Each week we find a new topic for our readers to learn about in our AI Education column.
Most of us are familiar with the phrase “where the rubber meets the road,” meaning the most crucial point in time or location for something—where ideas are put into practice. Literally, it’s the contact point between your tires and the road. Today we’re going to talk about where the AI rubber meets the real-world road.
Welcome to AI Education, where this week’s discussion will focus on physical AI. Think of this as an outgrowth on our discussion of AI infrastructure, where now, instead of talking about the physical underpinnings that AI runs on, we’ll instead talk about applying that AI back to the real physical world.
We often encounter AI in a software environment—meaning, we interact with chatbots and AI assistants across the web—but AI connects to the physical world when it is asked to perform tasks like sifting through x-ray films to find anomalies, take accurate notes at a meeting or listen for astronomical phenomena—or extraterrestrial life—in our sky. AI is squarely crossing over into our physical world when it is used to power autonomous vehicles and manufacturing robotics.
What Is Physical AI?
Physical AI is the technology that allows autonomous systems to operate in the real world. Automated physical systems pre-date AI—think robotic manufacturing arms, which have been around for decades. These systems are somewhat limited—the spaces they work within and the items they work with have to be predictable, if not identical. Until recently, automated machines only worked because the materials they worked with were carefully processed by people.
If that all sounds a little bit like data and generative AI, then we’re on the right track. Until the recent generations of AI, we had a lot of software that could do useful things with our data, but only if we spent a lot of manpower and time preparing that data to be used by that software.
Physical AI systems are generative—meaning they retain the ability to learn or be trained to multi-task or to react to different stimuli in real time. Generative AI, as we typically discuss it, is trained on volumes of data like text, web pages and images—but physical AI uses similar technology to digest information about spaces and objects not just in three dimensions, but also how these things and environments change and evolve over time.
How Does Physical AI Work?
Implementing and training physical AI involves concepts we’ve previously discussed in AI Education like reinforcement learning, digital twins and computer vision. Machines enabled with physical AI use sensors that record real-time information about their environment—they might sense variables like light and shadow, temperature, humidity, pressure, weight or velocity—that are able to provide the technology an image of where it is physically working and what it is working with.
Maybe it isn’t as detailed an image of reality as we get from our brain, which translates the light hitting our eyes and information from our other senses to give us a picture of the world around us—but in physical AI it will at least be enough for the technology to complete its intended tasks. We can also use physical AI to feed our machines a more detailed and nuanced view of the physical world than our own senses.
Instead of millions of images or text files, physical AI is trained with millions of simulations involving the digital twins of objects and spaces. This entails creating a highly detailed and accurate three-dimensional digital environment. The AI models are rewarded for delivering a desired response within different scenarios—over time, the models become more likely to deliver the correct responses. By the time physical AI is implemented, repetition is used to teach the software to perform reliably when encountered by different environments and scenarios.
Training and implementing physical AI is usually far beyond the capabilities of a personal computer—it requires banks of GPUs and specialized AI chips that can not only process volumes of information being read by a system’s sensors in real time but also deliver a physical response to that information immediately.
Applications of Physical AI
The first application of physical AI that probably comes to most people’s minds as of this writing at the end of summer 2025 is in autonomous driving. Physical AI allows autonomous vehicles to see the roads and streets they are driving upon and vehicles, pedestrians and other objects they may encounter. Furthermore, physical AI allows these vehicles to read and understand road signage and their driving setting (urban versus rural, for example, or limited access highway versus one-way street) and also adjust to weather and traffic conditions in real time without the intervention of a human driver.
We’ve also mentioned robots, specifically in automobile manufacturing, but we haven’t discussed in detail what today’s physical AI is enabling robots to do. Imagine a robot that can occupy not just one space in an assembly line but could move itself almost any space in that assembly line and take over a task without having to be retrained. Imagine robots with the dexterity to pick peaches off of trees and pluck grapes off of a bunch without crushing them. Imagine surgical robots that can make life-saving decisions on the fly, without human intervention, when microseconds might be at stake. Imagine intelligent humanoid robots that can integrate into any workplace and serve as chefs, concierges, couriers, stockbrokers personal assistants, bodyguards, house cleaners, retail clerks, security guards, librarians—anything.
A third use, related to robots, is the creation of so-called smart spaces, where an entire environment is wired with physical AI. Smart spaces could be indoor or outdoor spaces—a personal home can be made into a smart home using the internet of things, for example—but the best application of smart spaces to date is in high-traffic areas like warehouses and factories, or large public venues like stadiums and arenas. Smart spaces use cameras and computer vision to deploy resources—either people or technology like robots—to the areas where they’re most likely to be needed, and to direct traffic around complex areas. So, a smart city might use cameras and other sensors to help move vehicles around congestion or accidents or to divert commuters to or from different modes of public transit. A smart stadium might be used to personalize fan experiences or to position mobile vendors near the fans most likely to spend their money. A smart care facility might track its IV pumps and wheelchairs to make sure that resources aren’t being lost or hoarded, or track wait times in an ER waiting room in real time to determine whether additional staff need to be called in.
