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

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. Paul Appleby, CEO of Virtana, joins Craig Smith to discuss the findings of their AI Factory Reality Check study, a research report that reveals a striking and underappreciated gap between the pace of AI infrastructure investment and the governance needed to run it safely and efficiently. Six in ten enterprises, the study found, cannot automatically identify root cause when an AI workload fails, a problem that compounds fast once you're running critical services on AI infrastructure at scale. The conversation covers the mechanics of Virtana's observability platform, capturing 20,000 metrics per second across the entire AI stack, correlating them in real time, and increasingly using agentic capabilities to remediate failures automatically, but its most important insights are structural. Appleby makes a sharp observation that cuts through a lot of AI optimism: token costs are falling, but token consumption is exploding, meaning the total cost of running agentic AI systems is still going up even as the per-unit price drops. He also tracks a cultural shift inside enterprises - IT resilience reporting that used to happen annually now happens weekly - as evidence that technology risk has become a board-level conversation in a way it simply wasn't before. The result is a conversation that's less about the promise of AI and more about what it actually takes to make it work at production scale. Subscribe to Eye on A. I. for weekly conversations with the people building and deploying the future of AI.

• 0:00Speaker 00

  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 saying, we're entering a phase where AI ROI will be judged or AI will be judged less by model performance and more by operational efficiency, ergo, cost.

• 0:30Speaker 01

  And business impact, you know, it might not just be a cost dynamic, the kind of gold rush to be in the AI investment cycle is overriding some of the good governance principles that companies have. We'll see the equilibrium come back.

• 0:46Speaker 00

  So let's start by having you introduce yourself to listeners.

• 0:51Speaker 01

  Thanks, Craig. Um, yeah, I'm Paul Appleby, the CEO of Virtana. And we're a company that exists really for, for one really important purpose, where, you know, companies across many industries, whether it's banking, telecommunications, healthcare, retail, airlines, whatever, are so reliant on their technology. In fact, you know, a lot of chief risk officers say that the single biggest point of catastrophic risk of failure for a business now is their technology and infrastructure. Virtana is in the world of trying to protect that. We live in this, this world called observability, and that's all about business resilience and operational efficiency.

• 1:36Speaker 01

  How do we make sure those services that are critical to your business and your customers stay performant and available? And even more relevant in this era of accelerated AI adoption, of course.

• 1:49Speaker 00

  Yeah. And how long have you been with Virtana? And what was your background before coming?

• 1:56Speaker 01

  I've been here now for a couple of years. Inherited an amazing business that in the past, in fact, under its prior name of Virtual Instruments, was run by John Thompson, the, the former chairman of Microsoft. So the company's been around for some time and has an amazing history with its core technology. But I came to the company two years ago with a charter from the board to really lean into this, this world of, you know, the broad digitization of services and the scale out adoption of AI. Prior to that, I'd, I'd been working in technology for years, sometimes in startups,

• 2:40Speaker 01

  because I love, you know, that whole idea of the scale up. But sometimes in much larger companies, you know, through, through phases of transformation and growth, like South Horse, where, where I was for a number of years, and also companies like Elastic Search, more recently as their president. So I've got a long background in, in enterprise software and enterprise technology and growth and, and scale of businesses.

• 3:10Speaker 00

  Yeah. In Virtana, we were talking before we started recording, is an observability platform for, for large scale operations or tech, technology operations, not necessarily AI. I mean, it, it existed before the, the, the generative AI boom, is that right?

• 3:37Speaker 01

  Yeah, I, I think it's a couple of really important things to say. I mean, this class of software, Craig's been around for a long time, for as long as, as, you know, technology's been supporting critical business services, there's been a need to monitor that infrastructure to make sure it stays available and performant. But interestingly, if you think about observability, what really is its purpose? Its purpose is to identify threats and risks in real time.

