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Chronic kidney disease affects 10% of the world’s population, including 37 million adults in the U.S.; 90% of those impacted may be unaware they are afflicted with the condition. In this episode, Marcello Tonelli, President of the International Society of Nephrology, sits down to share opportunities for early intervention, improved screening practices, and how kidney care fits into the broader cardio-kidney-metabolic (CKM) framework.

Tune in to learn how global organizations can best support their employees to improve health outcomes, and the bottom line.

Guest: Marcello Tonelli, Nephrologist and President of the International Society of Nephrology

Liquid biopsy tests, blood tests with the potential to detect up to 50 types of cancers, are being embraced by some in the health care community but viewed with careful consideration by others.

In this episode, Jason Oke, medical statistician and lecturer at the University of Oxford’s Centre for Evidence-based Medicine, discusses the promise of these multi-cancer, early detection tests, alongside their limitations when it comes to real world application. He explains how effective screening tests of all kinds are measured, shares data from randomized trials that early detection tests show, and reveals how sensitivity, specificity, over-diagnosis and “healthy screenee bias” contribute to test value.

Employees are excited about the potential of these tests. Employers should listen in for an unbiased, data-backed perspective to determine if benefits programs should evolve to include this offering.

Guest: Jason Oke | Medical statistician and lecturer in medical statistics and cancer research at the University of Oxford

Two market trends are driving the need for a higher CPU:GPU ratio in AI infrastructure. First, the inferencing market is accelerating. A higher CPU:GPU ratio is required for inferencing compared to training, especially as agentic AI becomes prevalent. Second, Reinforcement Learning is now being used in complex 3D simulation environments that require a high number of CPUs. Together, these two trends demonstrate that CPUs play a strategic AI infrastructure asset, acting as the control plane for AI systems. Future AI infrastructure planning must explicitly model CPU growth as a first-class driver of cost, performance, and energy-per-token economics.

Intel IT plays a pivotal role in Intel’s business transformation, especially in our role of driving data quality across both the Foundry and Product domains and maintaining master data. In 2025, we evaluated our data, analytics, and AI (DAAI) maturity level. The evaluation revealed that, while our work to date (such as master data governance, a modern data lakehouse, and a centralized data marketplace) represents best-in-class traditional data management capabilities, we have significant work ahead to truly enable AI-ready data. Areas of focus include improving data lineage observability, increasing AI literacy across the enterprise, and establishing better unstructured data management.

The 2026 Enterprise Cloud Index, a survey of IT professionals, reveals tension between the need for IT oversight and the reality of easy-to-build-and-deploy containerized apps. Demand for AI capabilities is driving up shadow IT use, forcing IT teams to manage more risks.

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Jason Lopez:: This is the Tech Barometer podcast. I'm Jason Lopez. Nutanix released its eighth annual enterprise cloud index this month, and two of the key findings stood out. Containers are rapidly becoming the foundation of how modern applications are built and run, and Shadow AI is spreading through organizations largely unmanaged. We talked about this issue of Shadow AI with NAND Research chief analyst, Steve McDowell.

Steve McDowell::You're like, "You know what? These IT guys, they're out of their mind. I need to use this because it's going to help me and I'm just going to pull the trigger and make the decision." Yeah, sure. The employee's getting a lot of benefit from this engagement, but you have no idea it's happening. I don't know where it's happening and I don't know what data's being exposed and where that data's going to. I think we see this kind of every big technology transition. Users are going to make their own decisions. Your employees are going to make their own decisions about the technology they use. It may or may not intersect with your corporate guidelines for IT. I remember when smartphones entered the world, that caused a lot of consternation among enterprise IT teams because how do I manage these devices? And they even coined a word, right?

BYOD, bring your own device. That was a hot topic for several years until Apple came out with kind of enterprise management tools and things smoothed over. And CloudWorld kind of did the same thing a half a decade later. We're doing that now with AI. I mean, AI is so beneficial. Now, scrolling through LinkedIn and reading all the AI-generated slop, it's not clear everybody knows how to use it, but they're using it.

