Company news, media coverage, and conversations on AI simulation, physics intelligence, and our foundation model for physics.
In this episode of BUILDERS, Hardik Kabaria explains how Vinci approached its initial beachhead, why the company started with heat transfer in semiconductors and electronics engineering, and how that decision shaped both product strategy and go-to-market execution.
In this Pulse 2.0 interview, Hardik Kabaria discusses the company’s origins, the technical motivation behind building a foundation model for physics, and how Vinci is working to make physics continuously computable inside engineering workflows.
In this Electronic Design article, Hardik Kabaria argues that modern hardware design is no longer limited by compute alone, but by how and when physics can be evaluated in the workflow.
In a conversation on AI Across the Product Lifecycle, Michael Finocchiaro speaks with Hardik Kabaria and Andy Fine, founder of the Fine Physics Consortium, about Vinci’s approach to building a foundation model for physics for hardware design, engineering simulation, and other complex physical systems.
In this Semiconductor Engineering article, Satish Radhakrishnan explains why episodic simulation workflows are increasingly misaligned with the pace and complexity of modern semiconductor design.
In this SemiWiki interview, Vinci CEO Hardik Kabaria discusses why engineering requires AI systems that are repeatable, physically grounded, and usable in production workflows.
In this TechTV panel discussion, Sarah Osentoski joins Bill Mew and other industry experts to discuss how the AI infrastructure landscape is evolving as inference demand grows.
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.
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.
This Electronics For You article covers Vinci’s thermo-mechanical simulation capability for automated warpage analysis in hardware designs.
Vinci’s physics AI foundation model delivers deterministic, solver-accurate warpage analysis across extreme scales—already in production use at leading hardware companies
In this Pulse 2.0 article, Vinci discusses its $46 million in total funding, its emergence from stealth, and its platform for semiconductor design and simulation.
In this SiliconANGLE article, Vinci discusses its $46 million in funding and its platform for AI-powered chip simulation.
In this Reuters article, Vinci discusses its $36 million Series A and its work building software for hardware simulation in chip and other design workflows.
Validated by early deployments at leading semiconductor companies, Vinci’s physics-based AI software operates up to 1000x faster than conventional simulation tools, at higher accuracy, and without training on customer data