• 4:06Speaker 01

  You know, identify what the cause of those was and remedy those. So as a consequence, we've been building deep AI and ML capabilities, yeah, for over a decade. So AI is not new to us. It's really part of our core reason for being, you know, streaming in all of that event data in real time and doing, you know, the, the mapping and correlation needed to identify, you know, risks and threats. What we've done more recently, of course, is lean in heavily to, you know, a lot of agentic capabilities, you know, to support automating a lot of these IT operations. But the other thing that we've done is recognize that as companies scale out the, you know,

• 4:52Speaker 01

  yes, kind of the true industrialization of AI with massive AI data center investments, that the market loves to call AI factories, is, is build full end-to-end observability for the AI factory. So what we've essentially done is taken an incredible legacy of observability and fully embrace this whole, you know, AI era to support the, you know, companies as they scale up their AI investments.

• 5:24Speaker 00

  Yeah. And before we get to the study, you were saying that this is a layer that sits above the technology stack, even above the orchestration layer.

• 5:37Speaker 01

  Correct, Craig. And you know, what I'd say is, if you, if you think about any critical business service, let's call it a trading platform or internet banking is, you know, something that, that, you know, is core to any retail bank. You know, we all do most of our banking online these days. In fact, I do most of mine on my, on my mobile phone. And, um, you know, what supports our ability to interact with our bank is a really complex system, if you like, of, of, you know, data and applications and networks and compute and storage infrastructure. Some of it potentially in the cloud, some of it in legacy infrastructure, some on-prem.

• 6:21Speaker 01

  And, and really what observability needs to be is something that uses the whole system. Cause I like to think about it as a system that's incredibly, you know, in most cases heterogeneous, in most cases, you know, hybrid, which is the, the reality for most companies today.

• 6:41Speaker 01

  And, and the ability to, you know, dynamically observe that whole system and, and manage that system is, is where our class of software sits.

• 6:53Speaker 00

  And, um, the, it, it's becoming increasingly critical, this observability, as these systems scale, right? And, and they're scaling not only to keep up with, I mean, some are scaling to keep up with customer, customer base, but some are scaling simply because the business is growing and becoming increasingly complex. I mean, you're not only looking at customer facing or public facing applications. You're, you're looking at any system that, that has AI in the stack.

• 7:34Speaker 01

  You're absolutely correct. So you think of, of any system, you know, even for large retailers, some of their most important systems aren't just the customer facing systems, their website or, or their point of sale environment, but it's the whole distribution, supply chain, infrastructure as well, all of the back office functions of a retailer. The same with an airline, you know, obviously we experience the airline through baggage handling and ticketing and, and, you know, all of those things. But there's all of the crew scheduling and flight scheduling and everything else that exists. And, you know, the thing that, you know, obviously makes that even more complex is this world of remote and hybrid, hybrid work.

• 8:17Speaker 01

  So supporting huge remote and hybrid workforces is, is key as well. So when we look at a large company, we're thinking about all of these critical systems that are key to the operation of the business. And, you know, you think about, you know, healthcare companies, you know, the way we experience healthcare today, you know, with things like, you know, digital medicine and, and remote diagnostics and, you know, remote visits and all the rest of it, as wonderful as a consumer to be able to do that. But the medical practitioners are consuming all of that as well as they're able to access your electronic health records and collaborate with other practitioners.

• 9:00Speaker 01

  So it's, it's being able to support all of that infrastructure for a big healthcare org or a big airline or a big bank, that's, that's where we sit.

• 9:09Speaker 00

  Right. And, and the study that you've done, the AI factory reality check, I believe it's called. You're, you're, you're finding that these AI factories, to use that term, but, but these systems are growing faster than, than.

• 9:32Speaker 00

  Then there is ability to to to operate them or or maintain them or.

