π‘ Aerospace Engineers have a composite risk score of 33/100 (Frey-Osborne probability: 2%, GenAI exposure: 88/100). With 68,440 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 68,440 workers and a median wage of $135K,aerospace engineers represent a significant portion of the US workforce. Their GenAI exposure index is 88%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
33/100
Employment
68,440
Median Wage
$135K
GenAI Exposure
88%
β οΈ Top Risk Factors
AI coding assistants reducing developer demand
Automated data interpretation and insight generation
AI-optimized manufacturing process design
π‘οΈ Tasks AI Can't Easily Replace
Novel engineering design for unprecedented challenges
On-site problem-solving in variable physical conditions
Client communication and technical consultation
Cross-disciplinary collaboration on complex projects
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Engineers
81% skills overlap Β· $106K median wage
Political Scientists
61% skills overlap Β· $139K median wage
Electronics Engineers, Except Computer
73% skills overlap Β· $128K median wage
β Frequently Asked Questions
Will AI completely replace aerospace engineers?
Partially. Some tasks will be automated, but the core role will likely adapt and evolve.
What is the AI risk score for aerospace engineers?
Aerospace Engineers have a composite AI automation risk score of 33 out of 100, classified as "Moderate".
How many aerospace engineers are there in the US?
There are approximately 68,440 aerospace engineers employed in the United States.
What do aerospace engineers earn?
The median annual wage for aerospace engineers is $135K.
What skills should aerospace engineers develop?
Focus on tasks AI can't easily replicate: novel engineering design for unprecedented challenges, on-site problem-solving in variable physical conditions, client communication and technical consultation, cross-disciplinary collaboration on complex projects. These human-centric skills will become more valuable as routine tasks are automated.