Paul Appleby

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246 appearances 1 recordings 1 series first heard Jul 2026 last heard 15 Jul

Paul Appleby’s voice in public audio — every appearance, attributed to the second.

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Eye On A.I. · 6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana · 15 Jul 2026 podcast

as you said, AI factories, which is a phrase that's bandied about a lot by the likes of NVIDIA and Dell.

These AI factory investments

We're not seeing the governance that needs to come with the kind of controls to optimize the resilience and efficiency of those data centers.

You know, the investments in governance and controls aren't running at the same pace as the investments in the infrastructure, which increase risk.

And that's what we're calling out with this study.

It's like, hey, checkpoint here, you know,

Huge opportunities in so many different industries for adopting AI at scale.

Make sure the governance and controls that you've got built into all of your, you know, legacy infrastructure get replicated in, you know, this AI data center world.

Craig, what I'd say is that root cause is hard to discover either way, but it's much harder in an AI factory or a massive scale AI data center because of the complexity of that system, if you like.

from the data pipelines to the AI workloads and AI orchestration into the infrastructure layer of computing and network and storage, et cetera.

If there is either a slowdown or a failure of an AI workload,

The challenge is, where did that occur?

And it's really quite fascinating is that the vast bulk of these failures don't have an identified root cause.

Now, the problem with that, of course, is if you don't know what causality is, you get to remediation.

And that leads to a really, really high percentage of AI workloads actually failing.

Now, that doesn't make sense.

If you're going to operate at scale with critical services, you can't have a really high percentage of AI workloads failing.

And that's just one element of it.

The second element of it is incredibly powerful compute infrastructure and capability with these GPUs.

If AI workloads are getting throttled and GPUs are sitting idle, you've made these massive investments and you're not getting a return on the investment.

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