Computer and Mathematical · United States (national)
Mathematicians
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is highly exposed to automation, and the bigger shift looks near-term.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 57.0 to 81.0
Physical / roboticsrange 0.0 to 20.9
When the change is likely
Near-term, highlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Act within the next 1 to 2 years
Displacement for this role is near-term. Start a concrete reskilling plan now, while you still have income and leverage.
2
Lean into the human edge
The exposed parts are routine and cognitive. Invest in judgment, client relationships, and complex problem-solving that AI still cannot do.
3
Pivot toward Computer and Information Research Scientists
It is skill-adjacent and less exposed (50.0 vs 69). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
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Which of my tasks are exposed
Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.
69.0
Disseminate research by writing reports, publishing papers, or presenting at professional conferences.
50.0
Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.
100.0
Apply mathematical theories and techniques to the solution of practical problems in business, engineering, the sciences, or other fields.
50.0
Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.
50.0
Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
50.0
Perform computations and apply methods of numerical analysis to data.
69.0
Assemble sets of assumptions, and explore the consequences of each set.
69.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.
Task: Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.
Lower-risk adjacent roles
Computer and Information Research Scientists
50.0
Bioinformatics Scientists
54.5
Data Scientists
59.4
Physicists
63.6
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Mathematicians to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 69.0, robotics 8.9.
The timeline comes from the higher axis (69.0), shifted for local demand (stable), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.