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.
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.
Some relationship component. Grant panels, cryptographic reviewers and internal clients do care whose name is on a proof — a Fields-adjacent reputation buys benefit of the doubt on a 200-page argument — but the deliverable is still the theorem or the model, which is checkable independently of who produced it, putting this at 7 rather than in physician-or-therapist territory.
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.
Physicists EXPOSED
Has AI actually changed your work?