The core issue is that while companies are spending billions on AI factories, 60% cannot automatically identify the root cause of AI workload failures. This problem is compounded by the complexity of modern AI systems, where most failures have no identified cause. Virtana's platform addresses this by capturing 20,000 metrics per second across the entire AI stack, enabling real-time correlation and automated remediation. A key insight is that falling token costs are misleading, as token consumption is exploding, driving total costs higher, especially with agentic AI systems. This has led to a cultural shift where IT resilience reporting has moved from annual to weekly, reflecting board-level concern. The conversation also covers the heterogeneous nature of AI data centers, the hybrid reality of enterprise workloads, and the need for clear ROI metrics before scaling AI deployments.