Your Company Needs an Energy Strategy for AI’s Next Phase

2026-06-04 · Source: Feeds - HBR.org · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, long

Summary

As AI adoption accelerates, the primary competitive bottleneck is shifting from models and GPUs to electricity itself, transforming AI's economics into an industrial concern. The International Energy Agency's 2026 update projects global data-center electricity use will nearly double from 485 terawatt-hours in 2025 to 950 terawatt-hours by 2030, with AI-focused data centers tripling their consumption. This shift, termed the "Great Value Loop," indicates that value pools migrate down the technology stack from connectivity and compute to energy and physical infrastructure like cooling, land, and grid connections. Companies like Meta are already securing gigawatts of new power, including nuclear generation, as infrastructure hedges. Efficiency gains, such as DeepSeek's 2025 cost reductions, are offset by the Jevons paradox, increasing overall demand.

Key takeaway

For CTOs and VPs of Engineering managing AI deployments, your company's competitive edge now hinges on a robust energy strategy. You must move beyond merely optimizing models and GPUs to actively managing electricity consumption, procurement, and infrastructure location. Implement an AI-energy dashboard and establish a Compute and Energy Council to ensure major AI deployments are vetted for efficiency, workload flexibility, and energy risk, securing reliable, affordable power for future growth.

Key insights

AI's escalating energy demands are making electricity, cooling, and grid access the new strategic competitive advantage.

Principles

• Value pools migrate to the next underlying scarce resource.
• AI economics are increasingly industrial, not purely software-based.
• Efficiency gains can paradoxically increase total demand.

Method

Companies should implement a five-step energy strategy: make energy intensity visible, reduce demand before buying supply, contract for optionality, redesign compute locations, and assign accountability.

In practice

• Track AI energy cost per workflow and tokens per kWh.
• Route simple AI tasks to smaller models and cache queries.
• Utilize long-term power-purchase agreements (PPAs).

Topics

• AI Energy Strategy
• Data Center Power
• Generative AI
• Energy Management
• Competitive Strategy
• Cloud Infrastructure

Best for: Investor, Entrepreneur, Director of AI/ML, Executive, CTO, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by Feeds - HBR.org.