[Related: AI’s Next Wave]

Jason Lopez: The Enterprise Cloud Index is a snapshot of an industry in the middle of a profound transition, and it raises a question worth asking. What exactly is driving all of this and where does it lead? Dan Seruli has watched this transformation from the inside. As the cloud native product leader at Nutanix, he spends his days talking to organizations navigating this shift firsthand. Ken Kaplan, editor-in-chief of the forecast, interviewed him and asked him to step back and survey the arc of technology he's lived with for more than a decade. And what he's hearing now from the people on the front lines.

Dan Ciruli: I think it is safe to say at this point that containers in general and Kubernetes in particular are, I'm going to say it out loud, the de facto standard for developing and deploying new applications. It is no longer something that is a science experiment, which it was eight years ago. It's no longer a viable option, which it might have been three or four years ago. I would say these days, building your application to be packaged in containers and deployed on Kubernetes is the defacto standard for how applications are being developed. And I'm pretty comfortable saying that out loud.

Ken Kaplan: What are you seeing the success and struggles now in this new period compared to the early days? How have things changed?

Dan Ciruli: One thing that people are wrestling with is that when it was in that phase that it was more of a science experiment or just some of the new applications, you had certain developers who would lean in and certain developers who were not affected by it. And now I think we're reaching the stage that everybody needs to be comfortable with it. Everybody needs to be ready for whatever application you're working on the next time they ask you for a thing like, "Okay, it better be packaged up in a container. We better have some Kubernetes running wherever it is. We need to deploy that application." In becoming the de facto standard, that has means we're no longer, there's a pocket of the organization who's doing in Kubernetes. Now we're in the phase that everybody needs to be comfortable talking about it, using it, deploying it, running things on it, SREing on it.

Ken Kaplan: You're talking to more customers probably than ever before. You would like to talk to more. What are you learning from them?

Dan Ciruli: One of the things we're trying to help our customers with is as they approach that transition is how they standardize. When you have pockets of people leaning into a technology, then you have pockets of people leaning into a technology and some might lean in a slightly different direction than others. Some people might be making a certain architectural choice, a certain security choice using a certain technology, which works for that team, but might not work for the organization as a whole. And as you adopt this technology wholesale across the organization, then you have to think about, well, how do we standardize? What policies do we want to set that apply to everybody? How do we centralize this so that this can be run by a centralized team rather than by individual teams? So it's a big transition. And as I say, it's become the de facto standard.

And I say that with confidence, but for many organizations, they are still figuring out how that will affect them and how they, as an organization, do that in an efficient way that gives them the benefits that developers want, that give them the ability to innovate quickly, deploy frequently and scale as needed.

Ken Kaplan: And do you see a correlation or it's similar to what happened with cloud and people could go out and use a credit card and get compute services for the first time. And then there was all these projects going on and IT didn't know about it. Do you see some similarities?

Dan Ciruli: I had never thought about that way, and I think that is a fantastic way to look at it. Cloud was very much the same way. I was part of a team that we got frustrated with internal IT and the length of time it was taking them to get us literally just VMs and the amount they were going to charge us back. And we said, "Somebody on your corporate card, go to Amazon and start using this stuff." Yes. So I think Kubernetes started the same way where a team was like, "Hey, I don't want to deploy in VMs. I'm just going to start using Kubernetes." And then another team is. And then another team is, at some point, someone higher up and more central in the IT organization said, "Wait, there's way too much of this going on. This is not efficient. We've got different teams.

We're spending too much money on this. We're duplicating effort all over the place. And from a security posture, we're at risk." So someone centrally said, "We need to, for all of those reasons, we need to centralize this. We need to standardize this and we need to do this in a way that does give the teams what they want, but does it in a way that doesn't put us at risk financially or from a security perspective?" That's an excellent analogy.

[Related: Ecosystem Scorecards Help CIOs Avoid Vendor Lock-In]

Ken Kaplan: Yeah. You hear about smart cloud strategy now. It used to be cloud first, now smart, but those people who have lived through that transition and are getting smart about their use of cloud, those are probably lessons that they can be applying to Kubernetes and containers. Why would the companies benefit now from saying, "That stuff's useful. Let's do it here together on this platform that can do the old and the new."

