data-centers
4 free lessons tagged data-centers across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Is the AI buildout a bubble or a bet?
Hundreds of billions of dollars a year are being spent on AI infrastructure. Is that rational investment or a bubble? This lesson gives you the tools to reason about it: what capex is and why it dwarfs AI revenue today, the depreciation trap of fast-aging chips, what circular financing means, and the two coherent cases, for and against, so you can judge for yourself.
Why electricity, not chips, is the real limit
The surprise of the AI buildout is that the scarcest resource is not chips or money but electricity, and the grid was not built for this. Learn why power became the binding constraint, why connecting a big new load takes years, why data centers are turning to gas and nuclear, and why an unglamorous device, the transformer, quietly gates the whole thing.
Inside the machine that runs AI
An AI data center is not a warehouse of ordinary computers; it is a single supercomputer built from tens of thousands of specialized chips wired together. Learn why GPUs beat regular processors for AI, why the network between chips matters as much as the chips, why one company dominates the market, and the cost structure that makes these buildings so expensive.
Why AI turned into a compute problem
Modern AI got better mainly by getting bigger, and bigger means more computation, which is why the story of AI is now a story about hardware, power, and money. Learn what compute actually is, why scaling laws made more of it pay off so reliably, the difference between training and inference demand, and why this buildout is unlike previous technology booms.