Tech Barometer – From The Forecast by Nutanix: Recent Episodes

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In this Tech Barometer podcast, disruptive technology investor and analyst Jeremiah Owyang explains the rise of AI agents and a future shaped by a multiplying AI-first mindset.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Podcast transcript:
Jeremiah Owyang: The biggest industry right now in AI is the AI agent space. It’s destined to grow 10 times every five years. Right now it’s estimated at 5 billion, which will go to over 65 billion, and then it’s estimated to be 500 billion just within the next several years. This is the hot market growing within the AI space right now. People use the internet. You physically go out and find different websites to get information. You fill out tasks and you order flights, you order e-commerce, you get things done even inside of your enterprise. You have to fill out expense reports and timecards. All of these things are wasteful and not great experiences in the future. Your AI agents are going to go out and do those tasks for you. The information will be reassembled in the way that you want when you want in a multimodal way, whether it’s text, video, ar, vr, whatever time of day that you want, and in the amount of information that you want. It’s no longer up to the web designer. It’s no longer up to the website or the app designer. It’s up to you.

[Related: 4 Trends Defining the Future of Enterprise AI]

Jason Lopez: What you heard are excerpts from a keynote speech by Jeremiah All Yang, general partner at Blitzscaling Ventures. He also leads an event called the LAMA Lounge and AI community of hundreds of AI company founders. We recently interviewed Jeremiah to tell us more about agents.

Jeremiah Owyang: AI agents are dependent upon large language models.

Jason Lopez: In just a moment, Jeremiah explains the importance of LLMs to agents. But let’s start here. An agent is software code that allows a device to understand its environment process information then acts autonomously to take actions to achieve specific goals. AI agents can do more than common AI assistance that respond to specific user prompts. An AI agent can self task, it can learn over time and even recruit other agents to help. They operate across multiple apps, often working silently in the background even while we sleep. A smart home thermostat or robot vacuum or autonomous vehicle, or just a few examples of AI agents in action. In many ways, AI agents are less like tools and more like digital organisms adapting and evolving in a world built from code.

Jeremiah Owyang: As they become more advanced, they operate in sequence, in combination with each other. In concert, which is called the agent crews or fleets or groups or swarms. In many cases, they are like low level human employees.

[Related: Managing Enterprise AI Sprawl]

Jason Lopez: So how does an agent know what to do?

Jeremiah Owyang: They get it from large language models. They’re dependent, so it’s not if they’re a replacement of large language models, they’re actually executing the tasks. So think of large language models as your brain with all that knowledge and the things to do, but the actual clicking and typing and doing the actual physical tasks like your limbs, your appendages, your body are the AI agents, and you need both in combination to be an effective quote creature.

Jason Lopez: From the point of view of an enterprise, you might be wondering if IT infrastructure needs to change as companies shift from just using LLMs to deploying AI agents. The answer would be yes. An IT team needs to prepare for how agents will interact with the company’s systems. This means choosing the right tools or partners and deciding on what permissions and access an agent should have for the tasks it could do.

Jeremiah Owyang: It could fill out your expense report after looking at your credit card. It could create meetings or meeting summaries. It could build your monthly reporting deck and grab data from disparate places and aggregate it into one location. It could be like a virtual colleague. It could be just the two of us on a call. We could have three, four, or five assistants that are not just recording, but actually talking, collaborating, taking notes, taking actions, and generating diagrams in real time.

[Related: Enterprise AI Reality Check: Implementing Practical Solutions]

Jason Lopez: Many companies are already using AI assistance for customer service, handling basic questions using company data, and Jeremiah says, companies are using Gen AI in all sorts of interesting ways. He said 10% of Pfizer’s marketing content is being generated by ai. This can help workers be faster and more productive.

Jeremiah Owyang: Now, imagine if that worker who just wants to focus on making good decisions or the relationships with customers or relationships with internal stakeholders could allow the AI agent to do all that busy work for them. Now at your company and many companies who has an executive assistant, typically it’s only provided to those at the top of the hierarchical pyramid. Now, imagine a world where every worker has one assistant wait, two assistants, wait, wait, wait, 10 assistants, 10 assistants that help with data or meetings or scheduling. Just all of those things. Imagine the level of productivity that can increase for those knowledge workers, and I think we’re on the cusp of that because if you interface with just an AI agent, it means you just don’t need that many enterprise apps anymore.

[Related: A New Generation of Data Centers Spreads Use of Enterprise AI]

Jason Lopez: This isn’t to say that an AI agent is just a simple addition to a team. Some companies are exploring what it would be like to rely on AI led teams.

Jeremiah Owyang: So it’s really critical that you as a worker lean into AI and lead that within your company and for your career, or you might have to be updating your LinkedIn profile. So I think it’s critical that we lean into this technology.

Jason Lopez: We’ve reported before as we head into the age of ai, there’s a skills gap. Companies like Nutanix have AI teams that can build AI into products or create apps that boost business productivity, but using AI as a skill that will be needed across different business units to enhance employee competency. Many companies onboard AI capabilities like Microsoft Copilot or Glean or services like writer.com. Many workers take upon themselves to acquire knowledge and certifications for AI expertise. They turn to YouTube or platforms like Coursera or continuing ed from colleges and universities.

Jeremiah Owyang: Every worker is responsible for their continued growth. Now, for executives, there’s a new role. There’s a chief AI officer leading the charge within the organization to use these tools. Most of the innovation is happening with the young startups, many of them who spun out a big tech or spun out of big companies and they say, I’m going to move much faster.

[Related: Business Steps for Using Agentic AI]

Jason Lopez: Jeremiah says, small teams are capable of building a global enterprise AI software suite in one year with rapid adoption.

Jeremiah Owyang: Crew AI only has 16 employees, and they’re already one of the dominant enterprise agent leaders because they’re using AI for everything.

Jason Lopez: Crew AI is a San Francisco based firm, which makes development tools to build AI agents that do tasks in apps like CRM or ERP systems.

Jeremiah Owyang: They are what is called an AI-first mentality. So let’s talk about an AI first mentality. This is a common mental framework within Silicon Valley, amongst the AI leaders. If you have a problem in your life, you first see if there’s an AI that exists off the shelf, whether it’s an app you download or an enterprise app that’s an existing enterprise software. If it doesn’t exist, then you try to build it and step three, if it doesn’t, you can’t build it. Then you hire somebody to build it. So you follow that lineage to think about, how do I move fast? And it’s always about leveraging AI first. So that’s the AI-first mindset.

[Related: AI and Cloud Native Skills Reshape Careers]

Jason Lopez: Building on the rise of enterprise AI platforms and the growing availability of ready-made templates. The next logical step is even more transformative. As tools become more accessible in development, more streamlined, the creation of AI agents is starting to shift as well, possibly even to the agents themselves.

Jeremiah Owyang: We are probably going to see AI agents create AI agents. There’s already no-code AI, so developers can be even more efficient and can create even faster. I anticipate that developers and software engineers, they’re still needed. We need them more so they can just produce more. I don’t see them going out of a job. That’s our take in this

Jason Lopez: Space. Still, the development of AI agents in the enterprise lags behind the customer side. A lot of that has to do with security.

Jeremiah Owyang: One rogue AI system within an enterprise, potentially from a nation that is not friendly with your nation, could upend your company. It could grab data and send it back to the home country. So I think there are appropriate concerns around ensuring that the AI is safe for the enterprise and the organization eventually, which would help with the customer relationships. So I think that’s why we see that lag.

[Related: CIOs Sharpen Skills for the AI Era]

Jason Lopez: Jeremiah points out that while AI agents can be like coworkers, they’re not human, but powerful tools, tools that aren’t neutral because they mirror the intentions of people.

Jeremiah Owyang: The old adage in Silicon Valley that you and I know quite well is that if the product is free, then you are the product. If you’re paying a premium, whether it’s enterprise or consumer for the AI agent, then it is more likely to be aligned for your benefit. If not, then it is more likely to be towards the benefit of the builder. And I think the old business models in Silicon Valley apply here. They’re tools, and we have used Fire for cooking, which helped us to expand our brains with more nutrients by having clean food. We’ve used steel to build amazing vehicles and buildings, but we’ve also used fire and steel for weapons, and that choice is very much a human thing where we choose how to use these tools and technologies. The difference here with AI though is that it is a thinking machine and it’s trained off what humans will do, and it starts to think or simulate thinking on its own, and that’s something we haven’t seen before in every possible way you look at this, it’s an embodiment of the human condition, and that is a wild thing to think about, that we’re creating a new species.

Jason Lopez: Jeremiah Al Yang is the founder of Lama Lounge AI events, and he’s a partner at Blitzscaling Ventures. You can find out more about his events at lu.ma/lama lounge. This is the Tech Barometer podcast. I’m Jason Lopez. Thanks for listening. Tech Barometer is a production of the forecast where you can find more stories about artificial intelligence as well as cloud and enterprise computing. Look for those at the forecast by nutanix.com, all one word, theforecastbynutanix.com.

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In this Tech Barometer podcast, Nutanix CEO Rajiv Ramaswami describes why a thriving IT ecosystem enables enterprises to maintain investments in traditional infrastructure and applications while evolving to newer innovations such as cloud native and AI technologies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Podcast transcript:

Jason Lopez: One of the key messages from the 2025 Nutanix .NEXT Conference in Washington DC: the expanding partner ecosystem. This is the Tech Barometer Podcast, I’m Jason Lopez. dot-NEXT is where enterprise IT professionals come together with an eye on building their future on the Nutanix software platform. One thing we learned this year is companies like Pure Storage, NVIDIA, and Dell will play a key role in delivering integrated solutions to customers, in areas like enterprise AI and infrastructure modernization. The Forecast’s Editor in Chief, Ken Kaplan, recorded this walk-and-talk with Nutanix CEO Rajiv Ramaswami as he headed to another session at the .NEXT conference, which continues to build in numbers of attendees and partners.

Rajiv Ramaswami: Yesterday I was at the Partner Summit. I believe there were 1,600 partners in attendance. So a lot more. The partner network is expanding. Our ecosystem partners are growing. This year I think we had about 85 plus, 86 I think, sponsors for the event. A few years ago it was 25. So I think we’ve come a long way.

[Related: Get a Grip on Data Storage in Quest for Enterprise AI]

Ken Kaplan: Yeah, the theme for me is partners and Pure Storage was a big announcement. What is it like to work with NVIDIA, Dell, all these partners you’ve been talking about for years, but you’re really close with them now.

Rajiv Ramaswami: It all comes down to can we work together to create something of value that each of us brings together and create a solution for the customer. That is the core of every partnership. You look at NVIDIA, it’s all about enabling enterprise AI. If you look at Pure, it’s about providing customers with choice. Many of the large install base of customers out there with Pure Storage and now they all have an option to be working together with the Nutanix Cloud Platform and Pure. It’s all about providing a better solution and experience for customers.

Ken Kaplan: There was Cisco, Dell, NVIDIA. What’s driving all this togetherness now? Is it a push for AI? Is it a push that there’s really a lot of innovation and we got to get together on the same page? Why is this happening now?

Rajiv Ramaswami: It’s across all the themes that we talked about. Modernizing infrastructure, building cloud native applications, enabling enterprise AI, and depending on the partner specifically, it’s one or all of these vectors.

[Related: What’s Driving IT Decisions Around Enterprise AI and Cloud Native Technologies]

Ken Kaplan: Do you anticipate more of this happening or does this thing start to close up or are we in a real open go, go, go period?

Rajiv Ramaswami: We’ve always been a company that’s focused on creating a platform and a platform always has an ecosystem around it. I do expect our ecosystem to continue to grow and flourish. Do you see the customers wanting this kind of openness? Absolutely. This is creating a huge value for customers because they know that we can’t provide everything and so combining solutions together, if we can make it work well, yes, absolutely.

Ken Kaplan: Have you seen some of the customers, once they have these capabilities, start going in new directions? You mentioned a customer who came on stage with you and now they’re getting into AI.

Rajiv Ramaswami: Their needs are evolving as well. They may have been running traditional infrastructure stuff on us. They may have been running VDI workloads on us. They’re running mission critical databases on us these days. Now they’re running AI on us. Their needs evolve and we as a platform continue to work hard to evolve with them.

[Related: AI Lifecycle’s Impact on IT Infrastructure]

Ken Kaplan: The last thing here: we’re seeing a lot of people talking about VMs and having cloud native. Are they doing these things and don’t know it or is it just something they have to do now going forward?

