Hardik Kabaria on why hardware design requires a different kind of AI

Most AI conversations today are centered on large language models. This episode focuses on something different: what changes when AI has to reason about physical systems, where the laws of physics do not negotiate and a wrong answer cannot simply be patched after a product ships.

Hardik joined Maribel Lopez to discuss why physics-based AI is built and evaluated differently from generative AI, why determinism is a requirement rather than a preference in hardware design, and what it means for organizations building physical products to adopt AI responsibly inside real workflows.

At Vinci, we believe this distinction matters. Physical-world AI is not just another application layer on top of general-purpose AI. It introduces a different qualification bar: repeatability, solver-grounded validation, secure deployment, and trust that can hold up in production engineering environments.

What this conversation covers

• Why physics-based AI is a different modality than large language models, and why that changes how it must be built and evaluated
• Why determinism matters in hardware design, where engineers need the same answer every time, not a plausible variation
• How AI can expand access to physics analysis beyond a small pool of highly specialized experts
• Why security requirements are higher when AI is used on sensitive hardware designs, and what deployment models address that
• How organizations should think about AI across the full product lifecycle, from concept exploration through manufacturing sign-off
• What “trust but verify” looks like in practice when benchmarking AI in high-stakes engineering workflows

Why this matters

The bar for AI changes when the output is used to evaluate a physical system.

In language workflows, probabilistic behavior can be acceptable. In engineering workflows, it is a liability. If the system is not deterministic, solver-grounded, and stable enough for meaningful comparison, it cannot support production decision-making.

That is the distinction this conversation explores — and why physical-world AI must be held to a different standard.

Hardik Kabaria appears on The AI with Maribel Lopez in Episode 76, “Physics AI Explained: Why Hardware Design Requires a Different Kind of AI.” The episode runs 28 minutes.

Listen Now

Physical-world AI requires a different standard.

Vinci is building a deterministic, solver-accurate foundation model for physics that can support real engineering workflows.

Explore the Platform