WHITEPAPER

Why physical AI breaks at scale

And why the data layer
is the missing reliability layer

Every major technology company is investing in physical AI: the systems that perceive, decide, and act in the real world. Autonomous vehicles, surgical robots, factory-floor systems, and drones operating where connectivity is uncertain.

The investment is flowing into the layers everyone can see: compute, models, training data, networking. But there is a foundational layer that barely features in the conversation, and it is the one that determines whether these systems work reliably in the field or fail silently. In the lab, data infrastructure failures are invisible. In the field, they are irreversible.

This white paper traces how data layer failures propagate through the control loop and the audit trail, examines why power loss, flash wear, and latency drift behave differently in physical AI than in cloud workloads, and maps the requirements across automotive, aerospace and defense, medical, industrial automation, and smart energy.

Access this white paper to find out what physical AI requires from the data layer.