Rajiv Ramaswami: They’re all going to have a mix of their traditional applications at the same time be looking at building modern applications and they actually have to do both. They have to run their traditional ones and they have to at the same time be building modern ones and figuring out how to run all of these very efficiently.

Jason Lopez: Rajiv Ramaswami is the the CEO of Nutanix. Ken Kaplan is the Editor in Chief of the Forecast, the producer of this Tech Barometer podcast. They spoke on the floor of the .NEXT conference in Washington DC. .NEXT will move to Chicago in April 2026.The Forecast reports on the enterprise computing industry can be found at theforecastbynutanix dot com. I’m Jason Lopez, thanks for checking in.

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In this video interview, Harmail Chatha, senior director of cloud computing operations at Nutanix, describes the growing challenges of managing data centers as business demands for enterprise AI applications climb.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Harmail Chatha: You have GPU clouds available like AWS Google and Azure, but they’re kind of the true ias PaaS platforms. Now you actually have clouds that offer you bare metal with GPUs. And what’s happening across the industry is there’s a lot of companies that are deploying these in traditional data centers and taking up a lot of the power and space as well, because we’ve always had this concept of hyperdepth racks where we optimize for vertical growth versus horizontal growth. But what’s happening within the data centers now is a lot of horizontal growth because of not enough power available, not enough infrastructure available to support the power needs of GPU environments. And obviously cooling isn’t there as well. So I don’t think AI is necessarily pushing the limits within data centers yet, but it’s going to very, very soon if data centers don’t start to adapt to new technologies, new cooling infrastructure, new power densities that are required as well. So I think we’re going to start seeing the limitations within data centers, but I don’t believe it’s there yet. But as more and more consumption goes in and customers identify workloads that they’re going to be running with ai, I definitely see it hitting a limit.

[Related: AI Lifecycle’s Impact on IT Infrastructure]

They’re at the heart of the power problem in the data centers right now as well, right? These newer generations of CPUs with GPUs are consuming anywhere from 30 to 50% more power within the servers. Hence, the industry is really taxed from a power consumption perspective. Whereas you can deploy a full rack of the older gear now you can only deploy half a rack. So how do you solve that problem? So now, whereas we historically have been 17.3 kilowatts per rack, fully maximizing the rack. Now our new design, it’s going to be 34 plus kilowatts per rack with liquid cooling to the rack and ultimately getting to the chip as well. So we’re really at the onset of designing our data center of the future also, because what’s legacy is not going to work any longer. It’s going to be super inefficient. Cooling challenges within the data center. Air cooling is not going to be enough with this new AI technology going in and the consumption of power within the GPUs as well.

[Related: Report Shows Enterprise AI Driving Big Investment Burst in Cloud Services]

Companies have to start really honing in or zooming into their environments. Not so much holistically at a data center level, but what does a workload look like and how do you measure the emissions of that workload in itself? Right? And we’re just kind of touching the surface on scope one, scope two, how do you really measure scope three, which is the most challenging one? It’s basically considered everything else that’s not direct emissions, indirect emissions, but scope three being all encompassing. How do you get to embody emissions as well as an industry? We’re not there yet embodied emissions of a server. So we’re talking about VMs to workloads, but embodied emissions means what’s that single little cable within the system, the server itself, and how do you measure the emissions of that? There’s thousands, hundreds of thousands of parts that go into a server. How do suppliers measure the transportation cost and the development cost of those components as well? So really it’s all about zooming in right now, right? As we continue to mature in this space, there’s a lot of effort that’s going to go into measuring, and there’s so many companies, new startups coming out that are starting to just touch the surface of how do you measure emissions in itself?

[Related: Guiding Enterprise IT Hardware Buyers into the AI Future]

ROI has a lot of interest from companies wanting to learn and understand sustainability. It’s no longer like a hypothetical topic or a conversation, but kind of roll your sleeves up and you have to take the step, take the initiative to first kind of educate yourself, what is it that you need to do, understand the different scopes there are, and really start to measure what your footprint looks like. So what I’m seeing is, of course a lot of interest in the industry, but I think what’s happening with some of the, for example, us who’ve been on this journey for the last three plus years now, or some of the more mature companies that are been measuring their carbon footprint, we’ve been doing this at a very holistic data center level, at a building level, and then kind of have gone down to a customer’s environment level.

[Related: Get a Grip on Data Storage in Quest for Enterprise AI]

So we have data halls, we have cages, so we’re able to measure our footprint there. But now, as we announced just earlier today in the opening keynote is worse within (Nutanix) Prism Central application, we can measure the electrical consumption of a node, and ultimately you can get to a cluster. So we’re going a step deeper versus just being holistic at a data center level now. So this is a good step in the right direction, but where we ultimately need to go and continue to do more work is you got to get to the VM level. Once you can measure the vm, then you got to get to the workload level, and that’s when you’re going to be able to make smart and intelligent decisions on what a workload consumption looks like, correlate that back to the emission factor, and then intelligently you’re able to move those applications around to more sustainable data centers that might have lower P use more renewable energy as well. So I think that’s the journey we’re on. I’m glad we’re measuring at the node level, but ultimately we got to get to that VM and application level as well.

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In this Tech Barometer podcast, David Kanter, co-founder of MLCommons, talks about intellectual curiosity and how it led him to the forefront of the enterprise AI revolution.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

David Kanter: The current era of machine learning is very much informed by the supercomputing community. I think the seminal moment for this era of machine learning was AlexNet and ImageNet, which demonstrated that if you took enough compute power, you could train a machine learning model, a neural network that outperforms humans at image recognition. And that was 2011, and that kind of launched the modern era.

[Related: Importance of AI Data Storage Performance]

Jason Lopez: The voice of David Kanter, co-founder of MLCommons, the organization which developed the benchmarks to measure the performance of machine learning and artificial intelligence hardware, software, and computer systems. This is the Tech Barometer Podcast. I’m Jason Lopez. For the past two decades, computer scientists have been working on the performance of machine learning models. Kanter and his colleagues have explored how computer systems across a range of functions can keep up with AI training. Their benchmark suite, MLPerfStorage, reveals the performance of storage systems when running AI.

David Kanter: Debo Dutta and I had sort of brainstormed about this.

Jason Lopez: Debo Dutta is the chief AI officer of Nutanix and a co-founder of MLCommons.

David Kanter: He said, you know, we’ve got this wonderful professor at McGill that we’ve worked with. She was at Nutanix, I think, for a period of time and runs a wonderful lab up at McGill. And so we started some of the brainstorming. And from there, sort of the idea emerged that, hey, we should look at what is the performance that you need out of a storage system to support AI training. That’s really the goal of MLPerfStorage is answering that question.

[Related: Measuring the Prime Ingredient in Enterprise AI]

Jason Lopez: Measuring performance is complex, especially at different scales. Machine learning workloads vary significantly, and they work on a variety of applications and use different levels of computational power.

David Kanter: If you’ve got a system that has, you know, 65 of these generation of accelerators, five nodes of Nutanix may be right for you. Or, you know, maybe you’re looking to build a cluster that’s a bit more expandable. And maybe you should get 10. But that’s ultimately the kind of what the benchmark is telling us. And, you know, we’ve got three different workloads in the benchmark, 3D images and 2D images and scientific workload.

Jason Lopez: Machine learning operates across a big range, from small energy efficient models to very large systems.

David Kanter: One of the things I like to say is it spans from microwatts to megawatts, because some of the largest MLPerf submissions on the compute side for MLPerf training and MLPerf HPC, we had one submission on Kudaku at the time, I think the world’s number one supercomputer. And then we’ve had systems with 10,000, 11,000 accelerators. Those are some of the largest supercomputers out there.

[Related: Get a Grip on Data Storage in Quest for Enterprise AI]

Jason Lopez: One of the most exciting applications of AI is in scientific research, where machine learning can enhance simulations to make them more efficient.

David Kanter: You can do an exact simulation, but that’s very computationally expensive.

Jason Lopez: An exact simulation means, for example, in the case of charting the transformation of a molecule, the system expends the time and energy to go through an exhaustive analysis of how atoms and molecules interact and how they bond and change over time.

David Kanter: What happens if instead of doing pure simulation, maybe we can use machine learning to do some prediction that might be a lot more affordable computationally than an exact simulation. So one of the ways that people are looking at AI for science is training the machine learning model on these exact simulations so that it can approximate them and make very efficient predictions. And then you can use the exact simulation only on the ones that you already believe to be safe.

[Related: Creating AI to Give People Superpowers]

Jason Lopez: Machine learning, by extension AI, isn’t just for research institutions anymore. Its tools and infrastructure are becoming more mainstream, with applications extending far beyond traditional supercomputing environments.

David Kanter: AI is taking a lot of those same tools and infrastructure, including the storage, mainstream and operating it in a different way. Now you have all the leaders in AI and machine learning having those same needs for totally different data. We’re taking the storage that you needed for supercomputers, and now there’s a much broader pool of people who need them for a wide variety of different applications.

Jason Lopez: One field where this shift is already visible is in autonomous driving, where AI must analyze complex data in real time.

David Kanter: The data needs there are super complicated. You’ve got 2D images, you’ve got 3D volumes, you’ve got LiDAR that gives you depth, you’ve got radar. And so you have to combine all of this together and analyze it so that we can get cars that will help inform me as a driver, hey David, you’ve got to stay in the lane or this deer is coming out of nowhere.

[Related: Alex Karargyris’s Path to Becoming a Pioneer of Medical AI]

Jason Lopez: As the field of AI grows, so do the options for access. Companies must decide whether to buy or lease computing power, a challenge that Kanter and his team considered while designing MLPerf benchmarks.

David Kanter: There’s a lot of folks who are going to be doing that in the cloud who may not want to buy, they may want to lease, or it may be a combination of the two. And just the variety of approaches there is really astounding. And that was all something we had to take into account when we built the benchmark to be as inclusive as possible. I read science fiction and fantasy copiously as a kid, but I also loved video games because they could bring that imagery to life. So that got me interested in, well, what computers are good for running video games and why? And those were questions I started to ask myself when I was middle school, high school. That ultimately led me down the path into computing.

Jason Lopez: In some ways, Kanter’s work with MLPerf is just an extension of what he’s been doing since he was a kid. His father was a doctor and he said he was raised in an atmosphere of inquiry.

David Kanter: I didn’t learn until later in life that actually most people don’t talk about medical cases over the dinner table. That was one of the ways in which I became maybe a little bit better rounded in college.

[Related: Enabling AI-Powered Computational Biology in Pursuit of Precision Medicines]

Jason Lopez: His passion for technology carried him through the dot-com boom and into a career where he helps define the future of computing.

David Kanter: In the late 90s, I got really interested in the internet and was starting to read some of the early websites on computers and reviews. Anand Tech, Tom’s Hardware Guide, a lot of the early review sites I read when I was in high school. And then when I was in college, I ended up getting involved with Real World Tech, which was actually kind of the granddaddy of some of these and still is a website for computer engineers, not the general public, focused on understanding computer technology. And so I started writing there while I was in college, actually, and ended up running the website when I moved out to Silicon Valley in 2005. And that’s when I first came into contact with Intel, Itanium, and the transition to 300 millimeter wafers and all sorts of marvels that we bring to life.

Jason Lopez: Today, as AI continues to evolve, the challenge for Kanter remains the same. How to define progress.

David Kanter: Part of the goal is getting the whole industry oriented around what does it mean to be better? And part of that is helping customers understand what they should be buying. And which, again, coming back full circle, that’s why I was reading review websites in middle school and high school, because I was trying to figure out what computer to buy.

[Related: Bridging the Gap Between AI’s Promise and Fulfillment]

Jason Lopez: From figuring out what computer to buy, Kanter’s now helping people figure out how best to implement enterprise AI to run organizations. The work he’s doing for MLCommons is big. Many challenges lie ahead, like power consumption of inference and training systems. Some of the new work they’re doing, measuring AI risk, reliability and safety.

David Kanter: I’m thrilled that inference we delivered a couple of years ago and then training, which is a bit more complicated because they’re bigger systems. We were able to get the first power measurements for AI training systems late last year. And we’ve got hopefully more coming soon with MLPerf training.

Jason Lopez: David Kanter is the co-founder of MLCommons, a consortium that develops benchmarks such as MLPerf Storage for evaluating machine learning performance across hardware, software and cloud platforms. You can find them at mlcommons.org. In our ongoing series on AI leaders, listen to our follow-up segment where David Kanter discusses how they’re creating benchmarks for data storage. This is the Tech Barometer podcast. I’m Jason Lopez. Thanks for listening. Tech Barometer is a production of The Forecast. Find us at theforecastbynutanix.com.

