EXPOSED
Lecture drafting, problem-set generation, worked solutions, and syllabus writing are already commodity AI output, and intro-physics content is the most thoroughly pre-solved material on the internet. What holds is the embodied part: running lab sections with real optics benches and oscilloscopes, diagnosing why a student's mental model of angular momentum is wrong in real time, and supervising undergraduate research. The real pressure on this occupation is structural — enrollment shifts, large-section consolidation, and adjunct staffing — more than a model replacing a professor outright.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $89,590 → $100,310 -10.4% 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
+2.5%
Percentage only. The projection counts a different population from the 13,090 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 +2.5% 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.
~1,300 openings a year on average, including replacing people who leave.
TeacherLecturerProfessorInstructorPhysics LecturerAcoustics TeacherAdjunct ProfessorCollege ProfessorPhysics ProfessorScience ProfessorAdjunct InstructorBallistics TeacherNuclear InstructorPhysics InstructorScience InstructorAssistant ProfessorAssociate ProfessorAerodynamics TeacherAstrophysics TeacherBallistics ProfessorHydrodynamics TeacherAerodynamics ProfessorAstrophysics ProfessorAtomic Physics Teacher
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier At 12 rather than 6, the tasks that resist are the ones that happen with a student in front of you — watching someone set up an interferometer wrong and knowing whether to intervene, reading a confused derivation on a whiteboard mid-office-hour, advising a senior thesis on a problem with no textbook answer — but the bulk-hours work of writing lecture notes on Gauss's law, generating and grading Physics 101 problem sets, and building the syllabus is now near-instant output, which is what keeps this out of the 14+ band.
Some physical or field component A 10 reflects that the lecture hall and grading queue are screen-substitutable, but lab sections are not: aligning optics on a rail, checking that a student wired a scope probe to ground and not to the 120V side, calibrating force sensors, and enforcing laser and radiation-source safety are hands-on in a room where students break things, and machine-shop or cryogenics access in research supervision pushes it higher than pure classroom teaching would.
No licence, no signature requirement There is no state physics-teaching licence at the postsecondary level — the credential is a PhD and a department hiring committee, and an institution can and does staff the same PC1010 section with an adjunct, a postdoc, or a graduate TA, so the 3 reflects that nothing statutory prevents substituting who or what delivers the course.
Meaningful discretion At 12, the discretionary calls are real and unappealed — deciding whether a lab report shows fabricated data, setting a curve that determines who fails and loses financial aid, judging whether an undergraduate is ready to run an experiment unsupervised — but the accreditation-driven learning outcomes, departmental common finals, and appeals processes bound those decisions in a way that keeps them below the 14+ band.
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 (12/20 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 (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 29 of this occupation's 51 points (57%).
Embodiment (10/20) 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 physics 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 63/100, still EXPOSED.
If content delivery (lecture, problem sets, worked solutions) is fully offloaded to AI/OER and the remaining assigned load becomes lab supervision, oral exams, and research mentoring, the residual job is the judgment tier. Watch for departments formally restructuring intro courses into 'studio physics'/SCALE-UP models where contact hours are lab and coaching rather than lecture — already done at NC State, MIT TEAL, and spreading via AAPT.
If institutions make faculty the named accountable party for AI-assisted grading and academic-integrity determinations — e.g. honor-code rules requiring a human instructor of record to personally adjudicate AI-detection flags and sign the sanction, plus radiation/laser safety officer responsibility for undergrad lab and research supervision under state and NRC/OSHA rules — the role owns consequential calls with personal exposure.
If accreditation or program review makes hands-on wet-lab hours non-substitutable — e.g. ABET-accredited engineering physics sequences and state articulation agreements refusing to accept simulation-only lab credit, as several state systems tightened after the 2020-21 remote-lab expansion — the lab-supervision core becomes a fixed physical requirement.
If a visible tier of students/parents pays specifically for small-section human instruction as a differentiator (liberal-arts colleges marketing 'no AI-taught courses', faculty-senate policies requiring disclosure when course content is AI-generated — such disclosure resolutions have already been passed at several campuses), the human-taught section becomes the priced product.
If radiation-source and Class 3B/4 laser licensing requires a named, credentialed responsible individual per NRC/Agreement-State rules for teaching labs, that creates a narrow personal-signature requirement — but it attaches to a designated safety officer, not to physics faculty generally, so the route is real but small.
The limit. The binding constraint is budgetary, not technical: large-section consolidation and adjunctification cut headcount regardless of how high these dimensions go. Raising task_resistance and judgment_accountability protects the work but not the number of jobs.
| New York-Newark-Jersey City, NY-NJ | 870 | $104,690 +4% |
| Boston-Cambridge-Newton, MA-NH | 510 | $128,640 +28% |
| Los Angeles-Long Beach-Anaheim, CA | 440 | $172,510 +72% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 340 | $95,430 -5% |
| Chicago-Naperville-Elgin, IL-IN | 330 | $83,830 -16% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 290 | $103,750 +3% |
| Houston-Pasadena-The Woodlands, TX | 230 | $99,480 -1% |
| Austin-Round Rock-San Marcos, TX | 220 | $100,430 +0% |
| Los Angeles-Long Beach-Anaheim, CA | 440 | $172,510 +72% |
| College Station-Bryan, TX | 80 | $167,090 +67% |
| San Diego-Chula Vista-Carlsbad, CA | 120 | $152,190 +52% |
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.