• 9:42Speaker 01

  Yeah, I think that's that's a great way of putting it, Craig. I think that, um, you know, the I think we'd all agree that there there's been a wild explosion of investment in AI infrastructure and AI data centers and and, you know, even above and beyond, you know, these AI platforms that we're all very familiar with. Um, as we think more about large enterprises and and governments adopting AI, um, the the clear narrative is that, um, you know, most of this AI infrastructure or a lot of it is going to be built on-prem. Um, you know, to support these, you know, huge AI use cases for for these companies.

• 10:25Speaker 01

  And, you know, what, um, our study has found is that whilst companies are leaping into investing in AI data centers and and, you know, as you said, AI factories, you know, which is a a a a phrase that's, you know, bandied about a lot by, you know, the likes of Nvidia and and Dell. The these AI factory investments, um, we're not seeing the governance that needs to come with the kind of controls uh, to optimize the um, the resilience and efficiency of those data centers. Um, you know, the investments in governance and controls aren't uh, aren't running at the same pace as the investments in the infrastructure,

• 11:09Speaker 01

  which increase risk. And that's, 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 through 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 AI um, data center world.

• 11:35Speaker 00

  Yeah, and I think one of the of the the the top findings is that, uh, nearly, uh, well over half, I think, six and 10 enterprises cannot automatically identify the root cause cause across AI infrastructure domains when AI workloads fail. Can you talk about that? Uh, why root causes so much harder in an AI factory than in traditional uh, enterprise IT uh, to discover?

• 12:15Speaker 01

  Craig, what I'd say is that root cause is hard to discover either way, but it's much harder in an AI AI factory or a massive scale AI data center because of the complexity of that um, system, if you like, from the data pipelines to the AI workloads and AI orchestration into, you know, the the infrastructure layer of computing and network and storage, et cetera. Um, if there is either a slowdown um, or a failure of uh, an AI workload, the the challenge is where did that occur?

• 13:00Speaker 01

  And and it's really quite fascinating is that, you know, the vast bulk of um, 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 can't get to remediation. And um, that leads to, you know, 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 you know, a really high percentage of AI workloads failing. Um, and and that's just one element of it. The second element of it is, you know, incredibly powerful compute infrastructure and capability with these these GPUs.

• 13:44Speaker 01

  But 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. So how do you make sure you're maximizing utilization and throughput to get great ROI? And then the final thing is we're all aware of the massive energy consumption um, and environmental implications of AI data centers. So if we're actually operating them efficiently, we can actually significantly reduce the um, you know, the the energy requirements and and ultimately the footprint and the impact that AI data centers have. So it's really at multiple levels that we need to be thinking about this whole system. And unfortunately, right now, Craig,

• 14:28Speaker 01

  what, you know, what exists for many companies is, you know, bits of telemetry out of each level of the stack that they're trying to stitch together to identify, hey, what's really going on and how do we actually better manage this environment? And that's not the answer.

• 14:44Speaker 00

  Yeah, well, as a matter of fact, that was going to be my question. What do people do without a platform like Virtana? Because this isn't a new problem. It may be increasing in its uh, the risk uh, as things scale. But what what have people been doing or what are they doing as they scale? They can't just be holding their breath and hoping everything's okay.

• 15:11Speaker 01

  Um, well, then they're not just holding their breath. Uh, you know, there there's clearly data coming from you know, certain elements of that, you know, AI factory stack. And what it's left to is that the IT professionals whose job is to run and operate the the the these data centers to try and stitch that data together. Um, that's why when you you you have a a service failure or a service disruption, you end up with a group of people of, you know, historically it would have been physically in in a, you know, in a in a knock, but now it's combination of people in the in the knock and a bunch of people on on a call like this.

• 15:52Speaker 01

  Um, trying to work out what the hell is the problem and how do we fix it? And it's trying to stitch together all of this data. Now, that's the reality for most companies today. In in fact, you know, that they've got four or five, sometimes six different sources of data that they're somehow trying to weave together. And I think the the clear message moving forward as we think about critical services at scale in this AI world is that you need visibility into the full system. Um, you have to be capturing rich telemetry and doing that dynamic mapping and correlation in real time to be able to really truly govern these systems.

