jacomo corbo

PhysicsX demonstrates that large physics models trained on simulation and real-world data can supersede traditional CAE, enabling high-fidelity simulation across the entire product lifecycle from design to manufacturing operations.

PhysicsX is moving engineering simulation from legacy numerical methods to AI inference, achieving 10,000x to 1Mx speedups while training on both simulation data and real-world measurements from wind tunnels, test dynos, and operating equipment. This enables high-fidelity simulation to be used downstream in manufacturing, testing, and operations — not just detailed design — creating a new software layer for industrial production.

The full data center stack — gas turbines, chilling units, step-down transformers, and internal equipment — represents a massive industrial surface area where AI-driven simulation can optimize design and manufacturing, pulling in partners like Nvidia and Siemens.

Established data and model-size scaling laws for physics models — where larger models trained on more data generalize better zero-shot — give PhysicsX conviction to invest heavily in larger pre-trained models and pursue a land-grab across multiple industrial verticals simultaneously.

PhysicsX has established data and model-size scaling laws for pre-trained physics models analogous to those in language and robotics: larger models trained on more data show better generalization and zero-shot performance. This gives the company conviction to invest heavily in larger, more capable physics models, implying sustained demand for specialized AI training compute in industrial domains.

A new wave of neo-manufacturers across semiconductors, quantum, fusion, and aerospace are reaching production scale and becoming major engineering organizations, creating a growing customer base for AI-native simulation platforms.

PhysicsX's work with semiconductor equipment manufacturers and fabs on advanced packaging and process control indicates the semi supply chain is a beachhead for AI-native simulation, with global footprint requiring global service capability.

Real-time AI control of industrial processes like aluminum smelting cell baths and gas turbine operations can materially reduce energy consumption and improve yield, linking industrial AI directly to power generation efficiency.