deliver on its promises. The challenge lies in AI’s growing need for distributed processing. AI models are no longer confined to centralised data centers; they are deployed across cloud platforms, edge devices, and enterprise environments, each with unique latency and bandwidth requirements. AI inference – the real-time application of trained models and the most common use of AI – depends on ultra-fast data transfers between these locations. Traditional cloud architectures, which rely on unpredictable public Internet routing, introduce performance limitations that become unacceptable when milliseconds matter. Whether an AI model is making real-time decisions in a self-driving vehicle or processing predictive analytics at a financial firm, the network must be able to handle large-scale, high-speed data movement without congestion or extensive packet loss. So how can that be achieved? AI workloads require direct, low-latency pathways between cloud providers, edge computing sites, IoT devices, and enterprise infrastructure to ensure real- time responsiveness, while at the same time, AI’s appetite for data is growing exponentially, with models requiring continuous updates and retraining based on new inputs. This requires a shift away from traditional data center-centric architectures toward a more distributed, dynamically interconnected model. Interconnection provides the missing link in AI’s infrastructure challenge by creating
direct, high-performance pathways between cloud providers, data centers, and edge computing environments. Unlike traditional networking models that rely on multiple hops over the public Internet, interconnection establishes private, low-latency connections that optimise dataflows and ensure AI workloads can move seamlessly. Security and reliability are also critical concerns. We know that AI models depend on vast amounts of often sensitive data, requiring secure, predictable network environments. Ensuring that AI traffic moves through secure, dedicated pathways rather than competing for bandwidth on congested public networks will become essential in meeting compliance obligations and mitigating cyberthreats. Put simply, as AI models become more sophisticated, enterprises will need greater control over their dataflows. And it’s that same element of control that will eventually unlock AI growth. Almost every business conversation at the moment is dominated by AI. It’s difficult to think of a technology that has experienced such far-reaching breakthroughs in such a short span of time – but those breakthroughs alone aren’t enough. The real challenge is building the digital infrastructure necessary to support AI at scale, ensuring that latency, security, and bandwidth constraints do not hold back its potential. As organisations push AI deployments beyond isolated use cases and into widespread real-world applications, our focus must expand from developing new AI capabilities to ensuring the underlying infrastructure is ready to sustain them. MWC 2025 showcased the next generation of AI-driven technology; now the industry must take on the harder task – building the foundation need to make next-generation this generation.
AI’s appetite for data is growing exponentially, with models requiring continuous updates and retraining based on new inputs.
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