AI Network Growth Trends: Scale Up, Scale Out, and Scale Across
Michael Crook
Published: November 13, 2025
Data centers are home to complex fiber optic ecosystems that enable a variety of AI applications (machine learning, natural language processing, and predictive analytics) at an unprecedented scale. Collectively, these AI use cases are compelling network operators to consider several forms of capacity expansion, and all of them will need to decide how to best use their capital dollars to meet their networking goals.
One of the most pressing challenges is the need for scalable growth to meet the demands of AI networks, both now and in the future. This means data center operators must continuously add network capacity to their existing infrastructure to support data-hungry AI applications.
As AI systems grow larger and more sophisticated, networks have several forms of connectivity growth to select from — scale up, scale out, and scale across. These three solutions each take a different approach to increasing network capacity.
Scaling Up: Expanding computational power at the core
Scaling up is all about increasing computational power by adding more resources within the existing backend AI network node—creating more capacity to accommodate the skyrocketing data demands of AI systems. The simplest way for a data center operator to scale up a GPU node is to add additional servers with GPUs.
Interconnecting these GPUs in the expanding cluster involves using high speed interconnects between network switches and the AI server. AI applications, like large language models, are growing in both size and complexity. By adding new servers and GPUs to the existing node, the operator is incrementally adding low latency capacity to help ensure the node has enough network bandwidth to support future AI applications.
Fiber optic solutions play a critical role here. High-bandwidth cables and connectors, such as Corning’s innovative high-density fiber solutions, that incorporate our Corning® SMF-28® Contour fiber, allow data centers to scale up their performance by increasing throughput without compromising reliability. For example, AI workloads often require the ability to transfer enormous datasets between servers and storage systems with minimal latency. Fibers designed for ultra-low loss and high-speed transmission ensure that these performance upgrades remain sustainable and efficient.
Increasing computational power at the core is just the first phase of inevitable network growth. It is important to consider the need to grow outward — and that brings us to the next growth trend.
Scaling Out: Expanding physical infrastructure
If scaling up refers to adding capacity to a single AI node, scaling out refers to increasing the number of nodes to accommodate increasing demand. This trend is particularly relevant for AI networks, which often require distributed architectures to process data across multiple nodes simultaneously.
The challenge here is ensuring that this physical expansion doesn’t lead to bottlenecks or inefficiencies. High fiber count cables, high density fiber housings, and advanced mesh networking components play a vital role in enabling scale-out growth by providing flexible, modular solutions that support rapid deployment and expansion. For instance, Corning’s preterminated optical fiber systems simplify installation and reduce downtime, making it easier for data centers to expand their footprint without disrupting operations.
Scaling Across: Interconnecting distributed systems
Finally, scaling across is about connecting multiple data centers or AI clusters to create a distributed network. As AI applications become more widespread, the need for interconnection between geographically dispersed facilities grows. For example, a company may need to link its primary data center in one city to another primary data center in another region to build an even larger AI cluster.
High density fiber optic cables are essential tools for this type of scaling. As data center operators and carriers deploy conduits with limited space, maximizing fiber density is key. Corning’s high fiber count cable portfolio, like the Contour™ Flow cable, are designed for interconnection deployments, supporting the scalability of AI networks across regions and use cases.
The path forward for scalable AI networks
As I work with customers across the industry, I see firsthand how critical it is to align fiber infrastructure with the unique demands of each AI growth phase. At Corning Optical Communications, we’re committed to helping our customers navigate these challenges. Our portfolio of innovative fiber solutions is designed to support scalability at every phase, enabling data centers to grow efficiently. AI is changing the world, and we’re proud to play a role in building the networks that make it possible.
The journey ahead is exciting, but it’s also complex. By understanding the distinct needs of scaling up, out, and across, data center operators can position themselves for success in this rapidly evolving landscape.
