Building the Backbone of AI: How Internet Exchanges and Network-as-a-Service will Power the AI Era

The rapid evolution of artificial intelligence is fundamentally reshaping global network requirements, as the focus shifts from centralized training to massively distributed, real-time inference. AI inference is expected to overtake training as the dominant AI workload as early as 2027. And this changes everything: No longer is the network a subordinate infrastructure in the AI value chain. With inference, the network is increasingly serving as the primary orchestration and management layer, influencing where processing should occur and what pathways data should take. While all eyes have been focused on the extraordinary growth of AI compute in recent years, it’s important to remember that, without high-performance networks, AI cannot succeed, which is why software-defined Internet Exchanges (IXs) are increasingly adopting Network-as-a-Service (NaaS) principles, combining automated interconnection with standardized APIs to enable programmable AI-ready connectivity.

AI’s shift to real-time, distributed inference makes connectivity mission-critical

Inference workloads demand ultra-low latency and real-time responsiveness to meet customer expectations, unlike training workloads, which can tolerate distance. And it’s not just about a seamless user experience: Lags in the processing of AI inference will impact the bottom line of AI service providers and their customers. As AI agents and autonomous systems interact at machine speed, network latency and reliability become core determinants of performance.

What’s more, suboptimal network performance directly reduces GPU utilization, increases costs, and degrades user experience. This means that connectivity is no longer secondary – it is as critical as compute and storage in AI architectures.

Latency-sensitive inference is being embedded into every application, device, and workflow. And this latency sensitivity is heightening the challenges for ensuring optimal performance of every AI-supported functionality. We know from the content distribution business that this shift requires compute to be closer to users and data sources, across edge and regional environments, to achieve the scale and agility necessary. To minimize latency to workloads, AI demands the same distributed geography at even greater density.

The solution is to distribute compute and optimize the network layer globally. Achieving this requires network operators to collaborate using open standards. As AI infrastructure becomes more distributed, openness and interoperability become increasingly essential. This allows providers of every scale and geography to work together to meet the needs of shared customers. Realizing these capabilities across multiple providers depends on standardized APIs and interoperable frameworks that enable providers, IXs, cloud platforms, and enterprises to automate service delivery consistently across a multi-provider ecosystem.

This is where the complementary roles of IX operators, network service providers, cloud platforms, and industry alliances naturally converge. IXs provide the high-performance interconnection fabric that brings networks together, while open standards, common APIs, and interoperability initiatives make it possible to automate service delivery across multiple providers and technology domains.

Standardized lifecycle service orchestration APIs can provide a common framework for service ordering, provisioning, orchestration, and ongoing management. This helps turn programmable interconnection from a capability offered within individual platforms into one that can operate consistently across a wider, multi-provider ecosystem.

See also: Private AI Takes Center Stage: How Ultra Ethernet is Redefining Interconnected Infrastructures at Scale

Internet Exchanges enable the proximity, performance, and control AI requires

IXs function as the physical and logical aggregation points of the AI ecosystem, bringing together workloads, data, and users in high-performance environments. They provide direct, private interconnection between networks, offering significant advantages for operators in the AI value chain and their customers. Ultra-low latency: IX-based interconnection can reduce latency from tens or hundreds of milliseconds to sub-millisecond speeds within metros, enabling real-time AI inference. They also support deterministic performance, with direct routing eliminating “best-effort” Internet variability and ensuring consistent latency and throughput. IXs connect AI providers directly to cloud platforms, data sources, and end users in dense ecosystems, enabling efficient access to relevant data. In addition, peering reduces dependency on single transit providers and allows isolation of network issues (such as DDoS attacks), improving resilience and security and protecting AI workloads.

NaaS transforms connectivity into a scalable, programmable AI resource

While IXs already provide the interconnection fabric, NaaS-enabled IX platforms operationalize it for the AI era by making connectivity agile, automated, and cloud-like.

Modern NaaS capabilities include on-demand provisioning, API-driven control, Infrastructure as Code integration, and dynamic scalability, allowing connectivity services to be spun up in seconds rather than days, networks to be configured and managed programmatically, and bandwidth and routing to be adapted instantly to changing AI workload demands. As such, NaaS-enabled IX platforms can be aligned with AI and cloud deployment pipelines, while tools like Terraform enable connectivity to be embedded directly into AI workflows.

NaaS allows connectivity to be consumed with the same flexibility, speed, and automation as compute, making it viable to support dynamic AI workloads at scale.

See also: What Are Neoclouds and Why Does AI Need Them?

Software-defined interconnection + NaaS create a new AI-ready connectivity model

The convergence of IX infrastructure and NaaS platforms creates a software-defined, globally distributed interconnection layer capable of supporting AI at scale. The latest generation of high-capacity switching infrastructure, including the emergence of 800 Gigabit Ethernet, can support the enormous volumes of data moving between AI compute environments, clouds, data sources, and users. Dense interconnection ecosystems bring together cloud providers, neoclouds, content delivery networks, carriers, and enterprises, creating neutral marketplaces through which AI traffic can move efficiently.

Automation across provisioning, configuration, monitoring, and lifecycle management can further reduce friction and accelerate AI deployment. Combined with real-time observability and analytics, it gives providers and enterprises greater control over data flows, routing, capacity, and performance. This architecture shifts networking from a static constraint to a strategic enabler of AI innovation and scale.

Direct impact on AI economics, scalability, and adoption

For AI service providers, strengthening AI adoption and meeting customer expectations in terms of performance requires the implementation of a cost-efficient and profit-oriented AI distribution solution. As the network evolves into the AI control plane, connectivity becomes increasingly key to AI architectures.

The combination of software-defined IXs and NaaS has measurable benefits that directly influence AI growth. For example, lower latency reduces idle time, increasing GPU utilization and overall compute efficiency. Equally, efficient data paths and scalable bandwidth reduce infrastructure waste, lowering operational costs. Distributed interconnection supports expansion into new regions and edge environments, fostering global scalability. Finally, instant provisioning accelerates deployment of AI services, enabling a faster time-to-market. These improvements are essential to sustain the projected explosive growth of the AI inference market.

See also: NaaS for AI Takes Center Stage at GNE 2025

Open standards and interoperability are foundational to the future of AI infrastructure

Advancing AI-ready digital infrastructure to capitalize on the potential of AI will require closer collaboration across the connectivity ecosystem. IX operators, network service providers, cloud platforms, technology providers, and industry alliances must work together to advance interoperability, standardized APIs, automation, and common trust mechanisms across the wider digital infrastructure ecosystem.

Together, IXs, NaaS, standardized APIs, and ecosystem collaboration are transforming connectivity into a programmable, software-defined resource that will be essential to supporting the next generation of real-time, distributed AI inference. A market that, according to Grandview Research, is forecast to grow to over US $250 billion by 2030.

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