The Case for Distributed Computing: Building the Right Infrastructure in the Right Places at the Edge

By Roger Sartain, Telamon

Note: This blog was produced under WIA’s Innovation and Technology Council (ITC). The ITC is the forum for forecasting the future of the wireless industry. Participants explore developments in the wider wireless industry, from 5G network monetization trends and streamlining infrastructure deployment to future spectrum needs and cell site power issues. These views are not a WIA endorsement of a particular company, product, policy or technology.

For years, the industry has talked about edge computing as the next major evolution in digital infrastructure. The opportunity was clear, but adoption depended on something that had not fully materialized – applications that could justify the investment.

Artificial intelligence is changing that equation.

As organizations move beyond experimenting with AI and begin applying it to real business challenges, the conversation around distributed computing is becoming more tangible. Manufacturing, healthcare, logistics, utilities and other industries are identifying opportunities to improve efficiency, automate processes and make faster decisions. Those applications are creating a clearer path for where additional computing capabilities may be needed.

That is the biggest difference between this moment and previous discussions around edge computing. Earlier cycles often involved building infrastructure in anticipation of future demand. Today, the demand is helping shape the infrastructure.

The opportunity, however, is not simply about building more infrastructure. It is about understanding where compute needs to exist, how it connects to the broader network and what infrastructure is required to support it.

Building the connectivity foundation for distributed compute

While AI is accelerating interest in distributed infrastructure, it is important to be precise about what is being built today. Not every distributed facility should automatically be considered edge compute.

A significant amount of current investment is focused on transport infrastructure that moves data between major computing locations and expands the connectivity foundation that future distributed compute will depend on.

At Telamon, much of the work we are supporting today involves shelters and facilities designed primarily for transport infrastructure, including long-haul fiber networks. These facilities are built to move data efficiently rather than process workloads locally.

That is not a limitation. It is a necessary step.

You have to build the highways before you can determine what destinations need to exist along them. Distributed computing requires the right combination of fiber, power and physical infrastructure.

The economics also matter. You are not going to build miles of fiber simply to connect a single edge location. Instead, edge deployments will naturally develop where connectivity, power availability and specific business needs come together.

The continued expansion of transport infrastructure provides an important connectivity layer that future distributed compute deployments can leverage.

Over the next one to three years, we will likely continue to see pilots and targeted deployments as organizations determine where AI can deliver the greatest value. Over the three-to-five-year timeframe, broader adoption of inference workloads will continue as more organizations move from experimentation to implementation.

Organizations no longer need to wait for edge infrastructure to begin exploring AI capabilities. Hyperscale data centers are already enabling many of these applications today. As workloads mature and requirements around latency, efficiency and performance evolve, distributed compute will enhance those capabilities.

The wireless industry is ready for the next phase of distributed infrastructure

This is where the wireless infrastructure industry brings a unique perspective.

For decades, the wireless industry has built distributed networks across thousands of locations and solved many of the same challenges that will shape the next generation of digital infrastructure: identifying the right locations, deploying in diverse environments, managing power and connectivity requirements, and creating systems that can scale. AI introduces new workloads, but the infrastructure challenge is familiar.

As distributed compute develops, infrastructure providers across the ecosystem will need to determine where they can create value. For tower companies and other infrastructure providers, that may mean continuing to provide critical real estate and connectivity infrastructure, or it may create opportunities around additional services such as colocation and distributed facilities.

There will not be one blueprint for how AI infrastructure develops. Some workloads will remain in hyperscale environments. Others will require regional facilities, edge deployments or infrastructure integrated directly into enterprise operations.

The wireless industry knows how to build efficient distributed infrastructure. AI represents a new application for those capabilities, but the challenge itself is familiar. The opportunity is not simply building more capacity. It is building the right infrastructure in the right places and ensuring that the systems supporting AI can evolve as technology and business needs continue to change.