Why AI Efficiency Is Now A Board Governance Issue

Global electricity consumption tied to AI data centers rose roughly 50% in 2025 alone, according to the International Energy Agency’s April 2026 report on energy and AI, a pace of growth the agency expects to keep climbing as adoption accelerates. In the U.S., the Lawrence Berkeley National Laboratory projects data centers could consume between 6.7% and 12% of total U.S. electricity consumption by 2028, up from roughly 4.4% a few years earlier. For many executives, that growth has been an abstraction, something happening in a data center a state away. AI efficiency and responsible AI use are increasingly becoming a governance question instead, one that management and boards should understand across three distinct layers: the AI platforms an organization chooses, the physical infrastructure underneath those platforms and the data operations running inside that infrastructure. Leaders who fail to understand all three of these layers risk mistaking a partial fix for a complete one.

The Platform Layer: AI Efficiency Starts With Design

France Hoang, founder and CEO of AI collaboration platform BoodleBox explained the relationship between AI tokens and energy usage. “When you send a prompt, that prompt gets packaged up and sent in the form of tokens,” he said. “Tokens grow because when you prompt an AI model, it doesn’t just send the current prompt; it sends all the prompts that came before it,” which includes attached documents. AI models use inference to interpret prompts and create output. “The more inference used, the more tokens consumed, the more power that is used, and ultimately the larger the impact on our environment,” he said.

The Infrastructure Layer: Utilization Before Construction

The Data Layer: Where Inefficiencies Compound

Governing the Stack for AI Efficiency

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