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An estimated 80% of all existing digital data isn't neatly organized in rows and columns, but that unstructured data can unlock significant value and fuel enterprise AI applications, explains data solutions specialist Kailin Hart.

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Kaitlin Hart: I'm Caitlin Hart, director of strategic partnerships at Pryon. I help build together ecosystems and technologies with our partner to build full value driven solutions for our customers.

Ken Kaplan: Now tell me how you got into this.

Kaitlin Hart: Well, I'm a data nerd. I've been a data nerd in all kinds of different capacities of the market for almost 20 years. I have spent a lot of time in machine learning before it was popular. And then most recently I've moved into unstructured data transformation at Pryo.

Ken Kaplan: And what pulled you to the other side?

Kaitlin Hart: To the other side of data? Well, so I spent so much time in structured data trying to help customers solve really critical problems there, but it's only 20% of all data is actually structured. And so the types of problems that they would often talk to me about, data discoverability and being able to build more full applications, we actually couldn't serve in structured data. And so when you think about 80% of all data is unstructured, it's a huge opportunity for us to drive the needle, deliver more value. And that's really what we're doing here at Prime.

Ken Kaplan: And what is some of the unstructured data examples that people wanted to tap into and weren't able to?

Kaitlin Hart: It's PDFs, it's Word docs, it's websites. It could be research documents. It could be legal briefs. It could be really any ... There's about ... I'm trying to think about dozens of different types of unstructured data. Even getting out to multimodal type of content like video data, audio. It's a very large mass of data. And the difference between unstructured and structured data, structure is very organized, right? So it's columns and rows and it's very lightweight and size. Unstructured data because it varies so much in the type of content it actually is. So then does the size of that data. So it makes it very complex to actually transform it at scale.

Ken Kaplan: How are people coming to Priyan and what do they want and how do you help them?

Kaitlin Hart: Yeah, people who are focused on security, they're really driven to us because we take that as a day one priority. We've been engineered from day one not to train on customer data to make sure that their IP is protected at scale. And that's how we have customers like Nvidia and Leidos and some of the market leaders who take their data super seriously.

Ken Kaplan: And take us under the hood a little bit of ... Describe how it works.

Kaitlin Hart: Yeah. So well, I'm a data nerd, so I don't want to go too deep or I might lose everybody. But when you think about transformation, ETL, structured data, it's a similar process. It's a lot more complex because again, that data's a lot more complex. So there's a series of different types of models, machine learning based models that we've developed ourselves that handle the vectorization, the semantic understanding, query routing to question mapping. All that stuff is actually really complex, especially again at scale and to do that out of the box without training on a customer data. So we do all of that and at the very end we have the LLM, which does the smoothing and consolidation of the answer and we're agnostic to what LM folks might use.

Ken Kaplan: And so tell me what a customer the first time that they try and they get something done that they want done, what's their reaction and what's the next thing they want to do?

Kaitlin Hart: Well, this is ... Think about unstructured data. It's been called content, right? As a market, we've separated so far out that we don't even call it data. So when people are actually able to use it like the data that it is, it opens up in a whole new world of possibilities because with a strong data foundation, anything is possible. So they get really excited about all the different things that they could potentially build and all the ways that they might be able to evolve in the future with this. What's next in AI and data changes every single day. We're always on the cutting edge of something new it feels like. But I do think that data is a central really fundamental piece of this working long term. It has to be based on real data. It has to be factual. And then when you go to the next step of that at scale it has to be valuable and you can't sacrifice too much security.

So as you kind of think about building your own solutions internally, those are the types of fundamentals you should be looking at.

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Pharmacy costs have an outsized impact on overall healthcare spend for employers. But much happens before a drug’s price is set. It is important to understand how the complex and lengthy drug development process really works and where there are opportunities to adapt the policy, regulatory, scientific and market forces that influence the lifecycle of a drug - and the costs.

In this podcast we sit down with Rachel Sachs, Professor of Law, Washington University, St. Louis School of Law. Professor Sachs is one of the nation’s leading experts on drug pricing, policy, and innovation and she shares the drug development process in a clear, accessible way to explain how the costs that patients and purchasers pay are ultimately determined - and why they may be different depending on who is paying.

She also discusses the patent process including what they cover, why drugs often have multiple patents, what that means for exclusivity and future competition, skinny labeling and more. We also dig into the R&D process, FDA approval standards, the gap between approval and launch, coverage negotiations, the importance of biosimilars, and more.

