Martin Szerment
AuthorPublished on February 9, 2026
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Picture a warehouse robot navigating the aisles at full speed, or a port crane stacking containers with millimeter-level precision. These aren't machines running pre-written scripts — they're AI systems making decisions in real time. This is what the era of Physical AI looks like.
Physical AI refers to intelligent systems capable of sensing, interpreting, and acting in the real world. Autonomous vehicles weaving through city traffic, robotic arms assembling components with surgical accuracy, smart energy grids responding instantly to load changes — these are just a few examples.
At the heart of this transformation is the digital twin: a continuously updated virtual replica of a physical object or system. Digital twins mirror reality with remarkable fidelity, allowing AI to test scenarios, predict outcomes, and guide actions instantly. But behind this technology lies something equally critical: the network. Without fast, secure, and reliable connectivity, Physical AI simply cannot function.
The next step in scaling Physical AI is the development of Multimodal Large Language Models (MLLMs) — AI systems capable of understanding and processing multiple data types simultaneously: text, images, video, audio, LiDAR, and more. When such a model is connected to a physical environment and real-time sensor data, it effectively becomes an "LLM for the physical world."
Digital twins support these models in two ways: as simulation environments for testing and refinement, and as live references during real-time operations. Together, they give MLLMs the accurate, up-to-date context needed for smarter decisions. Without a robust, intelligent network — the backbone connecting assets, twins, and AI models — none of it works.
From Design Tool to Autonomous Partner
For years, digital twins served primarily as design and simulation tools. Engineers tested jet engines before manufacturing them, modeled factory floors to identify inefficiencies. Physical AI fundamentally changes their role.
Today, twins are part of a continuous control loop — constantly updated with sensor data, generating predictions, and sending commands back to physical machines. This entire cycle happens in milli- or even microseconds, which means the underlying infrastructure must move enormous volumes of data at extraordinary speed.
Consider a delivery robot in a warehouse. Its cameras, LiDAR sensors, and ultrasonic detectors gather data every microsecond. The digital twin processes that information, anticipates hazards, and plans a route. The robot receives instructions instantly, adjusts its movement, and continues. When connectivity fails, the entire chain breaks.
Every Microsecond Counts
The demands placed on Physical AI networks go far beyond traditional connectivity. When a port crane relies on its digital twin to coordinate the movement of multi-ton loads, even a few hundred microseconds of delay can result in an accident. Physical AI operates effectively only when latency — the time between a sensor reading and a system response — is reduced to an absolute minimum.
Edge computing makes this possible by processing data close to where it is generated. Digital twins can reside at the edge for maximum responsiveness, or in the cloud for broader scalability. In either case, the infrastructure must deliver seamless, end-to-end performance.
The network must also bridge edge and cloud smoothly: real-time local decision-making on one side, and analytics, long-term data storage, and AI model training on the other. And since Physical AI operates in demanding conditions — dust, moisture, extreme temperatures, constant vibrations — the infrastructure itself must be engineered to withstand them.
Systems Hungry for Data
High-fidelity digital twins don't just operate fast — they consume enormous amounts of data. A single autonomous vehicle can generate terabytes per hour from its cameras, radar, and LiDAR sensors. While most processing happens onboard, the most critical data must flow seamlessly to the cloud or edge servers. Any bottleneck causes the twin to fall out of sync with reality — and when that happens, the AI's decisions can no longer be trusted.
Networks supporting these systems must not only deliver massive throughput but also maintain absolute precision and reliability in real time.
The Network as Nervous System, Security as Immune System
Physical AI frequently manages critical infrastructure: manufacturing plants, transportation hubs, medical robotics. In these environments, network downtime or a security breach can have serious consequences.
If Physical AI is the brain, the network is the nervous system — carrying sensory data, enabling processing, and triggering physical actions. Security plays the role of the immune system, defending against external and internal threats.
That's why resilience must be built in from day one: redundant connections, advanced fault tolerance, encryption, authentication, intrusion detection, and network segmentation. By integrating security directly into the network infrastructure, organizations maintain consistent protection across physical, cloud, and virtual environments — without sacrificing performance.
From Lab to the Real World
Industries like automotive and logistics are already demonstrating what Physical AI makes possible. Their experience consistently points to the same prerequisites for success: ultra-low latency, high bandwidth, reliability, and robust security.
Ultimately, the success of Physical AI comes down to one thing: infrastructure built to match the ambition. Networks must deliver speed, intelligence, and resilience from day one — not as an afterthought.
Organizations that invest in solid, secure connectivity today will be the ones that transform Physical AI from an interesting concept into a genuine competitive advantage.
If the topic of digital twins and industrial simulation caught your attention, we invite you to explore Simultus — software designed specifically for digital twin applications. Simultus allows you to simulate virtually any device, connect it to a real PLC controller, and test your concepts in a 3D virtual environment before a single physical component is built. Available in versions for both education and industry.
Learn more: www.simultus.pl