AI Inference vs AI Training: Understanding Infrastructure Requirements
How Enterprises Are Optimizing AI Inference Costs at Scale
GPU vs CPU: Choosing the Right Compute for AI Workloads
Hidden Costs in Cloud Billing: What Your Provider Isn’t Telling You
The AI Agent That Wiped a Company’s Entire Database in 9 Seconds – Lesson the Industry Needed
What the PocketOS incident reveals about the real risks of giving AI agents access to production infrastructure and the storage architecture decisions that made it so much worse.
The Complete Guide to NVIDIA GTC 2026 – Including Side Events, Meetups, and Parties
The Rise of Neo-Clouds: Russ Artzt on the New AI Infrastructure Stack
Russ Artzt discusses how the AI boom is driving a shift in infrastructure, with neoclouds emerging as specialized GPU providers to meet surging compute demand. The new AI stack emphasizes integration, workflow-centric storage, and the importance of operational expertise and standards compatibility.
The Global AI Data Center Gold Rush: Why Data, Not Compute, Will Decide Who Wins
The article highlights that while AI data center construction is booming, long-term data storage and governance are being overlooked. It warns that data, not compute, will drive future infrastructure risks and costs, urging builders to prioritize storage strategies for lasting success.
The Jevons Paradox of AI Compute and the Hidden Cost of Storage Growth
AI efficiency gains are driving exponential data growth — the Jevons Paradox reborn. As compute gets cheaper, storage costs and energy use surge. This article explores how to design infrastructure that scales intelligently, not infinitely.
Inference Is Cheap, Data Isn’t: The New Cost Curve of AI Infrastructure
As compute gets cheaper, storage becomes AI’s new bottleneck. This article explores how falling inference costs collide with stagnant storage pricing — and how CIOs can align data architecture with AI’s new economics.
