Why Energy—not AI—is Emerging as the Strategic Variable in the Global Economy

The global economy is entering a phase where growth, competitiveness, and technological leadership are increasingly shaped by a foundational constraint: energy. As economies electrify and artificial intelligence scales, the ability to generate power cheaply, reliably, and at scale is becoming a central macro consideration.

That framing set the tone for a keynote discussion at Citi’s Year Ahead Conference 2026, January 13-14, where Adam Pickett, Head of Global Macro Strategy at Citi Research, spoke with Robert Gibbins, Founder and Chief Investment Officer of Autonomy Capital, about why energy systems now sit at the center of structural change in the global economy.

Gibbins argued that much of today’s debate is clouded by narrative. While energy transitions are often framed through climate goals and policy commitments, markets ultimately respond to economics—specifically price, scale, and capacity. When a new technology becomes materially cheaper and capable of scaling faster than incumbent systems, broader structural change follows, often irrespective of policy intent.

That dynamic is increasingly visible in electricity generation. The discussion highlighted a sharp divergence in how major economies are expanding power capacity. China has added generation at a pace and scale that far exceeds other large economies, driven primarily by solar power and supported by large-scale battery systems. Gibbins emphasized that this outcome reflects technological economics rather than environmental alignment: solar benefits from rapidly declining costs and an ability to scale quickly once deployment reaches critical mass.

By contrast, electricity generation capacity in Europe has grown more slowly. Despite ambitious climate objectives, overall power supply has remained relatively flat. According to Gibbins, constraints on capacity expansion can translate into broader pressures on industrial activity and competitiveness.

The conversation also focused on the role of policy framing. Carbon pricing mechanisms, originally designed to encourage substitution toward lower-emissions energy, may function differently once newer technologies become the lowest-cost option at scale. In that context, such mechanisms may act less as incentives and more as direct costs on production. Regulatory complexity and permitting constraints can further slow deployment, contributing to a gap between policy goals and economic outcomes.

Energy’s role becomes even more pronounced when viewed through the lens of digital infrastructure. Gibbins described artificial intelligence not as a standalone technology race, but as part of a broader “electric stack” that includes power generation, storage, data infrastructure, robotics, and automation. Advanced computing systems are energy-intensive, and their effectiveness depends as much on access to reliable electricity at scale as on hardware or software capabilities.

Pickett noted that this perspective helps explain why discussions around AI competitiveness increasingly intersect with debates over grid resilience, generation capacity, and long-term infrastructure planning.

The discussion underscored that shifts in energy systems are increasingly shaping economic capacity at a structural level. As electricity becomes more central to industrial activity and digital infrastructure, the ability to expand power generation at scale—and at lower cost—emerges as a critical macro consideration. Viewed through that lens, energy moves from a background input to a central factor in how competitiveness and growth are interpreted over time.