The hidden arms race of AI supercomputers: who controls the machines that control intelligence

The hum of power

Picture a data center on the edge of a desert plateau. Inside, row after row of servers glow and buzz, moving air through vast cooling towers, consuming more electricity than the surrounding towns combined. This is not science fiction. It is the reality of the vast AI compute clusters, often described as “AI supercomputers” for their sheer scale, that train today’s most advanced models.

Strictly speaking, these are not supercomputers in the classical sense. Traditional supercomputers are highly specialized machines designed for scientific simulations such as climate modeling, nuclear physics, or astrophysics, tuned for parallelized code across millions of cores. What drives AI, by contrast, are massive clusters of GPUs or custom accelerators (Nvidia H100s, Google TPUs, etc.) connected through high-bandwidth interconnections, optimized for the matrix multiplications at the heart of deep learning. They are not solving equations for weather forecasts: they are churning through trillions of tokens to predict the next word.

Still, the nickname sticks, because their performance, energy demands, and costs are comparable to, or beyond, the world’s fastest scientific machines. And the implications are just as…