A range of interlocking challenges and competing priorities have created a perfect storm of complexity across the data center industry that’s rapidly altering every aspect of facility design and operations.

AI-ready data centers aren’t just bigger; they’re smarter, faster, denser, and quicker to evolve.

Each new wave of technological advancement has a knock-on effect echoing across the entire data center ecosystem, and as the backbone of operations, physical connectivity must be amongst the first elements to respond.

The extent of AI-driven changes is so complex and far-reaching that simply scaling up capacity is no longer enough. Instead, connectivity must scale in all directions: inside data centers, between facilities, and across the white space.

To explain the nature of this shift, Rudy Musschebroeck, senior director of strategy at CommScope, shares his insights into how and why networks are changing, and the ways in which the company is supporting end-to-end scalability on multiple fronts.

Scaling up and out: Rising densities

In the past, before AI-ready data centers became commonplace, traditional CPU-based servers provided the required compute power. Musschebroeck explains: “A top of rack leaf switch aggregates traffic from ten to 30 servers, reducing the connectivity of the rack to a single fiber pair, and that is sufficient to fully leverage those resources for the tasks they perform.

“However, the architecture of modern AI-ready facilities and their GPU-filled racks has escalated that density by a factor of ten or more, and each new generation of GPU platform raises that density even higher. In addition, the backbone network of the data center must fully connect each individual GPU to every other GPU.”

This reality demands a vast increase in the density of the fiber fabric to make these high-speed ‘peer to peer’ style connections between racks, clusters, and even multiple sites. With hundreds or even thousands of connections in a single rack, this trend continues to curve upward.

“More GPUs are needed to build ever bigger data centers and train foundational large language models as well as application-specific models,” says Musschebroeck. “For a model to be more performant, it needs a more complex model, using more parameters, and training on more data – hence the need for more GPUs in the physical world.”

This scaling up process connects more GPUs in a rack that acts as a single computing resource. But even this is not enough to meet demand; multiple racks must be connected through a switching fabric, using Infiniband or Ultra Ethernet, to scale out capacity as well.

“This trend is continuing at pace in order to meet growing AI demand. Essentially, this means increasing the number of nodes inside a neutral network and in the physical world, which then translates into more GPUs that work together to accelerate the training of critical AI models,” says Musschebroeck.

Market demand for AI applications is catapulting this shift forward. As more industries, businesses, and individuals begin utilizing AI, the technology is evolving rapidly from large language models toward next-gen agentic AI. Musschebroeck expands on what this transformation means for network designs and performance:

“Increasingly, there’s huge demand for flexibility. We see customers that want to use their invested capital to push different workloads across the same infrastructure. And when it comes to AI inference capabilities, it makes sense to be located closer to the end user.”

Meshing is a network design approach that creates a robust interconnected architecture, providing multiple fiber paths between nodes that supports consistent performance, built-in redundancy and fast, scalable growth. This strategy has become a go-to for delivering the dense fiber connectivity modern operations demand.

The relationship between density and efficiency

The vast power requirements of data centers – particularly AI data centers – means that network efficiency is often just as much a priority as network performance. The availability and affordability of sufficient power is a significant constraining factor facing many data center builds worldwide – even more constraining than obtaining building permits or lining up capital.

“Rising AI usage necessarily involves fortified infrastructure and power generation,” says Musschebroeck. “A common bottleneck is limited power availability to a given location, but the industry is beginning to tackle this challenge by employing a strategy of geographical diversity – spreading out multiple sites across low-latency infrastructure to create a distributed data center, which can operate more cost- and energy-efficiently than a single large site with less reliable access to sufficient power.”

Musschebroeck continues, “Looking at individual sites, there are innovations now hitting the market that can reduce the energy use associated with cooling, which is a significant part of running an AI data center full of high-density GPU racks. For instance, direct-to-chip (DTC) liquid cooling offers a significant operational advantage over traditional air-cooling methods, greatly increasing efficiency and helping control operating costs. DTC cooling is scalable and has the added benefit of reducing ambient noise in the data center as well.”