• 16:37Speaker 01

  And, you know, as we were chatting about earlier before, you know, the this conversation started, um, it's then you can actually apply agentic capabilities, because if you've got a full system view, you could then use agents to accelerate um, getting to causality and then actually automate remediation.

• 16:57Speaker 00

  How does Virtana's platform work? I mean, do you have multiple models that are uh, cross referencing uh, each other? Do you have sensors in different uh, servers that are watching things? I mean, and and then all of that is is fused uh, and and analyzed. So, yeah, can you just talk about the architecture of it all?

• 17:29Speaker 01

  Yeah. The the answer, the short answer is yes, yes, and yes. But let me talk a little bit more about what I mean there, Craig, because um, to to be able to do the kinds of things we're talking about of getting to true autonomous operations, you need to capture telemetry from the whole stack. So over, you know, a period of many years, we've developed deep integrations into every layer of the stack and both your traditional infrastructure and now, of course, in this AI infrastructure. Um, and and we're capturing 20,000 different metrics um, in a sub-second um, timeframe

• 18:12Speaker 01

  and correlating and mapping uh, those uh, metrics in near real time. Um, so it's it's by capturing all of those metrics and correlating and mapping those and using our models to to, you know, to see patterns that that identify causality and of course, the patterns that recommend remediation um, are really core to what Virtana does. But it it really is a really important thing to understand. And one of the things I understood by having worked in other organizations in this observability sector is that you can't really apply agentic capabilities

• 18:57Speaker 01

  and expect to drive true autonomous outcomes unless you've got system-wide visibility and and that kind of real time, you know, dynamic mapping and correlation, because it just doesn't work. Otherwise, you've got some automation to a part of the stack as opposed to the full system. And that's real, that's really what, uh, you know, uh, uh, Virtana does. And it it is a really, really tough problem to solve, Craig, that ability to, you know, stream it in jest, you know, that that volume of metrics and then analyze, correlate, understand and recommend dynamically um, is really core to, you know, what Virtana's story is all about

• 19:42Speaker 01

  and why it's so powerful now in this AI era. Um, because we can bring those core capabilities into these so-called AI factories um, and play a really important part in helping govern them.

• 19:55Speaker 00

  Yeah, and the uh, uh, does, we also talked briefly about cybersecurity, whether this enhances uh, your cyber attack monitoring, because this is uh, LLM adjacent, right? You use uh, models to do some of the analysis.

• 20:21Speaker 00

  Are most of the anomalies detected, things that you've seen over and over again? And so the you you you, the system has a pretty good bead on uh, on why that's um. You know, why that's happening, or does it find new things that no one's seen? And in those cases, is it capable of addressing them autonomously?

• 20:53Speaker 01

  Um, yeah, I mean, a lot, a lot to unpack there, Craig. So let me, let me take them piece by piece. You know, on the, um, the, the question of cyber, you know, we don't position ourselves as a cybersecurity solution. We're primarily focused on the infrastructure. But the, the, the point that we were talking about earlier, of course, is that if you've got this ability to ingest all of these, ingest all of these events and analyze them, correlate them and identify anomalies, um, you know, and it's such a powerful engine, it can be applied for cybersecurity use cases.

• 21:34Speaker 01

  So in fact, although we don't position ourselves as a cybersecurity company, we've got a bunch of customers who, you know, who've gone, wow, this data is so rich. We can actually augment what we're doing in terms of our cyber capabilities by using the ability to detect anomalies. So, so that has certainly happened, but it's not really the core of our business today, although it is a very powerful engine. As it relates to, you know, your other questions, um, yes, we do see, you know, because we have so much rich data and because, you know, a lot of these events follow similar patterns, we can identify those patterns and get to causality really quickly.

