Over the past few years, the wave of artificial intelligence has primarily occurred in the digital world. The development of large models has significantly advanced text generation and algorithm optimization. Beyond these advancements in digital AI, a more profound transformation is underway: AI is moving into the physical world, embedding itself in robots, AGVs, AMRs, industrial cameras, and intelligent sensors, directly perceiving, deciding, and acting upon the real environment. Physical AI has become a hot topic in the industry.

The difference between Physical AI and digital AI lies in its output: motion commands, stop signals, path decisions, and mechanical actions. Delays or interruptions do not result in error messages but may lead to equipment damage, production line halts, or even safety accidents. In this process, high-quality wireless network communication is essential among the carriers of Physical AI. While wireless communication may not play a crucial role for digital AI, for Physical AI, the collaboration between physical devices reshapes the role of wireless networks, making wireless connectivity an integral part of the Physical AI control loop. Against this backdrop, 5G private networks are poised to rapidly emerge as a key infrastructure supporting the deployment of Physical AI, forming a symbiotic relationship where demand and technology mutually drive each other.

Demand for Physical AI Drives Growth in the 5G Private Network Market

Wireless private networks are not new. As early as the 2G and 3G eras, various private networks existed, such as the GSM-R dedicated network used in railway systems, which effectively ensured railway scheduling and safe operations. In the early 2010s, the deployment of dedicated LTE networks began to support an expanding range of industries. However, for a long time, private networks remained a niche market within the wireless infrastructure domain. In the 5G era, the demand for digital transformation across industries has brought private networks into the mainstream. While they have not yet reached a tipping point, they have become a highly sought-after sector. In the future, the stringent requirements for wireless communication posed by various Physical AI terminals and industrial automation will further drive the development of 5G private networks.

Market data is confirming this trend. A recent report by SNS Telecom & IT projects that annual investments in vertical industry 5G private networks will grow at a compound annual growth rate of approximately 34% between 2026 and 2029, exceeding $6.6 billion by the end of 2029. A significant portion of this growth will come from localized network deployments for Physical AI and industrial automation. Currently, global industrial giants such as Tesla, BMW, Toyota, Hyundai, Foxconn, BASF, and Airbus are deploying multi-site, cross-border 5G private networks in their existing and new facilities.

Notably, Physical AI is likely to become a major driver of 5G private network procurement. Many industrial enterprises rely on 5G private networks to connect AGVs, AMRs, drones, cranes, forklifts, mining vehicles, quadrupedal robots, and even semi-humanoid robots to perform complex tasks. These devices themselves are core carriers of Physical AI. For example, an automotive parts supplier coordinates 100 semi-humanoid robots for handling tasks via a 5G private network, with these robots continuously evolving into typical representatives of Physical AI in industrial settings. It can be said that the large-scale deployment of Physical AI will directly translate into demand and investment in 5G private networks, becoming a strong growth engine for the private network market.

5G Private Networks: An 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, in terms of 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 each delay can be enormous. In terms of uplink capacity, machine vision, LiDAR, and telemetry data continuously flow from terminals to edge computing nodes, creating a pattern starkly different from traditional networks dominated by downlink traffic. High-resolution video streams alone can overwhelm networks supporting ordinary services. In terms of mobility and coverage, complex interference caused by metal structures, mobile equipment, and dense layouts in some factories, combined with assets continuously moving over large areas, demands smooth and predictable handovers.

5G private networks systematically meet these demands. As is well known, 5G private networks achieve generational leaps in throughput, latency, reliability, availability, and connection density. Their URLLC (Ultra-Reliable Low-Latency Communications) and mMTC (Massive Machine-Type Communications) capabilities make them a wired-alternative solution for industrial-grade communication between machines, robots, and control systems. Through quality-of-service guarantees, traffic isolation, and more advanced wireless scheduling, 5G private networks provide higher determinism. Their configurable uplink capacity accommodates the uplink-intensive traffic characteristics of Physical AI, while dedicated spectrum avoids interference from unlicensed bands. At the security level, 5G private networks offer device-level identity authentication, network slicing isolation, and controlled access, reducing the attack surface posed by massive connected machines.

The actions of some key vendors confirm the infrastructure status of 5G private networks. In February this year, NTT DATA and Ericsson announced a strategic collaboration 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 globally on a managed services basis and directly run edge AI agents on Ericsson's enterprise edge platform, supporting Physical AI deployment and enabling real-time intelligence and autonomous decision-making at the data source. An IDC analyst commented, "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 of Physical AI combines "private networks + edge computing," where 5G private networks collaborate with edge computing nodes to complete inference and decision-making close to the action points, reducing reliance on remote clouds while ensuring data sovereignty and operational resilience. Connectivity and computing jointly support the operation of Physical AI.

Challenges Remain for 5G Private Networks Supporting Physical AI

For 5G private networks to become the infrastructure foundation for Physical AI, several obstacles must be overcome.

First, the integration complexity between 5G private networks and Physical AI systems is high. Analyses point out that while 5G private networks are the infrastructure for scaling AI production, integration complexity often remains a barrier. Physical AI systems need to interface with existing operational platforms like MES, SCADA, and ERP, coordinate robots, sensors, and other devices from different vendors, and reconcile diverse data formats and protocols. Some interoperability issues hinder large-scale deployment.

Second, deployment and operational barriers are significant. Building an industrial-grade deterministic private network involves spectrum acquisition, wireless planning, core network deployment, edge computing configuration, and IT/OT security system integration, testing both technical capabilities and financial investment. For Physical AI scenarios, professional integration and managed services are still required for rapid private network deployment, and they are not yet mature enough for plug-and-play adoption.

Third, organizational integration between IT and OT teams remains challenging. The deployment of Physical AI requires deep collaboration between IT and OT teams, whose cultures, processes, and evaluation systems still need time to align.

Fourth, performance verification in extreme environments is essential. The failure costs of Physical AI are physical, meaning private networks must prove their long-term reliability in real, complex industrial environments, not just perform well in pilots. Transitioning from pilots to production-grade operations requires extensive engineering practice.

The relationship between 5G private networks and Physical AI exemplifies the dynamic where "demand pulls supply, and supply creates demand." The stringent requirements of Physical AI for determinism, uplink capacity, and mobility open up more market space for 5G private networks, while the technological capabilities of 5G private networks enable Physical AI to move from niche applications to large-scale deployment. As wireless technologies evolve and continue to strengthen, along with a smooth transition toward 6G in the future, the private network + edge intelligence foundation serving Physical AI will continue to solidify.