EXPOSED
The bulk of an econ professor's week — building lecture decks, writing problem sets and exams, grading, summarizing literature, running regressions and drafting papers — is exactly the text-and-numbers work current models do at usable quality, and online sections are the most exposed of all. What persists is the live seminar, thesis and dissertation supervision, letters of recommendation, and being the named human who assigns a grade and defends a research claim. The real threat here is structural: enrollment pressure, adjunctification, and larger sections shrink headcount faster than AI replaces the teaching act itself.
This fall is concentrated in 2020 and has not recovered since.
Median pay $104,370 → $123,920 -5.0% 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
+2.1%
Percentage only. The projection counts a different population from the 11,560 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +2.1% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~1,200 openings a year on average, including replacing people who leave.
TeacherLecturerProfessorInstructorFaculty MemberAdjunct ProfessorCollege ProfessorFinance ProfessorEconomics LecturerAccounting LecturerAssistant ProfessorAssociate ProfessorEconomic InstructorEconomics ProfessorCollege Faculty MemberEconometrics ProfessorLabor Economics TeacherEconomics Faculty MemberMacroeconomics ProfessorMicroeconomics ProfessorLabor Economics ProfessorUniversity Faculty MemberAdjunct Economics ProfessorEconomic Adjunct Instructor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Micro/macro problem sets, multiple-choice midterms, Stata/R regression scripts and lit reviews are all reproducible by a model today, which is why this sits at 9 rather than mid-teens; the residue that holds — running a live 15-person seminar where you push back on a student's identification strategy, reading a dissertation chapter that isn't working yet, and writing the recommendation letter a hiring committee will actually weigh — is real but is a minority of contact hours in a 200-seat Principles course.
Some physical or field component The physical demand is standing in front of a lecture hall for three 50-minute sessions, working a chalkboard or clicker, and holding office hours — a controlled indoor environment with no equipment to handle, which is why this is closer to the desk end than to lab-science or clinical faculty who must be in a specific room with specific apparatus.
No licence, no signature requirement There is no state licence to teach economics; the credential is a PhD and a departmental hiring vote, and accreditors (SACSCOC, AACSB for b-schools) audit faculty qualifications at the program level, not by issuing you a personal ticket that can be pulled — so nothing legally requires a named human to deliver the content of ECON 101.
Meaningful discretion Grade curves, academic-integrity referrals, deciding whether a dissertation is defensible, and choosing what to claim from noisy identification are genuine discretion, but they run inside faculty handbooks, departmental grade norms and IRB/committee review, and a wrong call is appealed rather than catastrophic — that combination lands at 10, not at the level of someone whose single judgment ends a career or a life.
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 (9/20 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 (10/20) 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 27 of this occupation's 45 points (60%).
Embodiment (9/20) 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.
Law Teachers, Postsecondary EXPOSED
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 59/100, still EXPOSED.
Enforcement or tightening of the US Department of Education's 'regular and substantive interaction' requirement for distance education (34 CFR 600.2) — plus accreditor standards (SACSCOC 6.2, HLC) requiring a credentialed faculty of record who personally certifies grades and academic integrity findings — makes a named economist legally necessary for Title IV eligibility of every online section. Currently under active negotiated-rulemaking attention.
Institutional academic-integrity policy shifting AI-misuse adjudication onto the course instructor as first-instance decider with due-process exposure (already spreading in honor-code revisions post-2023); combined with grade-appeal regimes where the professor must defend a contested grade in writing.
Task-mix shift: if departments respond to AI-written problem sets by moving assessment to oral exams, in-person proctored finals, and supervised replication projects — as some econ departments have begun doing — the remaining day is live viva, thesis supervision, and case-by-case judgment rather than deck-building and grading. Genuine two-tier occupation.
Journal and funder authorship rules that bar AI as author and require a named human to attest to data provenance and results (COPE guidance, AEA Data and Code Availability Policy, NSF/NIH AI disclosure terms) — if replication-failure or research-misconduct proceedings begin attaching personal consequences to the signing economist for AI-generated regressions.
Employer- and grad-admissions-side reliance on named human letters of recommendation with verified authorship (e.g., econ PhD programs explicitly discounting or banning AI-drafted letters), plus AACSB/liberal-arts marketing of small human-taught seminars as the paid-for differentiator against AI tutors.
The limit. The binding constraint is not capability but headcount economics: adjunctification and section-size growth can cut positions even if every dimension above rises, since the levers protect the role of 'faculty of record' rather than the number of them. No plausible route raises embodiment.
| New York-Newark-Jersey City, NY-NJ | 930 | $132,140 +7% |
| Boston-Cambridge-Newton, MA-NH | 530 | $174,910 +41% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 360 | $155,060 +25% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 290 | $127,430 +3% |
| Chicago-Naperville-Elgin, IL-IN | 260 | $124,360 +0% |
| Los Angeles-Long Beach-Anaheim, CA | 250 | $137,740 +11% |
| Atlanta-Sandy Springs-Roswell, GA | 230 | $133,460 +8% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 170 | $103,430 -17% |
| Lexington-Fayette, KY | 30 | $220,690 +78% |
| College Station-Bryan, TX | 50 | $219,160 +77% |
| San Francisco-Oakland-Fremont, CA | 50 | $203,720 +64% |
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 45. 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.