EXPOSED
Lecture slides, problem sets, solution keys, syllabi, grant boilerplate and literature reviews are already produced at usable quality by current models, and much of an engineering professor's week is exactly that. What resists is running a physical lab, supervising senior design and thesis work, judging which research directions are worth funding, and being the named mentor whose recommendation letter and advising a student's career depends on. Scored for the modal tenure-track/teaching-faculty worker; adjunct lecturers teaching high-enrollment intro courses sit meaningfully lower, since online AI-assisted course delivery attacks that tier first.
Headcount grew steadily across the period.
Median pay $101,010 → $109,270 -13.5% 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
+8.1%
Percentage only. The projection counts a different population from the 40,270 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 +8.1% more of these jobs by 2034, and at 52/100 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.
~4,100 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorDesign TeacherMining TeacherDrafting TeacherAdjunct ProfessorCollege ProfessorSurveying TeacherAdjunct InstructorDrawing InstructorHydraulics TeacherMetallurgy TeacherResearch ProfessorAeronautics TeacherAssistant ProfessorAssociate ProfessorElectronics TeacherEngineering TeacherRobotics InstructorShip Design TeacherTechnical ProfessorEngineering LecturerEngineering Professor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Statics problem sets, thermo lecture decks, ABET syllabus mapping and NSF broader-impacts boilerplate are the recurring deliverables of a semester and a model drafts them at usable quality, which caps this at 11; what holds it above the automatable band is standing in the lab when a student's tensile rig is misconfigured, scoping a senior design project that must actually build, and calibrating how hard to push a struggling MS candidate.
Some physical or field component You are in the machine shop, the wind tunnel, the clean room or the concrete lab several hours a week — signing off on instrument use, spotting the unsafe fixture, running the demo that doesn't work on the projector — but grading, proposal writing, committee service and lecture prep are screen work, which is why this lands at 10 rather than the 15+ of a field engineer.
No licence, no signature requirement No licence gates the classroom: most engineering faculty never hold or maintain a PE, ABET accredits the program rather than certifying the individual instructor, and nothing you sign carries personal legal exposure — the 3 reflects only the institutional gatekeeping of the PhD and tenure review, not any statutory protection of the work itself.
Meaningful discretion You decide which research direction to spend three years and a graduate stipend on, whether a thesis is defensible, whether an academic-integrity case goes to the dean, and how a senior design team's failing prototype is graded — real ambiguous calls with career consequences for others, though bounded by department curricula, promotion committees and IRB/EHS review rather than borne alone, which is what keeps it at 13.
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 (15/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) 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 31 of this occupation's 52 points (60%).
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.
Civil Engineers 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 75/100 — SAFE.
Genuine two-tier structure: if slide decks, problem sets, solution keys and lit reviews are absorbed, the residual week is hands-on lab supervision, senior design review, thesis defense judgment, and lab safety sign-off — tasks with no current substitute. The score rises mechanically as the routine tier leaves, without any new rule.
Institutional shift to proctored, oral, and bench-based assessment (oral qualifying exams, in-person design juries, lab practicals) in response to AI-assisted cheating, moving assessment labor into forms that require a present expert examiner.
Expansion of the role's gatekeeping function: serving as named PI on grants where agencies (NIH/NSF) require human attestation that proposals and reviews were not AI-generated — NSF already bars generative AI in merit review — plus personal accountability for research-integrity and authorship decisions in supervised student work.
ABET accreditation criteria (EAC) tightening to require named, credentialed faculty of record to personally attest to student outcome assessment and to certify that capstone/design coursework was evaluated by a human — plus PE-licensure boards (NCEES) requiring that engineering coursework counting toward licensure be taught and graded by identified faculty, not AI-delivered modules. ABET already requires 'sufficient qualified faculty'; an explicit attestation clause is a checkable next step.
Regional accreditor (SACSCOC/MSCHE) substantive-change rules or state authorization law defining AI-generated instruction as a change requiring approval, with a licensed/credentialed instructor of record legally responsible for academic integrity determinations and grade appeals — currently the weakest link, as grade disputes already land on the named instructor.
Growth of federally funded hands-on infrastructure (CHIPS Act workforce programs, NSF Engineering Research Centers) shifting faculty effort toward cleanroom, fab, wind-tunnel and machine-shop instruction where an accountable human must be physically present for equipment authorization and OSHA/EH&S lab safety compliance.
Employers and graduate admissions continuing to weight named-faculty recommendation letters and identified thesis advisors, with letter-writer identity verification becoming explicit as AI-written letters proliferate; if professional societies (IEEE, ASME) or graduate schools formalize verified human-referee requirements, the premium attaches to the person rather than the course.
The limit. Two structural caps. First, the adjunct/intro-lecture tier — high-enrollment statics, circuits, thermo — has no realistic route to any of these shields; enrollment-driven cost pressure and existing online-course precedent point the other way, and gains here accrue to tenure-track and lab-owning faculty only. Second, headcount is set by enrollment and state appropriations, not by task resistance: even a fully shielded role can shrink if the department consolidates sections. Liability shield realistically tops out around 8-10; unlike a PE stamp or a medical license, nobody is personally sued for a bad lecture.
| New York-Newark-Jersey City, NY-NJ | 2,600 | $123,210 +13% |
| Boston-Cambridge-Newton, MA-NH | 2,010 | $135,480 +24% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,700 | $79,210 -28% |
| College Station-Bryan, TX | 1,160 | $129,110 +18% |
| Los Angeles-Long Beach-Anaheim, CA | 1,130 | $137,760 +26% |
| Pittsburgh, PA | 760 | $77,870 -29% |
| Ann Arbor, MI | 710 | $140,450 +29% |
| Austin-Round Rock-San Marcos, TX | 630 | $132,130 +21% |
| San Francisco-Oakland-Fremont, CA | 230 | $175,200 +60% |
| Riverside-San Bernardino-Ontario, CA | 90 | $163,110 +49% |
| San Jose-Sunnyvale-Santa Clara, CA | 520 | $154,760 +42% |
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 52. 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.