Dan Ciruli: So the interesting thing about me saying very strongly, this is the defacto standard for how applications are written. That doesn't change history and it doesn't change the fact that at essentially every enterprise, they have decades worth of applications running in virtual machines that are for the most part going to stay in virtual machines. That isn't changing. And while the new stuff is all containerized, the old stuff was virtualized and will continue to be virtualized. Very few of those apps will be rewritten to be containerized, which means as you centralize operations for all of this, now you've got one IT team that is going to be for the next decade, two decades, responsible for running tons of VMs and a growing number of containers that some point will outnumber the VMs, but those VMs will still be there. Companies have a choice. They can build up two separate groups of people, pieces of hardware, networking strategies, security technologies to manage those, in which case they're building literal silos in data centers of which hardware can run which applications, building silos in their organizations of which people can work on which organizations and actually hampering integration.

You might have a new application that needs to get an old database, old piece of data that is in a virtual machine. And if you're building those things entirely separately, that's a challenge every time you do it. Whereas companies that say, "Let's combine these on one." They allow themselves to run essentially any application. It doesn't matter if it's virtualized or containerized, you can run it in the same locations. It means the same people who work on running the infrastructure can run that infrastructure, whether it is running virtualized or any combination of containerized applications. And it means you can do things like set security policies, set backup policies, set disaster recovery, networking policies. All of those can be consistent and it becomes much easier to interoperate between the new and the old. So I think it's a really strong driver for companies to invest in ways to run all of their both containerized and virtualized applications in a way that makes sense.

[Related: Clouds With Borders: IT Teams Design for Geopatriation]

Ken Kaplan: And when they're doing that, are the apps that are running on VMs and containers, are they able to tap into the same storage or databases or different aspects of the system? Or is it you have this ability to manage them both, but they're still kind of separate?

Dan Ciruli: I mean, I talked to some organizations who they have their VMware environment and their OpenShift environment. They are literally buying separate teams, buying separate hardware, run by separate people. From a integration calling back and forth, it might as well be separate companies because you've got different networking, you've got different security, right? And what we advocate for is running a hypervisor on all of that hardware that you buy. And then within that, some of those, it's just an app running in a VM and some of those it's Kubernetes running in those, but there's the same networking underneath. And with Flow, you can write a networking policy that says, "This containerized application needs to talk to this virtualized application." That we feel is a tremendous advantage. You don't end up with hardware silos. Hardware silos are always a bad idea because inevitably one team has more hardware than they need.

We bought a huge cluster and we're only using 60% of it for our, say, containerized applications. Meanwhile, we've got this other cluster that's running virtualized applications. It's 100% full, but we have another virtualized application that we need to run. In that case, the only way to do that is to go procure more hardware. Whereas when you're running things more homogeneously, one system that can handle either, you can say, "Oh, no problem. We'll run more VMs on it. " So the hardware siloing is a really big deal. And that same thing that feeds down into the networking, "I want this application to talk to this application." Well, that's complicated when you've got completely different networking solutions on the two side.

Ken Kaplan: In the report, it said, "Currently, 71% are running their AI enabled applications on a mix of traditional apps and virtual machines and modern apps in containers on VMs." While 14% are running their AI enabled apps directly on bare metal servers, is that significant and is it something you see might be changing?

Dan Ciruli: I think what that's pointing to is AI is, in some cases, it's an experiment. Companies are buying hardware specifically to run these AI applications. So we do see people doing that, but what most enterprises will quickly realize is that you want AI embedded in all of your applications. I'm firmly convinced that in three years we won't differentiate between applications and AI applications. We'll just call them applications. It doesn't matter if it's just a business process workflow, if it's your email, if it's your sales database, you want AI everywhere. As I said before, some of those applications aren't going to move out of a VM, which means you need to be able to figure out how to, maybe it's an API call out that is going to hit a new service, which is an agent running in a container, but you need that logic embedded in that traditional application.

