Riverside Event Title
Physics Has a ChatGPT Moment: AI, Simulation, and the Future of Engineering

What happens when AI stops guessing and starts solving physics?

In this episode of AI Across The Product Lifecycle, I’m joined by Hardik Kabaria, co-founder and CFO of Vinci, and Andy Fine of the Fine Physics Consortium, for a sharp discussion on one of the biggest shifts in engineering software: AI-native physics simulation.

Vinci is building a physics intelligence layer: a foundation model for physics designed to answer real engineering questions around heat transfer, thermo-mechanical deformation, high-fidelity simulation, and manufacturing-resolution analysis. Hardik says Vinci is already deployed with tier-one hardware companies and can run simulations from hundreds of millions to over a trillion degrees of freedom.  

This is not vague AI hype.

We dig into what makes AI simulation credible, why deterministic physics matters, how engineers can validate results, and why thermal problems are becoming mission-critical across semiconductors, electronics, batteries, EVs, data centers, robotics, and advanced manufacturing.

If your product generates heat, deforms under load, consumes power, or depends on simulation to avoid expensive failures, this conversation matters.

Timeline
00:00 — Introduction: Vinci, Fine Physics Consortium, and the “OpenAI moment” for simulation
01:11 — What is physics intelligence?
02:18 — Why physics is universal and governed by differential equations
03:08 — Physics-based AI vs. surrogate models
04:01 — What makes a physics foundation model credible?
06:51 — Why business value beats white papers
08:33 — Where Vinci fits in the engineering workflow
10:16 — Heat transfer, fluid dynamics, and choosing the right wedge use case
11:14 — Vinci’s focus: semiconductor and electronics thermal problems
13:23 — Thermo-mechanical deformation and why materials warp
14:49 — Multi-physics simulation as a long-standing engineering holy grail
16:06 — Yield, reliability, and manufacturing risk in electronics
17:04 — ROI: faster design loops and thousands of analyses per day
19:23 — Uncertainty, validation, and trust in AI simulation
20:08 — Training on 45TB of physics simulation data
21:47 — Residual norms and transparency at inference time
24:42 — 300 million to 1.2 trillion degrees of freedom
25:51 — GPU requirements and why Vinci is built for modern hardware
27:09 — Quantum computing, GPUs, and future scalability
30:22 — Wedge use cases: chips, boards, servers, batteries, defense, robotics, steel plants
31:45 — Who buys AI-native simulation software?
33:50 — Why thermal engineers are Vinci’s first target users
35:06 — Power, cooling, throttling, and data center energy constraints
36:25 — What throttling means in chips, EVs, and thermal runaway scenarios
39:58 — Deployment, IP protection, Docker containers, cloud, and on-prem
41:27 — How to convince skeptical engineers
43:00 — Path to adoption: start with the customer’s real benchmark
44:16 — What engineering leaders should do next
45:47 — The physics brick in the AI factory of the future
46:03 — Final debate: can there ever be one general foundation model for all physics?

Join us for a practical, skeptical, deeply technical conversation about what AI can actually do for simulation, hardware design, and the next generation of engineering software.

#AI #Simulation #EngineeringSoftware #PhysicsAI #DigitalThread #Semiconductors #ThermalEngineering #CAE #ProductDevelopment #AIAcrossTheProductLifecycle #TheFutureOfPLM #BetterCallFino