Tune in for a clearer understanding of why so many of today’s headlines about prescription costs trace back to the drug development process and how it influences labeling, launch timing, pricing and the patient access decisions that matter most.

Speakers: Rachel Sachs, Professor of Law, Washington University, St. Louis School of Law and Ellen Kelsay, President and CEO, Business Group on Health

Intel IT runs Intel data center services like a factory, accomplishing change in a disciplined manner and applying breakthrough technologies, solutions, and processes. This enables us to meet Intel’s business requirements while providing our internal customers with effective data center infrastructure capabilities and innovative business services. Building on previous investments and techniques, our data center strategy has generated savings exceeding USD 13.36 billion from 2010 to 2025. Using disruptive server, storage, network, infrastructure software, and data center facility technologies can lead to unprecedented quality-of-service levels, TCO reduction, operational efficiency, and environmental responsibility.

In this Tech Barometer podcast, database experts from a U.S. insurer, a Saudi bank and Nutanix describe the growing need for automated enterprise database management as organizations scale their AI readiness.

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Kurt Richards: I've seen a lot of retirements in our company and I'm sure a lot of other companies are going through the same thing.

Jason Lopez: It's not about AI taking jobs and it's not about whether older workers can keep up. Those retirements can be translated to knowledge walking out the door, knowledge the IT team needs to deal with the complexity of databases.

Kurt Richards: Previously that storage guy, he retired long ago that was familiar with our old storage system. Had a couple DBAs retire. It's in every area. If I could sum it up, it's doing more with less now.

Jason Lopez: That's the voice of Kurt Richards, database administrator at Penn National Insurance. He was one of the featured speakers at Nutanix's 2026 NEXT conference along with Musab Umer Malik, senior database consultant for Saudi Arabia-based Alinma Bank. This is the Tech Barometer Podcast. I'm Jason Lopez. The life of a database administrator involves a lot of vigilance, which means patching, migrating, cloning, and recovering systems through processes that are often fragile and take a lot of time. And for DBAs, time is one of their top issues.

[Related: Data Storage Must Evolve to Support AI, But It’s Playing Hard to Get]

Musab Umer Malik: This kind of issues stopped us from growing. Once we talk about the roadmap of around like 700 databases and couple of clusters, when we wanted to move from these databases to extend or decentralize, we couldn't do that because we didn't have enough time and it was too complex and too time consuming for us.

Jason Lopez: Mosab says a database that could have taken hours was taking days. A migration that could have been routine had become a crisis.

Musab Umer Malik: It was around 50 terabytes. It used to have around 12 hours of downtime for the migration. We actually decentralized all the systems from a single Windows server failover cluster to around 25 Windows server failover clusters. There was like storage and infrastructure performance bottlenecks and we were not able to decentralize because a single Windows cluster would take around two weeks. This was the standard time to build the Windows clusters.

Jason Lopez: And then there was the latency. In database terms, latency is the lag between a request and a response. You can sense a lag of a thousand milliseconds, that's about a second, but at 15,000 milliseconds the system can crash.

[Related: Dual Native Plugs Containers into VMs or Bare Metal]

Musab Umer Malik: The largest database we have is around 60 terabytes, 6-0. And this is a central database which is used for logging different applications. And previously when we used to have this database at around 10 terabytes, we had performance issues. The disk latency, the worst case was like 15,000 millisecond of latency and SQL Server Crash. This is the threshold for SQL Server.

Jason Lopez: The safety net you have to have in database operation, which is backup and restore, had become its own bottleneck. Kurt described his experience in terms of whole working days, keeping project teams waiting.

Kurt Richards: Backup was taking six, seven, eight hours. Then the restore was taking another amount of time like that and it just wasn't working. We needed to turn that around quickly every day and keep the project teams up on their testing schedules.

Jason Lopez: You can take your database to the cloud and have someone else deal with its complexity. There are managed database services such as AWS, Azure, and Google. You can take it to them, but not everyone has that option.

[Related: Agent Gateway Enforces AI Token and Traffic Control]

Ashish Mohindroo: In certain regulated industries, it's a mandate because of data residency laws and so on that you cannot even go to the cloud. So that managed database option is not really even available to you as well.

Jason Lopez: Ashish Muhindru, who leads Nutanix database services, told the gathering that databases are too critical and too complex to leave to improvisation. We're now at an inflection point, shifting the database administrator who's faced with doing more with less from a hands-on IT role to that of an architect of sorts, overseeing the operation. Solutions such as Nutanix's NDB were built to fill this need.