So there is some experimentation going on where people are just going to buy some new hardware and run AI there. In the long run, you're going to want to run your containerizer and your virtualized applications together. You will also want that to be, I'll call it AI enabled. You will want to be able to, from anywhere, embed logic that depends on maybe an agent or maybe an LLM that is independent of whether or not that original application is deployed in a VM or deployed in a container.

[Related: AI Flips the Data Storage Paradigm]

Ken Kaplan: Since we're on this topic, like AI agents, you just think of AI agents as another app that's probably cloud native.

Dan Ciruli: Almost certainly said before, it's become the defacto ... Containerization has become the defacto standard for writing and deploying new applications. The word agentic is only about 18 months old. All of these agents are new applications. They're all being written to be deployed in containers. And so yeah, all of that. And what I think of as agents is these are AI that can do things.That's how I think of AI. In LLM, you ask it a question and answers and an agent is actually doing things. It might be validating someone else's answer. It might be going to actually interact with a system, but the agents is where AI actually does things. And yes, we have already moved from the phase of AI where I just wanted to help me answer a question too. I want it to go do a thing for me.

Ken Kaplan: Yeah. I'm going to set up a playbook and have them run my playbook. And yeah, it's happened pretty quickly. All right. Here's my last question here. Does it take specialized skills to manage containers and Kubernetes and is that changing IT teams?

Dan Ciruli: It still does take additional knowledge to run containerized applications, to run Kubernetes. It does mean running Kubernetes, and there are still things that you do need to learn. There's no doubt about that. In part, I think that this gets solved through education and people learning to use new tools. If you think about how the data center evolved. At some point, Linux became a pretty kind of, I won't say de facto standard, but super, super common. And people had to learn how to navigate in Linux, right? If people in infrastructure and operations. At some point, virtualization went from being a science experiment to the standard way things were networked and people had to learn the concepts of virtualization. The same thing is happening with the Kubernetes landscape and more and more people are having to learn these concepts. But the other thing I think that is happening at the same time is that as an industry, and this is someplace that we are leaning in as a company, how do we make that process easier?

How do we not just say, make that easier by giving lots and lots of education, but how do we make it easier for the average operator to run a Kubernetes cluster? How do we make the tools easy to use? How do we make the tools smart so that the tools are doing more and more of the grunt work? And I think this is a case where AI already is making their lives easier. The analogy I like to use with this is when I learned to drive in the 1980s, it was very common that you knew how to change the oil in your car, certainly check the oil level. You had to know where your dipstick was. You were going to be doing that. You probably knew where your carburetor was. You definitely knew where your spark plugs were. You knew how to clean a spark plug, you knew how to replace a spark plug.

[Related: Why the Future of IT Belongs to Open Systems]

Not every driver did, but most did. That was just common things, right? When was the last time anyone did any of those things in their car? The car has gotten better. It is tuning itself constantly. It is so much better at operating. The car in effect has gotten mechanically smarter, and now in some cases, actually computationally smarter. Well, our systems are doing the same thing. As vendors we are trying to make just as the car is continually tuning itself, we want Kubernetes to continually be tuning itself. We don't need to teach everybody in the world to be a mechanic. We want to make this machine easier to operate. And that's a big part of where we're investing is how do we make it easier for those operations teams for all the reasons we discussed are having to run Kubernetes. How do we make it so that doesn't feel like a burden for them, but it's just another tool in their toolbox.

Jason Lopez: Dan Serulli is the general manager of cloud native at Nutanix. He's also co-founder of the OpenAPI initiative, which established the universal standard for how software systems describe and communicate with each other. Ken Kaplan is the editor-in-chief of The Forecast. The forecast produced this interview. It's part of the Tech Parameter Podcast series. I'm your host, Jason Lopez. You can find articles and other podcasts at the forecast. Just go to the forecast by Nutanix. That's all one word, theforecastbynutanix.com.

