EXPOSED
The core of the job is studio: standing over a student's drawings and physical models, giving desk crits, running pin-ups and juries, and teaching hand and digital craft in a shared space — none of which current AI or robotics can do. What AI does absorb is the surrounding paperwork: lecture decks, syllabi, history/theory reading summaries, precedent research, rubric-based written feedback, and grant and accreditation narrative drafting. The real pressure on this occupation is budgetary, not technological: enrollment in architecture programs and the adjunctification of teaching load determine headcount more than automation does.
Dipped in 2020, then grew past where it started.
Median pay $87,900 → $96,870 -11.8% 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%
Percentage only. The projection counts a different population from the 7,700 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% more of these jobs by 2034, and at 58/100 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.
~900 openings a year on average, including replacing people who leave.
LecturerProfessorInstructorFaculty MemberAdjunct ProfessorCollege ProfessorAdjunct InstructorAssistant ProfessorAssociate ProfessorArchitecture ProfessorCollege Faculty MemberArchitecture InstructorInterior Design ProfessorUniversity Faculty MemberInterior Design InstructorArchitecture Faculty MemberArchitectural Design ProfessorInterior Design Faculty MemberLandscape Architecture TeacherLandscape Architecture ProfessorArchitectural Drafting Instructor
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Desk crits on an in-progress model, mid-review course corrections, and teaching a student to see why their section is lying require reading unfinished work in real time — but the same person also writes structures lecture notes, grades codes-and-materials quizzes, and assembles precedent decks, and that half of the load is already draftable, which lands it at 13 rather than 17.
Some physical or field component You are on your feet in studio for hours, handling chipboard and basswood models, running the laser cutter and shop orientation, and leading site and building tours — physical, but in a climate-controlled studio and shop rather than a scaffold or a live jobsite, so 11 not 16.
Certification preferred, not legally required Most tenure-line architecture faculty hold a professional licence and many keep a practice, but NAAB accredits the program, not the individual instructor, and nothing in your teaching contract requires a stamp — the licence is a hiring credential and a credibility signal, not a legal chokepoint, which is why this sits at 5.
Meaningful discretion You decide whose thesis is ready for a jury, whether a scheme is worth pushing another three weeks or killing, and how to grade design where there is no answer key — real, consequential calls, but bounded by department curricula, NAAB student performance criteria, and appealable grades, so 13 rather than 17.
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 (13/20 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 (5/20) is whether the law requires a licensed human to sign. Trust premium (16/20) 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 34 of this occupation's 58 points (59%).
Embodiment (11/20) 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 architecture 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 70/100 — SAFE.
Formal delegation to studio faculty of AI-use adjudication: program handbooks and honor-code procedures naming the instructor as the person who determines whether a student's generative-design output constitutes their own work, with appeal records. This is already appearing in university academic-integrity policies post-2023 and makes an unavoidably ambiguous, consequential call the faculty member's own.
NAAB (the sole US accreditor for professional architecture degrees) writing into its next Conditions for Accreditation an explicit requirement that a named human faculty member of record certify each student's attainment of Student Criteria, and that AI-generated assessment cannot substitute — parallel to how the 2020 Conditions already require documented evidence of student work reviewed by faculty. Also: institutional/board rules requiring the studio instructor of record to hold an NCARB-registered license for professional-practice and structures courses, which ties teaching to a personally-held credential.
Task-mix shift as the lecture/history/theory tier is absorbed: if programs consolidate survey and precedent-research courses into AI-assisted large sections and reallocate the surviving faculty lines to desk crit, model shop, and jury hours, the remaining job is almost entirely the tier machines cannot do. Watch for course-catalog restructuring that raises studio contact hours per FTE while cutting lecture sections.
Already high and near its practical ceiling; the only visible upward mechanism is accreditation or state-board rules requiring minimum in-person studio contact hours for a professional degree, which would make the human-taught studio a purchase requirement rather than a preference.
The limit. None of these touch the actual binding constraint. Headcount here is set by enrollment in professional architecture programs and by the substitution of adjuncts for tenure lines; a stronger accreditation shield can protect the role's content while the number of roles still falls. A high register score and a shrinking occupation are fully compatible.
| New York-Newark-Jersey City, NY-NJ | 1,460 | $123,760 +28% |
| Boston-Cambridge-Newton, MA-NH | 320 | $121,660 +26% |
| Los Angeles-Long Beach-Anaheim, CA | 260 | $79,100 -18% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 250 | $105,770 +9% |
| Portland-Vancouver-Hillsboro, OR-WA | 170 | $79,650 -18% |
| College Station-Bryan, TX | 160 | $128,670 +33% |
| Dallas-Fort Worth-Arlington, TX | 160 | $84,000 -13% |
| Chicago-Naperville-Elgin, IL-IN | 130 | $104,420 +8% |
| College Station-Bryan, TX | 160 | $128,670 +33% |
| New York-Newark-Jersey City, NY-NJ | 1,460 | $123,760 +28% |
| Boston-Cambridge-Newton, MA-NH | 320 | $121,660 +26% |
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 58. 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.