Robotics is forcing a fundamental rethink of AI compute, data, and systems design

Partner Content Physical AI and robotics are moving from the lab to the real world— and the cost of getting it wrong is no longer theoretical. With robots deployed in factories, warehouses, and public settings, large-scale simulation has become tightly coupled with real-world operations.

Physical AI companies need new types of infrastructure to continuously build, train, simulate, and deploy models that operate in dynamic, physical environments. With the cloud's current limitations, the next wave of physical AI won't scale.

Here are three reasons why the infrastructure stack needs to be purpose-built for physical AI.

1. The need for — and scarcity of — training data

Physical AI can't be trained on internet text, like an LLM. It requires context-specific data — from images and video to LiDAR, sensor streams, and motion data — that maps directly to actions and outcomes. With variation across environments, tasks, and hardware configurations, this data is ...