Older adults make up roughly 14% of the U.S. population but account for about 50% of health care spending. And as the country has fewer births with each generation, the health care system is challenged to support a “rectangular population” versus the “pyramid population” for which it was designed.

In this episode, Dr. John Rowe, geriatrician and Professor of Health Policy and Aging at Columbia University, explores how this population shift is transforming work, retirement and health care. He explains why core institutions must be re-engineered to better support older individuals and why longevity science has yet to deliver longer, healthier lives. Employers will gain insights for redesigning benefits across the employee life course, from paid parental leave and social connection to flexible retirement pathways, to build a more resilient, productive, age-inclusive workforce.

Guest: John Rowe, Geriatrician and Julius B. Richmond Professor of Health Policy and Aging at Columbia University

Andreas Eisenreich, CTO at Deutsche Telekom, explains why data sovereignty isn't about location — it's about control, informed decisions, and the ability to act on your strategic goals without vendor permission.

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Andreas Eisenreich: So welcome dear architects, strategists, and decision makers. You have arrived where the rules are being rewritten. The utopia of a borderless internet where one click deployments were possible and any location in the cloud is slowly dissolving. The IT galaxy is becoming more complex, more political, and yes, a bit more uncomfortable. But on very, don't panic. We have long lived in a kind of autopia. National borders didn't matter to us. Infrastructure was global and deployments could be distributed across the globe with a single click. The location was just a variable in a conflict file. That error is coming to an end. The topic of serenities, unfortunately, not just an abstract buzzword, but a real challenge. The impact on us as the IT industry is hard to foresee. After all, we all had the same access to technology, no matter where we in the world and almost regardless of the available budget.

The democratization of innovation and technology has made us significantly more agile. Personally, I'm more of one learning by doing guy. So the impact this will have on my way of learning, for example, remains to be seen.

[Related: Data Sovereignty Drives Enterprise IT Decisions] 

Jason Lopez: That's Andreas Eisenreich, reading from his 2025 LinkedIn article entitled IT Sovereignty: The Hitchhiker's Guide for Architects and Decision Makers. He's the CTO for cloud and infrastructure at Deutsche Telekom. This is the Tech Barometer podcast. I'm Jason Lopez. We thought having him read from his article would be a good way to start this story about how sovereignty is now a top concern. You need a way to define it before you can make a good decision about it. And firms like Gartner, IDC, and Forrester are now treating this as a primary market force, not just a footnote. We talked to Andy shortly after his team worked with Nutanix to develop a sovereign data solution for hybrid cloud. In our interview, he began with this. Data sovereignty isn't just about where your data lives. It's about whether you can actually use it without a vendor's permission. He reminds his clients that physical control means nothing if your data is locked in a proprietary format that becomes inaccessible the moment you lose a license.

Andreas Eisenreich: Phase one, let's try to free up your data. And that's not super hard, but most of the time users are involved.

Jason Lopez: A good example, he says, is Microsoft 365. The moment you commit to any proprietary platform is the moment your data sovereignty journey and your responsibility to protect it begins.

Andreas Eisenreich: We all use it. We love it. We are partnered with Microsoft, great partners. I love the product, but that has to be an option. So if I decide I want to go with Microsoft, my responsibility from that point in time, that's the beginning of a sovereignty journey when it comes to data.

[Related: AI Trends in 2026: Finding the Right Compute Platform]

Jason Lopez: Andy pointed to the idea of making fully informed decisions about how data is stored and who ultimately controls it. The moment someone else holds the encryption key, regardless of what they call it, you don't own your data, you're just borrowing access to it.

Andreas Eisenreich: If there's someone telling you, "Yeah, that's now your storage box, that's completely sovereign. Just place it in your office, you're able to access your data at any time, no problem." From a technical implementation perspective, the data are encrypted and the encryption key is part of their license model. That's the opposite of an informed decision, so that's a problem for sovereignty. But as long as you are able to understand the implications and take that informed decision, for me, that's sovereign.