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In this Tech Barometer podcast, MLCommons Co-founder David Kanter talks about creating the MLPerf benchmark to help enterprises understand AI workload performance of various data storage technologies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

David Kanter: Data is the prime ingredient in modern machine learning. I think there was an economist headline saying data is the new oil. Of course, where does that oil live? That oil lives on storage. Storage is kind of like Baskin and Robbins, except it’s possible there might be more than 31 flavors.

Jason Lopez: David Kanter is the co-founder of MLCommons. This is the Tech Barometer podcast. I’m Jason Lopez on this podcast, storage and the critical importance it plays in the age of AI. In this episode, David Cantor explains MLPerf Storage, a benchmark suite of MLCommons. This benchmark is designed to measure machine learning workloads in the context of storage systems, which hold the data used to train models. He says, to unlock the full potential of AI, storage systems need to be tailored to the process of machine learning, which is quite critical to AI training. And this is how the idea for the MLPerf Storage benchmark came about. Data fuels AI’s ability to learn and make decisions. The more data you have, the more powerful your models, the greater the breakthroughs. This is at the heart of the AI scaling laws developed by Greg Diamos, a co-founder of MLCommons. His work on AI scaling laws, neural network optimization, and GPU acceleration has had a big influence on the field of AI development.

[Related: Get a Grip on Data Storage in Quest for Enterprise AI]

David Kanter: The lesson of the scaling laws is that if you get enough data and you get a big enough model and enough compute to combine those together, that’s when you really get these qualitatively different outcomes, whether it’s a self-driving car or the ability to recognize an image better than a human. Data is really the top priority here.

Jason Lopez: But it’s not just about having data.

David Kanter: It’s how you process the data, how you manipulate the data. And there’s a ton of work that really shows that data is a first-class citizen. And of course, data needs a place to live, and that’s storage. You’re going to take batches of data and feed it into your compute system. And then you’re going to compute a forward pass and see how good the model is at predicting on that batch of data, compute the errors, and then adjust the model so it gets better, and then over time, the model will hopefully and often converge to an answer.

Jason Lopez: After enough iterations, the model becomes good at recognizing whatever you’re training it. It has converged to an optimal solution, but it can take a lot of data, which means a lot of storage. And to take it one step further, the faster and more efficient that data storage, the better for training, inference, and tuning models. In MLPerf’s first storage evaluations, the Nutanix Unified Storage Platform was a benchmark leader, and Cantor commented on these benchmark tools.

David Kanter: We built up a great set of infrastructure using some tooling from Argonne National Lab to help us measure sort of that data loading for AI. And critically, we can do it without having to have accelerators. You can actually run our benchmark and ask the question, hey, what would it take to feed 4,000 accelerators without having to shell out for 4,000 accelerators?

Jason Lopez: That’s how artificial intelligence is changing the way we process and analyze information. Making a model to see how something works is nothing new, but AI is raising the bar dramatically. It does this by requiring massive amounts of data to train the system.

David Kanter: I think the really critical thing is the size and type of data. We’ve got three different workloads in the benchmark, 3D images, and 2D images, and a scientific workload. So if we’re doing image recognition for, say, smaller images, you know, each image might be 100 kilobytes or so. When you want to feed your compute system, you’re going to be pulling in a batch of images, so your storage system has to be able to keep up. If you have really big images, then each image fetch is going to be a lot of data all in one fell swoop. But if you have smaller data, like let’s talk about large language models, right, you’re going to be working on a lot of text. That’s much smaller data, and it’s going to have actually a pretty different impact on the storage system.

Jason Lopez: Which leads to this insight. Image size is an issue, but it’s not just about images.

David Kanter: It’s actually about how many data samples, you know, whether it’s a 3D volume, whether it’s a 2D image, whether it’s a sentence, you know, whatever it is, how many of these samples are you getting? And critically, how many accelerators can you keep busy with a given storage system? If you’ve got a system that has, you know, 65 of these generation of accelerators, you know, five nodes of Nutanix may be right for you. Or, you know, maybe you’re looking to build a cluster that’s a bit more expandable, and maybe you should get 10. But that’s ultimately what the benchmark is telling us.

Jason Lopez: What the benchmark reveals helps users to understand how their system choices work together. That’s the big overview. But when you zoom in, the benchmarks evaluate a range of things, such as the safety of chatbot-gen AI systems measured in the AI Luminate benchmark, or the performance of large language models and other AI workloads on PCs in the MLPerf client benchmark.

David Kanter: An initiative that we’ve started recently at MLCommons is trying to turn our expertise in AI measurement to how can we make sure that these AI systems are going to be responsible and doing the right thing for us as we intend.

Jason Lopez: One intention, which gets a lot of headlines around AI, is safety. But another intention is building systems which don’t break the bank and don’t gobble up inordinate amounts of power. The owners of data centers benefit from the MLPerf storage benchmark with insights into how to manage costs or how to build data centers for optimum performance. Today, companies are rethinking how they build data centers.

David Kanter: As we’re shifting into the AI era, a lot of these systems use so much power that we may have to double the footprint of data centers. Obviously, that’s a big deal. And so we’re seeing people looking for new sources of energy and thinking more about where data centers are with energy in mind.

Jason Lopez: Older data centers often lack the electric capacity and cooling infrastructure needed to support hardware like GPUs, as well as train large-scale models, which use far more power than traditional computing. The next generation of data centers needs to be powerful and efficient.

David Kanter: Some of those older generation of data centers that we built in the 90s and 2000s just don’t work for AI. I think part of what we’re seeing is we need new data centers that can do AI. And that’s, you know, stressing the whole system. But the great news is they’re actually way more efficient than what we had before. And the systems we build are more efficient. Every day we’re discovering new things.

Jason Lopez: Kanter says you can’t optimize what you don’t measure, in this case, energy use. Training a single large AI model can produce as much carbon as five cars over its lifetime. Tools like MLPerf Storage measures AI performance per watt, helping developers choose more efficient hardware and software.

David Kanter: From a sustainability standpoint, we focused on measuring energy usage because you can measure it. We want to measure the power consumption of inference and training systems. I’m thrilled that inference we delivered a couple of years ago and then training, which is a bit more complicated because they’re bigger systems. We were able to get the first power measurements for AI training systems late last year. And we’ve got hopefully more coming soon with MLPerf training. The goal of my organization in many ways is how can we measure things in the AI world and help to make AI better for everyone. And where better means faster, more capable, more energy efficient and safer.

Jason Lopez: David Kanter is the co-founder of MLCommons, the organization which developed the benchmark suite MLPerf Storage. Some of the key players in this organization we’ve interviewed for Tech Barometer previously, Debo Dutta, chief AI officer at Nutanix, Greg Diamos, one of the key developers behind generative AI apps and discoverer of scaling laws. And Alex Karagris, the co-founder and co-chair for the MLCommons Medical Working Group.

Look for these podcasts and print stories at our Forecast news page, theforecastbynutanix.com. That’s all one word, theforecastbynutanix.com. Tech Barometer is a production of The Forecast. I’m Jason Lopez. Thanks for listening.

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In this video interview, the inherent group President Pierre Jean Beylier describes the state of digital transformation across Europe, including data sovereignty and regulations, public and hybrid cloud, IT resiliency and flexibility, and sustainable energy strategies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Pierre-Jean Beylier: Today we’re a French player with a small business starting in Belgium, but we’re really a French player. So we’re very focused on what’s happening in France in terms of cybersecurity. And already it’s complex, but we’re dealing with one big government agency that deals with cybersecurity Europe. Each country has its cybersecurity agency. Each country will want you to be certified with their local agency if you want to work with certain government agencies and so on so forth. So it’s a lot of work. It’s additional costs in many cases because you don’t have a certification that allows you to work smoothly around all the European countries. Now, there are some rules in place that give you equivalence. So if you have this level in France, then it works in Germany with some countries, but not with all. So it is very complex in France compared to the rest of Europe. In Northern Europe, we are late in the move to cloud, whether it’s private cloud or public cloud. So we are catching up on that, and we’re also late securing our data and our networks and our infrastructure. So small, medium sized companies have a lot of work to do on that front. In France, 60% of the small companies that get attacked get out of business within 24 months after the attack if the attack was successful.

[Related: Managing Enterprise AI Sprawl]

So there’s more and more education around those risks, and we see that evolving. Obviously, that creates a lot of opportunities for us helping our customers with securing their data, their network, and with helping them define what is the best setup of the infrastructure. Are there things that they want to keep on-prem and it makes sense to keep on-prem? What do they put in a private cloud? Is there a need for them to go to the public cloud? We advise them on that and then deploy it with them and manage it for them. Larger companies can co-manage or let them manage themselves if they want, but smaller companies in general, it’s a fully managed service.

[Related: IT Analysts Discuss the Search for VMware Alternatives After Broadcom Acquisition]

They need a lot of things. They need more reliable, resilient connectivity, and higher speeds. With that comes the need for more security. They have a lot of their systems. If you look at the French market, we are late in terms of moving to the cloud. So they have a lot of systems on-prem, and they’re asking themselves, does it make sense? Wouldn’t I be more secure if I put my systems in an environment that has been built for that and managed by people whose job day after day it is to do that. So typical needs of companies whose business model is evolving towards more and more e-commerce digitalization of all their processes, they’re coming to us. So we are really focused on mid-market companies from 50 employees to 5,000. And then we have also larger companies that come to us when they’re fed up with the big telcos and the lack of agility and response they’re getting.

[Related: 4 Trends Defining the Future of Enterprise AI]

At the beginning of the process, the customer was not sure of what he wanted. It often happens or they think they know what they want. And when you start going through different scenarios with them, no, actually this sounds good. That’s not what we had planned for, but this sounds good. And we wanted a technology that allows us for maximum flexibility because we were not sure where we would land. Maybe some servers would stay on-prem in some stores in the bigger stores, some private cloud they had in mind to have some in frying the public cloud as well. So we wanted the technology that allows us to manage that very smoothly wherever the infrastructure is located. So Nutanix came on top of the list to achieve that. And then we also wanted to go into a hyperconverged solution. So that was a key element in the decision.

[Related: Enterprise IT Teams Jump Into AIOps]

We have 14 data centers in France, seven that we own. And we are looking at a, what can we do better? So what’s interesting in our industry is that 80% of the carbon consumption, carbon footprint comes from terminals. Data centers are very small for us because in France, most of our energy comes from nuclear energy and it’s a green energy, with very low carbon emissions. So energy consumption is not a big issue. Water consumption is, and we’ve put in place cooling systems in our data centers that don’t use water. So we’re quite on the forefront of that. And then in terms of terminals, last year we collected from our customers a bit over 14,000 routers and all kinds of different equipment. And it’s roughly one third we refurbish and reutilize. One third we sell for other people to utilize them, and one third we recycle, destroy, recycle properly. That’s a big objective for us, and we think we are ahead of a lot of our competitors in that 80% of our carbon footprint comes from the equipment at customers, premises, routers for fiberlink, et cetera, et cetera. So that’s a big area of focus for us.

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In this video interview with The Forecast, Simon Robinson, principal analyst at Enterprise Strategy Group, discusses the complexities of managing data in cloud and hybrid multicloud environments, a challenge that is growing more acute with the rise of enterprise AI applications and data.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (edited):

Simon Robinson: We talk a lot about storage, but the important part is the data. That’s the stuff we care about. Managing data according to policy in a cloud environment or multicloud environment is much more complex because it’s much bigger and there are strict rules about what data can go away. So enabling that longer term I think is a really important problem that the industry is focused on addressing. One of the big challenges in the future is going to be: how do we get enough and the right sort of data into those models? So that has a couple of dimensions. One dimension is if I’m building massive GPU clusters, I want to be able to feed data very, very quickly. So I need highly parallelized capabilities on the storage side to keep those GPUs saturated. So that requires some very particular, very high performance storage capabilities.

[Related: 4 Trends Defining the Future of Enterprise AI]

So that is definitely happening. More broadly, I think for the typical enterprise, they’re now at the point where they’re thinking, okay, so we’re going to maybe have some models, build some models ourselves. We’re going to be thinking about what these applications may be. They may be running at the edge. Where is the data going to be for those applications and how do we get data into those models? Storage today is very, very siloed and it’s trapped into different locations. It’s in different flavors of storage. You’ve got sand, you’ve got nasa, you’ve got object storage, you’ve got storage in the cloud, you’ve got storage at the edge, you’ve got storage in multiple data centers. What is the right data that I need? Right? Organizations struggle with visibility into their data. They always have, and there’s always been kind of, well, what is the benefit of solving that problem? And I think AI could be a major enabler in persuading organizations to finally solve that problem. If they don’t have visibility into their data, they’re not able to optimize their AI environments. So I think that’s where we see a real opportunity in some really interesting things going on in the industry in terms of innovation there.