Ashish Mohindroo: What we are really doing with NDB is giving that same cloud-like experience to our customers so they can get the database automation on their on-premises data centers or on the public cloud as well and then be able to deliver the kind of productivity gains and cost savings that comes along with that. You can think about it as managed database services without the control of the cloud but the flexibility on their own terms. All of the top five most popular databases are supported on NDB today. So you can have Oracle, SQL Server, Postgres, MySQL, and MongoDB.

Musab Umer Malik: When we use the same database to migrate from the old infrastructure to NDB, it took less than one hour.

Kurt Richards: Our 12-hour process to back up and restore these databases we're now taking just a few minutes, which really opened up things for us and really allowed us to take the testing process a little bit further.

Musab Umer Malik: Now we have around 30 plus application logging everything and from the thousand average millisecond, now we have a 0.4 average millisecond of latency. That's a huge success for us and we are able to expand even further.

Jason Lopez: Beyond the improved stats, another change was the way the databases were maintained, less improvised and more consistent.

[Related: The Rapid Rise and Future of Neoclouds]

Musab Umer Malik: After moving to NDB, for the applications we don't have any downtime as of now. And it's not only that once we do the patching, it is always successful like 90% of the time it is successful, 10%. Even when it fails, there is no downtime.

Jason Lopez: That shift to automation that Musab and Kurt experienced shows it's not only about the performance leap and better uptime, but especially about supporting best practices. Every database adheres to the same standards.

Ashish Mohindroo: Every time they set up a new database, it was set up to the right specs. That's when issues happen on databases is not provisioned right. You don't have enough memory, you don't have enough storage, whatever it may be, now you're enforcing that.

Jason Lopez: As Musab put it, the goal is simple to describe and hard to achieve.

Musab Umer Malik: The long-term goal is to enable automation of all the manual tasks we used to do with the classic DBA.

Jason Lopez: Musab Umair Malik is a senior database consultant at Alinma Bank. Kurt Richards is the database administrator at Penn National Insurance. Ashish Muhindru is the general manager and SVP of the Nutanix databases services platform. Ashish has spent 30 years watching enterprise technology reinvent itself starting in CRM and e-commerce and for the last several years he's been helping shape what comes next at Nutanix. You might check out the article at the forecast entitled Mastering a Myriad of Databases in the AI Era, which features Ashish, as well as the video, how AI is reshaping database management. You can find those stories and more at theforecastbynutanix.com. That's the forecast by Nutanix, all one word.com.

While it is crucial for employees to perform at their highest level in any organization, it is especially critical when it comes to national security. In this podcast episode, Dr. Jennifer Posa, former Chief Well-being Officer at the Central Intelligence Agency, offers a roadmap for building psychological strength and readiness to help employees navigate the most challenging environments.

Tune in to learn how to evolve a set of well-being programs into a company-wide strategy that emphasizes peak individual performance. By prioritizing well-being and workplace culture, and focusing on resilience, employers offer employees the tools to successfully navigate high-stress situations.

Dr. Posa also discusses the challenges presented by artificial intelligence (AI) as they relate to employee well-being. She explains that AI can feel like disruption - to employees and leaders alike - but with transparent communication and measurable assessments as part of an implementation strategy, employers build trust and improve well-being.

Speakers:

Dr. Jennifer Posa, Organizational Psychologist and Transformational Change Advisor

Ellen Kelsay, President and CEO, Business Group on Health

As AI becomes a mission-critical capability across industries, successful enterprise deployments increasingly depend not just on powerful models, but on the infrastructure, operational frameworks, and expertise required to deliver them reliably and at scale. This white paper offers insights into systems integration challenges that span hardware design, software orchestration, data engineering, network optimization, and continuous operations. This paper also describes evolving AI infrastructure landscapes, from training clusters to production inference platforms, and maps the diverse requirements that define real-world use cases.

The reference AI inference stack presented in this paper illustrates the layered ecosystem required to operationalize AI effectively, while the exploration of deployment challenges reveals why specialized proficiency is indispensable for success. Intel IT technical leadership has the relevant practical expertise to partner with technical leads in Intel Sales and Marketing as well as with Intel business units (such as Data Center Group, AI Group, and Platform Engineering and Architecture) to help Intel customers with their AI initiatives, accelerate time-to-value, and unlock the full potential of their AI investments.

For each AI use case, a “minimum scalable unit” is proposed, which includes cluster size, specifications for server infrastructure (CPUs, GPUs, and DRAM), network (frontend and backend), storage (local and shared), and data center (space and power). It also introduces the applicability of disaggregated servers, patented by Intel, along with a full software stack that includes IT manageability capabilities and operation lifecycle automation.

