Image generated using ChatGPT. Image of neural intelligence interfacing with robotic manufacturing technology. January 26, 2026.

‍

Artificial intelligence is entering a new phase.

Over the last decade, AI has transformed digital systems. Language models reason over text, vision systems interpret images and video, and decision systems optimize complex workflows. But nearly all of this progress has remained confined to the digital realm. The next shift is about intelligence that operates in the physical world. Systems that can perceive reality, interpret, reason, and take action within it. The next shift is about intelligence that operates in the physical world, systems that can perceive reality, interpret, reason, and take action within it.

This transition is often described using familiar terms like robotics or autonomy, but those terms are incomplete. Those concepts are part of the story, but they don’t fully explain what is emerging. What’s changing is not just where AI is deployed, but how physical intelligence itself is built.

That shift is best captured by the term physical AI.

‍

A clear definition of physical AI

The defining characteristic of physical AI is generalizable physical reasoning: the ability to learn how the physical world works and apply that understanding across tasks, environments, and embodiments.  Systems that rely primarily on fixed rules, task-specific process, or manually engineered world models, even if deployed on physical hardware, do not meet this definition.

Rather than being engineered for a single task or environment, physical AI systems learn internal representations of the real world (think objects, spatial relationships, dynamics, constraints, and cause-and-effect) and use those representations to guide action. Since this intelligence is learned rather than hand-specified, it can adapt as conditions, tasks, or hardware change.

Crucially, physical AI is not defined by hardware alone. It is defined by the interpretation layer that connects perception to action.

That’s why physical AI can appear in different forms:

• ‍Embodied systems, such as robots that can perform multiple tasks without being reprogrammed for each one.‍
• Software-only systems, such as simulation or digital-twin environments that reason about physical processes (factories, logistics networks, power grids) and generate actions that are later executed by machines or humans.

In all cases, the core capability is the same: a model that understands and reasons about the physical world, rather than reacting to predefined signals and programming.

‍

What makes physical AI distinct

Historically, physical systems concentrated intelligence at the edges. Sensors captured data, controllers executed actions, and humans designed everything in between.

Physical AI changes where intelligence lives.

It introduces a model-driven interpretation layer that builds world models instead of task-specific pipelines, reasons over space, time, and physical dynamics, predicts outcomes and tradeoffs, and enables skills to transfer across tasks and platforms.

This is the first time the physical sense–act loop can be closed by a general model, rather than by task-specific engineering. The result is a shift from automating tasks to learning how the world works.

‍

How this differs from traditional autonomy

This difference becomes clearer when contrasted with how autonomy has historically been implemented.

Autonomy systems typically follow a sense → plan → act loop, but they are built to solve specific problems on specific hardware. They are usually designed hardware-first, with software layered on top. Even when positioned as “software-enabled,” these systems struggle to adapt to changes in the underlying hardware architecture and capabilities are tightly bound to a task and platform. As a result, changes in environment or hardware often require re-engineering, and skills do not transfer cleanly across systems or use cases.

Physical AI shifts this paradigm. By learning a shared interpretation of the physical world, behavior becomes software-defined and transferable, rather than hard-coded and task-bound.

Put simply: Autonomy is engineered per task. Physical AI is learned and generalizable.

‍

A framework for thinking about physical AI

A useful way to reason about physical AI is to separate it into three tightly coupled layers.

1. Interface Layer

How the system connects to the real world: multimodal sensors (vision, depth, RF, tactile), actuation and mobility, edge compute, and power. This layer defines what the system can observe and how it can act.

2. Interpretation / World-Model Layer

The core of physical AI: models that construct internal representations of the physical world, reason over space, time, and causality, predict outcomes and consequences, and enable simulation-to-real and cross-domain transfer. This layer enables generalization.

3. Action / Policy Layer

Where intelligence becomes behavior: learned policies that map interpreted state to action, adapt to new tasks without bespoke engineering, and improve through feedback loops.

Historically, most systems invested heavily in the interface and action layers, with humans filling the interpretive gap. Physical AI moves intelligence into the middle, allowing behavior to emerge from models rather than from pre-programmed rules.

Most physical systems fail not because of sensors or actuators, but because the interpretation layer is missing or tightly bound to a specific task. Physical AI succeeds when this layer becomes reusable, extensible, and central.

‍

Why this framing matters

Physical AI is not about making machines better at a single job. It is about creating intelligence that can operate in the physical world across many jobs.

That shift, from task-specific control to general physical reasoning, is what defines the field, and why physical AI represents a distinct evolution in how intelligence is built.

‍

Coming next: As physical AI moves from research into deployment, its implications extend beyond technology alone. In a follow-on post, we’ll explore why physical AI is becoming an important consideration for national security and what that means going forward.