When markets talk about “$3 trillion flowing into AI,” the assumption is usually software, models, and abstract innovation. In reality, the bulk of the capital is being deployed into physical systems: data centers, high-density compute, networking, cooling, and the power infrastructure required to run them continuously. This is not a digital spending cycle—it is an industrial one. What’s unfolding is closer to the buildout of railroads, telecom, or electrification than a typi

After nearly two decades of relatively flat electricity consumption, the United States is entering a new era of power demand growth. This shift is not being driven by population growth, household consumption, or traditional heavy industry. Instead, it is being propelled by data centers—specifically those supporting cloud computing, artificial intelligence, and high-performance workloads. Federal energy forecasts now show that U.S. electricity usage is set to rise more over th

The reported agreement by SoftBank Group Corp.  to acquire DigitalBridge Group, Inc.  for approximately $4.0 billion is not simply another large-cap infrastructure transaction. It is a strategic signal to global markets that the next phase of artificial intelligence leadership will be determined less by software breakthroughs and more by ownership of the physical systems that make AI possible. The immediate market reaction underscored this reality. Shares of DigitalBridge sur

The global race to build AI infrastructure has triggered what looks, on the surface, like a data-center supercycle. New projects are announced weekly. Capacity targets grow larger by the month. Megawatts are promised freely and confidently. Yet beneath the headlines, a more uncomfortable reality is emerging: many of these facilities are not real data centers at all. As Jonathan Ross, CEO of Groq, who was recently acquired by NVIDA for $20BIL, bluntly put it in a recent inter

Executive Summary 2025 marked a historic acceleration  in AI-driven data center investment, with hyperscalers committing unprecedented capital to compute, land, and power. AI compute demand reshaped capital markets , supply chains, and semiconductor economics, concentrating value in GPUs, advanced nodes, and energy infrastructure. Power availability—not chips—emerged as the primary constraint , redefining site selection and project timelines. 2026 will remain a growth year ,

Why Microreactors Are Back — and Why NANO Nuclear Is First in Focus Artificial intelligence is no longer constrained by algorithms or silicon alone. It is increasingly constrained by power—and more specifically, by the kind of power that can operate continuously, predictably, and at scale. As AI systems grow in complexity and ambition, they are placing unprecedented demands on physical infrastructure. Training and operating frontier models requires electricity that behaves le

For the better part of the last decade, the story of artificial intelligence infrastructure has been a story about silicon. Who could secure the most advanced GPUs. Who had priority access to next-generation accelerators. Who could outspend competitors on hardware, packaging, and interconnects. That narrative is no longer sufficient. Today, the defining constraint on AI scale is not compute—it is power. Reliable, dispatchable, near-term power. And increasingly, the market is

In the second quarter of 2025, the world’s leading AI companies poured over $100 billion  into data centers, compute infrastructure, and...

As data centers cement their place at the heart of the AI and cloud revolutions, Texas and the broader United States are navigating the...

We’re entering a pivotal era where AI is not just transforming industries—it’s fueling an infrastructure revolution. At the heart of this...