Podcast
2.27.2026 | By Vinci
In a conversation with James Maguire on TechVoices, Hardik Kabaria explains why applying AI to physical systems requires a different standard than applying AI to language, code, or content. In hardware design, outputs cannot just be plausible. They need to be deterministic, physically grounded, and reliable enough to support real engineering decisions. Vinci is building that capability by making physics continuously computable inside engineering workflows.
Watch Now
Most AI systems today are designed for probabilistic tasks. This conversation focuses on something different: what changes when AI has to reason about heat, stress, materials, and physical performance, where the governing laws do not change and the answer has to hold up in production.
Hardik discusses why hardware design has historically depended on a small pool of deeply specialized simulation experts working through slow, manual, one-at-a-time workflows. He explains how Vinci is changing that model with deterministic, solver-accurate physics computation that operates directly on native design data, helping teams evaluate more designs earlier and with less manual setup.
At Vinci, we believe this shift matters because engineering teams do not just need faster answers. They need physics they can trust, repeat, validate, and use inside real product workflows.
The bottleneck in engineering is not only compute. It is the limited capacity to evaluate physical behavior quickly, repeatedly, and with enough confidence to guide real decisions.
Traditional simulation workflows are slow, specialized, and difficult to scale across the full volume of design questions modern teams need answered. That constraint limits exploration, delays validation, and increases the likelihood of late-stage surprises.
This conversation highlights a different model: physics as an always-available reasoning layer inside engineering workflows, with determinism and solver-grounded validation as the minimum bar.
Hardik Kabaria appears on TechVoices with James Maguire. The episode runs 14 minutes.
Vinci is a frontier lab building the foundation model for the physical world, with deterministic, solver-grounded systems already deployed in production engineering workflows on flagship programs.
In a new conversation with Maribel Lopez on The AI with Maribel Lopez, Hardik explains why AI for physical systems must be evaluated differently than AI for language, code, or content workflows. In engineering, determinism, validation, and deployment trust are not preferences. They are requirements.