[Related: Enterprise IT Teams Jump Into AIOps]

When we are talking about billions of parameters about generating insight from stuff that wasn’t created by humans, that can only at scale, that’s accessible globally, that can only be done in the cloud and very likely that will be the case for a great many years. Again, the question is how do we take some of those learnings and apply that to our on-premises environments? And it could be that actually we don’t need a large language model. We actually might need many small language models. So how do we build an environment that is able to ingest elements of the public cloud, of the model that’s in the public cloud and apply it for our own data? I think that’s the key. The challenge for organizations, they can become sort of paralyzed by, I dunno where to start. The important point is just start. Just do something. Because the sooner you get going, the faster you’re going to learn. The faster you’re going to perhaps fail or make some mistakes and then learn again and do it again. And the faster you’re going to get to a kind of maturity in terms of delivering some real capability to the business.

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Analyst Jean S. Bozman, president of Cloud Architect Advisors, explains how evolving traditional IT to hybrid cloud and enterprise AI forces IT teams them to manage complexity, acquire new skills and manage a myriad of costs.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Jean Bozman: It’s amazingly well accepted. It’s not controversial. Hybrid multi-cloud. You cannot almost go to a tech person that wouldn’t agree with that statement. There’ve been different terms. There was hybrid cloud than they said because there were multi-cloud. It’s hybrid, multicloud. I think it’s an admission. That data is everywhere. And so I’ve distributed data and that was fed by the fact that we had the hyperscaler guys. Some people call ’em CSPs hyperscale, where the big guys know who they are. And the data is not just sitting in one monolithic place, it’s sitting here, there, and everywhere. And even the people who used to think that it would just be on one or two systems, it is not that way anymore. Anyway, so it is a fact of life, but the question is how do you live with it? It’s that way. How do you live with it? How do you get the two things that have been running in parallel, which is distributed, we know that’s happening, but also the traditional enterprise stuff. How do you get those things to sync up and work together? And I think a lot of what we’ve heard here is about how do you get those enterprise capabilities, manageability, all the things we’ve always had to have and how do you make that happen in a world that may otherwise seem chaotic?

[Related: Stepping Stones to Cloud]

This is a challenge for the enterprise where people say, Hey, that’s not how I’ve been doing things for 20 years, or Hey, I didn’t learn how to do this, or I need these new skills. So there is a skills challenge and there’s also a cost challenge because we know what we were doing and the thing we are doing seems additive, but now we really want to kind of embrace everything and do it all at the same time. People were living separately, right? You had the people who always advocated for distributed and lots of little databases here and there, and that was cool. Especially forgive me for my sins, but the open source movement. So fun. But it’s okay to have fun, but you also need to run a company. So you need to put those business best practices. I want to say the best practices to work and that’s what could be a chaotic environment, but should be a smoothly running, unified environment.

[Related: Managing Enterprise AI Sprawl]

A lot of the AI stuff comes out of the same kind of people that have been doing the open work, but it’s applying, I think a new rigor to it. And also there is the demographic piece. I’m absolutely going to talk about that. Where traditional IT has been running for, what, 40 years or something like that. A lot of these things are a sudden shock to systems that have been reliably in place when you talk about let’s do backups, let’s do security. It was done a certain way. All that’s having to be adjusted, I think, and that’s clear to people.

[Related: 4 Trends Defining the Future of Enterprise AI]

AI was kind of, I don’t know, it was almost a shock to people. All of a sudden, it’s in your face now what do I do? I’ve got lots of data. I’ve got these models. I know other people here. We’re talking about the models and where they are and who’s going to do it. And there’s a very big difference in training the models, which takes up a lot of CPUs, GPUs, software. But there’s also the inference stuff, and that can be customized to your business. Healthcare, retail, finance, it’s customized. And also it’s easier to get your arms around it than trying to take in everything that was ever written just to write a haiku. That’s not an enterprise thing by itself. It can fit into the enterprise. People writing things, people being creative, but it’s not enough. There’s a lot of rigor that has to come around that controls guideposts and all of that.

[Related: Enterprise IT Teams Jump Into AIOps]

All the things we were always careful for all these years. So AI is tremendously exciting to people. By the way, I’ll give you my controversial statements, which is Tech GT is great, but AI is much, much bigger. We had ai. AI was in the background doing data management, finding financial fraud, looking at medical records. That wasn’t sexy enough. But now it is more sexy. It’s in my office, I can see, oh ai, that’s great, but it’s not enough. It has to blend in. Great tool, wonderful to learn about and use. Very useful, but it needs to mix in with everything else that’s been developing over the last 10 years.

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In this Tech Barometer podcast, go behind the 2025 Enterprise Cloud Index findings numbers with Nutanix AI and cloud native technology experts, who explain current trends and challenges impacting CIOs and IT decision makers.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (edited):

Debojyoti “Debo” Dutta: Wearing my optimism and imagination hat, enterprise AI will get their act together and will accelerate with better infrastructure to land gen AI workloads for the enterprise users. I absolutely believe that. And also, in order to do that, they will reskill themselves. AI will just become another workload. It’s just new today, but it will become an IT workload. So today’s IT manager will actually become an AI manager tomorrow.

Jason Lopez: Debo Dutta is the Chief AI officer at Nutanix. This is the Tech Barometer podcast, I’m Jason Lopez. Let’s talk ECI… the Nutanix Enterprise Cloud Index. The index is a global survey of IT decision-makers on cloud computing, hybrid cloud adoption, and IT infrastructure. It’s a resource to benchmark cloud strategies, to understand industry shifts and basically address the question, do I have the right infrastructure. This 7th annual index shows some interesting things about how IT is dealing with AI. One of the major findings is that 95 percent of customers say gen AI is changing their priorities and 90 percent say that security is a top concern.

Lee Caswell: That’s really important for Nutanix customers and prospects.

Jason Lopez: Lee Caswell is senior vice president of products and solutions marketing at Nutanix.

Lee Caswell: How do they take these LLMs (large language models) from the public cloud and then basically have them run securely on private data, either in their data center or with inferencing out of the edge?

Debo Dutta: Deploying a model in a private infrastructure where your team has complete control, that solves a lot of the problems. And then your team can select the right models that kind of pass a bunch of benchmarks that your teams decide that these are the qualities that the model should have. And that can be done today.

[Related: Report Shows Enterprise AI Driving Big Investment Burst in Cloud Services]

Jason Lopez: Debo reminds us that AI models don’t just process data, they absorb and internalize patterns from it, which creates new risks that traditional security and governance measures were not designed to handle.

Debo Dutta: Once you train a model with your own data, your data is still properly governed, but if you don’t govern the model itself, it can be used to reverse engineer your data in many cases, which means now you need to extend your corporate governance to models themselves.
We need to ensure that we put the right guardrails on the models for them not to violate societal norms as well as corporate governance. We in enterprise companies need to get ahead of this a little bit and track this space very well.

[Related: Building a Solid Enterprise AI Infrastructure Strategy]

Jason Lopez: The data from the ECI report includes highlights such as, 94 percent of those surveyed said they benefit from cloud native applications. Almost 90 percent said they’ve containerized their applications, and that proportion is expected to grow with the emergence of gen AI.

Lee Caswell: It could be that those containers are running in the public cloud. What we’re expecting, right, is this onslaught, this wave of containers coming on. Our NKP products, of course, are a huge beneficiary of this as we go and offer container management to our customers.

[Related: DataRobot CEO: Bridging the Gap Between AI’s Promise and Fulfillment]

Jason Lopez: Caswell said more organizations are moving toward cloud-native applications for scalability, security, and hybrid cloud management. Gen AI is a major piece of this as companies rapidly adopt it. ECI findings show that only 2 percent of companies surveyed said they have not begun a gen AI strategy, but over 80 percent have one. For some background, we interviewed the leader of cloud native product management for Nutanix, Dan Ciruli, who talked about the foundational principles that made containers essential in the first place. He said cloud-native computing enables companies to quickly make and deploy software.

Dan Ciruli: There are lots of other benefits too. As it turns out, it can be much more scalable. The cloud-native computing really started with companies like Google and Twitter and Airbnb, early web-scale companies who were building software in a new way and realized that running on commodity hardware, and that was very important at the time, running on standard Linux boxes, Google was able to build the most scalable, performant, and reliable piece of software anyone had ever seen. It was before we created this term cloud-native computing, but it was containerizing applications so they can be deployed very reliably, and then using some sort of container orchestrator to put those on compute nodes without the developer ever having to get involved in using automation. Cloud-native computing, fundamentally, is a set of technologies and practices that let you ship software faster.

[Related: Orthogonal Advantages of Cloud Native Technologies]

Jason Lopez: Ciruli said cloud-native computing really started a little more than a decade ago with the project Kubernetes. It has turned out to be the de facto container orchestrator platform.

Dan Ciruli: For the first five years or so of its existence, it was almost a science project, but at some point, a tipping point was reached, and as an industry, people decided this is the new way to write applications, and for the last five years, virtually all new applications are being written to be deployed this way. Well, gen AI, really, AI has been around for quite a while, but generative AI is new. I just said all new applications are written to be deployed in this cloud-native fashion, which means all the gen AI applications, virtually everything that’s being done in this space is happening in Kubernetes. It’s very scalable. It has been happening in the last several years, so kind of by default, all these new applications are written to run in containers

[Related: Enterprise AI Reality Check: Implementing Practical Solutions]

Jason Lopez: Before containerized applications… it was the era of virtual machines. Enterprises used tools in a VM ecosystem to handle things like application development, networking, and security.

Dan Ciruli: Those are the kind of tools that have spent 20 years developing in the VM ecosystem, a vast ecosystem of tools to help people understand the storage, the networking, the security, the health of their VMs, and so now there’s a whole second set of tools that do those same things for these container-based applications, and so I think, again, that 80% of people are saying, hey, help, now we’ve got a much more complicated landscape than we used to, so we need tooling to help with that.

[Related: AI, Cloud Native and Hybrid Cloud Fuse to Run Apps and Data Anywhere]

Jason Lopez: One more highlight from the ECI, 52 percent of organizations said they’re going to have to basically increase training in order to meet the demands of deploying and managing AI.

Lee Caswell: Makes sense, right? You’ve got new AI hardware elements for GPUs, right, or even CPUs. You’ve got new LLMs, and how do you take those new LLMs? Our NAI product, right, terrific for that, so a great opportunity now to take these new trends and translate those into actual prospects and sales.

Jason Lopez: Debo Dutta talked about the skills gap regarding AI and said it depends on a person’s role. For technical professionals like software engineers, it could mean using AI to help with software development, or learn more AI principles and how systems function and integrate into larger architectures.

Debo Dutta: If you’re not a computer scientist, you still need to learn AI to do your current job better, and that involves techniques like prompt engineering. How do you prompt AI to write better emails, to write better summaries of the context that you provide, and so I believe that there’s a whole new set of skills that can be learned by everybody, whether somebody’s technical or just trying to use AI to be more productive.

[Related: Nutanix Builds GenAI App to Empower Sales Team]

Jason Lopez: And as the Chief AI officer, he’s witnessed the learning curve firsthand, with a group of engineers who knew machine learning though not the new gen AI.

Debo Dutta: I built a team from scratch where we trained everybody. We all learned together the basics of gen AI, but we all knew how to build systems and we kind of pushed the envelope on the job. We all picked up gen AI skills very rapidly within the company. Now we are proliferating this skill set across the board. We have now a team building a chat bot for our SREs that was all built, you know, homegrown.

[Related: Enterprise IT Teams Jump Into AIOps]

Jason Lopez: Debo says as Nutanix’s engineers are getting better at AI through in house training, there are excellent resources outside of the company.

Debo Dutta: There is a lot of information and especially courses available for both engineers and non-engineers to really get proficient with AI. There are generic courses, if you Google generative AI for everyone, which I highly recommend on Coursera, that would be a great place to start. Then there are courses specifically for prompt engineering that will allow anybody to just pick up prompt engineering and start using the current AI models and be very productive at their job, whether somebody’s an accountant or an EA or a writer or just a middle school student like my teenager. Everybody can get productive.

