By Zhao Xiaofei, IoT Think Tank, Original
Over the past few years, the wave of AI (Artificial Intelligence) has primarily unfolded in the digital world. The evolution of large models has driven substantial progress in text generation, algorithm optimization, and more. Beyond these digital AI advancements, a more profound transformation is underway: AI is stepping into the physical world. Embedded in robots, AGVs, AMRs, industrial cameras, and smart sensors, it directly perceives, makes decisions, and acts upon the real environment, making Physical AI a hot topic in the industry.
What distinguishes Physical AI from digital AI is that its outputs are motion commands, stop signals, path decisions, and mechanical actions. If delays or interruptions occur, the result is not merely an error message, but potentially equipment damage, production line halts, or even safety incidents. In this process, high-quality wireless network communication is required among the carriers of Physical AI. For digital AI, wireless communication might not play a critical role; however, for Physical AI, the collaboration among physical devices reshapes the role of wireless networks, and wireless connectivity itself becomes an integral part of the Physical AI control loop. Against this backdrop, 5G private networks are poised to rapidly grow into a critical infrastructure supporting the deployment of Physical AI, forming a symbiotic relationship where demand and technology mutually drive each other.
Physical AI Demand Emerges as a Driver for 5G Private Network Market Growth
Wireless private networks are not a new concept. Various private networks existed as early as the 2G and 3G eras; for instance, the GSM-R private network used in railway systems effectively ensured railway dispatching and safe operations. In the early 2010s, the deployment of dedicated LTE networks began, further expanding the supported industries. However, for a long time, private networks remained a niche market within the wireless infrastructure sector. In the 5G era, driven by the digital transformation needs across industries, private networks have moved from a niche market into the mainstream spotlight. Although an explosive tipping point has not yet been reached, it has become a highly sought-after business model. Looking ahead, the stringent requirements for wireless communication posed by various terminals generated by Physical AI and industrial automation will further drive the development of 5G private networks.
Market data is validating this trend. A recent report by research firm SNS Telecom & IT shows that between 2026 and 2029, annual investment in 5G private networks for vertical industries will grow at a compound annual growth rate of approximately 34%, exceeding USD 6.6 billion by the end of 2029. A significant portion of this growth will come from localized network deployments for Physical AI, industrial automation, and more. Currently, the entities deploying 5G private networks include global industrial giants such as Tesla, BMW, Toyota, Hyundai, Foxconn, BASF, and Airbus, which are advancing multi-site, cross-border 5G private network construction in both existing factories and newly built facilities.
Physical AI may become a significant driver for 5G private network procurement. Many industrial enterprises rely on 5G private networks to connect AGVs, AMRs, drones, cranes, forklifts, mining vehicles, quadruped robots, and even semi-humanoid robots to execute complex tasks. These devices themselves are core carriers of Physical AI. For example, an automotive parts supplier coordinates 100 semi-humanoid robots for material handling via a 5G private network; these robots will continuously evolve to become typical representatives of Physical AI in industrial scenarios. It can be said that the large-scale deployment of Physical AI will also directly translate into demand and investment for 5G private networks, serving as a robust growth engine for the private network market.
5G Private Networks Are Indispensable Infrastructure for Physical AI
For many Physical AI scenarios, the requirements for wireless networks far exceed what traditional best-effort wireless technologies can support. For instance, regarding determinism and low latency, many Physical AI systems operate continuously in millisecond-level closed loops, requiring data to arrive with extremely high reliability within known time boundaries; the cost of any delay could be enormous. In terms of uplink capacity, data from machine vision, LiDAR, and telemetry is continuously transmitted from terminals to edge computing nodes, forming a pattern vastly different from the downlink-dominated traditional networks. High-resolution video streams alone are sufficient to overwhelm networks supporting ordinary services. Regarding mobility and coverage, metal structures, mobile equipment, and dense layouts in some factories cause complex interference. Assets move continuously over large areas, requiring smooth and predictable handovers.
