EXPOSED
Lecture prep, syllabus writing, quiz generation, literature reviews and first drafts of grant proposals and journal manuscripts are already within reach of current AI, and that is a real share of the week. What doesn't move: running teaching farms, greenhouses, soils and animal-science labs, supervising field trials and graduate research, and mentoring students who will be hired on your recommendation. No license gates the job, but accreditation, tenure structures and the land-grant extension mission slow displacement more than the task mix alone would suggest.
This fall is concentrated in 2020 and has not recovered since.
Median pay $83,260 → $98,700 -5.2% 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 8,920 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 +4.1% more of these jobs by 2034, and at 56/100 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.
~800 openings a year on average, including replacing people who leave.
ProfessorInstructorFaculty MemberFarm InstructorAgronomy TeacherPomology TeacherCollege ProfessorAdjunct InstructorAgronomy ProfessorFarm Crops TeacherIrrigation TeacherAgriculture TeacherAgronomy InstructorAssistant ProfessorAssociate ProfessorFisheries ProfessorSericulture TeacherViticulture TeacherFisheries InstructorFloriculture TeacherHorticulture ManagerHorticulture TeacherOlericulture TeacherSilviculture Teacher
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half the week — writing lecture slides on ruminant nutrition, generating exam banks, summarizing agronomy literature, drafting USDA/NIFA proposal boilerplate — is now first-draftable by a model, which is why this sits at 12 rather than 16; what holds the line is calibrating a soil-testing lab, diagnosing a sick calf in front of 20 students, and reading a graduate student's failing plot trial in person.
Some physical or field component At 12 rather than 4, this reflects real time in teaching greenhouses, livestock handling facilities, ag mechanics shops and rain-dependent research plots where you demonstrate AI breeding, tissue sampling or tractor PTO safety; it stays below 13 because the classroom, office hours and manuscript writing still consume most contact hours and the field work is on a departmental farm you control, not an uncontrolled site.
No licence, no signature requirement No state license, no board exam, and no personal signature standing behind your instruction — a 4 rather than 0 only because pesticide applicator certification, animal-care IACUC protocols and USDA lab compliance training create paperwork you must personally hold, none of which restricts who may teach the course.
Meaningful discretion A 13 covers deciding whether a thesis is defensible, whether a herd-health or pesticide demonstration is safe to run with undergraduates present, and how to grade contested capstone work — consequential calls you own, but bounded by curriculum committees, IACUC review, tenure-and-promotion criteria and accreditation standards that pre-decide much of the frame.
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 (12/20 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 (4/20) 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 (13/20) 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 32 of this occupation's 56 points (57%).
Embodiment (12/20) 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.
Chief Executives SAFE
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 70/100 — SAFE.
Task-mix shift: this job genuinely has two tiers. If syllabus/lecture/quiz/lit-review production is largely automated, what remains is field-trial design, IACUC and pesticide-safety protocol supervision, graduate committee work, and extension diagnosis of real producer problems — the judgment tier. Watch for institutions formally reallocating load away from content production toward lab/field supervision in workload policies.
If institutional policy names a faculty member of record as personally accountable for AI-assisted grading and academic-integrity determinations — as several university senates (e.g. Big Ten and California systems) have moved toward requiring a human instructor to render final grade and misconduct decisions — the role owns consequential, appealable calls rather than reviewing outputs.
If accreditation and program-review standards make supervised hands-on contact hours a hard requirement — e.g. state teacher-licensure rules for agricultural education (many states require documented Supervised Agricultural Experience/lab hours for ag-ed certification) or USDA/NIFA capacity-grant conditions tied to functioning teaching farms, greenhouses and animal units — the share of the week that must physically happen on-site rises and cannot be delivered by a model.
Narrow route only: research and extension roles that require named certified supervisors — IACUC animal-use protocol PI, restricted-use pesticide applicator license for teaching-farm operations, biosafety committee sign-off. If universities and insurers require a licensed/named individual (not a department) to certify these, the shield rises modestly. It will never resemble medicine or engineering.
If accrediting bodies or state ag-ed certification require that recommendation letters and competency attestations for licensure/employment come from a named human faculty supervisor with documented direct observation, employers keep paying specifically for the human's vouching.
The limit. Realistic ceiling is upper-60s/low-70s. There is no license gating the teaching itself, and the biggest downside pressure — enrollment decline and adjunctification in small ag programs — is an economic threat that no dimension here captures; a program can be cut regardless of how AI-resistant the work is.
| College Station-Bryan, TX | 530 | $126,050 +28% |
| Raleigh-Cary, NC | 380 | $102,560 +4% |
| Madison, WI | 240 | $98,920 +0% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 220 | $100,410 +2% |
| Auburn-Opelika, AL | 210 | $80,030 -19% |
| Lincoln, NE | 140 | $98,160 -1% |
| Portland-Vancouver-Hillsboro, OR-WA | 140 | $101,550 +3% |
| Sacramento-Roseville-Folsom, CA | 140 | $77,930 -21% |
| Fresno, CA | 30 | $136,710 +39% |
| New York-Newark-Jersey City, NY-NJ | 40 | $131,380 +33% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 110 | $129,920 +32% |
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 56. 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.