Physics AI Explained: Why Hardware Design Requires a Different Kind of AI

Not every AI problem is a language problem. I talk with Vinci CEO Hardik Kabaria about what changes when AI has to reason about the physical world.Full show notesMost of the AI conversation in enterprise circles is about large language models — text, code, maybe images. This episode is about something different: what happens when AI has to reason about physical systems where the laws of physics don't negotiate and a wrong answer can't be patched after the product ships.I talked with Hardik Kabaria, CEO of Vinci, about how physics-based AI models are built differently from generative models, why determinism is a requirement rather than a preference in hardware design, and what it means for organizations manufacturing physical products to think carefully about where AI fits in their workflow. The conversation covers data security, scalability, and the practical question of how to evaluate new AI tools when the cost of a mistake is measured in product recalls rather than content edits.This episode is most relevant for technology leaders at companies that design or manufacture physical products. But the underlying insight — that deterministic and probabilistic AI serve different purposes and require different evaluation criteria — applies to any organization building a portfolio of AI tools.What we cover:Why physics-based AI is a different modality than large language models, and what that means for how you build and evaluate itThe case for determinism in AI: why hardware design requires the same answer every time, regardless of who asksHow AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talentWhy data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address thatHow to think about AI across the full product lifecycle, from early concept to manufacturing sign-offWhat "trust but verify" looks like in practice: building benchmarks before deploying AI in high-stakes design workflowsTimestamps:Chapters:00:00 Introduction to AI and Vinci02:04 Understanding Physics Intelligence Layer04:20 The Role of Physics in AI Models07:04 Digital Twins and AI Scalability09:35 Misconceptions in AI for Physical Systems12:15 Determinism vs. Non-Determinism in AI15:01 Deployment Challenges for Physics-Based AI17:41 Signals of Success in AI Implementation20:20 The Future of AI in Hardware Design23:01 Preparing for the Shift to AI in Physical SystemsGuest bio Hardik Kabaria is CEO and co-founder of Vinci, an AI company building foundation models for the physical world. His background is in physics and geometry software for hardware engineering, with experience across the tools mechanical and electrical engineers use to design, simulate, and manufacture physical components. Vinci was founded two and a half years ago and is focused on making physics-based analysis accessible at the speed and scale of AI inference.Company: VinciResources mentioned:Vinci:  https://www.getvinci.aiLopez Research blog: https://www.lopezresearch.com/research/📢 STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch.com/research/Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/Follow me on X: https://x.com/MaribelLopez