AI Hyperscalers Analysis - the supply chain of AI

At TheMarketAI.com, we’ve been closely tracking the massive surge in capital expenditures flowing into data centers and AI-focused cloud infrastructure. The companies building and delivering the raw computing power behind AI — often called hyperscalers, a term traditionally reserved for giants like AWS and Microsoft Azure — are rapidly expanding. We recently highlighted CoreWeave’s astonishing 313% run-up in 4 months, which has pushed its valuation near $80 billion, underscoring just how explosive this space has become. Given the scale and momentum here, we’re making it a priority by dedicating a category to hyperscalers analysis and news. Mapping the supply chain of how AI is delivered to our fingertips.

just like the internet, just like electricity, needs factories….

…And these AI data centers, if you will, are improperly described. They are, in fact,

AI factories. You apply energy to it, and it produces something incredibly valuable.

- NVIDIA Co-Founder & CEO Jensen Huang Computex 5/2025

What are AI Hyperscalers?

AI hyperscalers provide vast distributed computing environments that can rapidly scale by adding entire new data centers rather than merely upgrading existing hardware. This approach enables them to handle exponential growth in workloads far beyond traditional data centers. By building massive facilities near power sources and interconnecting them with proprietary networks, hyperscalers make it possible for businesses — even small and midsize ones — to tap into AI capabilities without investing heavily in their own infrastructure. They typically offer access to pre-trained and custom models, AI-optimized chips like GPUs, and ensure data flows are secure and compliant with key regulations, effectively bringing supercomputing power to the cloud. This summary relied on insights from Dwealth News. Mapping the AI Hyperscaler supply chain?

When you ask an AI chatbot a question over the internet, that query travels from your AI provider (like ChatGPT) to powerful servers, most likely hosted by major cloud computing platforms such as AWS, Azure, or Google Cloud. These servers aren’t just a few machines—they’re vast clusters housed in enormous, highly complex data centers. To give a sense of scale, a single 32,000-GPU cluster requires roughly 1,000 kilometers (about 600 miles) of fiber cabling and around 80,000 fiber connections. Often, these data centers are owned by specialized infrastructure companies (see out piece on SWI group) that lease space to the cloud providers and hyperscalers. Meanwhile, the GPUs—the graphic processing units that do the heavy AI lifting—are predominantly supplied by Nvidia, with a handful of other chipmakers in the mix.

With demand for AI skyrocketing and only one primary source for the most advanced GPUs, there’s now a frenzied scramble to secure the latest Nvidia hardware. Companies that can acquire and deploy these GPUs in their leased data centers have a real shot at capturing market share. Right now, bottlenecks are everywhere: Nvidia’s own production capacity, available space and technical readiness at data centers, and even the cloud providers’ ability to scale up, install, and roll out their AI services.

These supply chain dynamics are somewhat reminiscent of the PC boom in the late 1980s and early 1990s. As demand for personal computers exploded, major companies struggled with their supply chains — especially since market trends and hardware requirements were evolving so rapidly. It was during that time that Dell emerged, leveraging a unique supply chain model that helped it navigate and even capitalize on these challenges.

While AWS, Microsoft Azure, and Google Cloud are the dominant players today, their sheer scale and operational diversity might make them too slow to react with the speed the AI market now demands. That’s where smaller, more specialized companies like CoreWeave find their opening. With data center construction buildout value up 49%, we expect to see a surge of activity in this sector, likely attracting tens of billions of dollars in new investment.

Below, we’ve included a few charts from Mary Meeker’s Bond AI trends report that illustrate just how massive the AI hyperscale race has become.

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