Fiber Connectivity Powering Future AI Infrastructure Growth
Brian Rhoney
Published: November 20, 2025
AI networks are evolving at an unprecedented pace, driven by exponential growth in AI capabilities and the surging demands for compute power. To meet these demands, operators are using these four core tactics to build robust, scalable, and innovative networks capable of supporting next-generation AI applications:
1. Connecting AI data centers for smarter operations
As hyperscale data hubs face constraints around power, land accessibility, and physical space within the data center, operators are shifting toward long-haul AI data centers. This trend involves dispersing AI workloads across interconnected campuses, creating fiber networks that link multiple data center locations over long distances.
For large language models (LLMs) and other AI systems, distributing computations, memory, and power across campuses enables improved performance and efficiency. This decentralized approach requires low-latency, high-bandwidth fiber optic cabling to support the intensive data processing demands of AI. Long-haul fiber networks are becoming the backbone of distributed AI infrastructure, enabling networks to pretrain and run massive AI models across multiple data centers while maintaining seamless connectivity.
2. Accounting for scalable growth today, not tomorrow
The most successful hyperscale networks account for tomorrow’s needs today as opposed to adjusting when the need arises. The need for scalability is a defining factor in a data center construction, especially as AI end user applications and workloads become more complex. Modern AI models require larger high-bandwidth GPU clusters, interconnected by extensive fiber networks to handle the large AI computational loads. These clusters are no longer confined to individual servers or server racks (scale up); instead, they are expanding across multiple racks, buildings, and even campuses — a growth trend known as “scale out”.
This evolution calls for bigger switches, multi-plane network fabrics, and dense cabling architectures to interconnect GPUs within scalable units. As AI nodes stretch across larger networks, the cabling requirements multiply, with generative AI networks already demanding 10x more fiber than traditional data centers. Historically, copper cabling was used in these architectures, but with the need for higher bandwidth and longer-distance links (reaching up to 100 gigabits per second per meter), fiber has become the more economical and space-efficient choice.
3. Accelerating network speeds with co-packaged optics (CPO)
CPO technology represents a transformative innovation in network design, merging optics and electronics into a single package to increase processing speeds and power efficiency. By integrating optics directly into switches, CPO eliminates the need for long signal travel before conversion to light, reducing latency and boosting performance.
The adoption of CPO technology allows operators to build larger, more efficient switches with higher port counts, overcoming the limitations of traditional pluggable transceivers. This shift is critical for handling the massive bandwidth demands of AI workloads, while also lowering total cost of ownership and improving scalability. As next-generation servers are designed for CPO, this technology is expected to play a central role in enabling hyperscale networks to keep pace with AI’s rapid growth.
4. Innovating Fiber Solutions to Power AI’s Future
The explosive growth of AI is pushing network operators to embrace bold innovations to support network infrastructure. From scaling out GPU clusters to building distributed campuses connected by long-haul fiber and adopting CPO technology, the ability to innovate is essential to staying ahead in the AI era.
Innovations under the Corning® GlassWorks AI™ Solutions portfolio are at the heart of these advancements, offering the bandwidth, latency, and efficiency required to meet the demands of AI networks. By investing in cutting-edge fibers and connectivity technologies, operators can create AI networks capable of supporting the training and inference of increasingly complex models while driving down operational costs.
The path forward for AI networks is clear: operators must focus on scaling, distributing, and innovating their infrastructure to support the next generation of AI applications. By embracing these four key trends — scalability, distributed networks, CPO technology, and advanced fiber solutions — operators can build tomorrow’s networks today, enabling AI to reach its full potential.