• 22:20Speaker 01

  But the other fascinating thing is because we're capturing so much rich data, even when we're identifying, you know, new or newer anomalies, we're very, very quickly able to learn what those patterns are and then start recommending remediation. Even if it's, you know, and, and depending on a company's policies, um, you know, we can either automate that remediation or, you know, pass the, you know, the recommendation together with the supporting evidence around the recommendation to a human operator to then act on as well. So, you know, it really depends on the, you know, the, the company, where the company's

• 23:01Speaker 01

  at on their AI and automation journey and, um, you know, you know, what policies they want to adopt associated with that. But what we found in going into a number of these AI labs of some of the larger, um, you know, technology providers and some of the larger customers is that we're identifying anomalies and usage patterns that, that they didn't even understand about their technology themselves because of, of our ability to do exactly what we've been talking about earlier.

• 23:36Speaker 00

  Yeah, uh, well, I have so many questions, but, um, so who owns this? I mean, this is not another alarm dashboard, uh, because most of the remediation is being done autonomously. But as you said, there are times when you have to pull a human into the loop. Who is that? Is that in the IT department? And then the other question is, um, your report shows a disconnect between executives and practitioners. Yeah. What does that say about how AI infrastructure decisions are being made?

• 24:16Speaker 00

  Or the, the, the purchase of Virtana, for example, maybe the, the guys in the IT department are saying, we can't handle all of this. We need Virtana, but the, but the C-suite is skeptical. I mean, yeah, can you talk about?

• 24:35Speaker 01

  Craig, Craig, you're the master at asking multi-layered questions. So I'm gonna, I'm gonna try, I'm gonna try and, um, and, and parse them all there.

• 24:47Speaker 01

  You know, what I'd say, I'll tell you a story. I can't name the company for obvious reasons, but I visited with a very large company here in the U.S. recently. And I met with, um, the, the person tasked with this, and in this case, it was the senior vice president responsible for IT operations. So that kind of senior vice president of IT operations is really a critical role in companies. And, and the exec that I was chatting with had been with the company for many years, a couple of decades and said, when, uh, when they first joined the company and were in that role, they, they reported out to the, the CEO and, and the C-suite about what they

• 25:28Speaker 01

  are up to and creating rigidity and resilience once a year. And then it became once a quarter. And then it became once a month. And, and what they shared with me is that they report out once a week on where they're at, you know, around providing resilience, rigidity and operational efficiency to the CEO and the CEO's leadership team. And, and what that says, and the reason why I wanted to share that anecdote is that this role of the person who's responsible for ensuring the resilience and efficiency of, of IT infrastructure and the services it supports has now become a C-level, virtually

• 26:12Speaker 01

  a board level, if you, um, so, so it's that persona that, that we sell to. And, and then, but, you know, beneath them is all, obviously all of the IT operators, the, you know, the, the, the specific role is called site reliability engineer and their job is to ensure the reliability of, of that infrastructure. It's, it's that, that group that we sell to. And I think what I'd say to the other layer of your question is that the disconnect between the IT operators and the business, if you look at any business today, every board is saying, what's your, what's your AI strategy and how are we embracing AI to stay relevant and compete and all of those things?

• 26:54Speaker 01

  So this huge focus on, you know, getting in and investing in AI and, and being an AI forward company. So that pressure is coming from the business. The, the IT operators, you know, these, uh, VPs and senior vice presidents of IT Ops are sitting there going, but hey, we need the governance and controls to make sure that, you know, we can, you know, protect the, the resilience of the, the business. So that's kind of the disconnect, the pressure coming on investment and, and investment at speed. And then that kind of, you know, IT and the professional IT executive turning around and going, hey, you know, we're, we're increasing our risk profile here and, and what are we going to do about that?

• 27:39Speaker 00

  Yeah.

• 27:41Speaker 00

  The, um, token prices are falling.

