How policy can support ‘efficiency-forward’ approaches to AI growth.
In an article for Fast Company, Moshe Tanach argues that “the Federal Aid Highway Act provides a valuable lesson for one of the biggest issues facing the tech industry today: How will we produce enough power to support the growing number of AI-focused data centers?”
As Tanach explains, the computational power needed to power the rise of AI doubles roughly every 100 days, calling for a strategy for addressing these growing energy needs.
This is where the highway model comes into play. We could choose to find ways to supply more energy to power AI (build more highways), or we could discover how to lower AI energy costs (invest in high-speed rail). One path leads to a power-sucking, climate-destroying future, while the other is sustainable and profitable.
Tanach describes an ‘efficiency-first’ approach that would focus on processing AI tasks with less energy and breaking “the cycle of increased usage leading to increased energy consumption.” Tanach particularly supports looking for ways to eliminate central processing units (CPUs) in AI Inferencing servers. “For the future of AI, we can either invest heavily in outdated ways of supplying power that put an additional strain on our current power grids or find a way to lower costs at the source—the AI data center itself—with baked-in systems engineering that does most of that heavy work.”
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