[Related: Managing Enterprise AI Sprawl]

Now, when it comes to engineers, there’s a plethora of courses. If you look at any top-tier US university today, their bachelor’s and master’s program, you will see a lot of introductory courses that start from a very simple material in the space of AI and go very advanced very quickly. So the next generation of engineers are being minted as we speak to fill that gap.

Jason Lopez: Debo Dutta is the Chief AI Officer for Nutanix. Dan Ciruli is the head of cloud native product management for Nutanix. Lee Caswell is senior vice president of products and solutions marketing for the company. This is the Tech Barometer podcast. I’m Jason Lopez. Thank you for listening. Joanie Wexler’s article which reports on the Enterprise cloud index, Study Shows Big Uptake of Enterprise AI and Cloud Native Technologies, is at the The Forecast website. You can find it at theforecastbynutanix.com.

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In this Tech Barometer podcast interview with Induprakas Keri, senior vice president and general manager for hybrid multicloud at Nutanix, learn why hybrid cloud IT systems help enterprises evolve their use of AI applications and data.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Induprakas Keri: The only place on the planet where you have enough computing power to create a foundational model is a public cloud.

Jason Lopez: This is the Tech Barometer podcast. I’m Jason Lopez. Induprakas Keri, general manager of hybrid multicloud at Nutanix, says within the AI lifecycle, there’s a minefield for it. AI deployment spans across different infrastructures for each of its three parts, training, augmentation and inferencing. The gist of this story is how these parts challenge the deployment of ai. So starting with part one, the training, this is where the foundational model is created. Keri says, public clouds are the place to do this because of the massive compute power needed, like a lot of GPUs, which you find on AWS Azure or the Google Cloud. In our interview, Keri described the first phase, the training as akin to the gargantuan effort of creating a new language. We move on to the second part of the process where the model undergoes augmentation.

Induprakas Keri: If you train a public foundational model with your proprietary data in the public cloud, you have basically given up ownership and control of the data. It’s now baked into that model and you can’t do much about it. And so what a lot of organizations are going to do is they’re going to take that foundational model, they’re going to augment it or do this thing called retrieval augmented generation and have that happen on-prem so that the data that they control or they own that is really specific to their own success is not in the public cloud. It remains proprietary and it remains under their control, so that creates this new model that, for example, you might have a generic foundational model that does support responses, but you might train it with your own data that makes it much more specific to your business or your sort of products.

[Related: Building a Solid Enterprise AI Infrastructure Strategy]

Jason Lopez: The third part is inferencing. Here, Kere circles back to the language analogy.

Induprakas Keri: Somebody invents the French language, but then the French language is spoken by 50 million people or a hundred million people, and so some group of people spent a lot of effort creating the French language, but it was maybe 20 people, 30 people, 50 people, a hundred people, and they put a lot of effort, but then the usage is in millions day after day after day after day. The law of large numbers I think applies here. Inferencing, which is using the model to make decisions, is something that you do millions of times. If you then save the energy that’s used in the usage and the inference in by let’s say 30%, you then save that energy, not once, but a million times or millions of times.

Jason Lopez: He says most organizations which want to establish AI can’t be choosers, meaning they can’t be rigid about preferring one type of cloud over another.

Induprakas Keri: You can’t say that I’m only going to do things on the public cloud or only on the private cloud because typically most organizations, unless you’re a Fortune 5 company, you’re not going to have the compute resources to go train foundational models and you need to go depend on the public cloud. But by the same token, you probably don’t want to train the foundational model with your specific data in the public cloud, and you certainly want inference close to the edge, which may and may not be in the public cloud. Sometimes public clouds provide inference and endpoints, but most often they’re at the edge where the decision needs to be made because of latency reasons or other reasons.

[Related: Building a GenAI App to Improve Customer Support]

Jason Lopez: Keri emphasizes that open AI’s foundational models while taking the world by storm exposed challenges to it and deploying these models in production. Companies need to make choices to avoid just throwing their data out there. You have to have a plan. In his job managing Nutanix’s hybrid cloud, Keri says there’s a big advantage to having the options hybrid brings like running AI over public cloud or on the company’s own infrastructure. This makes hybrid ideal for AI. A hybrid approach allows businesses to refine models in the public cloud and then move them to a private cloud and maintain control over sensitive data without exposing it unnecessarily.

Induprakas Keri: As users have become more aware of model capability, I think this whole notion of public data versus proprietary data has also started to become more of a consideration. Once they realize that there’s a clear boundary between private data and public data, and once they realize that they need to make sure that any foundational model they need to find a way to take a snapshot of that, create something that’s more in-house, the blast radius of training that with data is limited, and then I might want to go optimize that model so that I can deploy it for inferencing.

[Related: Study Shows Big Uptake of Enterprise AI and Cloud Native Technologies]

Jason Lopez: In talking about AI, Keri boils it down to math and describes it as performing lots of matrix vector multiplications in sequence that gradually transforms raw input data into something meaningful. For an analogy, he draws upon Isaac Asimov’s Foundation trilogy. In the story, there’s a fictional science that blends history, sociology and mathematical statistics to predict the future behavior of large populations of people.

Induprakas Keri: AI seems a little bit like that. I think it’s exceptional for predicting average behavior or generally a particularly prevalent pattern of behavior, but I think that’s also its limitation. The way that these converge, the way that a lot of this linear algebra operations converge is different from how humans reason.

[Related: Bridging the Gap Between AI’s Promise and Fulfillment]

Jason Lopez: He points out. Human reason is based on a kind of self-awareness. Humans have a view of the world, a top down view.

Induprakas Keri: Whereas recent AI, what it’s really done is take data and get structure from that, but without something that provides a top down view of the world. Newton didn’t come up with the laws of gravity by running regressions. It’s not like he took a billion apples and then sort of threw them from a billion trees or watched a billion apples fall did regression and said, oh, wait, there is this thing. He came up with a model and then he ran experiments to validate the model. Generative AI is definitely missing that, and I think unless you have a model of the real world, you’re always done to fall short. I think data only can give you so much insight.

Jason Lopez: Successful AI adoption is not a sure thing, particularly during the experimentation phase, but organizations are under pressure to invest in AI infrastructure quickly to avoid being left behind, even though it’s unclear which use cases will ultimately be successful. Fraud detection and AI copilots are too familiar use cases.

Induprakas Keri: Support is another one. I think if you go talk to a lot of online agents right now, they will interact with you in a very human light way, even though they’re really powered by an AI chat bot, but beyond those three, there’s a lot of fomo right now. I think organizations want to believe that AI is going to transform their business and they don’t want to be settling in that race.

[Related: Future of AI: 9 Predictions for IT Innovation]

Jason Lopez: He says the belief that a substantial investment in AI will pay off doesn’t yet match reality across various use cases, especially in early stages of adoption.

Induprakas Keri: I think what you really want to do in this phase of experimentation is you want to shorten the time to outcomes. You want to make sure that with the least effort possible with the least amount of hardware, infrastructure, investments or training or what have you with the least amount of effort possible, I can gain that insight that I can then scale, and I think that’s what we are trying to do for our customers. You don’t need to spend six months setting up your infrastructure. You don’t need to spend three months setting up your Kubernetes environment. You don’t need to worry about what happens when you take this model that you have trained inside the data center and take it to the edge and all of a sudden find 75 issues because it was a different hardware platform. Every time you interact with chat GPT, for example, you are with your insights. My son was looking for a booth summary the other day. Chad GPD produced something that was very stylish but completely wrong, and then he spent like seven minutes fits in it, and I was telling him, you’re making chat g PT better, but you’re not getting any value from that.

Jason Lopez: It’s a lesson for businesses.

Induprakas Keri: All you have to do is look at how much money OpenAI spends in terms of running its model. I mean, that’s not sustainable if you are even a hundred million dollars business,

Jason Lopez: And he says the massive data sets needed to train AI present this issue. When one person uses data, it doesn’t stop others from using it too.

Induprakas Keri: Data is non-rival risk. It’s an economic term. If I give you a phone for example, I might be able to kill that phone remotely, so that phone all of a sudden becomes useless, but if I give you a piece of data and if you write it down or if you make a copy of it, there is no secret button that I have that allows me to do an auto destruct on the data that you have.

Jason Lopez: Keri is confident that the ability to isolate parts of a model, not meant to be broadly shared, get better.

Induprakas Keri: It’s going to be hard to filter out public data from private data once the model has been produced. Unless we solve that problem, I think enterprises are going to be cautious in terms of how much private data they’re going to expose.

[Related: Role of CIO Expands with Enterprise AI]

Jason Lopez: AI models operate on a stack of hardware and software. The software stack, often open source and containerized runs on a software-defined infrastructure in a hybrid cloud. It brings together things such as storage, compute, power, networking and management tools.

Induprakas Keri: If you have to take that software infrastructure and run it on one platform in the data center and another platform at the edge and a third platform on the public cloud, then it’s your job as an IT organization to make sure that it runs the same way on all those three different infrastructures at the same level of performance and management and usability and all of that.

Jason Lopez: A software stack that works across public clouds, data centers and edge environments makes deployment and management easier. Workloads can be moved between infrastructures with minimal friction,

Induprakas Keri: Even though all the components that you need for running an AI model are containerized. It takes a lot of effort to put all those different Kubernetes components together, and so the pathogens of all those different components, making sure that they work together, making sure that they’re optimized for performance and the underlying hardware infrastructure, we are able to make that drop dead simple, and that’s the value for organizations that are looking to do a lot of experimentation with AI really, really fast.

[Related: Speeding Software Development with AI-Assisted Coding]

Jason Lopez: The interview we did with Keri unfolded along the lines of avoiding large AI investments toward an uncertain goal and toward the advantage of a software-defined infrastructure.

Induprakas Keri: Which means that storage, compute, networking, management, they’re all defined through APIs, they’re all controllable through APIs, they’re all managed through APIs. They’re governed through APIs, and so what that really does is that you can stand up an infrastructure and then you can change the behavior of that really simply by writing code that inverts those APIs. Let’s say that I find out that I need to rethink my cluster because I have too much compute and not enough storage, so then very simply, I add half a dozen storage heavy nodes and I rebalance the storage and compute needs of my cluster to be able to run this model really well, and I can do all of that through software.

Jason Lopez: Induprakas Keri leads the Nutanix Hybrid Cloud team. This is the Tech Barometer podcast. I’m Jason Lopez. We’ve got more tech stories covering enterprise computing and IT technology at the forecast by nutanix.com.

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In a video interview with The Forecast, DataRobot CEO Debanjan Saha discusses the need for finding value and building confidence to achieve enterprise AI success.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (edited for readability):

Debanjan Seha: I was in a Morgan Stanley investment conference in San Francisco, and of course every investor quizzed me about ChatGPT and GeneAI. On the way back, the Uber driver quizzed me for half an hour about ChatGPT. And I realized that AI has arrived. But there are limitations to what some of these models can do. So what are those models doing? I mean, we have trained them on a really, really vast amount of publicly available data and sometimes on proprietary data. And once you run out of those data sources, there is a limit to how those models are going to get better and better over time. Of course, there will probably be better and better models, but I don’t think it can get incrementally better for an infinite amount of time. That’s not going to happen. Now, we are already pretty much fed all of these models with all the written text data that humankind has produced over the last hundreds of years.

So there is not too much text data left that you can feed these models to. What is left, by the way, is a lot of audio and video data, and that data is much easier to collect. And there are a lot of things which you don’t write down. For example, when you lift a cup from a table, nobody writes it down, but there are a lot of videos which show how we go and grab the cup and pick it up from the table. So those are the kind of data sources which are now going to make these models more and more intelligent. But I do think there is a limit to which you can feed them information and make them better.

[Related: Building a GenAI App to Improve Customer Support]

I see two gaps that we are really focused on. One I call the value gap. People are spending a lot of money training models, but ultimately those models have to solve some business problems. They have to make things more productive or predict things more accurately or whatever it is. Business value has to justify the investment that’s going in there. And it’s not easy with AI to create that business value. And one of the things DataRobot does very well is to connect business problems with AI to solve them. So that is the first problem that we are focused on. The second thing, which I also see with any new technology, is you need to build trust. AI is kind of in that phase and there is a confidence gap. There are a lot of people who are creating, especially with GenAI, a lot of prototypes.