• 27:46Speaker 00

  How, how should, uh, CEOs and CIOs think about GPU spend differently as AI moves from pilot to production? And, and I think I've mentioned to you, I've been seeing people warning that the drop in, in token costs, uh, is great, but it tends to, to then people deploy all these agents. The next thing, your, your, uh, your cost is going through the roof, even though the token cost is lower because you're using more tokens.

• 28:24Speaker 01

  Spot on, Craig. And, you know, as we were saying, you know, just as we were chatting beforehand, um, things are changing on almost a daily basis. So yeah, sure. Token cost, tokens going down and will continue to fall, you know, you know, for, for the foreseeable. The token consumption is going through the roof.

• 28:46Speaker 01

  And, you know, it, it's really just even looking at, at labor arbitrage and that question of, you know, what is, you know, where, where is the work done most efficiently? There's been conversations over the, the, the last few days about, you know, is, is the labor arbitrage conversation headed in the right direction? And, and in fact, will AI be more efficient? And I think that is going to lead to some really important decisions that companies have to make about what use cases and business problems, um, we need to solve leveraging this kind of infrastructure, um, that are going to make a huge impact.

• 29:31Speaker 01

  And, you know, there, I can think of some great examples, you know, like accelerated drug discovery, reducing the time of bringing new medicines to market, making them far, far more highly targeted, um, and improving the efficacy of those is like, there's no argument about that. You know, using AI to, you know, better analyze, you know, medical imaging, um, using AI to better identify, you know, um, uh, fraud, um, or, you know, criminal financial transactions and those sorts of things. You know, there are a lot of, I could go on and on, a really powerful use cases for AI. But I think, you know, what companies are going to do, and I think they are doing right

• 30:13Speaker 01

  now, is looking at why, where do we deploy AI that's going to have a meaningful impact on customers, customer experience and, and, you know, and, and drive increased ROI? That, that's going to be key. But I think that, um, you know, a lot of companies are rushing headlong into the investment right now without thinking through the implications for ROI. And there's a lot of studies being done on this, that, you know, the hundreds of billions of dollars that are being invested right now. And when we, when the curve between the investment and the revenues will actually cross and, and, um, based on the data I've seen, it is not in the near future, Craig.

• 30:56Speaker 00

  Yeah. And, and so we're, we're entering a phase, what you're saying, we're entering a phase where, uh, AI ROI will be judged, or AI will be judged less by model performance and more by, uh, operational efficiency, ergo, cost.

• 31:17Speaker 01

  Yeah. And, and business impact, you know, it might not just be a cost dynamic. It could be, you know, a revenue dynamic, or it could be a, you know, customer experience and retention dynamic, but there'll have to be measurable impacts that's commensurate with the, the level of investment that we're making. And as I've said, you know, there are so many use cases where AI is just the, the only answer. But it's, it's about, you know, um, being really smart about how and where we deploy AI, um, building the right governance and controls around those, uh, investments and, um, you know, uh, continuing to scale thoughtfully.

• 32:02Speaker 00

  Yeah. And does Virtana help in tracking ROI metrics? Because the report says, I think nearly a third say they need clear metrics, ROI metrics before confidently scaling AI. Further. So, yeah. So what, what metrics do you think matter most? And, and how does Virtana handle that?

• 32:29Speaker 01

  Yeah, Craig, great question. You know, what I'd say is that if, if you think about any large scale IT organization, they're in a pretty tricky position, not just as it relates to AI investments, but just, you know, IT investments in general. Because they've been asked to do a couple of things. Support more and more complex services, AI being one of those things, with more and more complex infrastructure with incredible resilience. But they're also being asked to do that efficiently. So, you know, I haven't met a CIO yet that hasn't told me that there's got pressure on cost and operating costs.

• 33:11Speaker 01

  So it's like, there's this duality that's kind of, it's competing. You know, priorities. And so if you're going to play a role in supporting enterprise resilience, you've got to play a role in, in making sure that you help customers use their infrastructure efficiently. So that whole area of understanding and managing control for a, a cost for both on-prem or physical infrastructure, as well as cloud infrastructure, is a core part of our solution. The only thing that we don't include is the business metrics. Because we're not bankers.

