The Next Cold War Is Electric: The Under-Hyped AI Revolution, Energy, and the Battle for Global Control

By Farhad Reyazat – PhD in Risk Management

AI Entrepreneur | Academic Scholar | Advisor on Tech & Policy Strategy

Introduction: The Intelligence Shift of the Century

The arrival of advanced artificial intelligence marks more than a technological breakthrough—it is the beginning of a transformation on a civilizational scale. We are not merely building automation tools or digital assistants; we are engineering a new class of intelligence—one that will soon rival and then surpass human cognition across science, engineering, policymaking, and creativity.

Despite the noise, this revolution remains profoundly under-hyped.

Most institutions—governments, universities, and even leading tech companies—still underestimate the velocity, scale, and societal impact of what is unfolding. This isn’t about faster search engines or smoother customer service. This is about machines that can reason, plan, learn, and act autonomously.

The leap from traditional machine learning to generative AI—from backend tools to consumer-facing, real-time intelligence—is unlike anything that has come before. While past breakthroughs, such as search and smartphones, reshaped how we access information, LLMs like ChatGPT, Claude, and Gemini are poised to reshape how we think. They have the potential to give every individual “Da Vinci powers”—supercharged creativity, problem-solving, and communication—available on demand.

And when consumers drive change, the pace accelerates. Much faster than enterprise technology ever could.

Yet amid this cognitive revolution, a stark reality looms: we are not prepared.

This shift is not limited by software or talent. Its actual bottleneck is infrastructure—most critically, energy. AI systems require vast computational power, which demands electricity on a scale that our grids were never designed to handle. We are moving toward an age where intelligence is abundant, but the power to sustain it is scarce.

This isn’t just a tech evolution. It’s a planetary inflection point—and we must rise to meet it with vision, urgency, and responsibility.

From Pattern Recognition to Synthetic Reasoning

Over the last five years, AI systems have evolved from text prediction to autonomous planning and problem-solving.

The Real Bottleneck: Energy

While public discourse often centers on AI’s ethical risks, job disruption, and economic impact, the most immediate and systemic constraint facing the AI revolution is far more tangible: electricity.

Advanced AI models are not just data-hungry—they are energy-intensive at an industrial scale. The leap from GPT-2 to GPT-4 wasn’t just a leap in intelligence; it was a leap in power consumption.

 The Scale of AI’s Energy Appetite

The last decade of AI progress—encompassing image recognition, language generation, and recommender systems—has been marked by impressive advancements. However, what is unfolding now is genuinely transformative.

By 2024, models like GPT-4 and Claude 3 were already solving math problems at a PhD level, writing 60% of developers’ code, and reasoning across multiple domains. In the coming years, experts forecast that AI agents will be capable of planning, learning, and acting autonomously across systems, with some labs predicting human-level general intelligence by 2028 and superintelligence within a decade.

But the intelligence leap comes at a massive energy cost:

• Training GPT-3 (2020): ~1.3 gigawatt-hours (GWh)—the annual energy of 120 U.S. homes
• GPT-4 (2023): Estimated 50–60 GWh—40× more than GPT-3
• ChatGPT inference (2024): Requires over 100,000 GPUs daily
• Each NVIDIA H100 GPU: Draws ~700 watts
• 10,000-GPU cluster: Consumes 7 megawatts continuously (enough for 6,000 homes)

These figures aren’t theoretical—they are already in operation. One data center today equals the energy output of a small city.

“People are planning 10-gigawatt data centers today. That’s the energy output of 10 nuclear power plants—for just one facility.”
— Industry Insider, 2025

The Infrastructure Shock Ahead

According to the International Energy Agency (IEA) and Nature (2025), the outlook is increasingly urgent:

• Global data center energy use (2023): ~460 TWh (~2% of global electricity)
• Projected by 2026: 1,000 TWh (≈ Japan’s total electricity consumption)
• Google’s AI systems alone (2023): 2.3 TWh (more than Ghana or Laos)

“Without urgent policy coordination, AI demand will outpace national grid capacity in key countries.”
— IEA, 2025

Compute Cities Are Coming—And We’re Not Ready

We are witnessing the rise of what might be called “compute cities”—10-gigawatt clusters, built for training and running AI models 24/7. Yet, no current national grid, regulatory regime, or energy strategy is designed to handle this shift.

This is no longer about cloud computing. This is about infrastructure on the scale of industrial-era steel, oil, and electricity revolutions.

The Strategic Solution: An All-of-the-Above Energy Strategy

To meet this moment, the world must abandon ideological silos and adopt an energy approach grounded in physics, not politics. AI demands reliable, scalable, low-latency power—and lots of it.

 Renewables

Solar and wind must scale dramatically. However, their intermittency and location dependence mean they cannot carry the load independently.

 Nuclear

Modern Small Modular Reactors (SMRs) offer stable baseload power. France and China are already deploying SMRs strategically near digital infrastructure hubs.

Storage and Smart Grids

Grid batteries (like Tesla Megapacks) and AI-optimized demand response systems will be essential to manage fluctuations and prioritize AI workloads intelligently.

Geothermal + Hydro

Underexploited but high-potential, especially in regions with natural advantage, like Iceland, Kenya, and the Pacific Northwest.

 Transitional Fossil Fuels

In the short term, gas-fired peaker plants may be necessary to prevent blackouts. While not ideal in the long term, they provide the elasticity AI clusters require until more clean base load is online.

 A Call for the “AI Grid Initiative”

To prevent a global compute bottleneck and ensure democratic leadership in AI, a coordinated public-private strategy is essential:

• Fast-track permitting for clean energy near data hubs
• Public-private partnerships for AI-centric power plants
• Dedicated AI energy corridors connected to national smart grids
• National Compute Reserves—like a strategic energy reserve, but for AI infrastructure

This is not just an infrastructure problem. It’s a civilizational challenge. If intelligence is the new oil, then energy is the refinery. And without bold coordination, we risk building the future of human knowledge, only to discover we can’t afford to turn it on.

What the AI Industry Needs Now: A Four-Pillar Plan

To remain competitive—and responsible—in the global race for AI supremacy, governments must realign quickly around four strategic imperatives. This is no longer optional. It is foundational to national resilience, economic sovereignty, and the global balance of power.