[Related: Enterprise IT Teams Jump Into AIOps]

It’s not difficult to put together a chatbot which answers some questions. It’s hard to guarantee that they’re going to answer the question correctly, that it’s not going to create any misleading answers or create harm in more serious cases. And there are a whole lot of things we need to do in terms of managing the risk and creating a framework where you can define and associate risk with various different types of use cases and handle them with the right level of mitigation, compliance, testing and validation, et cetera. So those are the two things which are very, very important for wide scale adoption of AI.

[Related: Role of CIO Expands with Enterprise AI]

For example, the value gap. So the most interesting and most challenging thing about the value gap is that you need people who understand the business and the business challenges that they’re trying to solve. And you need people who understand AI and how those two things can be put together in order to solve a business problem. So we do ideation workshops with our customers. We sit down with customers and their people who understand the business and work with them to figure out their business challenges and what use cases they have. And then our team works with them to figure out how to address those use cases. Do they have all the ingredients…the right datasets? Do we know what business processes need to be updated if we insert AI into that? Those are the things we do first. Then people can use our platform either themselves or we can help them as co-pilots. And we sometimes have our other partners, the service provider partners, to work with them to solve those business problems and actually build those models, build those applications, and then put them in production.

Pretty much every industry you go to, there are enough problems that AI can help solve. And pretty much every business line you go to, there are enough problems that AI can help solve. But bringing AI to business, that’s still not easy for everybody because you need to speak both AI and the business language. And it’s very difficult to find people who speak both. So you have to have two in a box and solve those problems.

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In this Tech Barometer podcast segment, technology analysts Simon Robinson, Natalya Yezhkova and Steve McDowell discuss the Broadcom acquisition of VMware and how it’s impacting CIOs and IT decision makers.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (edited for readability):

Simon Robinson: Clearly the market is in flux. It has been in a transitional phase for the past several years.

Natalya Yezhkova: Broadcom is a mature company. It has a certain way of doing business, so VMware culturally business-wise was very different from Broadcom.

Steve McDowell: They’re laser focused on high revenue, high margin business, and every move that we’ve seen them make plays directly toward that.

Jason Lopez: Three technology analysts walked into a room and talked candidly about the Broadcom acquisition of VMware. This is the Tech Barometer podcast. I’m Jason Lopez. The analysts are Simon Robinson of Enterprise Strategy Group who covers infrastructure. Natalya Yezhkova, a research vice president with IDC who writes about enterprise workloads and emerging workloads infrastructure, and Steve McDowell, chief analyst with NAND Research. This was a press conference at .NEXT in Barcelona, Spain in May of 2024. One of the initial questions in the press conference: Why did Broadcom make this move? Natalya Yezhkova began by explaining why companies make acquisitions in the first place. One reason is to grow the new acquisition as a business unit,

Natalya Yezhkova: Or they go the Broadcom way, which I think happens rarely with companies as large as VMware small organizations. When they are acquired, they usually absorb very quickly. Broadcom wants to continue to do business the way Broadcom always did it. They wanted to go into the software business, and that’s what the acquisition of VMware was for them. But they didn’t see a need to adjust to the VMware culture or keep VMware as an independent company or more independent business units. So they want to absorb it quickly into the Broadcom way of doing business.

[Related: Nutanix CEO Paves Way for Those Leaving Broadcom VMware]

Jason Lopez: Simon Robinson said he’s heard no indication Broadcom would integrate or encourage collaboration between VMware and other business units.

Simon Robinson: The focus here is really the opportunity as Broadcom MO (modus operani) was to take a business that has established itself at the core of most data centers globally and enabled quite an incredible change in how data centers are architected over the last 15, 20 years. But I think Broadcom saw the way VMware did that was not efficient. So the capabilities were there, but the efficiency wasn’t, in their view. So that is the core focus. Well, one of the focus areas is to drive more efficiency into how they are structured, how they go to market.

Jason Lopez: Natalya Yezhkova said in terms of hyperconverged infrastructure (HCI), the overall market declined in 2023. Of those that grew, only a few vendors were not associated with vSAN.

Natalya Yezhkova: Customers now approach acquisition of HCI systems because of this uncertainty between Broadcom and VMware. There will be some additional impact on the HCI market. Overall we expect it’ll continue growing, but in the short term, there is some shrink attributed to customers being cautious with purchases of vSAN.

[Related: Wondering What Broadcom Will Do Switches to Mitigating VMware Risk]

Jason Lopez: The centerpiece of the press conference wasn’t necessarily the Q and A with journalists, but these exchanges between Yeshkova, Robinson, and McDowell, starting with the topic of the future lifecycle for VMware products.

Simon Robinson: The market is in flux, and that’s been driven by multiple things. Primarily, I would say from on-premises IT environments to cloud-based IT environments as the major force that we’ve seen over the past several years, and that’s had its own disruptions and impacts on the market. You’ve had the transition caused by the emergence of a whole new class of workloads and applications and the emergence of the technical architectural underpinnings of those things, predominantly driven by the emergence of containers and orchestration all around that. So you kind of got those two major shifts already in flight, and that was causing organizations to make some really big strategic decisions around workload, location, and optimization and building their strategies around that.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Natalya Yezhkova: And also to add to what Simon has said, we’ll see the impact across multiple years. So it’s not something that we will see in one quarter and it’s gone. There are customers who up for renewal with VMware. Now there are customers who up for renewal in three years from now, and they still have the old terms of licensing. For them, it’s like a delayed impact and they have time to absorb. But those customers who are currently in the position where they need to make a quick decision whether they want to continue with VMware by Broadcom and adjust to new pricing, definitely in a tougher situation than those who need to make this decision in three years.

Steve McDowell: When you look at VMware technology, what’s interesting about that –– but it’s not true of pretty much anything else except Oracle. Equate VMware and Oracle from this perspective –– it’s that they’re extremely sticky and it’s expensive as hell to change out. And the accountants of Broadcom [did] a really nice job of calculating what that switching cost is. The IT guys, they got to worry about this AI thing. They don’t have time to re-architect how I’m doing my workloads. So they’re pricing it just under the pain threshold for those customers they care about, and they never have to do another piece of innovative engineering ever because these legacy applications, it’s going to be like Oracle. It’s an annuity for VMware forever, mid-range of the market. Man, that’s in turn. I don’t think we know where they’re going to land. We’ve seen some movement back and forth, but those guys, I’d be scared if I were a small company relying on VMware. VMware is not going anywhere.

[Related: Enterprise IT Teams Jump Into AIOps]

Natalya Yezhkova: So I agree that VMware is not going anywhere. We had a range of technologies which went away, but they [are] still here. So mainframe tape… it has been said [often that the] mainframe was dead [and] tape is dead. They’re not. The market shrank significantly, but both still exist. I’m not saying that VMware is going this way, but it’s transforming as a business with this change. But I would agree that it wouldn’t go away. It would look very different like five years from now.

Steve McDowell: That’s a good analogy. If you look at IBM with mainframe, you look at IBM with Power [System Servers]. These are technologies that have not evolved in 30 years. They’re milking a captive user base, and that’s what Broadcom likes to do across almost all of their products.

Jason Lopez: So as one journalist asked, who could take advantage of that position.

Natalya Yezhkova: I can say that from the HCI business perspective, pretty much everyone [can take advantage of this situation]. So with all the uncertainty which exists, I think even companies who worked with vSAN, they start looking for alternative solutions for the HCI offerings. So I think it creates great opportunities for infrastructure vendors. What companies are looking for now [is] integration of resources between dedicated environments and public cloud. They look at security and resiliency. Security and resiliency [are] always on top of the list. They look at modernization of the storage and data management strategies. VMware was in this business as well of providing the tools for managing both dedicated and public cloud resources for investing in security. So they were also in this business, but there are companies outside of VMware who do just what companies are looking for. This whole situation with Broadcom made companies start looking for what else they consider. It’s a great opportunity for vendors to take advantage

Simon Robinson: Just to build on that, on the previous question, again, the dominant motion of the past 10 years has been from on-prem to cloud. In fact, one of the things that VMware talks about is about the risks to going all in on a public cloud. What generally we don’t have in the industry today is portability of workloads, data applications between clouds. That’s still really difficult,

Natalya Yezhkova: But customers want it.

Simon Robinson: But I think, again, this whole trend is going to reinforce [caution for] going all in on anything. [That] has its risks.

Jason Lopez: Steve McDowell followed up by reiterating. He doesn’t think this is fundamentally a technology question.

Steve McDowell: They all smell blood in the water. There’s money to be had, and it’s not a license [but] a switching cost. It’s not going to be a rush to the door because apart from a handful of small businesses, everybody’s on three and five year cycles. There was a rush to renew those licenses ahead of the Broadcom acquisition. So it’s going to unfold quarter over quarter for the next four years.

[Related: Focus Shifts to Migration in Wake of Broadcom’s VMware Acquisition]

Simon Robinson: Well, there’s also the ways in which they’re tied in, right through custom scripting and all of that, depending on the breadth of the adoption on that side. Technology vendors are nothing if not pragmatic. If they see that their customers are asking for something different, then they’re going to respond.

Jason Lopez: Simon Robinson writes about infrastructure for Enterprise Strategy group. Steve McDowell is Chief Analyst at NAND Research. Natalya Yezhkova is Research Vice President with IDC, covering Enterprise and Emerging Workloads Infrastructure. This is the Tech Barometer podcast. I’m Jason Lopez. Thank you for listening. Tech Barometer is produced by The Forecast where you can find more stories on technology and the people in tech. Just go to theforecastbynutanix.com.

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In this video interview, Taylor Linton of Hugging Face explains the thought process many IT decision makers go through as they select open source and proprietary AI software to run their businesses.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Taylor Linton: With open source. There’s really two categories of benefits with using open source models, and it’s really broken down into strategic and tactical reasons. To start off on the strategic side, it really comes down to ownership of an ip. Meta has done a great job commoditizing the models and other open source model trainers. So the fact that these companies can take their proprietary data and customize these models, that’s a great opportunity to be really a competitive differentiator from their competition, having their own models that they own that are trained on their proprietary data. Another big reason on the strategic side, it really comes down to cost. And what I mean by that is sometimes your incentives aren’t aligned when you’re using a proprietary model because they charge based off of input data and output data. So the more value you get, the more charged. And so the fact that you can build an open source model and you’re only paying for the commodity hardware, your goal is to get as much usage as possible, whether it’s getting it in front of more customers or getting more AI assistance used from your employees, not only are you able to train your own model and keep it in your environment, but there’s a lot of security benefits.

[Related: Role of Open Source in AI]

Customer trust is huge. Responsible AI is huge, and the fact that you can feel confident that you are sending your data to a model in your environment, it’s a great opportunity to really de-risk it. Another part on the tactical side comes down to latency. If you don’t own the model and you can’t control the infrastructure that it’s running on, there’s many times when requests might take four or five seconds to get a response. So when you have ownership of the open source model, it allows you to make sure that you get that ultra low latency to support your requirements.

So that’s one of the things that people really appreciate with open source models because you can download the model and run it in any environment you want to. So it could be their preferred cloud provider, it could be on-prem, it could be in their Nutanix environment too. But really it comes down to where do they want to run it? Where’s their preferred environment? We often suggest our customers to go grab their own proprietary model, test out the use case, see how it performs. It offers a good benchmark to know roughly where AI is to be able to support the project from an accuracy standpoint. Now, once that POC has been built, that’s where we work with them to say, okay, how should we build this with an open source approach? Now, open source AI is not a silver bullet. There’s really trade-offs too. So when you start looking at cost, these models, they scale very well from a cost standpoint, open source models, and the reasoning is because you’re paying for the commodity hardware that it’s running on.

[Related: Enterprise IT Teams Jump Into AIOps]

The downside to that is if you don’t have a large volume use case, it could require some expensive compute to host that model, and it might not make financial sense to go deploy a model for low usage. In those instances, it totally makes sense to be able to use a closed source proprietary model because you’re only charged for input data and output data. So it can be really affordable in lower volume projects. But of course, as you do scale it out, that’s where it gets very expensive and the scale tips pretty quickly. It’s where it does make sense to deploy an open source model on your own hardware.

Related: IT Team Modifies Open Source NetBox to Help Manage Hybrid Multicloud]

So it’s nearly impossible to try to automate the process of knowing which model to use and the best way to train or adjust the model for a use case. And so our engineers sit down with customers and look at what exactly are we trying to build, what type of data are we going to be sending to this model? What constraints are there, whether it’s cost or latency. And so we sit down with them to understand what is the best open source model for this particular project. And then from there we can sit down and figure out how should we customize this to improve performance, whether it’s speed, latency, or accuracy. These are all trade-offs that really you kind of have to pick one or the other. So we do our best to help them make the best decisions there.