• 33:51Speaker 01

  We don't run insurance businesses or whatever, but we can provide all of the, the cost analysis and metrics that then get combined with the business metrics to determine, you know, a return on investment and, and payback and, and the like.

• 34:06Speaker 00

  Yeah. The study found though, that

• 34:12Speaker 00

  a lot of this governance work is being deferred, just as complexity is increasing. So why is that? And, and how do you see that impacting enterprises?

• 34:28Speaker 01

  Yeah, it's, it's interesting. And I think that what's happening, Craig, is the, is the, the kind of gold rush to be, to be in, you know, in the AI investment cycle is overriding some of the good governance principles that, that companies have. I think that we'll see the equilibrium come back. And in fact, that's why we published the study to show that, you know, there is a delta between what your business executives think and what IT operators think. There is a huge failure rate for AI workloads. And that, you know, we didn't publish the, the findings to be naysayers around AI, because we're not.

• 35:13Speaker 01

  But what we are saying is, hey, as we, as we make these investments, we've got to catch up with the governance. I think we'll see that happen quickly, Craig. You know, especially as people see the data and are confronted with the data and the reality of, you know, putting governance and controls in place, particularly as it relates to AI, is not only in the public interest, but it's in the business interest as well.

• 35:40Speaker 00

  You've just extended AI factory observability to Dell, AI factory and AWS,

• 35:50Speaker 00

  Bedrock and Nutanix.

• 35:54Speaker 00

  What does that say about where observability needs to live on-prem? At the model layer?

• 36:03Speaker 01

  Yeah.

• 36:03Speaker 00

  Yeah.

• 36:05Speaker 01

  Um, you know, I, I think there, there are again, a couple of elements to that, Craig. The first thing that I'd say is that what we're seeing in terms of the, you know, AI data centers that are getting deployed is that they, they are leveraging as companies architect these solutions. They're leveraging in, you know, what they believe is the best solution for every layer of that stack. So you end up with this heterogeneous environment of different providers providing, you know, components, if you like, of the AI factory that come together. So deepening our relationships with the likes of AWS and with Dell and with Nutanix

• 36:50Speaker 01

  and many, many others, Nvidia, your deep integration into the Nvidia stack and, and their GPUs, of course. It, it's all about ensuring that we're capturing the richest telemetry. So it is really, I think, the, the, the first thing to note is that heterogeneous nature of these, these AI data centers, pulling on the, the best technology for each layer of the stack. And then for observability companies, it's our job to capture all that telemetry and weave that together for our customers. I think as it relates to, you know, on-prem or in the cloud, I think, you know, like we're seeing

• 37:32Speaker 01

  with, you know, a lot of enterprise technology, we're, we're going to see a hybrid world. You know, the, the, the world of hybrid is the reality for just about every large company on the planet. Certain workloads and data will remain on-prem and, and that's not going to change. Certain workloads and data will be in the cloud. And, and it will be a kind of a dynamic, you know, model where, where we'll see, you know, sometimes, you know, workloads and data shift depending on some kind of price arbitrage or, or, you know, speed to outcome or, you know, data protection and privacy issues.

• 38:12Speaker 01

  So what's the big responsibility for organizations like ours is to make sure that we can, you know, manage that complex hybrid world and take that complexity away for our customers. And, and, you know, hence the investments in, you know, clearly significant on-prem infrastructure capability, as well as partnering with the hyperscalers and, and Neo clouds to ensure that we're supporting customers wherever they need to be.

• 38:43Speaker 00

  Yeah. But, but the platform resides

• 38:49Speaker 00

  with the, the enterprise? Or is it, is it, are there instances of it? In every place that you have data flowing?

