During the Capital Markets day in November, Nokia shared that consumer AI traffic is expected to grow by 20%, and enterprise and industrial AI traffic by 50% over the next decade.
The rapid acceleration of Artificial Intelligence is driving the world toward a new technological "supercycle," but its next wave of growth faces formidable structural and logistical obstacles. According to industry executives, the core challenges lie not just in innovation, but in the physical infrastructure and operational logistics required to support AI's massive scale.
1. The Looming Grid Crisis and Computational Demand
The single most significant bottleneck for the next AI wave is the exponential growth in power consumption by data centers. Consumer AI traffic is expected to grow by 20%, and enterprise/industrial traffic by an astounding 50% over the next decade. Investment bank projections suggest that global power demand from data centers could grow by 175% by 2030, equivalent to adding a new top-ten power-consuming country.
The critical issue is not the lack of power generation, but the outdated electrical grid infrastructure. Data centers demand immense power instantly, requiring the grid to be reconfigured and adapted, a process that is time-consuming and costly, requiring hundreds of billions in investment. To mitigate this, network infrastructure companies are focusing on scaling up performance by improving efficiency and reducing the power required to transmit and process a single bit of data ("power-per-bit") through more integrated technology.
2. Supply Chain and Production Backlogs
Despite the massive demand for AI-ready network components and compute infrastructure, the supply chain is struggling to keep up. While not a severe crunch like the COVID-19 era, manufacturers face large backlogs because they cannot produce the necessary technology quickly enough. This inability to meet the market's clock speed acts as a brake on the technological boom, slowing the deployment of critical infrastructure needed for AI expansion.
3. Data Sovereignty and Security Concerns
Beyond the physical limitations, complex data governance issues pose a challenge. Questions around data security—how to ensure information is transmitted safely—and data sovereignty—determining which data can travel long distances and which must be kept domestically—remain unresolved obstacles that can slow down large-scale, cross-border AI deployments.
In summary, while the demand for AI is overwhelming, the industry's progression is currently being throttled by three key constraints: the slow adaptation of the electrical grid, limitations in the manufacturing supply chain, and critical unanswered questions about data policy and security.