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 notes

Most 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 it
The case for determinism in AI: why hardware design requires the same answer every time, regardless of who asks
How AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talent
Why data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address that
How to think about AI across the full product lifecycle, from early concept to manufacturing sign-off
What "trust but verify" looks like in practice: building benchmarks before deploying AI in high-stakes design workflows

Timestamps:

Chapters:
00:00 Introduction to AI and Vinci
02:04 Understanding Physics Intelligence Layer
04:20 The Role of Physics in AI Models
07:04 Digital Twins and AI Scalability
09:35 Misconceptions in AI for Physical Systems
12:15 Determinism vs. Non-Determinism in AI
15:01 Deployment Challenges for Physics-Based AI
17:41 Signals of Success in AI Implementation
20:20 The Future of AI in Hardware Design
23:01 Preparing for the Shift to AI in Physical Systems

Guest 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: Vinci

Resources mentioned:

Vinci: https://www.getvinci.ai
Lopez Research blog: https://www.lopezresearch.com/research/

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

The AI with Maribel Lopez (AI with ML)

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