• 39:02Speaker 01

  You know, the one thing that we've also tried to do is ensure that our technology can be deployed anywhere. So our, our, our technology is deployed both on-prem and in the cloud. So there are instances on-prem, there are instances in the cloud. And in fact, with a number of providers who are providing, you know, as a service infrastructure, like Hitachi, our platform is embedded in their platform. So, you know, the, the interesting thing is Virtana shows up in, in all sorts of different places. You know, really depending on, you know, how you're consuming the service.

• 39:42Speaker 01

  And, and the whole idea and the whole philosophy around that was, you know, providing ubiquitous access and not going, hey, we are purely going to reside in the cloud. And that is, that, that is our, our vision and strategy, because that's not where our customers are. Our customers leveraging the public clouds. They're leveraging service providers, managed service providers and consuming services there. And they've got their own on-prem infrastructure that they're managing themselves. We need to be available across all of those environments. And so we've made a choice to, to do that. And it's ended up being the right choice because it allows us to be wherever our customers need us to be.

• 40:25Speaker 00

  Yeah. And this is a SaaS offering. I mean, it's not a, a, you know, a single purchase and then they're.

• 40:33Speaker 01

  Yeah. No, where, you know, where, where, you know, we, we, the great thing about being, you know, a subscription software company, Craig, is that, that you have to continually earn the right to do business with your customers. And that's, that's what I've tried to encourage my team is that, that philosophy is, is that we have to continue to earn the right. And, you know, the amazing thing for us and that I'm incredibly grateful to all of our customers for is that almost every single one of our customers grows year on year. And they're not growing year on year for any other reason than that they're growing and scaling their usage of us across their infrastructure, because we're solving big, important problems for them.

• 41:17Speaker 00

  I want to ask as kind of an off the topic question, if I may.

• 41:23Speaker 01

  You've got me worried now, Craig.

• 41:25Speaker 00

  No, no, it's, you know, with, with the, the economics of deploying AI changing. And there's a lot of work on pushing inference out to the edge. And there's a lot of work on, you know, making it more power efficient and cheaper. Are we overbuilding data centers, do you think? I mean, that would argue to me that you can do more with less.

• 42:03Speaker 01

  Wow, that's a big question. How, how, how long do you think we've got to answer that one, Craig? In, in one minute? You know, I think that, that you all of those things are true. If we, we think about, you know, the promises of IOT. I mean, we, we stopped talking about the Internet of Things. But if you talk about, you know, city scale automation, you know, large scale adoption of autonomous vehicles, you know, massive scale manufacturing. We talked about physical AI earlier and the role of AI in manufacturing processes. You know, a lot of this has to occur out in the edge. There, there is no doubt about that.

• 42:49Speaker 01

  But, you know, what I would say is that, that, that's going to be additive. I think that the, the physical build out of AI infrastructure is going to be absolutely key, particularly as companies and governments adopt AI. They're going to be building out more and more their own AI data centers, AI factories. And, and you only have to listen to Michael Dell talk about the, the explosive growth that Dell is seeing in the adoption of their AI factories to realize that companies are doing that. So I think that'll happen in parallel, Craig. And maybe we'll see ultimately the promise of, you know, large scale, you know, autonomous vehicle adoption.

• 43:34Speaker 01

  We'll see the, the advent of, of truly distributed power generation and, and, and smart utilization of, of, you know, energy in the home and all, all of the things that have been promised for a long time. I think, you know, could potentially be deployed with AI out at the edge. But that's, I'm sure, a much longer conversation.

• 43:57Speaker 00

  Okay. Okay. Is there anything I didn't cover that you want listeners to hear?

• 44:05Speaker 01

  No, I think Craig, we've, we've, we've touched, we've touched on all of it. It's been a great conversation and it's fun chatting about what's going on out there in the world and kind of, you know, debunking some of the, the myths around AI, but also raising consciousness and awareness around the risks. And I think it was a really great conversation.

• 44:25Speaker 00

  Yeah.