A recent study commissioned by Nokia reveals that today’s network infrastructure fails to support the explosive growth in AI technologies. According to the findings, the current networks won’t scale to meet the increasing demands of AI workloads. This presents a critical challenge and opportunity for industries to modernize their infrastructure as AI usage continues to expand.
The study surveyed around 2,000 technology and business decision-makers across the US and Europe. Participants included telecom operators, data center providers, and enterprises deploying AI at a large scale. A significant majority believe existing infrastructure lacks the capacity for the next phase of AI growth, urging substantial investment to stay competitive.
To address this, Nokia is restructuring around mobile and network infrastructure. This strategic overhaul aims to capitalize on what it calls the “AI supercycle.” By focusing on AI-native networks, Nokia intends to position itself as an AI networking specialist.
Pallavi Mahajan, Nokia’s chief technology and AI officer, emphasized that understanding future AI demands will require advanced networks and considerable investment. “The first wave of the AI supercycle has already reshaped industries and accelerated innovation,” noted Mahajan.
Accelerated AI workloads require networks with increased uplink capacity, distributed data flow, and enhanced performance metrics like low latency and energy efficiency. Such requirements pose challenges not only for telecom providers and cloud operators but also affect national competitiveness.
AI-powered application-such as autonomous vehicles, smart factories, and remotely operated healthcare systems-produce substantial data at the network edge. These must be efficiently transmitted upstream, fostering strains on existing networks. Notably, Nokia has recently reduced its focus on private 5G networks, which often address similar data demands.
A more predictable regulatory environment could facilitate timely and necessary infrastructure investments across the network ecosystem. The study shows a shared perspective among operators, enterprises, and technology partners for updating networks. In the US, nearly 88% of respondents are concerned about trailing infrastructure expansion, noting priorities like fiber capacity, data flow optimization, and edge infrastructure development.
European participants echoed the need for network updates, with 86% indicating unpreparedness for AI adoption at scale. These respondents cited regulatory simplification, spectrum accessibility, and energy-efficient network investment as vital. The absence of rapid modernization risks both regions facing bottlenecks, potentially limiting future AI deployment scale and impact.
Addressing this looming challenge will require a collective, coordinated effort from AI stakeholders and government bodies alike. Without swift action, the AI boom might overwhelm today’s networks, stalling progress rather than driving it.
