If We Can't Kick the Habit, How Do We Manage AI's Massive Energy Demand

Mon, 01 Jan 0001 00:00:00 +0000

Why Has AI’s Energy Problem Suddenly Become So Urgent?

Simple answer: because the growth curve has decoupled from grid capacity. When the training energy consumption of a single model begins to be measured in “annual electricity usage of several cities,” it is no longer a lab billing issue but a national-level infrastructure stress test.

Remember the dividends brought by Moore’s Law? Transistors became smaller, performance improved, and power consumption decreased. But this law has significantly slowed or even become ineffective in the AI era, especially for inference and training of large neural networks. We are facing the brutal reality of “Huang’s Law” or “AI computing power demand doubling every six months,” behind which is an exponential increase in energy consumption. The International Energy Agency (IEA) clearly pointed out in its 2025 report that data center electricity consumption is expected to double between 2022 and 2026, with AI and cryptocurrency being the two main driving factors.

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If We Can't Kick the Habit, How Do We Manage AI's Massive Energy Demand

Why Has AI’s Energy Problem Suddenly Become So Urgent?