EXPOSED
Much of the workload — building slide decks, writing syllabi and quiz banks, summarizing readings, drafting case studies, grading written assignments and giving feedback — is exactly what current models do at usable quality, and business content is the most heavily replicated subject matter on the internet. What survives is live classroom presence: running discussion-based case sessions, coaching student teams through consulting and capstone projects, and the alumni/recruiter network students are actually buying with tuition. No licensure protects the role, and the modal worker here is a contingent or non-tenure-track instructor teaching intro accounting, management, and marketing sections — the tier most exposed to larger sections, shared AI-built course shells, and online delivery.
Roughly flat across the period, with year-to-year wobble.
Median pay $87,200 → $99,080 -9.1% 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
+5.7%
Percentage only. The projection counts a different population from the 82,150 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 +5.7% 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.
~8,100 openings a year on average, including replacing people who leave.
TeacherLecturerProfessorInstructorFaculty MemberBanking TeacherFinance TeacherAdjunct LecturerBusiness TeacherBusiness LecturerCollege ProfessorFinance ProfessorMarketing TeacherAccounting TeacherAdjunct InstructorBanking InstructorBusiness ProfessorFinance InstructorAdvertising TeacherAssistant ProfessorAssociate ProfessorBookkeeping TeacherBusiness InstructorManagement Lecturer
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Intro accounting, management, and marketing sections run on standardized content that a model can draft end to end — problem sets, cases, rubrics, discussion prompts — but the Socratic case session, cold-calling on a room of 60, and reading when a capstone team is quietly failing keep this out of the single digits' bottom, hence 9 rather than 4.
Some physical or field component You are physically in a lecture hall or seminar room on a fixed schedule, walking between team tables during group work and running in-person office hours, but the room is climate-controlled, the equipment is a projector, and a growing share of these sections already deliver fully online — that's a scheduling and presence constraint, not a hands-on one.
No licence, no signature requirement No state licence, no bar or CPA requirement to teach the material, and accreditation bodies like AACSB set faculty-qualification ratios at the institutional level — so the school can meet them with fewer bodies, adjuncts, or professionally-qualified practitioners, and nothing attaches personal legal exposure to you for what you teach.
Meaningful discretion You set grade boundaries, handle academic-integrity accusations, decide whether a struggling student gets an incomplete, and choose what goes in the syllabus — genuine discretion, but bounded by department curricula, common finals, appeals committees, and the fact that a bad call gets reviewed rather than becoming irreversible.
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 (2/20) is whether the law requires a licensed human to sign. Trust premium (13/20) 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 25 of this occupation's 42 points (60%).
Embodiment (8/20) 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
Gambling Managers 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 57/100, still EXPOSED.
Genuine two-tier structure: if AI absorbs slide decks, quiz banks, and first-pass grading, the residual job is live case facilitation, capstone/consulting-project supervision with real client firms, and academic-integrity adjudication — none of which current models run unsupervised. Task-mix shift raises resistance without any new rule, though it also shrinks headcount.
Executive-education and MBA pricing already depends on a specific named human in the room (Wharton/HBS-style paid case teaching, corporate custom programs). If more revenue shifts to short-form exec-ed where the buyer is a corporate L&D budget choosing a person, the paid-for-human component of the occupation rises even as intro sections thin.
AACSB accreditation standards continue to require 'faculty qualifications' and 'sufficiency' ratios computed from human faculty engagement in each program, and employers/recruiters keep treating named-faculty case sessions and letters of recommendation as the signal they pay for. If AACSB adds an explicit standard limiting the share of instruction delivered without a human instructor of record, the premium concentrates further.
Instructor of record is the signer on grade appeals and academic-misconduct findings. If institutions adopt policies (as several Big Ten and Cal State campuses have drafted) requiring a named human faculty member to review and sign every AI-detection-based misconduct allegation and every contested grade, the role owns a consequential, appealable call under ambiguity.
Narrow route only: state authorization and regional accreditor rules (SACSCOC, HLC) that require a qualified human instructor of record for credit-bearing courses, plus Title IV federal 'regular and substantive interaction' rules for distance education, which the Department of Education has already used to disqualify courses lacking instructor-initiated interaction. If ED enforces RSI against AI-only delivery, a human of record becomes financially mandatory — institutional liability, not personal, so the lift is small.
The limit. No personal licensure exists or is proposed for postsecondary teaching, so liability_shield cannot reach professional-signature levels. The upside is concentrated in elite, accredited, and exec-ed segments; for contingent intro-section instructors teaching large shared-shell courses, none of these levers reaches them, and task-mix shift raises the score of the surviving job while reducing the number of jobs.
| New York-Newark-Jersey City, NY-NJ | 6,840 | $104,770 +6% |
| Boston-Cambridge-Newton, MA-NH | 3,250 | $103,110 +4% |
| Chicago-Naperville-Elgin, IL-IN | 2,450 | $93,670 -5% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,260 | $107,170 +8% |
| Los Angeles-Long Beach-Anaheim, CA | 2,050 | $128,470 +30% |
| Dallas-Fort Worth-Arlington, TX | 1,870 | $100,610 +2% |
| Baltimore-Columbia-Towson, MD | 1,590 | $107,120 +8% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 1,590 | $128,370 +30% |
| Wilmington, NC | 170 | $187,380 +89% |
| Lawrence, KS | 150 | $169,130 +71% |
| Anchorage, AK | 50 | $168,440 +70% |
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 42. 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.