How memory and storage shape AI infrastructure

As AI inference becomes more common, organizations are rethinking how data centers are designed to handle realtime workloads. The article, sponsored by Micron and published by MIT Technology Review, explains that memory, storage, networking, and compute now need to be planned together rather than optimized separately. Analyst Jim McGregor argues that AI is not one workload but many, with different needs depending on the use case. That means companies must focus on latency, bandwidth, data movement, and caching, especially for systems like retrievalaugmented generation, robotics, healthcare, and customer service tools. The article says the main challenge is moving data efficiently across the system while balancing performance, cost, energy use, and scalability. It recommends modular infrastructure, closer coordination with suppliers, and ongoing reassessment of procurement choices as AI requirements and hardware evolve. In this view, AI infrastructure is becoming a business strategy issue as much as an engineering one.