At the recent World Economic Forum  in Davos, a reality long understood by infrastructure operators was stated plainly on the global stage. As articulated by Elon Musk , the limiting factor for artificial intelligence is no longer compute availability. It is power. That observation marks an inflection point—not because it is new, but because it has now entered the mainstream narrative. For those building at scale, this constraint has been visible for years. The Shift from Com

South Florida’s emergence as a serious data center market is no longer theoretical. It is operational, powered, and ready. The Freedom Center  stands as a clear signal that the region has entered a new phase—one defined by execution rather than ambition, and by infrastructure that meets the real demands of AI-driven workloads. Over the last two days, Rich Washburn , Managing Partner & Chief AI Officer of Eliakim Capital , spent extensive time onsite at the Freedom Center. The

Artificial intelligence is often described as a software revolution, but at scale it becomes an infrastructure problem. Every serious AI system eventually resolves into racks, chips, cooling, steel, and electricity. As AI and high-performance computing infrastructure expands, the industry is discovering a hard truth: intelligence scales physically. Compute is not virtual — it is industrial. From Algorithms to Infrastructure AI / HPC infrastructure has moved beyond experimenta

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 AI infrastructure market is entering its most revealing phase. Demand is real, but delivery is being separated from promotion at an accelerating pace. In the last 18 months, “AI data center” has become a label applied to everything from entitled land to partially designed shells—often with bold megawatt claims and thin execution behind them. That’s the backdrop for what Groq CEO Jonathan Ross has called “fake data centers”: projects that look investable in a deck, but c

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 ,

Every major technology cycle has a moment where value migrates silently—long before it becomes consensus. In AI, that moment is now. NVIDIA’s reported ~$20B strategic integration with Groq is not a conventional acquisition. It is a structural repositioning around what has quietly become the most constrained variable in AI economics: inference latency. This move does not signal a retreat from training dominance. It signals recognition that the next phase of AI value creation w

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

Executive Summary POWER’s inaugural Data Center POWER eXchange (DPX)  Summit convened utilities, hyperscalers, regulators, financiers, and developers to address the accelerating collision between data center demand growth and grid capacity. AI-driven load expansion is materially outpacing grid planning, permitting, and build-out timelines—raising reliability, cost, and political risks. Former federal energy regulators and utility CEOs highlighted a dual challenge: maintaining