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AI Infrastructure Is the New Battleground

Written by Rob Steele | Oct 22, 2025 8:12:25 PM

If you stop and look around, you’ll notice something interesting happening. The biggest shift in enterprise AI isn’t about the models or the apps everyone’s talking about. It’s deeper. It’s happening underneath all of that. 

Right now, the real fight is over the infrastructure that makes AI possible. The data pipelines. The GPU clusters. The hybrid cloud platforms that connect it all. 

In short, the plumbing of AI has become the front line. And companies like Dell Technologies, NetApp, HPE, and Pure Storage are all racing to define what the future foundation of AI looks like. 

This is the first in a series where we dig into how infrastructure is quietly becoming the real engine of AI. We’ll start by looking at what’s happening in the market and how Dell and NetApp are helping lead the way, with future pieces diving into HPE, Pure Storage, and others reshaping this space. 

 

Why does infrastructure matter more than ever for AI enablement? 

It’s easy to think of AI as software. Train a model, deploy it, move on. But the companies actually scaling AI will tell you: the real bottleneck isn’t the model. It’s the infrastructure behind it. 

A few reasons why: 

  • Data pipelines are the heartbeat: You can buy as many GPUs as you want, but if your data isn’t ready (clean, labeled, governed), you’re stuck. 
  • Hybrid is the new normal: Very few organizations are purely cloud or on-prem anymore. Data and compute now live across regions, clouds, and edge environments. 
  • Performance and efficiency matter: Training and inference at scale demand architectures that balance speed, energy, and cost. 
  • It’s a new source of competitive advantage: If your competitor can run workloads faster, cheaper, or more securely, that’s not just IT bragging rights. That’s business impact. 

That’s why the infrastructure layer has become the new strategic battleground. Vendors who used to sell servers or storage are now talking about full pipelines, managed services, and GPU factories. 

 

Dell’s big move: Building the “AI Factory” 

Dell is making one of the boldest plays right now. 

Earlier this year at Dell Technologies World, they rolled out what they call the Dell AI Factory with NVIDIA. It’s not a single product but an ecosystem. Servers, storage, networking, software, and services built to handle the entire AI lifecycle. 

Some of the highlights: 

  • PowerEdge servers purpose-built for AI, with air and liquid cooling and NVIDIA HGX B300 GPUs. 
  • ObjectScale storage tied to NVIDIA’s BlueField DPUs and Spectrum networking for massive throughput. 
  • Managed services that handle monitoring, updates, and optimization, so customers can focus on results, not maintenance. 

Michael Dell said it clearly: “Our job is to make AI more accessible.” And that’s exactly what this move is about… making enterprise AI infrastructure simpler, faster, and ready to scale. 

Analysts are already seeing the impact. Dell’s infrastructure business is growing fast, with much of that tied directly to AI workloads. They’re not selling boxes anymore. They’re selling a foundation. 

NetApp’s answer: Data as the core of AI 

While Dell focuses on compute and integration, NetApp is going straight after the data problem. 

At their recent conference, they announced the NetApp AFX  and AI Data Engine, both built for large scale AI pipelines. AFX is a high-performance, all-flash storage system designed for AI workloads. The AI Data Engine acts like the glue… it manages metadata, curates datasets, and makes data discoverable and ready for training or inference. 

In plain terms, NetApp is helping enterprises fix one of the biggest challenges in AI: getting the right data to the right place at the right time. 

They call this “intelligent data infrastructure,” and it’s a smart angle. Because no matter how powerful your GPUs are, you can’t do much if your data can’t move efficiently between systems or clouds. 

NetApp’s bet is that the companies who figure out their data foundation first will win the AI race later. And they’re probably right. 

 

What does the strategic impact of infrastructure on AI mean for business leaders? 

If you’re leading an AI initiative, this shift should change how you think.

Here’s what to pay attention to: 

  1. Start with your data pipeline, not your model: Is your data accessible, high quality, and secure? If not, fix that before adding more GPUs. 

  2. Think end to end:From ingestion to training to inference, you need a connected stack. Buying piecemeal parts will slow you down later. 

  3. Plan for hybrid by default: Edge, on-prem, cloud… it’s all part of the story now. Your infrastructure should handle movement between them seamlessly.

  4. Don’t overlook operations: Running AI at scale isn’t a weekend project. Managed services can remove a lot of friction. 

  5. Pick partners, not products: The companies making the most progress with AI aren’t buying tools… they’re aligning with strategic partners like Arctiq, who understand the whole ecosystem. 

 

A closing thought… 

The AI race won’t be won by whoever has the flashiest model. It’ll be won by whoever builds the strongest foundation underneath it. 

Dell and NetApp are both rewriting what “infrastructure” means in this new era. One is focused on compute and acceleration. The other on data and pipelines. Both are betting that enterprises want simplicity, scalability, and trust. 

If you’re serious about building AI into your business, start here. 
Pick one use case. Map out your full data and compute flow. Identify the bottlenecks. 
Then look for infrastructure partners who are already thinking this way. 

Because the companies treating infrastructure as strategy today will be the ones leading the AI market tomorrow. 
 
Contact Arctiq today to assess your infrastructure’s readiness for AI, identify optimization opportunities, and build the AI factories that power intelligent innovation.