Hardik Kabaria, CEO and Co-Founder of Vinci, explores how physics challenges are holding back AI hardware development and what his company is building to solve them. As AI training and inference demands escalate, engineers face complex physics problems spanning heat transfer, thermomechanics, signal integrity, and electromagnetics across memory, GPUs, servers, and data centers. Kabaria reveals how traditional episodic engineering approaches create bottlenecks and introduces Vinci’s approach to transforming this workflow into something fundamentally different—powered by a foundation model built on first principles of physics.
**Topics*
– The critical physics challenges facing AI chip and infrastructure design today
– Why current engineering workflows limit AI hardware optimization
– How Vinci’s continuous physics intelligence layer differs from traditional analysis methods
– The role of foundation models built on physical principles in hardware development
– How this technology impacts decisions from architecture through data center operations
📚 CHAPTERS:
0:00:00 – Introduction to WinI and AI Infrastructure Challenges
0:00:24 – Physics Problems in AI Hardware Design
0:01:04 – The Limitations of Episodic Engineering
0:01:18 – Building Continuous Physics Intelligence Layer
0:01:55 – Foundation Model for the Physical World
0:02:26 – Enabling Better AI Chip Development
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