AI Engineering: The Emerging Field Poised to Secure America’s AI Advantage
Dr. Pramod Khargonekar, ERVA Co-Principal Investigator, Vice Chancellor for Research - University of California, Irvine
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A new era is emerging where engineering drives AI—and AI transforms engineering
This week Matt Kirchner is joined by Dr. Pramod Khargonekar—Vice Chancellor for Research at UC Irvine and lead author of the ERVA report AI Engineering: A Strategic Research Framework to Benefit Society. Dr. Khargonekar unpacks the emerging discipline of AI Engineering, where engineering principles make AI better, and AI makes engineered systems better.
From robotics and energy systems to engineering education and data sharing, this episode dives into the flywheel effect of AI and engineering co-evolving. Pramod explains the real-world impact, the challenges ahead, and why this moment represents a generational opportunity for U.S. leadership in both innovation and education.
Listen to learn:
- How AI is changing every branch of engineering—from mechanical to civil to industrial and beyond.
- Why manufacturing, energy, and transportation are ground zero for “physical AI”
- What the 14 Grand Challenges of AI Engineering reveal about the future of innovation
- Why systems thinking is the key to building AI products that actually work
- How colleges must rethink engineering education—and what industry can do to help
3 Big Takeaways from this Episode:
1. AI is transforming every branch of engineering—from design and simulation to manufacturing and operations. Pramod explains how fields like robotics, fluid mechanics, and materials science are being reshaped by tools such as reinforcement learning and foundation models. This shift isn’t just about efficiency—it’s enabling engineers to solve problems they couldn’t approach before.
2. Engineering will play a critical role in advancing the next generation of AI. Pramod highlights how engineering disciplines contribute essential elements like safety, reliability, power systems, and chip design to AI development. These contributions are vital to scaling AI into real-world, physical systems—what he calls “physical AI.”
3. To lead in AI Engineering, higher education must integrate AI into every engineering discipline. Dr. Khargonekar outlines how universities can start with shared foundational courses, then build field-specific AI applications into majors like mechanical or electrical engineering. He also emphasizes the importance of short courses, professional development, and industry partnerships to support lifelong learning.
Resources in this Episode:
- Read the ERVA report: AI Engineering | A Strategic Research Framework to Benefit Society
- Learn more about the work of the NSF Engineering Research Visioning Alliance (ERVA)
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