AI inference era puts memory and storage at the center

The article, produced by MIT Technology Review's custom content arm, reflects a growing industry consensus that AI is shifting from training-centric models to inference-heavy deployments. As Jim McGregor of Tirias Research notes, AI is not one workload but millions, so the old approach of stacking the fastest processors no longer suffices. Instead, every part of the data center—compute, memory, storage, and networking—must be architected together to minimize data movement, which has become the primary constraint.

For business leaders, this turns infrastructure planning into a strategic exercise. The article warns that buying for generic "AI readiness" can lead to overspending while leaving actual bottlenecks unresolved. It suggests a modular, flexible approach that can adapt as workloads and technologies change, and it stresses that efficiency will matter for both cost and public perception, given scrutiny over power and water use.

Ultimately, the piece argues that competitive advantage will go to companies that integrate infrastructure with business outcomes and measure ROI rather than just performance. As McGregor puts it, executives should ask how AI will change their business model—because the answer determines the right infrastructure investments.