Why ​Physical AI Needs A New Architecture To Scale

Dr. Greg Ombach, CEO-Level Deep-Tech Operator & Board Member, Senior Vice President at Airbus.

Physical AI can perform impressive tasks, but today’s vision, language and action models, known as VLAs, face a scaling trade-off. Larger models offer broader capability but require extensive robot data and computing, making edge deployment difficult or impossible. Smaller models can run locally, but cannot yet transfer learned skills across robots, tasks and sites without substantial retraining and validation.

As physical AI moves from structured consumer electronics and automotive production into more variable aerospace assembly, and eventually hospitals and homes, reliable perception becomes critical. A robot must identify an object and its pose as conditions change, then determine the correct approach, force and likely consequences.

To scale embodied AI, we need a modular architecture that reduces dependence on large models, learns quickly from limited demonstrations, operates locally with low latency and transfers skills across robots and environments. It must maintain reliable spatial and physical understanding, predict consequences and operate within independent safety controls. When reality differs from the prediction, it must correct the action, request assistance or stop safely.

From Pixels To Geometric And Physical Understanding

Traditional computer vision recognizes objects through two-dimensional pixel patterns. Many VLAs convert camera frames into visual tokens derived from pixels, then infer three-dimensional structure while deciding how to act.

A geometry-first model can instead extract reusable features such as surfaces, edges, shape, position, orientation and movement. They are designed to remain stable across lighting and viewpoints.

Geometry alone does not predict how an object will respond to action. Physical understanding must add force, contact and learned dynamics, allowing the robot to predict the consequences of a movement.