The paper concludes with an exploration of real-world challenges in deploying AI inference solutions, including latency optimization, scaling strategies, and lifecycle management, followed by the articulation of the strategic value proposition of the Intel IT organization’s technical expertise to operationalize and optimize these complex systems.

Chronic pain affects one quarter of adults globally, costing employers roughly half a trillion dollars in lost productivity, absenteeism, and healthcare costs every year.

In this episode, Dr. Sean Mackey, a pain medicine specialist at Stanford University, explains why the number of individuals experiencing chronic pain is increasing and reveals the fascinating science behind pain as "a product of the brain" shaped by not only injury but also sleep, stress, mood, and past experiences.

Dr. Mackey also outlines the six treatment categories for chronic pain which include medications, procedures, mind-body approaches, physical rehabilitation, alternative medicines, and self-empowerment through education. He shares how psychological factors like anxiety and poor sleep can negatively impact surgical outcomes and offers practical advice for those seeking effective pain care.

We wrap up with emerging research on precision pain medicine, including biomarkers and "digital twin" technology, plus a lightning round dispelling common pain myths.

Speakers

Sean Mackey, MD, PhD | Chief of the Division of Pain Medicine at Stanford University

LuAnn Heinen | Business Group on Health

Omdia's Scott Sinclair discusses how Enterprise AI is driving infrastructure modernization, the critical role of Data Security, and the unprecedented pace of IT innovation.

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Scott Sinclair: I mean, it's hard to talk about any trend without talking about artificial intelligence, right? So that's something that's been pervasive across all organizations. Seen, of course, tons of interest and excitement. We saw a lot of investment from the neo clouds. Now enterprises are getting involved. So what does that mean? We saw this massive uptick in GPUs and excitement around compute several years ago. That's continuing, but now we're seeing the focus turn to data, which we all expect it to happen, but now enterprises are saying, wait a minute, we not only need to get our data under control, but we also need to make sure we store it correctly as well as move it. So there's thoughts around what does this mean for data storage? What does it mean for networking and what does it mean for infrastructure overall? At the same time related to just artificial intelligence, organizations as they become better, they start to better understand their data.

They realize, hey, data sovereignty, data locality matters. So what does this mean? It means, okay, now we're starting to think, okay, should we invest more on premises? Think about data center investments and start to take more of a hybrid cloud strategy, as we think about where data is and where it's stored and where it moves. And particularly as it relates to not just new applications, but also saying, okay, well, if we're taking a more hybrid cloud strategy to this new innovation around AI, what does that mean for existing workloads? And then that also turns back to a lot of things around, okay, what does it mean for my hypervisor, my VMs, and then how does that help me accelerate containers? So all of these modernization initiatives, it kind of spurred a little bit from artificial intelligence, but it's really permeating all aspects of IT and not just new apps, but also looking back to existing applications.

But the big challenge that every CIO and every CEO cares about is around cybersecurity, right? That's job one that has to be secured. And one of the fascinating things, particularly as it gets back to data and artificial intelligence, now you're talking about not just great new potential for business, but also new potential for not only bad actors, but potential for new areas of exposure, for how you're leaking sensitive data or your most important or your vital data out into the world.

The challenge for IT decision makers or CIOs is how do I help my business accelerate its adoption of new innovations, such as artificial intelligence, but do it in a way that is secure and doesn't add additional risk to our own operations. You have your known, and then you have the unknown, and I think that's really what it's about. As you start to adopt new technologies or integrate new initiatives, it's all about, okay, how do we take this unknown and turn it into a known, so we can ensure that we are putting the right level of security and controls on top of it and doing the right things we can to best protect our business.

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Nearly 40 percent of Gen Z and 35 percent of millennials say they’re stressed or anxious most of the time. While not always evident, the workplace impact is difficult to ignore.

In this episode, Dr. Smita Das, psychiatrist and professor at the Stanford School of Medicine and Betsy Schwarz, director of the Center for Workplace Mental Health at the American Psychiatric Association (APA) Foundation, address the unique toll that certain mental health challenges have on millennials and Gen Z and what employers can do in response.

The conversation covers generational pressures and hidden risks like substance use, gambling and suicide. Employers will gain research-informed insights to normalize mental health and respond with clarity, compassion and action.

Speakers:

Dr. Smita Das, psychiatrist and Stanford School of Medicine professor

Betsy Schwartz, director of the Center for Workplace Mental Health, American Psychiatric Association Foundation

LuAnn Heinen, Business Group on Health

Industry analyst Matt Kimball and Steve McDowell, hosts of the archived “DataCentric” podcast, reunite in this Tech Barometer segment to break through the hype surrounding enterprise AI, NewClouds, cloud native and data storage trends, and supply chain constraints.

