%).
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.
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 83/100, still SAFE.
Task-mix shift: once slide decks, item banks, syllabi and accreditation prose are drafted by AI as a matter of course, the residual job concentrates on bedside observation, remediation of failing students, and defensible pass/fail documentation — the tier that carries litigation risk. This raises task_resistance without any new rule, and is already visible in nursing programs adopting AI content generation while clinical hours stay untouched.
State boards of nursing tightening faculty rules so that clinical supervision ratios (commonly 1:8-1:10) are written as hard caps with named, licensed instructor-of-record attestation per student per rotation — and explicit language that AI/simulation platforms cannot substitute for the supervising RN's signature on competency validation. NCSBN's model rules and periodic state board revisions are the visible venue; several states already require a named faculty member of record for each clinical group.
Accreditor (ACEN/CCNE) standards revised to require faculty attestation that AI-generated test items, rubrics, and simulated case content were reviewed by a qualified nurse educator before use in high-stakes evaluation — moving AI output into a countersign regime rather than a free substitute.
Continued growth in student due-process litigation over clinical dismissal (a live and expanding area in nursing programs) making the documented, individually-owned failure decision the legally load-bearing act, with faculty deposed as the decision-maker. If institutions respond by formalizing a named-faculty adjudication role rather than committee diffusion, this rises.
State board caps on the share of clinical hours substitutable by simulation (many states cap at 25-50% post-NCSBN simulation study) being held or tightened rather than relaxed, keeping a floor of physical hospital-floor supervision hours per student.
The limit. Trust premium has no realistic route up: the buyer is a program administrator managing a documented faculty shortage and cost pressure, not a student choosing a human teacher. Faculty vacancy rates create pressure in the opposite direction — toward waivers, larger ratios, and more simulation substitution — so the liability and embodiment levers here are as likely to be loosened as tightened.
| New York-Newark-Jersey City, NY-NJ | 5,290 | $92,050 +15% |
| Chicago-Naperville-Elgin, IL-IN | 3,840 | $80,440 +0% |
| Los Angeles-Long Beach-Anaheim, CA | 2,350 | $79,680 -1% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,050 | $81,040 +1% |
| Houston-Pasadena-The Woodlands, TX | 1,580 | $103,010 +28% |
| Boston-Cambridge-Newton, MA-NH | 1,560 | $83,550 +4% |
| Phoenix-Mesa-Chandler, AZ | 1,420 | $71,740 -11% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 1,310 | $86,030 +7% |
| Waco, TX | 60 | $206,810 +158% |
| San Jose-Sunnyvale-Santa Clara, CA | 140 | $150,400 +87% |
| Fresno, CA | 110 | $135,080 +68% |
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.