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In this video interview, Hugging Face’s Taylor Linton explains how enterprises can use existing open source software instead of building AI applications from scratch.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Taylor Linton: Predating me joining Hugging Face. We started in 2016 to be a chatbot company. That’s where the fun hugging face name and emoji comes from, because it’s supposed to be a chatbot that’s your friend. So we wanted a friendly brand associated with it. But since I joined Hugging Face in 2021, we started out with 15,000 pre-trained models on the hub. And it was so interesting talking to customers then because it was, Hey, there’s too many models out there, I don’t even know which one to start with. And just seeing that grow from 15,000 to 650,000 in a few years, it really shows how much ai, especially open source has grown.

[Related: Enterprise IT Teams Jump Into AIOps]

It gets even more challenging. So since yesterday when you and I both looked at the models, there’s already 2000 more models. It’s crazy. And the tough part is you can’t really automate these decisions on which model to use. It really is context dependent. You need to know what your cost requirements are, what type of input data that you’re going to be sending to the model, because models are trained on different types of data and you need to try to back into the model that was exposed to your type of domain. During the training. It comes down to latency, the particular task. So these are all things that our engineers can sit down with customers and look at what are we trying to build here? What type of constraints are we working with? And we really walk through them, walk through with them the different trade-offs that you make when you’re picking a different model.

[Related: Role of CIO Expands with Enterprise AI]

A lot of CEOs weren’t even aware of what AI was a couple years ago, and then chat GPT came out and CEO’s kids were doing their homework with it and all of a sudden they started hearing about it. So there’s been a lot of interest in companies to try to take advantage of this technology. When folks talk to hugging Face, it’s because we’re the entire open source ecosystem around ai. And so when they want to explore and take advantage of these open source models, they might go to our hub to go grab one of the 650,000 open source models. But also, once they do grab that model and bring it into their environment, we have quite a bit of maybe 20 different libraries or tools that they use to actually go all the way from building to deploying these models. So that’s really when we get involved with folks is to help them take advantage of open source AI.

[Related: Building a GenAI App to Improve Customer Support]

The community around hugging face and just open source AI is incredible. So the fact that someone might release a paper that introduces a new optimization technique, the fact that the entire community can benefit from that and be able to use it on their models, it’s really exciting and it gets folks pretty motivated to be able to introduce a new novel technique and open source it to benefit a larger group of people. I think that open source AI is critical to be able to keep responsible AI across the world. The biggest reason is because the last thing that people want is to only have a few companies that have access to this technology. The fact that people can build in public and you can know certain data sets that were used to train the models. It’s a great opportunity for companies to make sure they are using models that were trained on permissive data. And bias is also a really big area that folks are looking into because of course, human bias exists in these training sets, and that ends up transferring to the model’s bias. So the fact that people can work together and in a community to really look into these data sets and do their best to try to identify and mitigate bias in these models, that’s very important.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

We partner with quite a few companies, but Nutanix has really stood out to me. It really seems progressive to make sure to offer the best tools and technologies are made available to their customers. So what I’ve learned with GPT in a Box, the fact that they’re integrating it with all of hugging faces, open source libraries, they’re making it frictionless for customers to be able to deploy open source models in their environment. You don’t see that often from, and they’re really going a long way to make sure that the best tools are available for their customers.

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In this Tech Barometer podcast segment, Justin Mason, associate director of vendor and operations at the University of Canberra, explains how moving to Nutanix software enabled their IT operations to run demanding AI and machine learning courses and keep students engaged in their online studies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Justin Mason: We are all AHV now. We don’t have any VMware. We’ve been in that place now for a number of years, so we’ve been fortunate enough to miss this whole debacle around VMware and Broadcom at the moment. I know a lot of other organizations are kind of feeling that pressure now, so that’s one less thing we have to worry about.

[Related: Birth of a Hypervisor That Unleashed the Hybrid Multicloud Era]

Jason Lopez: Justin Mason is the associate director of vendor and operations at the University of Canberra in Australia’s Capital City. This is the Tech Barometer podcast. I’m Jason Lopez. What we have here is a press conference from 2024, next held in Barcelona, Spain, as in our previous reporting. These press conferences are a bit different, favoring an informal face-to-face with tech journalists. So Mason started off with some background on how the University of Canberra’s IT organization adopted Nutanix. He oversees the IT strategy for the school, which numbers about 15,000 students and staff. About 10 years ago, they decided to move away from three tier architecture.

Justin Mason: We were early adopters in the sense of when we jumped on the Nutanix platform at the time, we started to look at, well, how can we do this better? How can we do things more cost effective? And that’s when we looked at Nutanix and hyperconverged. Back then, we were primarily a Hyper-V shop, so we started to run Hyper-V on Nutanix. That wasn’t as reliable as we’d hoped, but that was more on the Microsoft V clustering side of things. We then moved over to running VMware on Nutanix as our second POC or proof of concept. That went extremely well.

[Related: Wondering What Broadcom Will Do Switches to Mitigating VMware Risk]

Jason Lopez: Mason and his team decided to fully transition to hyperconverged in 2014,

Justin Mason: And that’s when we decided, okay, we want to go all in on this platform. It was doing what we needed to do as extremely reliable, and that’s when we started to look at it, move over all of our legacy architecture to Nutanix hyperconverged platform. A couple of years later, Nutanix came out with AHV because we were the kind of technology leads at the university. Were always encouraged to try new things, and at the time we were already paying additional licenses and VMware and could save some money in there. That area. It made sense. And fast forward today with Broadcom buying out VMware,

Jason Lopez: Mason explained how that early decision to move to Nutanix allowed the university’s IT efforts to be free of lock-in to a VMware license. Today, the university’s infrastructure hosts hundreds of VMs running on Nutanix clusters.

Justin Mason: We’ve probably about seven different clusters on Nutanix, 50 odd nodes, about 500 workloads, 500 odd VMs spread between the different cluster sets and some of our major corporate systems are running. So the way that things are panned out for the university, we have a cloud adoption framework that’s out of date.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Jason Lopez: But Mason says, as their workloads run either on a Nutanix private cloud or in SaaS services or even run a few things on public cloud, they’re revamping their cloud strategy.

Justin Mason: Getting onto public cloud is pretty easy. What we were finding is having skills to cover all the public cloud options. Like having an Azure expert, an AWS expert, a GCP expert wasn’t really financially viable to have that many different skilled resources across all those technology sets. But whereas Nutanix is kind of a single platform that we’ve had for a long time now. So our skill sets are pretty well developed across it. And obviously with a SaaS platform being that it’s a bit of a black box managed by other providers, that works well for us. So Nutanix is definitely going to play a large part in that cloud strategy moving forward.

Jason Lopez: The University of Canberra is a prime example of how antiquated the idea is that it runs anonymously in the background. Mason said the student experience plays a critical role in a university’s reputation, and that experience is significantly affected by the digital platform students use to sign up for classes, submit homework, send and receive messages with teachers and administrators, and to just stay connected to what’s going on on campus,

Justin Mason: Putting in systems now that can track the student experience and kind of flag when students might need additional help. They haven’t been to class in a long time. We have some systems around that like, well, hey, it looks like you haven’t put in your last assignment or something like that. Do you need additional help? So there’s definitely a lot of, and even more of it coming out the digital strategy and putting more importance on that to make sure that the students are at the front of that experience factor.

Jason Lopez: The university’s digital strategy launched earlier this year. It includes 30 initiatives aiming to enhance the student experience while ensuring staff and students develop the skills to use new digital tools effectively.

Justin Mason: It’s kind of one of those things where it’s a 10 year roadmap, years one to three is pretty much well bedded down and spelt out, but those other phases, while there’s a high overview of what they want to achieve, they’re really going to rely on what the outcomes of phase one is that’s going to feed into it. A lot of it is around the student experience, the student journey, and what we’re finding that comes into your question about the digital skills and training for users. I think that’s a really important aspect as well. Obviously, there’s a cloud strategy that’s coming out of the digital transformation. There’s a big piece around integration, especially universities, probably similar for other organizations, so many separate systems and getting them to talk to each other reliably.

[Related: Machine Teaching: How Schools Are Using AI in Education]

Jason Lopez: Then there’s AI. Mason said there are a lot of things coming at it at breakneck speed, or as he stated in the press conference, it’s when everyone’s running around with their hair on fire.

Justin Mason: But from a teaching point of view, it’s not new. We’ve had subjects in artificial intelligent and deep learning and machine learning for quite a number of years, and some of those subjects have been complimented by students using virtual desktops via Nutanix, powered by GPUs to give them the additional resources they need to do those subjects. So not exactly new in the academic world. And by leveraging Nutanix, that’s allowed the students to access to those resources from anywhere at any time. They don’t need to come into a specialized lab running big hulking machines. They can do it from a web browser on their normal machine, whether they’re on campus or at home.

Jason Lopez: He wrapped up with this comment. Organizations considering a shift from VMware to a HV often worry about reliability and skill requirements, but those experienced with VMware find a HV easy to manage due to Nutanix’s user-friendly design.

[Related: Nutanix CEO Paves Way for Those Leaving Broadcom VMware]

Justin Mason: What we found is if you’re good at VMware, then you’ll do AHV in your sleep, and that’s Nutanix credit for making it so easy to manage. So when we move from Hyper-V to a HV, that was a bit of a learning curve for us. But then once you learn it, you kind of learn it. We found them when we moved to a HV when we were doing the migration, and part of that migration was outsourced to Wipro. There’s not much pushback here, and they’re doing it really quick, and it was kind of like, well, I think they were seeing the benefits of moving to it. What about getting external resources in that you might need to help with Nutanix? We’ve never had to do that. We’ve never had to go looking. There’ll be times in the university where we’re doing certain projects or certain initiatives. We need to bring in a resource to do something specifically like a T Net expert or a Microsoft Dynamics expert. But we’ve never found ourself having to bring in a Nutanix expert, so to speak, because I guess that speaks for itself. It’s pretty easy to manage.

Jason Lopez: Justin Mason is the associate director of vendor and operations at the University of Canberra, located in Canberra, Australia. His press conference was recorded at 2024 dot-NEXT, which was held in Barcelona, Spain. This is the Tech Barometer podcast. I’m Jason Lopez. Tech Barometer is a production of The Forecast. It’s at the forecast by nutanix.com where we’ve got technology articles, podcasts, and video for you to enjoy.

That’s theforecastbynutanix.com. Thanks for listening.

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In this Tech Barometer podcast, Northeastern University professor Dr. Norman Jacknis explains why IT leaders must concentrate on delivering business value while understanding essential and emerging innovations such as artificial intelligence and machine learning.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Dr. Norman Jacknis: Part of the reason why you need to get started is you are learning how to do this stuff well. So you have to get started. And if you don’t get started, you’re going to fall behind. Right now, one of the reasons why companies that are using this are ahead of their competitors is they started a mile back. They’ve made the mistakes. They’ve figured out what works and what doesn’t work. And they’re running. And you’re in a race. If unfortunately your competitors who are ahead of you keep on running as fast as they are and you can’t run faster, you’re never going to catch up to them. I think also it’s important just for your own jobs because in most companies, it’s at least crossed the mind of the CIO to ask the CIO about how to use AI.

[Related: The CIO Mindset for Embracing AI for Enterprises]

Jason Lopez: Norm Jacknis teaches mid-career technologists how to be CIOs. He’s Professor of Practice, Innovation and Entrepreneurship at Northeastern University in Boston, Massachusetts. In his career, he was an executive at Cisco and the CIO of Westchester County in New York. He was also chairman of a chapter of the Society for Information Management, a prominent organization for IT leaders and CIOs. The role of the CIO has changed dramatically. He says, it used to be the CIO just made sure all the technical parts worked. Computers stayed connected. Email wasn’t lost. Software was correctly installed. As he refers to it, making the trains run on time. But then came the cloud. Here’s an insight he gives his students.

Dr. Norman Jacknis: I said, you need to ask yourself, what am I doing all day? Because the stuff I used to worry about, like the data center, big data center being up all the time, I’ve outsourced a lot of that stuff to the cloud provider. So they’re worrying about it, not me. A lot of software I’m getting is sort of off the shelf and probably most of it is in the cloud as well. And again, somebody else is worrying about it. So what do I do? Sit here all day and twiddle my thumbs? Well, everybody finds ways of filling the day. But I said, why don’t you think about what you can really do in your organization? Think strategically. So now your role is not necessarily just to have the trains run on time. You still have to worry about that. But it’s more, how can I, as a technology leader, make this stuff strategically useful for my company?

