EXPOSED
Lecture drafting, slide decks, exam item writing, syllabus updates, and first-pass grading of short answers are already handled well by current models, and much of the content-delivery layer of intro biology is commoditized. What resists is the wet lab — running dissections, microscopy, cell culture, and PCR benches with novices who break things — plus research mentorship, thesis supervision, and being the person a student trusts when they're deciding whether to go to grad school. The modal worker here is increasingly contingent (adjunct/lecturer) rather than tenured, and that tier is the most exposed: enrollment-driven, teaching-only, and easiest to substitute with larger sections plus AI-assisted course materials.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $83,300 → $84,620 -18.7% 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
+7.3%
Percentage only. The projection counts a different population from the 50,190 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 +7.3% more of these jobs by 2034, and at 53/100 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.
~5,400 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorBotany TeacherFaculty MemberLab InstructorBiology TeacherEcology TeacherZoology TeacherAlgology TeacherBiology LecturerBotany ProfessorCytology TeacherEtiology TeacherGenetics TeacherMycology TeacherTaxonomy TeacherAnatomy ProfessorBiology ProfessorCollege ProfessorLimnology TeacherMammalogy TeacherOsteology TeacherScience Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 11 reflects the split inside one job: the 200-seat intro-bio lecture, its slide deck, its multiple-choice bank, and its canned lab manual are all reproducible without you, while supervising an undergrad who just contaminated a cell line, calibrating a microtome, or reading a student's messy honors thesis draft for conceptual errors still requires you in the room — if you taught only lecture-hall genetics you'd sit near 6, if you ran a research lab full-time you'd sit near 15.
Some physical or field component 11 puts you above the desk-bound faculty because teaching lab means gloves on, autoclave running, dissection trays, fume hoods, chemical hygiene and BSL-1/2 protocols, plus field sections at a pond or forest plot — but it isn't a 16 because the environment is a controlled teaching lab with known reagents and a written protocol, not an unpredictable job site.
Certification preferred, not legally required 5/20 because a PhD plus departmental hiring is the only gate: no state licensure, no board certification, nothing that legally requires a credentialed human to deliver a biology course, and accreditation reviews credit hours and program outcomes rather than named individuals, so the institution can restructure who fronts a section at will.
Meaningful discretion 12 covers the calls you actually own — an academic-integrity report, a grade appeal, whether a student is ready to co-author, whether a protocol is safe for undergrads to run — but curriculum outcomes, prerequisites, and grading floors arrive from the department and program review, so the discretion is real but bounded rather than the unreviewed high-stakes judgment of a 17.
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 (11/20 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 (5/20) is whether the law requires a licensed human to sign. Trust premium (14/20) 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 31 of this occupation's 53 points (58%).
Embodiment (11/20) 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.
Physician Assistants 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 69/100 — SAFE.
Task-mix shift: if lecture/assessment production is fully absorbed by AI course-material platforms, the residual job concentrates in wet-lab instruction, biosafety-level supervision, undergraduate research mentorship, and IACUC/IBC protocol training — tiers current models cannot execute. Watchable signal: department job postings retitled toward 'laboratory coordinator / research mentor' with lecture hours reduced.
Narrow route only: graduate admissions and fellowship committees continue to weight named-human recommendation letters and PI mentorship, so thesis-supervising faculty retain a premium buyers actively pay for. This does not extend to the contingent intro-lecture tier, where no realistic premium exists.
Biosafety and animal-use compliance is the one real hook: institutional rules requiring a named credentialed instructor of record to sign BSL-2 protocols, chemical hygiene plans, and IACUC training attestations, with OSHA/NIH-guideline exposure attaching personally. If NIH or a state adopts explicit individual-signatory requirements for teaching-lab biosafety oversight, this rises. Also watch faculty-of-record signature requirements for transfer credit and Title IV eligibility.
If accreditors and licensing pipelines (e.g. nursing programs under CCNE/ACEN, pre-med prerequisite rules, MCAT-feeder requirements) hold firm that biology lab credit requires in-person hands-on hours rather than virtual simulation, the physical lab-supervision share of the job is protected. Watch state nursing boards and ABET/regional accreditor rulings on online lab equivalency, which currently vary.
If institutions formalize the instructor as the accountable adjudicator for AI-assisted academic integrity findings and for competency sign-off on lab practicals — a named human decision that cannot be delegated to detection software, as several university senates have been drafting since 2023 — the ambiguous-call ownership becomes explicit rather than informal.
The limit. The levers protect the lab-and-mentorship core, not the modal adjunct. Enrollment economics cap the upside: departments facing demographic decline consolidate sections regardless of what any dimension score says, and biosafety-signatory duties can be concentrated in one or two faculty per department rather than spread across the headcount.
| New York-Newark-Jersey City, NY-NJ | 3,070 | $98,250 +16% |
| Chicago-Naperville-Elgin, IL-IN | 1,760 | $101,960 +20% |
| Boston-Cambridge-Newton, MA-NH | 1,380 | $128,070 +51% |
| Los Angeles-Long Beach-Anaheim, CA | 1,250 | $127,800 +51% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,070 | $95,260 +13% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 960 | $82,520 -2% |
| Houston-Pasadena-The Woodlands, TX | 820 | $100,430 +19% |
| Dallas-Fort Worth-Arlington, TX | 650 | $82,470 -3% |
| San Diego-Chula Vista-Carlsbad, CA | 570 | $166,850 +97% |
| Madison, WI | 210 | $142,490 +68% |
| Riverside-San Bernardino-Ontario, CA | 230 | $138,120 +63% |
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 53. 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.