EXPOSED
Math is the subject where current AI is strongest: problem-set generation, worked solutions, lecture-note drafting, and even much of homework grading are already handled at usable quality, and online courseware plus AI tutors compete directly with intro calculus and college algebra sections that make up the bulk of the teaching load. What persists is live classroom presence, diagnosing why a specific student is stuck, defending grades and academic-integrity calls, and curriculum ownership — plus, for the tenure-track slice, original research and doctoral advising. Employment pressure here comes less from AI replacing the professor than from institutions using AI-assisted courseware to consolidate large service sections into fewer, larger, more adjunct-staffed offerings.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $73,690 → $79,940 -13.2% 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
+2.3%
Percentage only. The projection counts a different population from the 47,670 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 +2.3% more of these jobs by 2034, and at 48/100 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.
~4,400 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorMath TeacherMath ProfessorAlgebra TeacherAdjunct LecturerBiometry TeacherCalculus TeacherGeometry TeacherTopology TeacherAdjunct ProfessorCollege ProfessorAdjunct InstructorCalculus ProfessorGeometry ProfessorStatistics TeacherTopology ProfessorAssistant ProfessorAssociate ProfessorMathematics TeacherCryptography TeacherMathematics LecturerStatistics Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Generating problem sets, worked solutions, and even proof-checking is where LLMs perform best, and multi-section calculus and college algebra courses run on standardized syllabi and item banks that transfer directly to courseware — what holds this at 10 rather than 5 is the live board work, reading a room of 200 students who have gone quiet, and the office-hours diagnosis of a student who can differentiate but cannot set up a word problem.
Some physical or field component Chalk-and-board lecturing to a scheduled room, proctoring in-person exams, and walking rows during recitation are physical and location-bound, which is why this is not a 2 — but the environment is a controlled campus classroom and the pandemic proved most of these sections can run over Zoom, so it does not reach the teens.
No licence, no signature requirement No state licence gates postsecondary math instruction; the credential is a PhD or master's plus departmental hiring, and an institution can and does staff sections with adjuncts, ABD grad students, or vendor courseware without any statutory barrier — the 4 reflects accreditation faculty-qualification rules (SACSCOC, HLC) that require a credentialed human of record, not personal liability.
Meaningful discretion Setting the curve, deciding whether a student who bombed the final but nailed everything else passes, and adjudicating academic-integrity referrals for Chegg or ChatGPT-copied solutions are consequential calls you defend to a dean — but rubrics, departmental common finals, and appeals procedures constrain most of it, which puts this at 11 rather than the high teens.
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 (10/20 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 (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 29 of this occupation's 48 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.
No occupation passed every test: close enough to mathematical science 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 60/100, still EXPOSED.
If AI-assisted homework makes unproctored take-home assessment worthless, the graded core shifts to in-person proctored exams, oral exams, and live board work — as several math departments (e.g. moves to oral/whiteboard qualifying-style assessment in intro sequences) have already begun. That remaining tier (diagnosing individual misconceptions in real time, defending integrity findings) is what AI cannot do, so the daily task mix becomes harder even with no capability regression.
If institutions formalize AI-cheating adjudication so the instructor of record must make and document the finding that goes to the honor council — and detector output is explicitly non-dispositive (the posture Turnitin's own guidance and multiple university senates have adopted) — the professor personally owns a consequential contested call under ambiguity.
If accreditors or state authorization rules require a human faculty member of record with documented 'regular and substantive interaction' for federal aid eligibility (the existing ED distance-education RSI rule, currently under renegotiation), AI courseware cannot be the instructor of record and the human becomes the thing being purchased.
If a faculty union contract caps section size and bars AI-only instruction of credit-bearing courses — the pattern in recent CSU/CFA and community-college bargaining over 'AI shall not replace faculty' language — a named human signature on grades becomes contractually mandatory rather than customary.
The limit. Liability here is institutional and accreditation-based, never personal malpractice, so the shield has a low ceiling. The dominant threat is section consolidation and adjunctification, which none of these levers reverse — they protect the role's content, not the headcount.
| New York-Newark-Jersey City, NY-NJ | 3,600 | $98,130 +23% |
| Chicago-Naperville-Elgin, IL-IN | 1,810 | $68,050 -15% |
| Los Angeles-Long Beach-Anaheim, CA | 1,400 | $131,070 +64% |
| Dallas-Fort Worth-Arlington, TX | 1,360 | $77,230 -3% |
| Boston-Cambridge-Newton, MA-NH | 1,320 | $86,510 +8% |
| Houston-Pasadena-The Woodlands, TX | 860 | $80,570 +1% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 830 | $82,390 +3% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 790 | $81,880 +2% |
| Riverside-San Bernardino-Ontario, CA | 340 | $150,550 +88% |
| Ann Arbor, MI | 310 | $137,720 +72% |
| Fresno, CA | 100 | $135,890 +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 48. 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.