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Matt Kimball

Oh yeah, the Steve and Matt show. Let's talk about 2PIC, Steve.

Steve McDowell

2PIC, first let's talk about us. We are Steve and Matt, host of the Data Center Podcast.

Matt Kimball

Yes. Who are you? Are you Steve or Matt?

Steve McDowell

I'm Steve McDowell, Principal Analyst.

Matt Kimball

Look at you. I'm Matt Kimball.

Steve McDowell

Are you also a Principal Analyst?

Matt Kimball

Principal Analyst. I'm a bunch of things.

Jason Lopez

That's an excerpt from the Data Centric Podcast featuring Matt Kimball, Principal Data Center Analyst at More Insights and Strategy and Steve McDowell, now Chief Analyst at NAND Research. It ran six seasons from 2018 to 2023 for more than a hundred episodes and covered enterprise IT and data centers. This is the Tech Barometer Podcast. I'm Jason Lopez. One of the reasons their conversations work so well is because they're not pretending to do a point counterpoint and at the same time, they're not pretending to be friends as evidenced in exchanges like this at .NEXT 2026 before they knew we'd hit the record button.

Steve McDowell

So you're agreeing with me. Thank you.

Matt Kimball

I think I said that. I think you tried to repeat what I said but make it more contrarian. See, this is why we'll never be married or this is why we should be married. I don't know, one or the other.

Steve McDowell

I miss our podcast

Matt Kimball

Do you? Yeah, that's good.

Jason Lopez

But once they did know, here's what we got. A wide ranging discussion from the idea of their doing a podcast to begin with to whether AI is in a bubble to the evolution of data centers in three short years since their last data centric podcast.

Matt Kimball

We used to do these things regularly, right? So it's been about three years or so. Almost to the day actually. Yes. Since we last did one of these podcasts and we were talking about things like cooling, sustainability and we were talking about chips as well. How much has the world changed in the last three years?

Steve McDowell

We were way ahead of our time. Last time we were talking about why the world might need liquid cooling and the potential. Yep. Now we kind of back the wrong horse on that one but the next generation of servers are gonna be liquid cooled top to bottom. Yep. Right? AI's enabled that. Jensen said you can't, I mean have you seen the power numbers for Rubin? Oh yeah. They're coming up on two kilowatt. Yep. Right? So, you know, immersion cooling has taken over the world. You can't go to a show now without looking at spigots coming out of the back of your rack. Yeah. It still gives me the shivers. You know, and that's given permission I think for the CPU guys to also up their amperage. So when they start merging these things they're not capped by watts anymore. Right? So Moore's law kind of comes back a little bit.

Matt Kimball

It's funny though because a lot of what we were talking about was leading up to where we're at today. And you're right.

Steve McDowell

As industry analysts, we're paid to look forward, son. We, if we can't predict two years in the future with 70% probability we need to go back to our other jobs, right? I need to start writing code and you start writing the requirements for that code.

Matt Kimball

It's a different world. I'm not surprised that AI becoming more of a center focus. And by the way, we were talking about that during the liquid cooling discussion. I may be a little surprised at kind of how unsettled it is in the enterprise still.

Steve McDowell

We're still waiting for that killer app for the enterprise. AI is changing how a lot of our SaaS offerings work. I'm doing observability differently. Cyber security has completely changed how I do cyber. Right? On both sides of that wall. But those are things as an enterprise guy I'm buying from Palo Alto or whomever, Oracle. Why do I need inference in the enterprise outside of the apps I'm using? The only killer app we've found so far is IT Ops and software development. I'm not going to build a cluster for that.

Matt Kimball

Is it that we haven't found the killer app or we're just not able to enable the killer app?

Steve McDowell

No, I go on Reddit and I read Reddit. It's where an industry gets his best source of information. I read all of the things around IT. People are doing experiments with AI. And the number one question I see in all of these forums is, all right, I built thisogenic rack. Now what the hell do I do with it? I'm building virtual assistant. How much email summarization do I need?

Matt Kimball

I will tell you this. I don't necessarily buy into people who've built aogenic environments and don't know how they're going to extract them.

Steve McDowell

I have one in every day. It just sends me a new summary, right?

Matt Kimball

Sure. Are you…

Steve McDowell

I'm not going to buy a GPU for that.

