Since the first industrial revolution, humanity has undergone a series of monumental technology‑driven shifts. These shifts are defined by transformative breakthroughs that changed how societies live, work and communicate. Just as each has reshaped societies and economies, these industrial ages were all dependent on external factors to drive their success.
Now that we’re well into the fourth industrial revolution we must take a closer look at one particular external factor that can inhibit or influence AI’s impact on our world: digital infrastructure. Low latency, intensely-powerful infrastructure underpins the AI economy. It enables the IoT, connects to cloud environments and powers bandwidth-heavy workloads – and it has done so since before the AI boom.
But as with all previous industrial revolutions, as technology becomes embedded and we begin to truly understand its impact and its capabilities, the scope widens; we reset our expectations, we explore new possibilities and we uncover more problems that need solving. It’s happening right now with AI – and the world’s digital infrastructure must adapt.
As AI becomes embedded into everyday life, what’s needed isn’t just a quick upgrade for legacy networks; nor is it an empty promise around being ‘AI ready’ simply because providers can scale bandwidth: it’s a fundamental reimagining of the digital infrastructure required to power the global AI economy.
Physically, far-reaching changes in network builds have been taking place to optimise the world’s infrastructure for AI, including:
- Major investment in subsea cable builds from hyperscalers and network providers
- Rapid rollout of high-density fibre to boost bandwidth and resilience
- Accelerated innovation, with the development of new technologies within the network core that boost power and performance and cut energy use
- New technologies within data centres, saving energy and space and trialling different cooling techniques
- Industry-wide pilots, including multi-core fibre trials, pushing network capabilities further.
At the heart of this transformation lies a set of non-negotiables – priorities which must be baked into a digital infrastructure strategy for it to carry future AI workloads with ease. These imperatives can be grouped into three: the first focuses on performance and scale; the second prioritises trust, sovereignty and security; and the third is a commitment to building responsibility and operational simplicity.
1. Performance and scale
Focusing on performance and scale ensures networks have the power, reach and responsiveness required for distributed AI models, high volume data flows and real time inference. Four elements are essential: capacity, capillarity, scalability and latency.
Capacity: this goes beyond forecasting bandwidth and compute. It’s about finding ways to expand capacity sustainably and cost-effectively. Providers must explore technologies that deliver higher performance while reducing energy use, perhaps through collaboration and pilots.
Capillarity: this is the ability of a network to extend connectivity deeply and efficiently across endpoints, devices, edge sites and remote locations. It is the lifeblood of the digital ecosystem, ensuring that AI workloads can be placed close to users for better performance across globally-distributed infrastructure.
Scalability: scalability must be simple and immediate. Automated platforms and consumption-based models allow businesses to adjust network resources as demand evolves, without long lead times or manual intervention.
Latency: low latency is central to AI traffic. The need to reduce delay has existed since the telegraph era and it now applies at an entirely different scale. Real-time applications require latency below five milliseconds, which demands new network design and optimised routing.
2. Trust, security and sovereignty
These imperatives protect against risk and ensure data protection, governance and compliance are embedded at the infrastructure level.
Security: AI has reshaped the threat landscape and requires providers to rethink how networks are secured. Zero trust architectures and AI-driven threat intelligence must be built into every layer. Security needs to be adaptive, continuous and capable of protecting AI era workloads.
Sovereignty: a recent global survey found enterprises that prioritise data, agentic AI and GenAI sovereignty are seeing returns up to five times higher than organisations that do not make data and AI sovereignty a mission-critical priority. In the UK, a new Sovereign AI Investment Fund worth 500 million pounds will strengthen national capabilities across AI and compute. In networking terms, sovereignty means maintaining authority and control over data, infrastructure and operations while meeting local legal and regulatory requirements. It ensures independence from external influence and protects national or organisational interests.
3. Responsibility and operational simplicity
These priorities support sustainable, manageable and user friendly AI adoption.
Responsibility: The industry must lead in defining responsible AI practices. Responsibility starts with infrastructure. Fairness, transparency and sustainability must be built into network design and operation. With AI significantly increasing global compute usage, environmental responsibility is critical. Infrastructure must be designed to minimise environmental impact while supporting future growth.
As an industry, we need greater consistency and transparency. This means establishing a single, cohesive framework; aligning on shared standards that enable collaboration; co-investing in interconnected systems accessible through automated platforms; and building infrastructure based on this set of common building blocks that define the foundation of digital infrastructure worldwide.
This is more than “AI‑ready” digital infrastructure. It’s building infrastructure designed for the future: scalable, interoperable, secure and capable of supporting the next generation of intelligence‑driven services.