DTC cooling consumes more space within racks, so these systems must grow larger and deeper to accommodate them and relocate connectivity access away from the bulky DTC hookups at the rear of the rack. Traditional fiber panels are not designed for this kind of scale or flexibility.

“It’s these kinds of small practical challenges that people often overlook,” says Musschebroeck. “The cabling isn't necessarily part of the integral design of the system that operators want – and that's the value CommScope adds, encouraging customers to think about how to structurally insert a cabling topology that will work well across racks, across pods, across data halls and even across data centers.

“What these projects really need is to get any labor out of the data hall and into staging areas, either at the data center or at the manufacturing site, if prepopulated rack-scale systems are used. We collaborate with customers to provide tailor-made solutions for their projects – be it the right length of cable, the right system for their rack, or the best logistics services to make sure everyone is building in the right place at the right time.”

Another approach, immersion cooling, is also on the horizon. Immersion cooling submerges the servers in a nonconductive liquid, with the idea of increasing data center energy efficiency even higher. “While DTC cooling stands to increase cooling capacity and efficiency significantly,” says Musschebroeck, “immersion cooling would raise those benefits to an ever-greater degree.”

Two kinds of speed

The need for speed in network design is twofold: the rate of communication between data links must increase to meet the complex demands of AI workloads – and equally critical is speed of deployment.

“Speeds currently at 400G are rapidly migrating to 800G, 1.6T and beyond,” explains Musschebroeck. “And so, we’re providing flexible solutions that can move from serial optics to parallel optics without having to change out the entire infrastructure.”

Given the rate of technological advancement, getting the right network infrastructure in place to handle today’s demand and scale for tomorrow’s is central to driving continued growth. The answer lies in reducing complexity.

Cabling in a point-to-point fashion is complex and time-consuming with a high risk of human error. Such systems can require weeks of connector cleaning, labeling, inspection, and troubleshooting before being ready to turn on, during which time capital is stranded and revenue generation is kept on hold. Fiber density, optical performance, and rate of deployment are now as critical to AI output as GPU count.

“What we’re seeing now is that the rack really is the computer,” explains Musschebroeck. “So we're working to find efficient ways to pre-cable those racks when they’re being assembled and tested, so that vital connectivity can be validated at that point in time. The architecture itself prescribes it.”

Once pre-cabled and validated, racks can be rolled into place and be connected together more efficiently using mass insert fiber connector solutions. This rack-and-roll approach simplifies and streamlines deployment while reducing the necessity for human intervention. Whether achieved via a fully offsite prefabricated solution or with an onsite staging area, validating and installing cabling reliably and at pace delivers a valuable strategic edge.

Scaling supply: Global capability and local adaptation

Navigating this new era of complex connectivity requires an equally wide reach. “The ability to supply product in today’s market is a significant advantage,” says Musschebroeck. “That’s why CommScope is investing heavily in both innovation and manufacturing capacity, and meeting customers where they’re at across the world with proven solutions.”

Whether for an AI factory or not, the building blocks for delivering future-ready connective infrastructure are the same, and CommScope’s global manufacturing and logistical footprint is central to quickly and flexibly delivering the solutions today’s data centers demand.

This agility is complemented by tailored customer service and onsite collaboration that starts with understanding the customer’s current and future goals, as well as understanding the facility itself.

“There’s a day-one situation, and then there’s what may happen across the lifetime of the data center. For cabling, it’s crucial to understand the whole picture so we can adopt the right strategy using our knowledge of tried-and-tested approaches,” says Musschebroeck. “We do more than just manufacture products; we’re supporting customers from early conception, all the way through to installation, training, planning for upgrades, and site extensions.”

With more data centers emerging across different regions, plus a dramatically accelerated technology cycle, facilities will continue to take on increasingly diverse shapes and forms. This trend will escalate the existing landscape of complex demands for the connectivity architectures that underpin them.