Why AI Performance Starts Long Before GPUs

Gartner forecasts worldwide AI spending will reach $2.59 trillion in 2026, with infrastructure accounting for nearly half of that investment, according to an industry analysis arguing that network performance, latency, and data movement now matter as much as GPU compute. The analysis cites IDC's projection that nearly half of enterprises will deploy AI inference at the edge within the next few years, shifting processing closer to hospitals, factories, retail stores, and branch offices. The author argues that the next wave of enterprise AI will reward organizations that move data efficiently, securely, and predictably across distributed environments rather than those with compute access alone.

Insight and analysis on the data center space from industry thought leaders. Why AI Performance Starts Long Before GPUs AI outcomes depend on how predictably and securely data moves across data centers, clouds, and the edge. Designing for latency, resilience, and visibility is now as important as compute. The AI infrastructure conversation has become remarkably predictable. Nearly every discussion centers on GPUs, specialized silicon, power availability, and the race to build larger AI clusters. Those investments are essential, but they overlook an equally important question: How efficiently can AI move data where intelligence is needed? Compute creates intelligence. Networks deliver it. Over the past year, I've had conversations with enterprise IT leaders across industries as they work through the realities of deploying AI. The first discussion is almost always about models, compute capacity, or cloud strategy. By the third conversation, we're usually talking about networking. That shift isn't surprising. The first wave of enterprise AI rewarded organizations with access to compute. The next wave will reward organizations that can move data efficiently, securely, and predictably across increasingly distributed environments /edge-data-centers/will-edge-ai-make-ai-data-centers-less-relevant- . That's a very different infrastructure challenge. Data Movement Is Now Strategic Traditional enterprise applications generated fairly predictable traffic. Users connected to centralized systems, and networks were designed to provide reliable access between locations. AI doesn't behave that way. A single AI request may pull data from an enterprise repository, retrieve information from a vector database, access a large language model running in another environment, apply security policies, and return an answer to a user in just a few seconds. None of those steps happens in isolation, and none happens without the network. This is one reason AI infrastructure spending continues to accelerate. Gartner forecasts https://www.gartner.com/en/newsroom/press-releases/2026-05-19-gartner-forecasts-worldwide-ai-spending-to-grow-47-percent-in-2026 that worldwide AI spending will reach $2.59 trillion in 2026, with infrastructure accounting for nearly half of that investment as organizations expand the systems needed to support AI at scale. But spending on infrastructure alone doesn't guarantee better outcomes. One of the biggest changes AI introduces is that it turns data movement from an operational concern to a strategic one. Training a model is only part of the equation. The real challenge is consistently delivering intelligence across data centers, cloud platforms, branch offices, edge locations, and the growing number of applications that now depend on AI. That requires infrastructure designed for movement, not simply capacity. Network performance has always mattered. AI simply raises the stakes. Latency Has Become a Business Metric Infrastructure teams often focus on GPU utilization, storage performance, or model accuracy. Users don't experience any of those metrics directly. They notice how quickly an AI assistant responds, whether recommendations appear instantly, and whether an automated workflow feels seamless. For many AI applications, latency is no longer just a networking metric. It's becoming a business metric. Inference Is Moving to the Edge Inference /build-design/ai-inference-pulls-infrastructure-back-into-metro-data-centers is driving much of this change. While model training remains concentrated inside large cloud and hyperscale environments, inference is increasingly happening closer to where work takes place, such as hospitals, factories, retail stores, financial institutions, campuses, and branch offices. IDC projects that nearly half of enterprises will deploy AI inference at the edge within the next few years, reducing dependence on centralized processing while increasing demands on distributed infrastructure. Design for Predictability, Not Just Bandwidth I've worked through several major infrastructure transitions over the course of my career, from client-server computing to virtualization, and then to cloud computing. AI feels different because it isn't changing one layer of infrastructure. It's changing all of them at once. That includes networking. The conversation isn't really about adding more bandwidth. It's about building networks that remain predictable as workloads shift, the user base grows, and AI becomes part of everyday operations. Visibility, resilient routing, diverse connectivity, integrated security, and intelligent traffic management all become more important when AI is woven into business processes. Reliability also takes on new meaning. When AI supports customer service, cybersecurity, manufacturing operations, or financial decisions, a network disruption is no longer just a connectivity issue. It can interrupt business processes that increasingly depend on real-time intelligence. Security Must Move with the Data Security follows the same pattern. AI systems interact with sensitive corporate data spread across multiple environments. Moving that information safely requires networking and security to work together, guided by principles such as Zero Trust, segmentation, identity-aware access, and continuous visibility. Connecting Data to Intelligence None of this diminishes the importance of compute. GPUs, accelerators, and advanced processors /data-center-chips/gpu-lifespan-in-data-centers-physical-vs-economic will continue to define what's possible with AI. But infrastructure conversations need to become broader. The organizations getting the most value from AI aren't simply deploying larger models. They're building environments where data, applications, users, and intelligence can work together without friction. That requires thinking about networking much earlier in the design process than many organizations have in the past. For decades, networking connected people to applications. Today, it's increasingly connecting data to intelligence. I believe that's one of the biggest infrastructure shifts happening in enterprise IT today. Organizations that recognize it early won't necessarily have the biggest AI deployments, but they'll be better positioned to scale them, adapt to what's next, and realize meaningful business value from their AI investments.