AI startup Vinci is using artificial intelligence to dramatically reshape one of the most exacting domains of engineering: physics-based hardware design. In this TechVoices interview, co-founder and CEO Hardik Kabaria explains how the company is building a “physics reasoning layer” that applies AI not to language or graphics, but to deterministic physical laws governing heat, stress, and material behavior.
By grounding its models directly in first-principles physics equations, Vinci aims to make advanced simulation accessible across engineering teams, dramatically accelerate design cycles, and enable companies to analyze complex hardware systems at manufacturing-level fidelity without sacrificing accuracy or intellectual property security. The discussion explores Vinci’s newly released thermo-mechanical simulation capability for predicting hardware warpage, the difference between probabilistic AI and solver-grade physics intelligence, and how physics-aware AI could fundamentally alter how hardware products are designed and validated.
Core Takeaways
Physics AI moves beyond generative models: Vinci is developing AI systems grounded in physical equations rather than probabilistic language modeling, enabling deterministic simulation suitable for precise hardware engineering and manufacturing decisions.
Hardware design faces a growing complexity gap: As manufacturing reaches nanometer-scale precision while systems grow more complex, legacy simulation tools struggle to keep pace, limiting who can perform advanced analysis and slowing innovation.
Thermo-mechanical simulation targets real-world reliability risks: Vinci’s new capability predicts warpage caused by thermal stress in semiconductor packages and electronic systems, helping engineers identify manufacturing and reliability issues earlier in development.
Physics intelligence changes how teams design products: Faster, accessible simulations allow more iterations and encourage engineers to integrate physics analysis throughout the design process, increasing both speed and ambition in hardware development.
Key Quotes
AI Built on First Principles Physics
“Physics is fundamentally different from creative AI domains like text or images because the outcomes must be deterministic. When we started Vinci, we went back to the basics and built AI that understands the laws of physics from the ground up rather than fine-tuning a language model. Heat transfer, stress, deformation—these are governed by equations engineers study in undergraduate and graduate programs, and we teach those equations directly to the model.
The result is an AI system that always produces answers rooted in first-principles physics. It predicts solutions to partial differential equations and converges on solver-grade results, which means engineers can trust the output for real-world decisions where accuracy is critical and failure is not acceptable.”
Making Physics Accessible Across Engineering Teams
“Today, only a very small number of specialists can run advanced physics simulations, and they rely on legacy tools that have existed for decades. Meanwhile, hardware systems are becoming dramatically more complex while manufacturing processes reach nanometer-level precision. That gap between complexity and accessibility is growing every year.
Our goal is to create a physics reasoning layer where anyone in a hardware organization can ask questions about performance, heat transfer, or deformation at any stage of development. Instead of lengthy setup and preprocessing, engineers can simply bring in their design files and immediately analyze how the product will behave.”
Why Hallucinations Cannot Exist in Hardware AI
“In generative AI, hallucinations are often tolerated because the output is creative. In hardware, a hallucination could mean a product overheats or fails in the field. That is unacceptable. Our system cannot hallucinate because every answer is tied to solving the governing physical equations.
The AI accelerates how we reach the solution, but the result itself remains grounded in numerical physics methods. That distinction is essential. We use learning to make simulation faster and scalable, not to replace the deterministic nature of physics.”
How Physics Intelligence Changes Design Ambition
“When simulation becomes faster and easier, teams naturally run more iterations, which compresses development cycles. But what surprised us is that customers are also becoming more ambitious. Engineers want physics insight earlier and continuously throughout the design process rather than treating analysis as a separate step.
Instead of a sequential loop of design, simulate, and validate that can take weeks or months, physics becomes embedded in every stage. That shift allows companies not only to build products faster but also to explore ideas that were previously considered too risky or impractical to attempt.”
