COOKED
Almost everything a mathematician does — deriving proofs, building and validating models, running symbolic and numerical analysis, writing up results — is symbol manipulation on a screen, which is precisely where frontier models have improved fastest (olympiad-level proof, formal verification via Lean, automated model fitting). There is no license, no stamp, no physical component, and the buyer is usually an institution paying for results rather than a person paying for a relationship. What survives is at the top of the field: choosing which problems matter, formulating fuzzy real-world questions as tractable mathematics, and vouching for whether a machine-generated proof or model is actually correct and applicable.
Nearly all of this fall was the 2020 shock. It has been climbing back since.
Median pay $105,030 → $126,710 -3.5% 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
-0.7%
Percentage only. The projection counts a different population from the 2,030 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -0.7% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
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.
~100 openings a year on average, including replacing people who leave.
AlgebraistResearcherCryptanalystGeometricianCipher ExpertCryptographerMathematicianGame MathematicianResearch ScientistAgent-Based ModelerApplied MathematicianComputational ScientistEngineering MathematicianComputational MathematicianResearch Computing SpecialistCryptographic Vulnerability AnalystMath Researcher (Mathematics Researcher)
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Proof search, symbolic manipulation, numerical simulation, statistical model fitting and LaTeX write-up are all now partly machine-executable — Lean-verified lemma chains and automated fitting cover a real share of the working day — but the step of turning a defense contractor's or actuarial client's vague question into a well-posed theorem or model specification still has no reliable automated equivalent, which is what keeps this at 8 rather than in the 0-6 band.
Fully desk- and screen-based The entire job runs on a laptop, a compute cluster, and a whiteboard; the only physical requirements are showing up to a seminar or conference room, which is why this sits at 2 and not 0.
No licence, no signature requirement There is no mathematics license, no PE-style stamp, no board that can strike you off — an incorrect proof or a flawed model gets you a retraction or a lost contract, not a personal cause of action, so the 1 reflects reputational exposure only.
Meaningful discretion Choosing the abstraction, deciding which assumptions to relax, and judging whether a machine-generated proof step is actually sound or a plausible-looking hallucination are genuinely unbounded calls with consequences that show up years later in cryptographic standards or risk models — that is real discretion, but you are typically one contributor inside a peer-review or team structure rather than the single signatory, which caps it at 12.
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 (8/20 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 (1/20) is whether the law requires a licensed human to sign. Trust premium (7/20) 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 20 of this occupation's 30 points (67%).
Embodiment (2/20) 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.
Physicists 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 43/100 — EXPOSED.
Task-mix shift is genuine here: the field has a routine tier (lemma-chasing, symbolic manipulation, standard model fitting, literature-checking) and a judgment tier (problem selection, formulating messy domain questions as tractable mathematics, deciding whether a formal proof's hypotheses actually match the real system). If the routine tier is fully absorbed, what remains is measured almost entirely as judgment work, and a day's residual becomes harder to automate — visible already in how Lean/mathlib formalization projects (Terence Tao's PFR and Equational Theories projects) reallocate human effort toward statement design and blueprint decomposition rather than proof steps.
If model-risk governance regimes that already name a responsible modeler extend to AI-generated mathematics: Federal Reserve/OCC SR 11-7 model validation requires a named independent validator to attest that a model is conceptually sound, and EU AI Act Article 14 human-oversight duties for high-risk systems push the same way. A bank, insurer, or actuarial employer designating a specific quantitative person as the accountable validator of AI-derived models makes ambiguity-ownership formal rather than cultural.
Mathematicians as such have no license, but adjacent credentialed routes exist and can be pulled inward: the Society of Actuaries/CAS credentials and NAIC Actuarial Opinion requirements already require a named qualified actuary to sign reserve opinions, and CFA/FRM-style model attestation is increasingly written into bank policy. If a regulator or standards body requires a credentialed signatory on safety-critical or financial models regardless of whether AI produced them, the pool of mathematical work behind a personal signature grows. Also watch cryptographic standards: NIST post-quantum processes require named human review of security proofs.
Narrow but real: mathematical expert-witness testimony (statistical evidence, patent damages, redistricting) requires a human who can be cross-examined, and Federal Rule of Evidence 702 plus Daubert gatekeeping mean courts admit a person's opinion, not a system's output. If courts and arbitration panels keep excluding non-attributable machine analysis, the litigation-consulting slice of the occupation carries a durable human premium — though this covers a small fraction of the 2,030 workers.
The limit. Even with every lever above, this occupation stays in the lower band. The core reason is structural and not fixable by regulation: the work product is symbol manipulation whose correctness is machine-checkable, so the usual argument for a mandatory human signature — that only a human can be held to account for an unverifiable judgment — is weakest exactly here. Formal verification makes proofs MORE auditable without a person, not less. The occupation is also tiny (2,030), which means no professional body has the mass to lobby a licensure regime into existence, and most people doing mathematics for pay are classified under other SOC codes (actuaries, statisticians, operations research analysts) where the shields already sit.
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 200 | $164,470 +30% |
| New York-Newark-Jersey City, NY-NJ | 150 | $113,590 -10% |
| Providence-Warwick, RI-MA | 100 | $109,260 -14% |
| Seattle-Tacoma-Bellevue, WA | 70 | $185,520 +46% |
| Chicago-Naperville-Elgin, IL-IN | 60 | $130,970 +3% |
| Boston-Cambridge-Newton, MA-NH | 40 | $104,190 -18% |
| Las Vegas-Henderson-North Las Vegas, NV | 40 | $102,890 -19% |
| San Diego-Chula Vista-Carlsbad, CA | 40 | $156,480 +23% |
| Seattle-Tacoma-Bellevue, WA | 70 | $185,520 +46% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 200 | $164,470 +30% |
| San Diego-Chula Vista-Carlsbad, CA | 40 | $156,480 +23% |
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 30. 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.