π‘ Microbiologists have a composite risk score of 40/100 (Frey-Osborne probability: 1%, GenAI exposure: 84/100). With 19,760 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 19,760 workers and a median wage of $87K,microbiologists represent a significant portion of the US workforce. Their GenAI exposure index is 84%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
40/100
Employment
19,760
Median Wage
$87K
GenAI Exposure
84%
β οΈ Top Risk Factors
Large language model automation of analysis tasks
AI-accelerated data analysis and pattern recognition
Robotic sample preparation and experimentation
π‘οΈ Tasks AI Can't Easily Replace
Designing novel experiments and research methodologies
Collaborative scientific discourse and peer review
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
59% skills overlap Β· $226K median wage
Social Scientists and Related Workers
83% skills overlap Β· $93K median wage
Political Scientists
75% skills overlap Β· $139K median wage
β Frequently Asked Questions
Will AI completely replace microbiologists?
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
What is the AI risk score for microbiologists?
Microbiologists have a composite AI automation risk score of 40 out of 100, classified as "Moderate".
How many microbiologists are there in the US?
There are approximately 19,760 microbiologists employed in the United States.
What do microbiologists earn?
The median annual wage for microbiologists is $87K.
What skills should microbiologists develop?
Focus on tasks AI can't easily replicate: designing novel experiments and research methodologies, collaborative scientific discourse and peer review, interpreting ambiguous results with domain expertise, fieldwork in unstructured natural environments. These human-centric skills will become more valuable as routine tasks are automated.