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.

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.

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.

While European policy and industry rhetoric supports working with AI-native startups, commercial adoption and spend rates remain materially lower than in the US and Asia, creating a geographic disparity in industrial AI deployment velocity.

The compute, data, and model-size scaling laws PhysicsX has established for physics models parallel those in robotics for embodiment-free control, suggesting a shared trajectory for AI models that understand and predict physical world dynamics.