[Related: AI Sparks Rise in Shadow IT]

Jason Lopez: And he goes on to say, "True sovereignty isn't even necessarily about open source or open formats. It's about how understanding exactly what you're agreeing to and making that choice with your eyes fully open." Sovereignty may feel like a new buzzword, but the underlying struggle, who controls your data and on whose terms is as old as IT itself.

Andreas Eisenreich: Look at a bunch of non-functional requirements. Things like the European Sovereignty framework that they just released, they might help us to somehow have a common ground to start the discussion around that. But at the end, it's non-functional requirements. And as all architectures, I mean, they're not just built, they're maintained. If you start with something like your own infrastructure based on Nutanix down in your data center, that might be version one. It ticks all the boxes when it comes to your understanding of Serenity, but bring that then to version two, where you have to tick all the boxes as well, but maybe with another technology. And that is where it becomes interesting on a lifecycle perspective. And exactly for that point in time, we have to be ready as a service provider to take over the workload from those platforms into something more capable, I would say, to somehow stick to the promises, to the values that cloud gives us these days.

[Related: Tension Mounts Between Supply Chain Challenges and AI Adoption]

Jason Lopez: Sovereignty has become one of the biggest trends in tech, but Andy says too many people don't ask enough questions, and in fact, often only ask the one we began with, "Where does my data live?"

Andreas Eisenreich: That's not the point. Serenity is all about being able to act according to your strategic goals, to your values.

Jason Lopez: But true sovereignty encompasses far more than simply knowing which legal jurisdiction your data happens to reside in, and it's more than just a binary choice.

Andreas Eisenreich: It's not a switch that you can turn on off Sovereignty. I would say it's a scale, and it depends always where you are, what is important for you.

Jason Lopez: And ultimately, your definition of sovereignty has to align with everyone else at the table.

Andreas Eisenreich: That makes it super complex when we are in a service provider and client discussion. When a client just mentioned to me, "Okay, I need that sovereign." So the answer, of course, is yes, no problem. But what's the meaning of sovereign?

[Related: How Kubernetes Catalyzes Enterprise IT]

Jason Lopez: Andy's definition extends to SLAs, operational processes, and whether you actually have a lever to influence decisions that affect your business. For example-

Andreas Eisenreich: There's patch day for your more classical infrastructure, and the provider is just telling you, "Okay, next Saturday, we patch." So that has to fit somehow to your business plans on that day. And if there's no possibility to just raise your hand and say, "Yeah, that doesn't fit, sorry, that's not a sovereign solution," even if the data is stored in your backyard.

Jason Lopez: Once you've clearly defined what sovereignty means for your organization and how much influence you need across each relevant area, it makes sense to put that in writing and use it as a filter when evaluating potential partners. You can take a mathematical approach, but the challenge is that even with that kind of detail, interpretations still vary. The same criteria can look very different depending on which side of the table you're on.

Andreas Eisenreich: If you see your own infrastructure service provider is running your data centers, it feels for you kind of sovereign, of course. If you share the same basic values, you have to be very open about your business goals, about your capabilities. I mean, that's something where it's easy to find out how to handle those complex topics around sovereignty.

[Related: AI’s Next Wave]

Jason Lopez: He says a real partnership is a foundation of mutual honesty, and that honesty has to extend to the hard questions like, "How do I get out of your solution if I need to? " If a partner can answer that openly and the answer fits your requirements, you found the kind of relationship where sovereignty stops being a problem and starts being a conversation. Andy says for a decade, the IT industry gave everyone the same answer regardless of the question, and that answer was cloud. He says clients have been told they didn't understand their own problems, reshaping their business challenges until they happen to fit perfectly inside whatever cloud offered.

Andreas Eisenreich: I was tired about that, and that's true for AI use cases as well. I was really tired about that, and that is why we started with the idea of hybrid IT, where we, on one hand, have the infrastructure of Nutanix as a building block that might run on client data centers as well as on telecom data centers wherever. And so the client is able to decide per workload, so per application that he wants to run, if he wants to run that on the Nutanix stack with his premises, or just put in additional resources from the cloud, and that's completely transparent to the client. What we created together with Nutanix is a perfect example of what I want people to think about. I think the time where we do those pilots, that's over.