Matt Kimball

Are you trying to do supply chain optimization? Are you trying to…

Steve McDowell

If I'm doing supply chain optimization, am I going to... I don't even know supply chain software. Am I going to... My ERP system should have that built in. How much am I rolling my own on supply chain optimization?

Matt Kimball

I think you're going to have master agents that work with folks in the organization.

Steve McDowell

I think I'm going to buy those from my logistic software provider. I don't think I'm writing them as an IT guy.

Matt Kimball

Oh, I think you are a wholly optimistic kind of guy. I am. I think you're looking at this in a super kind of redactive way. And you're assuming that SAP is going to be SAP. Yes. And that… Absolutely. And that they're going to have the same footprint in the enterprise 10 years from now that they do today.

Steve McDowell

Maybe it'll be a different SAP. It'll be SAP's follow on SAP Prime. I don't think I'm doing it internally.

Matt Kimball

No, but you might be buying an agentic based system from another company.

Steve McDowell

Oh, a hundred percent.

Matt Kimball

Where's that sit, son?

Steve McDowell

In your agentic rack. How much software am I running on-prem right now? It depends. Right, I'm buying that as a SaaS. Depends on the company. I'm buying it as a SaaS. And if I run it on-prem, I don't know.

Matt Kimball

All right.

Steve McDowell

I'll buy a beefy server from Dell. All right. And I say all this, Dell has a $50 billion backlog in AI servers.

Matt Kimball

They most certainly do, don't they? By the way, anybody who's on Reddit trying to develop an IT strategy has a credit.

Steve McDowell

That's where they go to vent. That goes to vent. I think I'm relying on my software vendors to give me this because I have enough headaches. Okay. So is AI a bubble? No. Is any part of AI a bubble?

Matt Kimball

Yes. I think the chip craze is a bubble. It's going to burst at some point, but that's a maturity thing, right? Companies are absolutely over-invested in NVIDIA, absolutely over-invested in AMD, and eventually that's going to level out.

Steve McDowell

Are they over-invested in data centers?

Matt Kimball

No. Look, was the internet bubble in the late nineties into 2000, was that a bubble or was that just a normalization? Exactly.

Steve McDowell

I look at it in the same way, but in a more compressed timeframe.

Matt Kimball

Yes.

Steve McDowell

Right? I mean, the dot-com bubble was, we had a technology that ultimately proved that it was transformational. Yeah.Right? It's changed everything we do, but at the time we over-invested in what we thought would be short-term gains. That was the bubble.I think AI is similar. It's a transformational technology that's going to change how we do everything, but there are pockets where we're over-investing. You talked about NVIDIA chips, right?

I think whatever processor we're going to end up using for inference, that's wide open. So NVIDIA might be in a little bit of a bubble in certain markets, but not as a whole. Agree, yeah.Memory is clearly in a bubble right now. It's not sustainable, although it might redefine normal, because I don't know that if I were a memory company, I'm going to let the prices come back down all the way, right?

Matt Kimball

Let me ask you though, I mean, because this is interesting, right? Because memory is priced high because of supply chain. That's the reason why, right?When I think of bubble, I think of overpriced because of hype. So is memory really in a bubble, or is it?

Steve McDowell

Go ahead, sir. No, I mean, there's some artificial inflation, right? And yes, there's scarcity, but it's also a balance, right?The memory companies are prioritized in HBM and DDR5 to solve AI infrastructure problems. So I guess if you believe that AI infrastructure at that scale, data center scale, is a bubble, the memory is a bubble. But if you don't, then maybe not.Maybe you're right. Maybe it's just supply chain scarcity.

Matt Kimball

So when I think of bubble, I think of pets.com. I think of books.com. I think of these great ideas that had no real business behind them and no real revenue behind them.

Steve McDowell

Oh, then no, I don't see that in AI, outside of my image generator software I use to make funny videos, yeah.

Matt Kimball

So if there is a bubble, it's a bubble that's created because of supply chain, first and foremost, right? If there was enough NVIDIA chips out there, if TSMC could pump out enough chips and pump out enough, and Samsung and TSMC could pump out enough memory, then we wouldn't see these prices that we're seeing today. Fair enough.So let's talk about bubble, okay? So talking about storage, right? Let's look at some of these data companies and these incredible valuations they have, right?They're very highly priced. And when you look at normal kind of pricing to revenue ratios, they're valued a lot higher than they probably should be. And part of it's because of anticipation.

