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6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana

Eye On A.I.

21 HOURS AGO
Eye On A.I.

Eye On A.I.

21 HOURS AGO

Shownote

Companies are spending billions building AI factories, but most of them can't tell you why their AI workloads are failing, whether their GPUs are actually being used, or what their infrastructure is going to cost them when agents start running at scale. Pa...

Highlights

In this episode, Paul Appleby, CEO of Virtana, discusses the critical gap between massive AI infrastructure investments and the governance needed to manage them effectively. Drawing from Virtana's AI Factory Reality Check study, the conversation reveals that most enterprises lack the ability to automatically diagnose AI workload failures, leading to significant operational risks. Appleby explains how his company's observability platform captures thousands of metrics per second to provide real-time insights, and he offers a sobering perspective on the true costs of scaling AI systems.
07:54
60% of enterprises cannot automatically identify root causes.
28:24
Falling token costs lead to higher total spending
40:12
Edge AI and physical AI will develop in parallel.

Chapters

Why 60% of AI Workload Failures Remain a Mystery
00:00
The Hidden Cost of Cheaper Tokens: Why Your AI Bill is Still Going Up
24:46
Inside the AI Factory: Observability Across a Hybrid, Heterogeneous Stack
36:10

Transcript

Craig S. Smith: Six in ten enterprises cannot automatically identify the root cause across AI infrastructure domains when AI workloads fail. Why a root cause is so much harder in an AI factory than in traditional enterprise IT to discover? What you're sayi...