π‘ Mathematicians have a composite risk score of 24/100 (Frey-Osborne probability: 5%, GenAI exposure: 87/100). With 2,220 workers in the US, this occupation remains well-protected against automation. Full occupation profile β
π― The Verdict
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
With 2,220 workers and a median wage of $122K,mathematicians represent a significant portion of the US workforce. Their GenAI exposure index is 87%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
24/100
Employment
2,220
Median Wage
$122K
GenAI Exposure
87%
β οΈ Top Risk Factors
AI pair-programming and code generation tools
AI-driven cybersecurity threat detection replacing analysts
Automated testing and CI/CD pipeline intelligence
π‘οΈ Tasks AI Can't Easily Replace
Architecting novel systems requiring creative problem-solving
Stakeholder negotiation and requirements elicitation
Ethical AI oversight and bias auditing
Cross-functional collaboration on ambiguous problems
Crisis debugging of complex production incidents
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Advertising, Marketing, Promotions, Public Relations, and Sales Managers
61% skills overlap Β· $145K median wage
Architectural and Engineering Managers
67% skills overlap Β· $168K median wage
Engineers
63% skills overlap Β· $106K median wage
β Frequently Asked Questions
Will AI completely replace mathematicians?
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
What is the AI risk score for mathematicians?
Mathematicians have a composite AI automation risk score of 24 out of 100, classified as "Moderate".
How many mathematicians are there in the US?
There are approximately 2,220 mathematicians employed in the United States.
What do mathematicians earn?
The median annual wage for mathematicians is $122K.
What skills should mathematicians develop?
Focus on tasks AI can't easily replicate: architecting novel systems requiring creative problem-solving, stakeholder negotiation and requirements elicitation, ethical ai oversight and bias auditing, cross-functional collaboration on ambiguous problems, crisis debugging of complex production incidents. These human-centric skills will become more valuable as routine tasks are automated.