π‘ Physical Scientists have a composite risk score of 33/100 (Frey-Osborne probability: 43%, GenAI exposure: 35/100). With 259,000 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 259,000 workers and a median wage of $93K,physical scientists represent a significant portion of the US workforce. Their GenAI exposure index is 35%, meaning a minority of their core tasks overlap with current generative AI capabilities.
Risk Score
33/100
Employment
259,000
Median Wage
$93K
GenAI Exposure
35%
β οΈ Top Risk Factors
AI-accelerated data analysis and pattern recognition
Automated laboratory instrumentation and workflows
AI literature review and meta-analysis automation
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
Fieldwork in unstructured natural environments
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Dentists, All Other Specialists
65% skills overlap Β· $226K median wage
Social Scientists and Related Workers
84% skills overlap Β· $93K median wage
Political Scientists
76% skills overlap Β· $139K median wage
β Frequently Asked Questions
Will AI completely replace physical scientists?
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
What is the AI risk score for physical scientists?
Physical Scientists have a composite AI automation risk score of 33 out of 100, classified as "Moderate".
How many physical scientists are there in the US?
There are approximately 259,000 physical scientists employed in the United States.
What do physical scientists earn?
The median annual wage for physical scientists is $93K.
What skills should physical 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, fieldwork in unstructured natural environments. These human-centric skills will become more valuable as routine tasks are automated.