Jason Lopez: Andreas Eisenreich is the CTO for cloud and infrastructure at Deutsche Telecom. You can read his article, The Hitchhiker's Guide for Architects and Decision Makers at LinkedIn, and he says in the piece, "Don't panic." This is the Tech Barometer podcast produced by the forecast. I'm Jason Lopez. The forecast has a treasure trove of tech articles and podcasts, and you can find them at the forecast by nutanix.com.

Medical errors related to misdiagnoses contribute to an estimated $870 billion in waste in the U.S. each year. Artificial intelligence (AI) may be the most powerful tool to change that.

In this episode, Laura Adams, Senior Advisor at the National Academy of Medicine, shares how her experience with a near-fatal medication error changed the trajectory of her career and now her focus on AI-driven patient safety initiatives. Listen in as the author of the National Academy of Medicine’s Code of Conduct for AI shares how AI improves real-time detection and prevention and how employers can empower patients with greater access to AI tools to reduce the ripple effects of medical harm.

Guest: Laura Adams, Senior Advisor, National Academy of Medicine

With agentic AI, Intel IT is pursuing self-optimizing, human-aligned facility intelligence. To keep up with the pace of business, Intel’s Industrial Automation team, working with the Facility Operations team, is helping Intel facilities transition from mere automation to complete autonomy. Agentic AI combines machine learning, digital twins, and Human-in-the-Loop (HITL) oversight to orchestrate intelligence across building systems, utilities, and sub-fabs. This distributed, context-aware, multi-agent architecture integrates perception, reasoning, and action. Our vision for facilities automation extends far beyond predictive maintenance to achieve self-optimizing, autonomous facilities by the end of the decade, setting a new standard for Industry 5.0 operations.

Artificial intelligence reshapes enterprise storage infrastructure, transforming passive data repositories into active platforms that power AI applications. Vishal Sinha explains how storage evolution, data consolidation, and security challenges define the new data-ready era.

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Vishal Sinha: If you look at traditionally, applications and users were creating data which used to get stored in storage.

Jason Lopez: As artificial intelligence accelerates, the infrastructure we usually think of, running in the background, is undergoing its most profound transformation yet.

Vishal Sinha: Now, it’s the other way around. Now applications are using the data to build intelligence out of it and power those applications. So things have completely flipped in more recent days with storage. In the era of intersection between infrastructure and intelligence. We need infrastructure to run all the AI applications and workloads, and then you need data to power the intelligence for applications. 

[Related: Data Management Strategies for the AI Era]

Vishal Sinha: Virtualization really transformed compute. Earlier, compute was tied to a server. You had to buy a server or a desktop or a laptop to get the compute. Now, virtualization made compute portable. You could move compute around as VMs or containers. And this change really paved the way for everything software defined. So that was one big transformation that happened around 2000 to 2005 time frame. After that, the cloud transformation, that has been a big one. It has really democratized infrastructure. Today, if you want to build a new app, you don't have to build a data center to build that app. You can directly go to the cloud and build that app. Though it has created some more work, like now you have to manage the cost, you have to manage the security, you have to manage the networking. So it has added some complexity, but definitely it has made the infrastructure more accessible to everyone. And I would say the latest one is the AI. This is a brand new one. It is going to change things significantly. Even for us, the entire software stack will change to support AI. So this is still playing out. We'll see how that evolves over time.

[Related: IT Leaders Modernize Infrastructure to Run AI]

Vishal Sinha: AI needs data and it needs a data-ready platform. And that's where we see that in order to make a data-ready platform, you need to provide certain capabilities. First, being able to consolidate the data from different sources and bring it all together. The second piece is around making the data clean so that it can be consumed by the AI LLMs. And then the third part is providing fine-grained permissions so that LLMs learn only from the data it is supposed to. So clearly lots of transformation from a passive data repo for files, images, audio, video, to becoming a full data platform to power AI-powered applications. So that's the big change that we are seeing.

Ken Kaplan: Talking about the new trends, what was it like before?

