Physical AI for Smart Buildings

The current discourse around Physical AI is dominated by humanoid robots and autonomous vehicles, but the concept extends far beyond mobile machines. Physical AI is fundamentally about integrating sensing, understanding, and action in the real world into AI systems.

Viewed through this lens, smart buildings represent one of the most immediate and tractable applications of Physical AI — environments that perceive their surroundings, reason about occupant needs and energy dynamics, and act through mechanical systems to optimize comfort, efficiency, and safety.

Giving AI Knowledge of the Physical Building

A central concept in Physical AI is the world model — the internal representation that enables an AI system to understand and predict the results of specific actions in the physical world. Yann LeCun, who recently departed Meta to found Advanced Machine Intelligence (AMI) Labs, has argued that world models operating in abstract representation spaces — rather than generating raw sensory predictions — are the key to machine intelligence that goes beyond what large language models can achieve.

LeCun's insight is directly relevant to buildings: a world model for a building does not need to simulate every molecule of air. It needs to represent the relationships between equipment, spaces, occupants, and energy flows at the right level of abstraction to enable useful prediction and control.

For smart buildings, this world model is grounded in semantic ontologies and knowledge graphs. When properly integrated, these ontologies constitute a machine-readable world model that enables AI systems to reason about building behavior — predicting, for example, how adjusting a supply air setpoint will propagate through ductwork, affect zone temperatures, and impact energy consumption.

Sensing, Digital Twins, and Agents

The architecture of Physical AI for buildings maps directly to the perception–cognition–action loop found in all embodied intelligent systems.

Sensing is perception: IoT sensors, meters, and BMS data points provide the building's sensory input — occupancy, temperature, air quality, energy consumption, and equipment status. The digital twin is the cognitive model: instantiated from the world model ontologies, it combines the knowledge graph with a functional model to support understanding, reasoning, learning, decision-making, and planning. Agents are actors: AI agents translate decisions into physical actions through control sequences and actuation — adjusting HVAC setpoints, modulating lighting, reconfiguring ventilation zones — closing the loop between digital intelligence and physical outcome.

Three Phases, Not Three Machines

NVIDIA's "three computers" framework for Physical AI was designed with robotics and autonomous vehicles in mind. But for smart buildings, these three computers are better understood as three phases of a continuous workflow.

This cycle of plan, simulate, and act runs continuously, with sensor feedback updating the world model and informing the next iteration.

The Right Level of Abstraction

The promise of Physical AI for buildings lies precisely in the bounded nature of the problem. Unlike a general-purpose humanoid that must navigate an open-ended physical world, a building operates within a well-defined domain: known equipment, known spaces, known physics of heat transfer and fluid dynamics, and established standards for comfort and efficiency.

The ontologies underpinning the world model — ASHRAE 223P, 231P, Brick, REC, QUDT — set this level of abstraction, making the problem tractable for AI systems today. As generative models struggle with the kind of causal, physics-grounded reasoning that building control demands, the ontology-based world model approach offers a practical and rigorous alternative.