5G Private Network and Physical AI: A Mutually Enabling Development Process
Over the past few years, the wave of artificial intelligence has mainly taken place in the digital world. The development of large models has enabled substantial progress in text generation, algorithm optimization and other fields. In addition to these developments of digital AI, a more profound transformation is underway: AI is moving into the physical world, embedded in robots, AGVs, AMRs, industrial cameras and intelligent sensors, to directly perceive, make decisions and act on the real environment. Physical AI has become a hot topic in the industry.
The difference between Physical AI and digital AI lies in that its outputs are motion commands, stop signals, path decisions and mechanical actions. In case of delay or interruption, the result will not be an error message, but may be equipment damage, production line shutdown or even safety accidents. In this process, high-quality wireless network communication is required between the carriers of Physical AI. For digital AI, wireless communication may not play an important role, while for Physical AI, the collaboration between physical devices reshapes the role of wireless networks, and wireless connection itself is part of the control loop of Physical AI. In this context, 5G private networks are likely to rapidly grow into a key infrastructure supporting the implementation of Physical AI, and the two form a symbiotic relationship where demand and technology pull each other.
Demand for Physical AI Becomes a Factor Driving the Growth of 5G Private Network Market
Wireless private networks are not new. As early as the 2G and 3G eras, various types of private networks already existed. For example, the GSM-R private network used by the railway system effectively guarantees the scheduling and safe operation of railways. In the early 2010s, the deployment of private LTE networks started, and the industries it supported further expanded. However, for a long time, private networks were only a niche market in the field of wireless infrastructure. In the 5G era, driven by the demand for digital transformation in various industries, private networks have moved from the niche market to the public view. Although the explosive inflection point has not yet arrived, it has become a highly popular business form. In the future, the harsh requirements for wireless communication put forward by various terminals formed by Physical AI and industrial automation will further drive the development of 5G private networks.
Market data is confirming this trend. According to a recent report released by research institution SNS Telecom & IT, the annual investment in 5G private networks in vertical industries will grow at a compound annual growth rate of about 34% from 2026 to 2029, and will exceed 6.6 billion US dollars by the end of 2029, a large part of which comes from local network deployment for Physical AI, industrial automation and other scenarios. At present, the entities deploying 5G private networks include global industrial giants such as Tesla, BMW, Toyota, Hyundai, Foxconn, BASF and Airbus, which are promoting the construction of multi-site, cross-border 5G private networks in existing factories and new facilities.
It is worth noting that Physical AI may become an important driving force 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 perform complex tasks. These devices themselves are the core carriers of Physical AI. For example, an auto parts supplier coordinates 100 semi-humanoid robots to carry out handling work through a 5G private network, and these robots will continue to evolve into typical representatives of Physical AI in industrial scenarios. It can be said that the large-scale implementation of Physical AI will also be directly converted into demand and investment for 5G private networks, becoming a strong growth engine for the private network market.
5G Private Network is 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 example, in terms of determinism and low latency, many Physical AI systems operate continuously with millisecond-level closed loops, requiring data to arrive within a known time boundary with extremely high reliability, and the cost of each delay may be huge. In terms of uplink capability, such as machine vision, LiDAR and telemetry data are continuously transmitted back from terminals to edge computing nodes, forming a completely different mode from traditional networks which are mainly downlink-oriented, and high-resolution video streams are enough to overwhelm networks supporting ordinary services. In terms of mobility and coverage, metal structures, mobile devices and dense layouts in some factories cause complex interference, and assets move continuously in a large range, requiring smooth and predictable handover.
5G private networks just systematically meet these demands. As is known to all, 5G private networks have achieved 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 an alternative to wired connections in industrial-grade communication between machines, robots and control systems. Through quality of service guarantee, traffic isolation and more advanced wireless scheduling, 5G private networks can provide higher determinism; their configurable uplink capabilities adapt to the traffic characteristics of dense uplink of Physical AI; dedicated spectrum avoids the interference problem of unlicensed bands. At the security level, 5G private networks provide device-level identity authentication, network slicing isolation and controlled access, reducing attacks brought by a large number of connected machines.
The actions of key vendors confirm the infrastructure status of 5G private networks. In February this year, NTT DATA and Ericsson announced a strategic cooperation, combining Ericsson's 5G private network and edge platform with NTT DATA's full-stack enterprise network services to deliver 5G private networks on a large scale worldwide in the form of managed services, and directly run edge AI agents on Ericsson's enterprise edge platform to support the deployment of Physical AI, realizing real-time intelligence and autonomous decision-making where data is generated. An IDC analyst commented on this: "5G private networks are the backbone for the large-scale implementation of AI in the production environment, and autonomous systems must operate reliably and on a large scale." The cooperation between the two parties focuses on high-value scenarios such as manufacturing, port logistics, energy mining and smart cities, covering typical Physical AI applications such as automated quality inspection, predictive maintenance, real-time safety monitoring and autonomous operation.
It needs to be emphasized that 5G private networks do not play a role in isolation. The typical architecture of Physical AI is the combination of "private network + edge computing", that is, the 5G private network cooperates with edge computing nodes to complete reasoning and decision-making close to the action point, reducing dependence on the remote cloud, while ensuring data sovereignty and operational resilience. Connection and computing power jointly support the operation of Physical AI.
5G Private Networks Still Face Practical Challenges in Supporting Physical AI
To become the infrastructure base for Physical AI, 5G private networks still need to overcome a number of obstacles.
First, the integration complexity of 5G private networks and Physical AI systems is high. Relevant analysis points out that although 5G private networks are the infrastructure for large-scale AI production, integration complexity is often still a threshold. Physical AI systems need to be connected to existing operation platforms such as MES, SCADA and ERP, coordinate robots, sensors and other devices from different vendors, and reconcile different data formats and protocols. Some interoperability problems are obstacles in large-scale deployment.
Second, the thresholds for deployment and operation are also quite obvious. 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 is a test for the technical capability and capital investment of enterprises. For Physical AI scenarios, professional integration and managed services are still needed to achieve rapid deployment of private networks, and it is not mature enough to be out-of-the-box.
Third, the dilemma of organizational integration between IT and OT. The implementation of Physical AI requires in-depth collaboration between the IT team and the OT team, and the integration of the two types of teams in culture, process and assessment system still takes time.
Fourth, performance verification in extreme environments. The cost of failure of Physical AI is physical, which means that the private network must prove its long-term reliability in a real and complex industrial environment, rather than only performing well in pilots. Moving from pilots to production-level operation requires a large amount of engineering practice accumulation.
The relationship between 5G private networks and Physical AI is a typical relationship of "demand drives supply, and supply creates demand", that is, the harsh requirements of Physical AI for determinism, uplink capability and mobility open up more market space for 5G private networks; and the technical capabilities of 5G private networks enable Physical AI to move from small-scale application to large-scale deployment. With the evolution and continuous enhancement of wireless technologies, as well as the smooth evolution towards 6G in the future, the private network + edge intelligent base serving Physical AI will continue to be strengthened.
This article is from the WeChat Official Account "IoT Think Tank" (ID: iot101), the author is Zhao Xiaofei, and it is released with authorization from 36Kr.