Beyond the GPU: Cisco Says AI’s Biggest Challenge May Be the Network That Connects It All
Key Highlights
- AI traffic now accounts for around 30% of backbone network utilization, up from less than 1% two years ago, indicating a significant shift in network demands.
- Cisco's Agile Services Networking framework aims to simplify infrastructure by integrating routing and optical functions, reducing equipment footprint and operational costs.
- Edge architectures are evolving with compact, flexible systems that bring compute closer to users, reducing latency and supporting distributed inference workloads.
- Redesigned metro topologies enable shorter, more direct paths for traffic, effectively creating 'shortcuts' to AI services and enhancing performance.
- The middle mile is becoming a strategic battleground, with capacity upgrades often outpacing demand projections, underscoring the interconnected nature of AI infrastructure layers.
For much of the AI boom, the industry's attention has centered on GPUs, power availability, and liquid cooling. But according to Cisco Senior Business Development Manager Robin Olds, another critical constraint is rapidly moving to the forefront: the network itself.
Speaking with Data Center Frontier on the show floor at Fiber Connect 2026, Olds argued that AI represents a once-in-a-generation shift comparable to the birth of the commercial internet, fundamentally changing traffic patterns and forcing service providers, data center operators, hyperscalers, and emerging neoclouds to rethink infrastructure design.
"It's really like the internet when it was created," Olds said. "We're at another intersection in time where we could really see things happening."
AI Is Rewriting the Bandwidth Equation
The most significant change may not be compute density alone but the sustained demand AI places on transport networks.
According to Olds, service providers are already seeing AI traffic account for roughly 30% of utilization on backbone infrastructure; a dramatic increase from less than 1% only two years ago. As AI workloads continue to proliferate, those utilization levels are expected to rise further.
The next wave of agentic AI could amplify that trend.
Unlike consumer chatbots, which generate bursty request patterns, autonomous AI agents continuously interact with applications and external services, creating more persistent traffic flows.
"Everything's about chatbots," Olds observed. "It's very spiky—up, down. Agentic AI is going to maintain utilization because now I have agents working on my behalf."
For data center developers, network operators, and cloud providers alike, that implies planning not just for peak demand but for elevated baseline utilization across metro and long-haul infrastructure.
Compressing the Network Stack
Cisco's response centers on architectural simplification.
Olds highlighted the company's Agile Services Networking framework, which combines router and optical networking technologies with coherent optics to converge functions that historically occupied separate Layer 1, Layer 2, and Layer 3 platforms.
By embedding more intelligence directly into optical systems and collapsing multiple networking functions into a unified architecture, providers can reduce equipment footprint, lower power consumption, and decrease cooling requirements while freeing valuable rack space.
That reclaimed capacity may itself become an AI opportunity.
Olds suggested operators are evaluating deployments such as municipal video inferencing or colocated AI services within existing central offices, effectively transforming portions of telecommunications infrastructure into localized compute environments.
The concept mirrors a broader industry trend in which communications facilities increasingly resemble distributed edge data centers.
Bringing AI Closer to the User
The conversation also underscored how AI is reshaping edge architecture.
Cisco's Unified Edge platform packages compute and networking into compact 3RU and 5RU systems while supporting multiple storage environments and GPU ecosystems, including NVIDIA, AMD, and Intel accelerators. The goal is to provide a flexible deployment model managed under a single operational framework.
But the larger architectural shift may be occurring at the metro layer.
Olds described a redesigned Metro Edge topology that moves peering capabilities closer to users, enabling enterprise and consumer traffic to reach AI services through shorter, more direct paths rather than traversing traditional hierarchical networks.
The approach effectively creates "shortcuts" to cloud and AI applications, reducing latency while supporting increasingly distributed inference workloads.
For an industry that has spent years debating the meaning of "the edge," the discussion suggests that AI may finally be giving the concept practical definition: placing intelligence where proximity improves performance.
The Middle Mile Emerges as a Strategic Battleground
Perhaps the most revealing insight involved the middle mile.
Olds recounted discussions with a provider that upgraded directly from a 10 Gb network to 400 Gb infrastructure, bypassing 100 Gb economics altogether, only to discover that projected demand already requires tripling that new capacity.
The anecdote illustrates how AI-driven bandwidth growth is outpacing conventional planning cycles.
It also reinforces the increasingly interconnected nature of AI infrastructure. Data centers, neocloud operators, hyperscalers, internet service providers, and transport networks can no longer optimize independently; each layer depends on the others to sustain AI deployment at scale.
Data Center Infrastructure Is Becoming Network Infrastructure
For readers of Data Center Frontier, the interview highlights an important evolution in AI infrastructure strategy.
The industry's first challenge was deploying enough compute. The second has become supplying enough power and cooling. Increasingly, the third may be ensuring sufficient network capacity to move data among users, inference engines, training clusters, and distributed edge locations.
As inference workloads expand and autonomous AI agents generate continuous demand, networking is shifting from supporting role to strategic differentiator.
If the first phase of AI was about building bigger GPU clusters, the next may be about building the connective fabric that allows them to operate as a coherent, low-latency system, stretching from hyperscale campuses through metro networks and ultimately to the edge.
At Data Center Frontier, we talk the industry talk and walk the industry walk. In that spirit, DCF Staff members may occasionally use AI tools to assist with content. Elements of this article were created with help from OpenAI's GPT5.
Keep pace with the fast-moving world of data centers and cloud computing by connecting with Data Center Frontier on LinkedIn, following us on X/Twitter and Facebook, as well as on BlueSky, and signing up for our weekly newsletters using the form below.
About the Author
Matt Vincent
Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with his family, remaining an active musician in his spare time.
Voices of the Industry