Steve McDowell

No, you're right. You're right. I don't know that AI moves the needle on storage at all, honestly.And I mean, yeah, there's some data management problems we have to go solve. And the companies that are solving those just because they're attached to AI are seeing inflated valuations, 100%. That to me is a bubble.And even, you know, you talk about NetApp and Dell and AI factory storage, they're not making a nickel right now, right?

Matt Kimball

All right, so. Even as, you know, they get pulled into this, like, let's focus more on data, let's focus more on data, because that's kind of the shiny object, right? Even as they do that, they're still at the end of the day selling storage.Oh, sure. Yeah.

Steve McDowell

And I like the data stuff, it has secondary benefit of, it's a smarter way to manage my enterprise, regardless where that data is going to land.

Matt Kimball

Without a doubt, yeah.

Steve McDowell

Yeah. So it's a step function for storage guys. But you're right, I think some of the AI specific storage companies, yeah, their valuations and across the board are just really high.

Matt Kimball

They're very high, but you've got some publicly in, not publicly traded companies that are in that AI data space that have valuations that, you know, I was, I think you and I were having this conversation a few weeks ago about, when you were at a startup before, you know, a lot of money invested was $10 million, $20 million. You got a hundred million dollars, it was crazy. You're seeing billions of dollars being invested in tens of billions of dollars in valuations for companies that are not generating the revenue that would necessarily warrant that valuation.

Steve McDowell

So if I'm an IT guy, and I'm building out an infrastructure for AI, do I avoid the ones I think are overvalued and in a bubble because either they're not going to go away or they're going to be consolidated? Should I worry about that? Should I make the safe bet and call the guys I've always called for storage and compute?When do you roll the dice on somebody you think is overvalued? You used to run a data center.

Matt Kimball

That's a tough one. When you think about, so enterprise IT are, they're a conservative bunch, right? When it comes to adopting technology.And you know this, we go with what we know, we go with what's proven and we go with what we know is not be supported over time. In order for me to go with a new entry, there has to be something super compelling. There has to be that killer app for that thing that I'm going to bring into my data center.

Steve McDowell

Or I have to need it right now.

Matt Kimball

Or I have to need it right now and my trusted vendor does not have it. And that's the question, right? Are we at that point?And is there something Dell doesn't do that another company does or a net app doesn't do that one of these newer company does? And do I actually need it? That's the other.I'll be sold on needing it, but do I really need it at the end of the day?

Steve McDowell

The Neo clouds might need it, but does an enterprise guy?

Matt Kimball

Neo clouds are interesting. So how do you see storage in Neo clouds and data in the Neo clouds? Because they're different than the hyperscalers, right?They're more like a large enterprise in the way they deploy. A very large enterprise. But it's AI from the ground up.It's very bespoke architecture.

Steve McDowell

The only place AI touches storage in a meaningful way right now is on KB cache to give me a larger context window and minimize my GPU resources. If I'm a Neo cloud, I have other things I care about around multi-tenancy and distributed namespaces and things like that, that the CSPs, the AWSs of the world figured out a long time ago, right? And are using internal solutions. When you look at some of these software stacks that Neo clouds are deploying, whether it's Weka or Vast, they're duplicating a lot of functionality that already exists in the public cloud. Take AI out of the equation, these Neo clouds just have to solve a cloud storage problem. These guys like Weka and Vast come out in DDN, come out of the HPC world, so they know how to do big distributed storage. And where you see Vast in particular kind of innovating is around how to make that friendly for a cloud-like multi-tenant environment. But I don't think that's related to AI.

Matt Kimball

Speaking of the Vasts and the Wekas and the DDNs, tie this to Nutanix, right? We see announcements from Nutanix around partnerships with NetApp. Oh yeah. With Dell, with Everpure. They have a strong partnership with Lenovo. We don't hear about how Nutanix works with a Weka or a Vast. Part of it is because they're not in that market segment. They're not in the enterprise. Right. But there is aspiration for these companies to be in the enterprise.

Steve McDowell

That'll happen over time. I think Nutanix has taken a very deliberate approach to their partnership. They came out of the gate with Dell and Pure. This conference, they announced NetApp. I think we all know that the NetApp products trickle down into Lenovo. They're solving an enterprise problem. For Nutanix to grow out of its kind of HCI heritage, it's a problem they have to solve. I mean, say what you will about VMware, but vVols and vSAN, valuable technologies. If I'm going to move forward from that legacy kind of infrastructure, I have to go solve those problems. And while they're not working with Weka and Vast and DD, they're not in the same markets today. I think Nutanix is ultimately going to wind up in NeoCloud. It's a natural next step for them. And we both talked to Weka and Vast and their ilk on a daily basis. They have enterprise aspirations. So eventually those two are going to meet. I think first we're going to see Nutanix and HPE, right? So they're kind of moving down the priority list of what impacts revenue in the nearest term, right? That's a decision I would make.

