Season 1. Ep[isode 16. Unscripted with Keith Pijanowski

Every impressive AI demo sits on an enormous, expensive, and very unglamorous foundation of data. Keith Pijanowski is one of the people who built it. After a decade-plus at Microsoft (where he helped evangelize Azure before its public release) and data pipeline work at BNY Mellon, he now works on the storage infrastructure that AI training and inference actually depend on. He breaks down why hundreds of GPUs hammering storage for months makes training a workload the software industry has never seen, and why inference is no longer lightweight thanks to KV caching across GPU memory, system memory, and network storage. He predicts power ratings will soon rival performance ratings for GPUs, dismisses space data centers as science fiction while taking ocean data centers seriously, and argues the build-out is rational because the Jevons paradox guarantees efficiency gains produce more demand, not less. His deepest concern isn't the machines. It's people publishing content and shipping code they don't understand. Use AI as a productivity tool, but don't let it turn you into an operator.