π‘ Chemists have a composite risk score of 41/100 (Frey-Osborne probability: 10%, GenAI exposure: 74/100). With 83,250 workers in the US, this occupation faces moderate but manageable AI pressure. Full occupation profile β
π― The Verdict
Possible. Significant task automation is underway β workers should actively upskill.
With 83,250 workers and a median wage of $84K,chemists represent a significant portion of the US workforce. Their GenAI exposure index is 74%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
41/100
Employment
83,250
Median Wage
$84K
GenAI Exposure
74%
β οΈ Top Risk Factors
Machine learning models replacing manual hypothesis testing
AI literature review and meta-analysis automation
AI-powered research and literature review tools
Generative AI producing marketing and creative copy
π‘οΈ Tasks AI Can't Easily Replace
Ethical oversight of research involving human subjects
Collaborative scientific discourse and peer review
Fieldwork in unstructured natural environments
Interpreting ambiguous results with domain expertise
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Dentists, All Other Specialists
61% skills overlap Β· $226K median wage
Social Scientists and Related Workers
70% skills overlap Β· $93K median wage
Political Scientists
77% skills overlap Β· $139K median wage
β Frequently Asked Questions
Will AI completely replace chemists?
Possible. Significant task automation is underway β workers should actively upskill.
What is the AI risk score for chemists?
Chemists have a composite AI automation risk score of 41 out of 100, classified as "Elevated".
How many chemists are there in the US?
There are approximately 83,250 chemists employed in the United States.
What do chemists earn?
The median annual wage for chemists is $84K.
What skills should chemists develop?
Focus on tasks AI can't easily replicate: ethical oversight of research involving human subjects, collaborative scientific discourse and peer review, fieldwork in unstructured natural environments, interpreting ambiguous results with domain expertise. These human-centric skills will become more valuable as routine tasks are automated.