Vishal Sinha: Yeah, storage was a passive data repo like where customers put their files, their pictures, their videos, their audio. So it was truly looked at as a passive infrastructure mostly for storing data. And now it's very different because now it's an active part of an AI stack like where it needs to power all these applications with the data that it stores.

Ken Kaplan: So you see it more active.

Vishal Sinha: Absolutely, so it's an important part of the new tech stack for AI.

Ken Kaplan: Let's talk about customers and what you're hearing from them. What are their needs? Do they have some new needs they're struggling with?

Vishal Sinha: Yeah, so typically I will say two categories of customers. One who are already deep into AI. For them, the AI platform, data platform, I just talked about, that's very important for them. But then there's another big set of customers who are still not there. Their problem is how to manage data at scale. Like data is doubling every 18 months and now you have to support and manage that data. So that's their number one problem. Second one that comes often is the data is now fragmented, like it's everywhere. It's at the edge in your phone, like it is in your applications, it's on the desktop.

[Related: Can IT Infrastructures Meet AI Data Demands]

So how do you provide a consistent operating model to manage all this data? Then security is very important, like ransomware, probably many customers have hit ransomware. So I really want to know how the system, the storage solution itself can protect the data and not have to buy a separate solution. And also the cost, like the total cost of ownership is always a very important factor. Given the growth in data, they're always looking for a much better, most cost-efficient way of storing their data. So these are, I would say, the four things that I hear often from the customers that we talk to.

Ken Kaplan: Do you ever hear them say, I wish I could do this or I can't do this, but I needed to do that?

Vishal Sinha: Yeah, so that we hear a lot more around the scale part. Like so today, generally most customers have different solutions for running at the edge, in the core, in the cloud, and now they have four different solutions that they are running with. And that makes it very fragmented and they say, wish I had one console, one policy that I could apply across all of this data and that would have made their life so much simpler.

Ken Kaplan: Let's talk about your career a little bit. What got you into technology? What was that feeling like?

Vishal Sinha: Yes, I was always fascinated by technology. I always wanted to build products that could help customers solve their problems. And for first 20 years or so, I was primarily focused on building things. And the last seven, eight years, I found it equally rewarding to take this product or products to the market and help the customer achieve the outcome that they want from these products. So this, the whole journey, which is building the products that customers love, and taking that product to the customer market, is what has been very rewarding and something that motivates me to continue with the technology space

[Related: Smart Data Management is Critical for AI Success].

Ken Kaplan: And do you remember when you first started, what were some of the big challenges or exciting things that you wanted to work on?

Vishal Sinha: Oh, absolutely. So I still remember those days when if you had to make an international phone call, it used to cost two dollars for one minute. Look at today, it's almost free. You can use WhatsApp and make that call. So the first one for me was the whole telecommunications shift that happened between 1995 to 2000. And I was actually in the middle of it working for a startup, which was building multi-protocol label switching to really get the service provider infrastructure more seamless. And over time, it's really brought the cost down and made everything more accessible to the end users like me and anyone else.

Ken Kaplan: And when you were working in telecom, could you imagine hybrid multi-cloud, containers, VMs living together, like all the world that we're in now?

Vishal Sinha: So at that time, it was all monolithic. You had one infrastructure which ran everything. We didn't have the flexibility of a VM or a container that we could move around and solve the right problems with the right form factor.

I think after the virtualization, that definitely got simplified. Now we can have a very flexible, software-defined telecom infrastructure.

Ken Kaplan: What's the advice that you give people to motivate them?

Vishal Sinha: Learn the first principles of systems building, like don't focus only on the API layer. Understand how systems are built so that you can appreciate and you can make better things after that. The second one would be the ability to deal with ambiguity. Things are changing very fast and we have to be able to cope with uncertain things. And look at AI, for example, things will evolve and we have to be able to make progress with what we know and be ready to adapt to newer changes. That's very important. And third one is having endless curiosity. Things will change fast and if you're not curious enough, we'll not dig in and learn new things. So if we want to stay relevant, we have to be endlessly curious.