CIOs and IT professionals have endured dramatic changes over the past three decades. Data storage, once seen as a repository for stockpiling bits and bytes of digital information, today comes in many colors and flavors. IT teams can choose from block, files, object, direct-attached and network-attached storage and storage area network solutions. And these are powered by various hardware, including hard-disk or solid-state drives, all-flash or hybrid array memory. Since generative AI hit the scene, storage experts are rethinking their data storage strategies, according Vishal Sinha, senior vice president and general manager of Unified Storage at Nutanix.
Data as the lifeblood that needs to be at the beck and call of AI applications.
“Now applications are using the data to build intelligence out of it and power those applications. So things have completely flipped,” Vishal Sinha told The Forecast in a video interview.
Before, applications and users created data that was kept in storage chambers. With generative AI, Sinha sees this fundamental relationship in reverse, moving storage from a background component into critical infrastructure that actively fuels the needs of AI applications.
Three Waves of Transformation
Sinha has spent more than two decades building infrastructure technologies and endured three major transformations that brought the industry to this moment. The first arrived between 2000 and 2005 when virtualization liberated compute from physical servers.
“Compute was tied to a server, like you had to buy a server or a desktop or a laptop to get the compute,” he explained. Virtualization made compute portable, enabling it to be moved around as VMs or containers, paving the way for software-defined infrastructure.
Cloud computing drove the second wave, democratizing access to 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,” Sinha noted. He said that while cloud added complexity around cost management, security, and networking, it removed barriers to infrastructure access.
The third wave is artificial intelligence.
“It is going to change things significantly,” Sinha said. “Even for us, the entire software stack will change to support AI.”
Building Data-Ready Platforms
AI demands more than storage capacity. It requires data-ready platforms that deliver three capabilities. He said organizations must first consolidate data from disparate sources into unified repositories. Second, they need to clean that data so AI models and large language models can consume it effectively. Third, platforms must provide fine-grained permissions, ensuring large language models (LLMs) learn only from authorized data.
He talked about exponential data growth and fragmentation. He said data lives in a lot of different places, including at the edge in phones, within applications and on desktops. This drives IT teams to want “one console, one policy that I could apply across all of this data, and that would made the life so much simpler,” he said. Meantime, most organizations run different solutions for edge, core, and cloud environments, creating operational complexity.
He said security concerns intensify these challenges. Ransomware attacks have hit many organizations, driving demand for storage solutions with built-in protection rather than separate security products. Total cost of ownership remains critical given data growth rates, pushing customers toward more cost-efficient storage approaches.
Building a Data Storage Mindset
Sinha started his career in telecommunications during the industry’s transformation between 1995 and 2000. He worked for a startup developing multi-protocol label switching technology that reduced international call costs from $2 per minute to nearly free, enabling platforms like WhatsApp.
That experience taught him to focus on the fundamentals of systems.
“Learn the first principles of systems building,” he advises. “Don’t focus only on the API layer, understand how systems are built so that you can appreciate and you can make better things.”
He emphasizes two other qualities for navigating rapid change: the ability to handle ambiguity and learn new things.
“Things will change fast,” he said. “To stay relevant, we have to be endlessly curious."
This is critical for IT teams shifting from passive to active storage strategies. He said this transformation demands infrastructure that consolidates fragmented data, ensures security, manages costs at scale, and provides the clean, accessible data that AI requires.
Editor's note: Learn more about Nutanix Unified Storage capabilities. Read the blog post by Alex Almeida: Fine-tuning your data symphony: Nutanix Unified Storage 5.3 harmonizes usability, performance, and scale.
Video transcript:
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.
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.
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.
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.
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.
Ken Kaplan is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
Jason Lopez produced this video. He is executive producer of Tech Barometer, the podcast outlet for The Forecast. He’s the founder of Connected Social Media. Previously, he was executive producer at PodTech and a reporter at NPR.
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