π‘ Materials Scientists have a composite risk score of 35/100 (Frey-Osborne probability: 2%, GenAI exposure: 67/100). With 8,330 workers in the US, this occupation faces moderate but manageable AI pressure. Full occupation profile β
π― The Verdict
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
With 8,330 workers and a median wage of $104K,materials scientists represent a significant portion of the US workforce. Their GenAI exposure index is 67%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
35/100
Employment
8,330
Median Wage
$104K
GenAI Exposure
67%
β οΈ Top Risk Factors
AI coding assistants reducing developer demand
AI-powered research and literature review tools
Chatbot displacement of customer-facing interactions
π‘οΈ Tasks AI Can't Easily Replace
Collaborative scientific discourse and peer review
Designing novel experiments and research methodologies
Interpreting ambiguous results with domain expertise
Ethical oversight of research involving human subjects
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Dentists, All Other Specialists
69% skills overlap Β· $226K median wage
Political Scientists
80% skills overlap Β· $139K median wage
Social Scientists and Related Workers
78% skills overlap Β· $93K median wage
β Frequently Asked Questions
Will AI completely replace materials scientists?
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
What is the AI risk score for materials scientists?
Materials Scientists have a composite AI automation risk score of 35 out of 100, classified as "Moderate".
How many materials scientists are there in the US?
There are approximately 8,330 materials scientists employed in the United States.
What do materials scientists earn?
The median annual wage for materials scientists is $104K.
What skills should materials scientists develop?
Focus on tasks AI can't easily replicate: collaborative scientific discourse and peer review, designing novel experiments and research methodologies, interpreting ambiguous results with domain expertise, ethical oversight of research involving human subjects. These human-centric skills will become more valuable as routine tasks are automated.