{ "source": "Cooked Index — occupational AI risk register", "page": "https://cookedindex.com/jobs/mathematicians/", "methodology": "https://cookedindex.com/methodology", "notice": "Verdicts are re-examined as evidence accumulates. Re-fetch before relying on this; the page above always carries the current score.", "scored_at": "2026-08-11", "model": "claude-opus-5", "occupation": { "title": "Mathematicians", "soc_code": "15-2021", "category": "Tech", "us_employment": 2030, "median_annual_wage": 126710 }, "verdict": "COOKED", "risk_resistance": 30, "contested": false, "near_boundary": false, "dimensions": { "task_resistance": 8, "embodiment": 2, "liability_shield": 1, "trust_premium": 7, "judgment_accountability": 12 }, "reasoning": { "task_resistance": "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.", "embodiment": "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.", "liability_shield": "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.", "trust_premium": "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.", "judgment_accountability": "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." }, "rationale": "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.", "outlook": "Headcount in this small occupation stays flat or shrinks as AI absorbs derivation and analysis work, and the remaining jobs concentrate in problem formulation, verification, and security-cleared or regulated modeling roles.", "what_would_raise_it": { "levers": [ { "dimension": "task_resistance", "change": "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.", "plausibility": "already happening", "would_add": 4 }, { "dimension": "judgment_accountability", "change": "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.", "plausibility": "plausible", "would_add": 4 }, { "dimension": "liability_shield", "change": "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.", "plausibility": "plausible", "would_add": 3 }, { "dimension": "trust_premium", "change": "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.", "plausibility": "plausible", "would_add": 2 } ], "ceiling_note": "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." }, "adjudication": null, "employment_history": { "points": [ { "y": 2017, "emp": 2730, "wage": 103010 }, { "y": 2018, "emp": 2580, "wage": 101900 }, { "y": 2019, "emp": 2630, "wage": 105030 }, { "y": 2020, "emp": 2460, "wage": 110860 }, { "y": 2021, "emp": 1770, "wage": 108100 }, { "y": 2022, "emp": 2070, "wage": 112110 }, { "y": 2023, "emp": 2220, "wage": 116440 }, { "y": 2024, "emp": 2220, "wage": 121680 }, { "y": 2025, "emp": 2030, "wage": 126710 } ], "from": 2017, "to": 2025, "change_pct": -25.6, "comparable_from": 2019, "spans_soc_revision": true }, "pivots": [ { "slug": "physicists", "title": "Physicists", "verdict": "EXPOSED", "risk_resistance": 46, "median_wage": 172250, "overlap": 69, "skills_to_close": [ "Technology Design", "Quality Control Analysis", "Science", "Management of Personnel Resources" ] } ], "license": "https://cookedindex.com/terms" }