IN PARTNERSHIP WITH | | | The fevered contest over advanced semiconductors has captured headlines and shaped export policy across three continents. Yet beneath the spectacle of cutting-edge lithography and nanometer thresholds lies a more fundamental and enduring struggle: the race to secure the infrastructure that turns silicon into intelligence. The real competition is not for chips themselves, but for compute—the trifecta of energy, data-center capacity, and hardware deployment that powers the artificial-intelligence economy. | AI's expansion is creating geopolitical and economic pressures unlike any witnessed since the electrification of industry. By 2030, global data-center power demand is projected to reach 220 gigawatts, more than triple current levels, with AI workloads alone consuming 945 terawatt-hours annually—equivalent to Japan's entire electricity usage. In the United States, data centers are expected to account for 9 percent of total electricity demand by decade's end, up from 3.5 percent today. | Infrastructure Under Pressure | The strain is visible across the value chain. Data-center construction spending in the United States reached $40 billion at an annual rate in June 2025, a 30 percent year-on-year increase following a 50 percent jump the previous year. Single facilities now demand up to 2 gigawatts of power—four times the capacity of the largest existing hyperscale sites—and planned campuses could consume 5 gigawatts or more. McKinsey forecasts that meeting AI's appetite will require $5.2 trillion in global data-center capital expenditure by 2030. | Training frontier AI models now draws industrial-scale power, straining grids faster than they can adapt. |
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| | Policy and Power | Faced with these realities, governments are mobilizing. The European Union launched its Apply AI strategy in October 2025, committing €1 billion to accelerate domestic adoption and reduce dependence on American and Chinese technology. The strategy positions AI infrastructure as "a strategic asset essential for the bloc's competitiveness, security and resilience," explicitly warning that foreign control of the AI stack could be "weaponised". Brussels aims to triple EU data-center processing capacity within five to seven years, supported by simplified permitting, energy-efficiency mandates, and the expansion of AI Factories—supercomputing hubs optimized for scientific and commercial model development. | China, which controls 70 percent of global rare-earth mining and 85 to 90 percent of processing capacity, has tightened export controls on critical materials. In October 2025, Beijing expanded restrictions to 12 of 17 rare-earth elements, including neodymium and dysprosium essential for precision motors and high-performance magnets. The rules now require licenses for products manufactured abroad using Chinese materials or equipment, mirroring American export controls on semiconductors. Rare-earth dependencies create chokepoints across the AI supply chain—motors, cooling systems, and fabrication equipment all rely on elements with no near-term substitutes. |
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| |  | | | Presented by Brownstone Research | |
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| | | Capital and Consequence | This scramble for compute is reshaping investment flows and valuations. AI infrastructure capital expenditure is expected to reach $2.9 trillion between 2025 and 2028, with roughly half—$1.5 trillion—financed externally, including $800 billion from private credit markets. Data-center construction starts in July 2025 alone totaled $14 billion, nearly doubling the previous monthly record and pushing year-to-date spending to $26.9 billion, triple the prior year. The United Kingdom projects data-center investment will soar to £10 billion annually by 2029, a fivefold increase from 2024. | Industrial strategy is shifting accordingly. The UK's Modern Industrial Strategy allocates £2.8 billion over five years to advanced manufacturing research and development, including robotics and smart factories, alongside £40 million for a national network of robotics adoption hubs. The European Commission is establishing AI Gigafactories—facilities designed to integrate massive computing power with energy-efficient operations and AI-driven automation—to anchor regional ecosystems. Private equity firms, real estate investment trusts, and sovereign wealth funds from the Middle East are repositioning capital toward AI-optimized infrastructure, leveraging energy abundance and flexible regulatory environments. | The strategic calculus extends beyond infrastructure. Rare-earth supply chains, semiconductor fabrication, and grid modernization have become theaters of competition. The US is accelerating domestic rare-earth magnet production, with firms such as Noveon Magnetics ramping output in Texas to reduce reliance on China. Australia, Vietnam, and Myanmar are expanding mining and concentrate exports, though without separation capacity these sources cannot bypass Beijing's processing dominance. Europe sources 98 percent of its rare-earth magnets from China, a dependency the European Central Bank estimates leaves over 80 percent of large European companies within three supply-chain steps of Chinese producers. |
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| | | | | | | | Resilience and Governance | What sustainable AI dominance ultimately demands remains an open question. Energy resilience is foundational. Grid-interactive data centers, employing flexible workloads that respond to real-time signals, have demonstrated the potential to reduce power consumption by 25 percent during peak demand without degrading service quality. Off-grid and microgrid solutions, combining local renewable generation with storage, offer deployment timelines measured in months rather than years. Yet these approaches require regulatory frameworks, utility coordination, and technical standards that are only beginning to emerge. | Governance structures are equally critical. The EU's AI Act, sectoral strategies in the United States, and state-led initiatives in China reflect divergent approaches—risk-based regulation, market-driven innovation, and centralized coordination, respectively. | The race for compute is, in the end, a race for the capacity to act—to train models, deliver inference, and deploy intelligence at scale. Chips remain essential, but they are inputs to a larger system. Energy, cooling, grid access, regulatory certainty, and supply-chain resilience now define competitive advantage. The nations and enterprises that secure these pillars will shape the trajectory of the AI economy. |
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| | | | Deniss Slinkins,
Global Financial Journal |
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