5G private networks systematically meet these requirements. It is well known that 5G private networks have achieved a generational leap in throughput, latency, reliability, availability, and connection density. Their URLLC (Ultra-Reliable Low-Latency Communication) and mMTC (Massive Machine-Type Communication) capabilities make them a viable wired-connection alternative for industrial-grade communication among machines, robots, and control systems. Through quality of service assurance, traffic isolation, and more advanced wireless scheduling, 5G private networks can provide higher determinism; their configurable uplink capacity adapts to the uplink-intensive traffic characteristics of Physical AI; and dedicated spectrum avoids interference issues in unlicensed bands. At the security level, 5G private networks provide device-level identity authentication, network slicing isolation, and controlled access, mitigating attack risks brought by massive connected machines.
Actions by key vendors confirm the infrastructure status of 5G private networks. In February this year, NTT DATA and Ericsson announced a strategic partnership to combine Ericsson's 5G private networks and edge platforms with NTT DATA's full-stack enterprise network services. They will deliver 5G private networks at scale globally in the form of managed services, and directly run edge AI agents on Ericsson's enterprise edge platform to support Physical AI deployment, achieving real-time intelligence and autonomous decision-making at the point of data generation. An IDC analyst commented on this: "5G private networks are the backbone for scaling AI in production environments; autonomous systems must operate reliably and at scale." The collaboration focuses on high-value scenarios such as manufacturing, port logistics, energy and mining, and smart cities, covering typical Physical AI applications like automated quality inspection, predictive maintenance, real-time safety monitoring, and autonomous operations.
It must be emphasized that 5G private networks do not operate in isolation. The typical architecture for Physical AI is a combination of "private network + edge computing," where 5G private networks collaborate with edge computing nodes to perform inference and decision-making close to the point of action. This reduces reliance on remote clouds while ensuring data sovereignty and operational resilience, with connectivity and computing power jointly supporting the operation of Physical AI.
5G Private Networks Still Face Practical Challenges in Supporting Physical AI
For 5G private networks to become the infrastructure foundation for Physical AI, several obstacles must still be overcome.
First, the integration of 5G private networks with Physical AI systems involves high complexity. Relevant analyses point out that although 5G private networks are the infrastructure for AI scaling in production, integration complexity often remains a hurdle. Physical AI systems need to integrate with existing operational platforms such as MES, SCADA, and ERP, coordinate robots, sensors, and other equipment from different vendors, and reconcile diverse data formats and protocols. Some interoperability issues act as barriers to large-scale deployment.
Second, the thresholds for deployment and operation are also quite evident. Building a private network with industrial-grade determinism involves spectrum acquisition, wireless planning, core network deployment, edge computing power configuration, and IT/OT security system integration, which tests both the technical capabilities and financial investment of enterprises. For Physical AI scenarios, specialized integration and managed services are still required to achieve rapid private network deployment; it has not yet matured to a plug-and-play level.
Third, the dilemma of organizational integration between IT and OT. The deployment of Physical AI requires deep collaboration between IT and OT teams, and it will take time for the two types of teams to integrate in terms of culture, processes, and assessment systems.
Fourth, performance validation in extreme environments. The cost of failure in Physical AI is physical, meaning private networks must prove their long-term reliability in real, complex industrial environments, rather than just performing well in pilots. Moving from pilot to production-grade operations requires extensive accumulation of engineering practice.
The relationship between 5G private networks and Physical AI is a typical case of "demand pulling supply and supply creating demand," meaning the stringent requirements of Physical AI for determinism, uplink capacity, and mobility open up more market space for 5G private networks; in turn, the technical capabilities of 5G private networks enable Physical AI to move from small-scale applications to large-scale deployment. As wireless technology evolves and continues to strengthen, along with the smooth evolution toward 6G in the future, the private network + edge intelligence foundation serving Physical AI will be continuously reinforced.