AI at the Crossroads of Power: A Dual‑Edged Catalyst in the Global Energy Transition
When ambition meets disruption, leadership is tested. AI—long heralded as a force for transformation—stands at the nexus of global energy resilience and universal access. Yet its surge in power demand is creating an urgent dilemma: how do we harness AI’s promise without destabilizing the very systems we rely on?
Across high-income economies, utilities are rolling out AI-driven predictive maintenance to preempt infrastructure failures. Duke Energy and startups like Rhizome now deploy machine-learning models and real-time sensor networks to monitor transformers and detect early signs of malfunction. These systems have reduced storm-induced outages by as much as 72 percent, representing a powerful case for grid modernization (Mok 2025).
Field operators also benefit from wearable AI tools such as Avangrid’s First Time Right Autopilot, which improves field diagnostics and repair outcomes by offering real-time, context-aware instructions (Mok 2025). Demonstrating replicability, these tools herald a future where data-driven maintenance becomes a driver of national-level resilience—empowering leaders to secure both reliability and equity in infrastructure development.
Yet AI’s power surge isn’t without peril. Andreas Schierenbeck, CEO of Hitachi Energy, warns that data-center AI operations can trigger demand spikes up to tenfold in seconds—a volatility that traditional grids were never designed to absorb (Schierenbeck 2025). Such surges, compounded by renewable intermittency, could threaten energy access, particularly in regions with weaker grid systems.
The International Energy Agency (IEA) projects that data-center electricity consumption could approach 945 TWh by 2030, surpassing the entire annual usage of Japan (Schierenbeck 2025). In response, transformer manufacturers like Hitachi are investing over $6 billion to bolster capacity, while regulators in jurisdictions like Ireland and the Netherlands are drafting siting rules to limit destabilizing power demands (Schierenbeck 2025).
A Strategic Path Forward
This duality—a tool for resilience and a potential stressor—requires a holistic strategy characterized by:
1. Systemic Planning: AI’s promise is greatest when predictive maintenance, cloud operations, and demand flexibility are integrated into national energy infrastructure models. Grids must evolve through smart assets, responsive demand, and digital twins.
2. Contextual Extension: AI-driven innovations from mature grids should be adapted for rural electrification pilot sites in Africa and Asia, supporting local utility growth, farmer livelihoods, and school access with precision intelligence.
3. Proactive Regulation: AI data centers should be treated as industrial-scale loads—subject to pre-notification, grid-impact assessments, and incentives tied to low-carbon operation.
AI is no longer a peripheral force in energy transitions—it is central to universal access and climate ambition. Whether strengthening substations in one country or powering community micro-grids in another, its deployment must be intentional, governed, and equitable.
By integrating predictive maintenance, regional resilience planning, and energy-aware AI policy, global energy leaders can steer AI from being a grid liability to a universal engine of progress. The stakes have never been clearer: leadership today demands orchestrating AI’s growth so that it uplifts—not overwhelms—the energy foundations of tomorrow.
References
Mok, Aaron. 2025. “Utilities Are Modernizing the Grid With AI Amid Growing Energy Demands.” Business Insider, July 3, 2025. Retrieved July 5, 2025.
Schierenbeck, Andreas. 2025. “Hitachi Energy Says AI Power Spikes Threaten to Destabilise Global Supply.” Financial Times, July 4, 2025. Retrieved July 5, 2025.