What Is Physical AI? How AI Is Moving from Screens Into the Real World

For many people, artificial intelligence is most familiar as software accessed through a screen: a chatbot answering questions, a tool generating images, or an application analyzing information. But a growing area of AI development is taking intelligence beyond digital environments and into the physical world.

Known as physical AI, this field focuses on systems that can perceive their surroundings, interpret information, make decisions, and act in real-world environments. These capabilities are being developed for robots, autonomous vehicles, and industrial machines that must respond to changing conditions rather than simply follow a fixed sequence of instructions.

The distinction matters. Acting in a physical environment introduces challenges that software operating on a screen does not face in the same way. A machine must account for space, movement, objects, people, and the consequences of its actions. It must also respond appropriately when conditions change unexpectedly.

What Is Physical AI?

Physical AI refers to artificial intelligence integrated into machines that interact with the physical world. These systems combine AI models with sensors and mechanical components, allowing them to gather information about their surroundings, interpret what they detect, and carry out actions.

As NVIDIA explains in its overview of physical AI, autonomous machines can use inputs such as images, video, language, and sensor data to understand their environments and perform complex actions.

This is not simply another name for robotics. Robotics is the broader field concerned with designing, building, and controlling robots. Physical AI describes the intelligence that can enable machines to perceive, reason, and adapt as they interact with their surroundings. A conventional robot may be programmed to repeat a narrowly defined task, while an AI-enabled system can use sensor information to respond to conditions that vary from one situation to another.

A physical AI system typically brings together three capabilities:

• Perception: Sensors such as cameras, force sensors, and other detection systems collect information about the environment.
• Decision-making: AI models interpret that information and determine an appropriate response based on the task and current conditions.
• Action: Motors, robotic arms, wheels, or other mechanical components carry out the decision, while further sensor input helps the system respond to what happens next.

These capabilities work together in a continuous cycle. The system senses its environment, processes the information, acts, and then uses new input to guide its next response.

Physical AI in Action: What Amazon’s Robots Can Do

One prominent example of large-scale warehouse robotics comes from Amazon. In July 2025, the company announced that it had deployed its one-millionth robot across its operations. The milestone illustrates the scale at which robotics is being used in logistics, although the fleet includes different types of machines and should not be understood as one million physical AI systems.

Some of Amazon’s systems demonstrate capabilities associated with AI-enabled robotics. As reported by GeekWire, these include:

• Vulcan: Uses force sensors and AI to handle items with a sense of touch, helping it adjust how it interacts with objects.
• Proteus: Amazon’s first fully autonomous mobile robot, designed to navigate warehouse floors alongside people.
• Robin: Uses computer vision and suction to identify, sort, and transfer packages.
• DeepFleet: An AI system designed to coordinate robot movement across fulfillment centers and improve the efficiency of their routes.

These examples illustrate different ways intelligent systems can support warehouse operations, from handling objects to navigating shared spaces and coordinating movement. They also show why physical AI is not a single technology or a single type of robot. It brings together perception, decision-making, software, sensors, and mechanical systems to address different real-world tasks.

Why Physical Environments Make AI More Complex

An AI system that generates a written response or analyzes a dataset operates primarily on digital information. A physical AI system must also account for the consequences of acting in a real environment.

Consider a warehouse robot moving through an area where employees, packages, and other machines are constantly changing position. If a person unexpectedly enters its path, the system must detect the change, interpret the situation, and respond appropriately. Depending on the circumstances, that could mean slowing down, stopping, or adjusting its route to reduce the risk of a collision.

This requires more than recognizing an object in an image. The system must interpret spatial relationships, account for movement, and make decisions within the constraints of its physical surroundings. Its actions can affect people, equipment, and the wider operation.

That is why developing physical AI involves extensive testing and validation. Systems need to be evaluated in different conditions, including unusual or unexpected situations, before they are deployed in environments where mistakes can have physical consequences. Simulation can help developers test how machines respond to scenarios that may be difficult, expensive, or unsafe to reproduce repeatedly in the real world.

The challenge is not only to make a machine perform a task, but to make sure it can do so reliably and safely as its environment changes.

Where Could Physical AI Have the Greatest Impact?

Manufacturing and logistics are among the sectors where physical AI and advanced robotics are attracting significant attention. In these environments, machines can support tasks such as moving materials, handling products, inspecting equipment, and coordinating workflows.

The potential economic impact is also attracting attention. In a 2026 McKinsey interview on the future of robotics, McKinsey partner Ani Kelkar estimated that robotics and physical AI could create at least $1 trillion in economic value by 2040, with much of that value coming from manufacturing and logistics. This is an expert estimate shared in an interview, rather than a formal McKinsey forecast.

The opportunity extends beyond industrial settings. Autonomous vehicles, agricultural machines, and other systems designed to operate in changing environments also demonstrate how AI can be connected to physical tasks. However, the extent and pace of adoption will depend on whether these systems can meet practical requirements around reliability, safety, cost, and performance.

The Computer Science Foundations Behind Physical AI

Physical AI draws on several areas of computer science and engineering. Developing systems that can interpret information and respond to their surroundings requires knowledge of programming, data, algorithms, computer architecture, machine learning, and the systems that connect hardware and software.

A computer science education can provide some of these foundational concepts. At University of the People, the Bachelor of Science in Computer Science includes coursework in areas such as data structures, databases, communications and networking, operating systems, and data mining and machine learning. The program also includes learning outcomes related to designing computing systems, developing and testing software, analyzing problems, and considering computer ethics, privacy, and security.

These subjects provide a foundation for understanding how intelligent computing systems are developed and evaluated. They do not, on their own, constitute specialized training in robotics or physical AI, which also requires knowledge of areas such as robotics, control systems, sensors, and mechanical engineering.

As AI increasingly interacts with the physical world, the ability to understand how software, data, and computing systems work together will remain important across a range of technology fields. Physical AI brings these foundations into environments where the results of a system’s decisions are no longer limited to what appears on a screen.