Matt Kimball

Do you think there's a Nutanix play in the NeoClouds?

Steve McDowell

Yeah, I mean, what Nutanix excels at is control plane for workloads. I have a vast number of resources. I need to manage workloads across them. I need a control plane that understands that and deals with that. So, you know, the announcements that most excited me, and I think this points directly to a NeoCloud future at .NEXT, is the bare metal options, the multi-tenancy. These are things that they have to have. Some IT departments also need that as you do more self-service. I don't know that the NeoClouds are investing in that kind of control software.

Matt Kimball

Do you not think they have their own tooling already?

Steve McDowell

I think they have some scaffolding. The NeoClouds today, I think they look a whole lot more like an advanced rack space. They're running full CPUs, full clusters. It's mostly bare metal. I don't know they're sharing a lot of resources, but as we move into the age of inference, I think they will, and they're going to have to solve that. Can I do that with KVM and Kubernetes? Maybe. But if I look at something like a Nutanix, or even VMware Cloud, it gives me so much more. So I think that's a natural play.

Matt Kimball

There's also a lot of cost associated with that. The reason why the hyperscalers have moved to their own tooling is for cost. Well, cost because they have very bespoke architectures that require very bespoke tooling.

Steve McDowell

Well, a more interesting conversation might be, do NeoClouds exist in five years? They rose up because of a GPU scarcity problem that may not exist as we move forward.

Matt Kimball

I think they do. I will say, I've been a skeptic a long time of the Lambdas, the Coral Weaves, you know, the... I don't have an opinion yet. I'm seeing Coral Weave, and when you look at their revenue mix, shifting over the last few quarters and having conversations with them, I see the path. I kind of equate them to almost like an Oracle Cloud. They built a very specific infrastructure for a very specific set of customers and use cases, and it works really well for them. And if you believe that inference is going to be the size that it's going to be, they're really well prepared to do that better than the major CSPs today.

Steve McDowell

But from an enterprise side, it's not always about the best technology or the best technical capabilities. If I already have, you know, I'm running Exadata at Oracle, or I'm running RDS at AWS, why do I want to move that data back and forth just because they have a better GPU cluster? It's often about simplicity. But if I do, maybe I have a control plane like Nutanix that helps me migrate those workloads. The reality, though, is most enterprises have their data everywhere, right?

Matt Kimball

I mean, AWS just... They prefer not to. But they do, right? And AWS, not just. They have a interconnect, the spec they created. Oracle just announced that they're supporting it. Google supports it. It's an open spec that the NeoClouds are going to support it. So if I can move my data and minimize my latency and assure security, so on and so forth, does that being in four clouds distributed matter as much? But to what you're saying, though, a little bit, kind of, I'm going to go off on this a little bit, is when we talk about bubbles, right? NeoClouds are another example of bubbles. I think there are something like 200 NeoClouds right now that are being tracked. Five years from now, there ain't going to be 200 NeoCloud. And this is maybe more kind of aligned to the dot-com bubble. You're going to see a lot of these fade away. I think companies like CoreWeave that are expanding globally and are building out, kind of, this bigger footprint, they're going to do well. But there are a lot of these others that are getting investment and getting dollars just because.

Steve McDowell

I'm with you. I think CoreWeave, ultimately, is going to move and become more general purpose cloud-like, because that's what their customers want. And maybe there's another CSP and we're a little more distributed.

Matt Kimball

I think they'll become general purpose because AI will become that common in the enterprise. That AI workloads, because they're so normalized, CoreWeave and what they do becomes normalized. Not that they start taking on other kind of AWS, database as a service.

Steve McDowell

But the bulk of their money right now is not coming from any enterprise. No, but it's starting to shift. It's going to be training. And there'll always be a place for big training clusters. And I think that's where they're going to sit. But I don't know. I'll watch. It's early. It's going to be fine.

Jason Lopez

Steve McDowell is Chief Analyst at NAND Research. Matt Kimball is Principal Data Center Analyst at More Insights and Strategy. Their show, Data Centric, ran for more than 100 episodes from 2018 to 2023. This is the Tech Barometer podcast produced by The Forkast. I'm Jason Lopez. Thanks for listening. If you like this podcast and you want to discover more stories about technology, head on over to The Forkast by Nutanix. That's theforkastbynutanix, all one word, dot com.