EXPOSED
The paper layer of this job — lecture notes, syllabi, reading summaries, literature reviews, first-pass grading of undergraduate essays, artifact catalog descriptions — is already within reach of current models, and online intro sections are the most exposed part of the load. What holds is live seminar teaching, field school supervision at excavation sites, hands-on lab instruction in osteology and lithics, and thesis mentorship where a named human vouches for a student. The bigger near-term threat is not AI but shrinking humanities enrollment and adjunctification; the modal worker here is increasingly contingent, not tenured.
This fall is concentrated in 2020 and has not recovered since.
Median pay $86,220 → $99,650 -7.5% 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
+2.7%
Percentage only. The projection counts a different population from the 5,240 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 +2.7% more of these jobs by 2034, and at 51/100 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.
~500 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorCollege ProfessorPaleology TeacherAdjunct InstructorAssistant ProfessorAssociate ProfessorPaleology ProfessorAnthropology LecturerArchaeology ProfessorArcheology InstructorAnthropology ProfessorAnthropology InstructorArcheology Faculty MemberUniversity Faculty MemberEthnoarchaeology ProfessorAnthropology Faculty MemberAdjunct Anthropology LecturerAnthropology Department ChairAnthropology Adjunct ProfessorCultural Anthropology LecturerNear East Archeology ProfessorAdjunct Anthropology Instructor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An intro-to-cultural-anthropology lecture, its slide deck, its reading guide, and its multiple-choice midterm are all reproducible by a model today, but running a five-week field school where you decide which trench to open, teaching a student to distinguish a retouched flake from a natural break with the object in their hand, and directing a dissertation through IRB and committee revisions are not — that split is why this sits at 11 rather than down at 6.
Some physical or field component Field seasons put you in trenches with a trowel, total station, and screens in heat and rain, and lab sections mean handling human skeletal remains, ceramics, and lithics with students at the bench — but a large share of the teaching year is still classroom and office work, and many in this SOC line no longer run active excavations at all, which keeps this at 12 instead of the 16+ of a full-time field director.
No licence, no signature requirement There is no licensure board for anthropology instruction; the PhD is a hiring credential, not a legal one, and when a permit or NAGPRA compliance issue arises the liability sits with the institution, the state historic preservation office, or the permit-holding PI — the 3 reflects only that ARPA and NAGPRA obligations attach to named individuals on excavation permits.
Meaningful discretion You decide whether an undergraduate's field data are clean enough to publish, whether to halt a dig on encountering human remains and initiate consultation, and whether a candidate is ready to defend — real calls with consequences, but bounded by curriculum committees, permit conditions, and IRB review, which is why this reads 11 and not 15.
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 (3/20) 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 (11/20) 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 28 of this occupation's 51 points (55%).
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.
No occupation passed every test: close enough to anthropology and archeology teachers, postsecondary on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
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 66/100, still EXPOSED.
Genuine two-tier structure: intro survey lecturing and first-pass essay grading are the routine tier; the residue is method training in lithics/osteology, thesis supervision, and site interpretation under incomplete evidence. If institutions consolidate intro sections into AI-supported large formats while retaining faculty for methods and capstone supervision, the surviving job is denser in unautomatable work — but this raises the score of the role while cutting headcount.
Cultural resource management is the licensure-adjacent edge: Section 106 review reports and state permit applications already require a Secretary of the Interior-qualified principal investigator to sign. If SHPOs or the ACHP issue guidance requiring that signer to personally attest that AI-generated site descriptions, artifact catalogs and literature reviews were human-verified — mirroring the attestation language spreading in engineering and appraisal boards — the faculty who hold PI qualification gain a genuine, personal, non-delegable signature.
Field school accreditation tightening: if the Register of Professional Archaeologists / RPA-recognized field school standards or state SHPO permit conditions require a named, on-site supervisor with a fixed low student-to-supervisor ratio for excavation credit, the physically-present supervision hours become a mandated core of the job rather than an optional summer add-on. Same effect if NAGPRA repatriation consultation and osteological handling are restricted to in-person credentialed instruction.
IRB and NAGPRA governance load: if universities respond to AI-assisted research misconduct by requiring named faculty to personally certify the provenance and consent chain of every human-remains or ethnographic dataset used in coursework and theses, the ambiguous calls (repatriation disputes, descendant-community objections, whether a dataset is ethically usable) concentrate on the faculty member rather than the committee.
Thesis and dissertation vouching is the only durable premium: if graduate admissions and hiring committees formalize a requirement for named-human letters with verified direct supervision hours (as some AAA and SAA discussions of mentorship documentation suggest), the signature-of-a-known-scholar function becomes something students explicitly select programs for.
The limit. Every lever here raises the score of the surviving position without protecting the modal worker. The binding constraint is humanities enrollment decline and adjunctification, which AI exposure scores do not measure: liability and accreditation gains accrue to PI-qualified or tenured faculty, and contingent instructors teaching intro online sections capture none of them. A realistic combined ceiling is the mid-60s, and that ceiling describes a smaller occupation.
| New York-Newark-Jersey City, NY-NJ | 500 | $101,450 +2% |
| Boston-Cambridge-Newton, MA-NH | 190 | $106,480 +7% |
| Los Angeles-Long Beach-Anaheim, CA | 180 | $131,660 +32% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 140 | $105,960 +6% |
| Sacramento-Roseville-Folsom, CA | 130 | $129,380 +30% |
| Chicago-Naperville-Elgin, IL-IN | 100 | $81,050 -19% |
| Portland-Vancouver-Hillsboro, OR-WA | 100 | $105,240 +6% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 90 | $101,710 +2% |
| San Francisco-Oakland-Fremont, CA | 40 | $199,310 +100% |
| Riverside-San Bernardino-Ontario, CA | 40 | $159,680 +60% |
| Los Angeles-Long Beach-Anaheim, CA | 180 | $131,660 +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 51. 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.