Innovation in capital markets rarely comes from novelty alone. More often, it comes from reassessment — from asking sharper questions about assets that already exist. Not what was this built to do , but what else can this support ? A recent infrastructure project in Switzerland offers a useful illustration. At a decommissioned satellite teleport in the Alps, massive satellite dishes — once critical to global communications — are being repurposed as solar-tracking energy syste

The idea of data centers in space has officially escaped the realm of speculation. At the World Economic Forum in Davos, global leaders framed orbital computing not as science fiction, but as a structural response to the most pressing constraint facing artificial intelligence today: Earth itself. Power shortages, cooling limits, land scarcity, permitting delays, and carbon pressure are colliding just as AI demand is accelerating beyond anything existing infrastructure was des

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

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

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

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

When a power system fails during a natural disaster, there is no luxury of long planning cycles, regulatory indecision, or theoretical optimization. Hospitals need electricity. Communications must come back online. Water systems, logistics hubs, and emergency command centers cannot wait for utility upgrades or multiyear grid studies. In those moments, power is deployed the only way that matters: fast, reliable, and sufficient to meet real demand. What is becoming increasingly

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

There is a habit, common in fast-moving industries, of mistaking the most visible problem for the most important one. In artificial intelligence, that habit has taken the form of an obsession with chips. We speak of shortages, races, allocations, and breakthroughs, as if the future of AI were decided solely by silicon. It is not. Artificial intelligence does not ultimately run on chips. It runs on power, land, and permission. And those are in far shorter supply. A recent anal

Executive Summary AI compute demand is growing faster than the U.S. grid can supply; traditional renewables alone cannot meet 24/7 load requirements. Nuclear—large-scale fleets, SMRs, and long-duration baseload PPAs—is re-emerging as a critical enabler of hyperscale data-center reliability. The overlooked story: nuclear is not just a clean-energy asset; it is a long-duration capital instrument , allowing operators to lock in stable, predictable economics for decades. Transmis

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