EXPOSED
Law professors do two things AI is already good at — producing doctrinal explanation and drafting written analysis — and one thing it is not: running a live Socratic classroom where students are pushed to defend positions under pressure, and then vouching for those students to employers and courts. Case summaries, model answers, exam-question banks, outline generation, and first drafts of law review pieces are all substantially automatable today; cold-calling, clinic supervision, moot court coaching, and letters of recommendation are not. The real protection here is institutional rather than technological: ABA accreditation standards require full-time faculty, tenure limits headcount churn, and the bar-passage pipeline is a credentialing monopoly — all regulatory scaffolding that can be revised.
Dipped in 2020, then grew past where it started.
Median pay $113,530 → $128,500 -9.5% 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
+2.2%
Percentage only. The projection counts a different population from the 20,060 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 +2.2% more of these jobs by 2034, and at 59/100 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.
~2,200 openings a year on average, including replacing people who leave.
TeacherProfessorInstructorLaw LecturerLaw ProfessorFaculty MemberLaw InstructorAdjunct ProfessorCollege ProfessorAdjunct InstructorAssistant ProfessorAssociate ProfessorLabor Law ProfessorTorts Law ProfessorParalegal InstructorAdjunct Law ProfessorLaw Adjunct ProfessorBusiness Law ProfessorClinical Law ProfessorCollege Faculty MemberCriminal Law ProfessorBusiness Law InstructorContracts Law ProfessorLegal Writing Professor
Holding it up: trust premium . Weakest point: embodiment .
Mixed — a routine tier and a judgment tier Doctrinal lecture prep, casebook note-writing, hypothetical drafting, and grading multiple-choice or IRAC-formatted answers are all reproducible today, which pulls this below the safe band, but cold-calling a 1L through a line of argument he hasn't thought through, supervising a live clinic docket with real clients, and coaching an oral argument keep it at 12 rather than 6.
Some physical or field component The work is classroom, office hours, and Zoom, so 7 reflects only the physical fragments that do exist — being bodily present in a lecture hall for accreditation-relevant contact hours, supervising a clinic that appears in court, and judging moot court rounds in person — none of which involves uncontrolled environments.
Certification preferred, not legally required Most law professors hold a JD and many a bar licence, but they are not personally liable for teaching content and no statute requires a licence to lecture on torts; the 8 comes from clinical faculty, who are the actual attorney of record on client matters and answer to state bar discipline for them.
Exists to be accountable for ambiguous calls Deciding whether a student's professional-responsibility lapse in clinic gets remediated or reported, setting the curve that determines who makes law review, and choosing what unsettled doctrine to teach as the rule are calls with no procedure behind them and consequences that follow the student into practice.
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 (8/20) is whether the law requires a licensed human to sign. Trust premium (17/20) is whether buyers specifically pay for a person. Judgment and accountability (15/20) 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 40 of this occupation's 59 points (68%).
Embodiment (7/20) 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 law 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 75/100 — SAFE.
Genuine two-tier structure: if outline generation, case briefing, model answers, and exam banks are conceded to AI, the residual job is cold-call sequencing, clinic case triage, and Socratic pressure-testing — none of which is asynchronous text. Task-mix shift alone raises this without any rule change, visible already where schools license commercial AI study tools and reassign faculty time to experiential teaching.
ABA Standard 304/306 revisions that require identified full-time faculty of record to certify supervision hours for experiential and distance-education credits — plus state bar clinic rules (e.g., student-practice orders in NY, CA) naming a licensed, malpractice-liable supervising attorney for every clinic matter. If clinic and experiential credit minimums rise (Standard 303's 6-credit floor moving up, as the ABA has repeatedly floated), the share of the job that legally requires a bar-licensed signer grows.
ABA Standard 402/403 (full-time faculty must teach a substantial majority of credit hours) being retained or tightened rather than relaxed during the ABA's periodic accreditation review; conversely a variance regime permitting AI-delivered doctrinal credit would cut this. Watch the Council on Legal Education's agenda items on distance ed caps.
Character-and-fitness and academic-misconduct roles hardening: as AI use in exams proliferates, faculty become the accountable adjudicators of honor-code findings that determine bar admission eligibility, and state bar C&F committees rely on dean/faculty certifications. Formalizing faculty-signed AI-use attestations on graded work would make this an owned, contestable call.
Already near ceiling at 17 — recommendation letters to judges for clerkships (federal judiciary hiring plan relies on named-faculty vouching) are the mechanism, and it is hard to raise further. If clerkship and BigLaw hiring formally discount or ban AI-assisted references, the named-human premium is reinforced rather than increased.
The limit. Embodiment has no route; law teaching is not physical. The dominant risk is not AI capability but enrollment and cost pressure: if the ABA relaxes full-time faculty and distance-ed limits, or state supreme courts expand non-JD licensure pathways (Washington, Oregon, Utah alternatives to the bar exam), headcount falls regardless of how high liability_shield and trust_premium score for the survivors. The credentialing monopoly is the whole game, and it is a policy variable.
| Los Angeles-Long Beach-Anaheim, CA | 2,940 | $114,330 -11% |
| New York-Newark-Jersey City, NY-NJ | 2,180 | $144,130 +12% |
| San Francisco-Oakland-Fremont, CA | 760 | $130,010 +1% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 740 | $134,090 +4% |
| Boston-Cambridge-Newton, MA-NH | 710 | $170,310 +33% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 510 | $128,560 +0% |
| Dallas-Fort Worth-Arlington, TX | 430 | $125,440 -2% |
| Chicago-Naperville-Elgin, IL-IN | 320 | $101,810 -21% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 60 | $218,970 +70% |
| Boston-Cambridge-Newton, MA-NH | 710 | $170,310 +33% |
| New York-Newark-Jersey City, NY-NJ | 2,180 | $144,130 +12% |
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 59. 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.