An aerial view of an electrical power substation along the Columbia River in Oregon, circa March 2026. A Stanford University and Together AI study indicates that electrical infrastructure, not chips, is the real bottleneck on AI. (Shutterstock/Hrach Hovhannisyan)

The Grid Doesn’t Care How Smart Your Model Is

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New research on“intelligence per watt” suggests the real AI race may be won on energy efficiency, not frontier chips.

For three years, the contest for artificial intelligence (AI) has been scored one way: whoever trains the largest frontier model, on the most advanced chips, in the biggest data centers, wins. That premise drives US export controls, which aim to deny China the hardware to build ever-larger systems. It drives the buildout now straining power grids from Virginia to Texas, where firm electricity, not capital, has become the binding constraint on how much compute comes online. And it rests on an assumption worth examining: that the frontier model in the cloud is where the game is decided.

A study published in November 2025 by researchers at Stanford University and Together AI, an American AI infrastructure firm, gives reason to doubt it. The authors propose a single yardstick for AI efficiency: intelligence per watt, the task accuracy a system delivers for each unit of power it burns. Running more than a million real-world queries across 20-odd compact models and eight types of hardware, they found that models running locally, on the kind of chip in a modern laptop, correctly answered 88.7 percent of single-turn chat and reasoning queries. From 2023 to 2025, intelligence per watt improved 5.3 times, and the share of queries a local model could handle climbed from 23 percent to 71 percent.

That trend does not stand alone. The price of intelligence has been falling just as fast. OpenAI’s flagship model cost $30 per million input tokens when GPT-4 launched in early 2023; a little over a year later, GPT-4o did the same work for $2.50, and a cheaper variant........

© The National Interest