[Related: How Nutanix Built a GenAI App to Improve Customer Service]

Jason Lopez: Today, a CIO has to think not just about moving data around, but what that data means. And with the rise of machine learning and AI, it’s become critical that CIOs take this on.

Dr. Norman Jacknis: The leader of this really should be the CIO. Unfortunately, in a lot of organizations, that’s not the case. Sometimes it’s been led by chief marketing officers and in other cases by a chief innovation officer. But when that happens, that’s a poor reflection on the CIO. The CIO has not done his or her job properly, if that’s the case.

[Related: Seeing AI’s Impact on Enterprises]

Jason Lopez: But here’s the kicker in what Jackness is saying. A CIO who’s the point person in an organization’s development of AI isn’t just doing a software migration in the background, but is actively part of the business strategy.

Dr. Norman Jacknis: I always used to joke that the IT staff always used to go around telling everybody they had to change, they had to adopt this new system, this new technology. And if you looked at their own behavior, they were always the last people to adopt change themselves. But yeah, that’s part of your job. You are a change agent. You’re a change leader. Particularly when you’re talking about something like artificial intelligence, which is threatening to some people. Think about it. If you’ve got some use of artificial intelligence that identifies where there might be some new potential customers, and you also even have a way of phrasing the marketing material that would go out, and you present this to the chief marketing officer, sometimes you’re going to get a reaction that’s, I’ve been in marketing for 40 years and you think you know what? And so there’s a significant change. This is worse than bringing in, when you brought in, for example, an SAP system, it was aggravating to people because they had to change procedures, but you weren’t threatening their professional identity. You can do that with artificial intelligence. Now you have a really strategic technology. You have the opportunity to think about how it can be strategic for your organization. That’s going to get you the invitation to sit down with a CEO and other C-suite colleagues. We go through the major algorithms, but the focus is on how you will use it.

[Related: IT Leaders Get AI Ready and Go!]

Jason Lopez: Jackness says the course materials reflect the real world. There is work focused on AI, data centers, and cybersecurity, but also that real world component. Again, students are in their mid-careers. So each semester they have to defend their ideas before a panel of industry experts, as if they were going before a capital investment committee or VCs. The goal is to empower them to be able to implement things and put together teams.

Dr. Norman Jacknis: How can I get this off the ground without asking the CEO for $15 million? And for that matter, we even go over the issues of change management. Okay, so you’ve done this thing. How do you figure out which groups you start with in your company? How do you get it deployed? So we’re really helping them very much to sort of think about this as a leader, in addition to what the technical issues are. The later semester as well, we deal with operational issues. Okay, you’ve got this great idea of yours and your thesis approved. What are the day-to-day issues that you have to deal with, including, for example, ethical issues, as well as operational issues, dealing with staff, stuff like that. Think about how a CIO spends his or her day. Over the course of a year, we want to make sure we touch on all those various questions that cross that person’s mind and bring the best knowledge we have, the most recent research about what works and doesn’t work, and we want to bring that to bear and actually have them apply it.

[Related: AI in the C-Suite: Using Artificial Intelligence to Shape Business Strategy]

Jason Lopez: Aside from the basic tech knowledge, which is baked into the curriculum, he brings his experience into play by emphasizing what’s beyond the classroom.

Dr. Norman Jacknis: I think they need to keep up with the world, you know, so they should spend at least some of their time just keeping abreast of developments in artificial intelligence, because their colleagues are looking to them as the technology leader, right? You’re the CIO. You’re supposed to know more about technology, including artificial intelligence, than the rest of us do, right?

[Related: Enterprise IT Teams Jump Into AIOps]

Jason Lopez: And one of the advantages of today’s computer science, you don’t have to spend a lot of money to get experience, just a laptop and a connection.

Dr. Norman Jacknis: I mean, there’s even software now that will provide a lot of the algorithms for free, open source software. There’s a lot of open data. The efficiency of the algorithms has really improved. So it’s not going to use up as much of the cloud resources as you might have in the past. Some of the stuff you can even start out doing on your own laptop. And then when you go to production, you can move it to the bigger data centers in the cloud. But at least you can get a start. You can be able to show somebody, hey, I can do a good job of identifying what customers might be highly probable of churning. So we can focus in on maybe doing something extra with them.

[Related: The Amalgamation of AI and Hybrid Cloud]

Jason Lopez: The program at Northeastern awards a Master’s of Science, but Jackness points out it’s learning by doing. It emphasizes leadership skills in a technology environment. Perhaps think of it this way. What can you learn to bring to a company to help it succeed?

Dr. Norman Jacknis: Well, I’m not very academic, I suppose. So it’s been easy for us because basically what I do is teach some of the theoretical stuff and then also have them apply it to their jobs. I teach a course on artificial intelligence and machine learning for technology leaders. I go through the major algorithms so they understand what they’re supposed to do. But their assignments are not to do heavy duty data science work, but instead to figure out, OK, strategically, how is this going to apply to my company? And at the end of the semester, they’re actually writing a memo to their CEO on what the AI machine learning initiative ought to be for their company. So it’s very much a marriage of these two sides. And actually, it’s one of the reasons why I moved to Northeastern, because Northeastern is known for what’s called experiential learning, which is basically a philosophy of education that you will learn best when the concepts that you learn are actually applied to your life, to your work.

Jason Lopez: Norm Jacknis is Professor of Practice, Innovation and Entrepreneurship at Northeastern University in Boston, Massachusetts. This is the Tech Barometer Podcast. I’m Jason Lopez. Thank you for listening. We also have another podcast with Professor Jacknis, and that focused on the role of CIOs implementing AI in their organizations. The Tech Barometer Podcast is produced by The Forecast. You can find more stories on technology and people in tech at theforecastbynutanix.com. That’s theforecastbynutanix.com.

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In this Tech Barometer podcast, Dr. Norman Jacknis explains how CIOs can adapt to rapid change driven by AI and the value it can bring to organizations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Dr. Norman Jacknis: If I ask you to travel from New York to Boston, you pretty much know what your options are. You can fly, you can take a train, you can drive, you can get a map from Google, whatever. There’s a whole bunch of things. It’s pretty straightforward, and you might run into a traffic jam or something like that. It’ll sell you down, but basically you know where you’re going and what it’s going to be like when you get there. With ai, particularly generative AI these days, it’s more like the Louisiana purchase in the Lewis and Clark Expedition. You’ve got this whole big territory that you have to explore and you’re not going to know what its value is. I mean, ultimately in the long run, it’s the best $3 million investment the United States government ever made, but you don’t know when it’s going to start delivering. You don’t know exactly why it might deliver. The whole nature of artificial intelligence is it’s an iteration, it’s an exploration, and so that’s one of the reasons why the people who expect to see a return in the next quarter or the next two quarters are really missing. The point about what this is about.

[Related: AI and Cloud Native Innovation Spark Explosion of Business Apps]

Jason Lopez: Norm Jacknis is Professor of Practice Innovation and Entrepreneurship at Northeastern University, which is located in Boston, Massachusetts. Before coming to Northeastern to teach technology leadership, he was an executive at Cisco and the CIO of Westchester County in New York. Those are just a couple of his consequential stops throughout his career. And in this podcast, you’re going to hear about the role of the CIO in establishing AI and organizations. What makes this compelling is that as a professor, he’s had to gather together his deep experience in technology and organize it to teach future CIOs, and we gleaned some of his best insights. This is the Tech Barometer podcast. I’m Jason Lopez, so let’s pick up from where we started this podcast with his comment about understanding what AI in the enterprise is.

[Related: IT Leaders Get AI Ready and Go!]

Dr. Norman Jacknis: There was some hype clearly, but I think there’s been pushback, particularly from CEOs, they see the value in this stuff these days. What those people are talking about is generative ai, the more traditional machine learning for prediction, for improving interactions with customers so forth. That’s already a given. You can see companies that have been using that and have been making money from that, but even on the generative AI stuff, I think they hyped it up too much, and I think outsiders who haven’t done this successfully don’t understand the nature of working with ai. It’s different from a traditional software project.

Jason Lopez: Jacknis says the hype around generative AI has put so much attention on language processing. Perhaps it’s easy to forget about machine learning, something he reminds students who are mid-career executives.

Dr. Norman Jacknis: There’s still a lot of low hanging fruit, if you will, more traditional machine learning. That stuff can be used to help identify potential customers, help identify problems ahead of time that you can fix all that sort of stuff. So that kind of thing is still very useful and can be done, and the generative AI stuff properly used also can do some things to make it easier for your users and customers to have access to the computing resources they need instead of having to be subject to the constrictions. The restrictions that have come up about over decades of computer science history

[Related: How Nutanix Built a GenAI App to Improve Customer Service]

Jason Lopez: Those decades ago before it was AI or generative AI or machine learning, it was called computer automation where for example, you took what was on paper and put it on a screen.

Dr. Norman Jacknis: Yeah, no, I suppose it was an improvement. I’m not sure how much it was annoying, and of course, they’d ask the same questions over and over again the way you did on paper forms. Now with generative ai, one of its advantages is it can be much more conversational and it can be smarter about what to ask. So if I put down than I’m mail, it’s not going to ask me how many months pregnant I am and things like that. It’s a lot smarter and it makes that whole human computer interface a lot easier.

Jason Lopez: He talks about AI as a strategic tool. There are ways to deploy it that aren’t so great and ways that are successful.

Dr. Norman Jacknis: One of the things about the successful uses of AI is that they help break down the silos in companies because you have to, the folks who are expert on the algorithms don’t know the business. For this to work, they need to speak to the business, and the nice thing about it is AI is sexy enough. Everybody’s quite willing to work together on this. So I think that’s actually kind of helpful. So there’s a strategic value in this as well.

[Related: AI in the C-Suite: Using Artificial Intelligence to Shape Business Strategy]

Jason Lopez: Jacknis gave us an insight in what he teaches students in his technology leadership courses, how a business can get started in ai.

Dr. Norman Jacknis: Don’t go out and hire a whole bunch of data scientists and programmers to write your own AI cloud providers, as you know, offer most of the various options for AI tools. There also is open source stuff. One of the things I use with some of my students is a product called nine K-N-I-M-E, which is from Europe, open source and free. Got a lot of these algorithms there. But start off with that. So the focus is on how do I want to get the data and present it to the computer so it can come up with the best predictions and not worry about getting stuck writing all kinds of blower level code.

Jason Lopez: Once a CIO’s team has become successful in AI deployment in the cloud, measuring that by an uptick in revenue, Jacknis says about 5%. Perhaps it might be time to swap out some capabilities for something more proprietary, but he emphasizes use the cloud and operate on laptops before doing anything on-prem.

Dr. Norman Jacknis: When this started out maybe a few years ago, a lot of the people in AI and machine learning wanted to use the internal data centers that their companies had because there was a big concern about data being proprietary. They didn’t want a lot of people know it and all that sort of stuff. And then the CIOs who were not part of this process pushed back and said, you want to run this program, which is going to eat up gobs of computing resources while I’m trying to run a payroll or a CRM. No way. So in some respects, they were told internally, push this outside, put this in the cloud because we’re not going to let you do this internally.

[Related: Seeing AI’s Impact on Enterprises]

Jason Lopez: And another piece of advice from Professor Jacknis, create the culture around AI deployment. He’s seen failure around treating AI in the enterprise as a technology project, treat it as a business project

Dr. Norman Jacknis: As opposed to we have a set of tools that will help us learn more about the environment in which we’re operating in, help us identify customers better, all that kind of stuff. Instead of saying, Hey, this is a joint effort of everybody who’s got some experience worthwhile in our company to work together. They treat it as a technology thing and then it fails partly because typically what they’ll do is they’ll think, oh, I have a whole bunch of data. Lemme throw it at the computer and see what happens. Oh yeah, the computers are good, but not that good.

Jason Lopez: He goes on to say that when establishing an AI component to business operations, make sure you bring in some people who truly get the business side of it. When he spoke about this, he referred to research showing just several years ago that new AI teams were made up of mostly data scientists.

[Related: Making IT Infrastructure AI Ready]

Dr. Norman Jacknis: Ninety percent of the people in their AI projects were all data scientists. A couple of years later, it was maybe a quarter or 20% because what they found was they needed people who understood the business side at the beginning of this process, and they needed people who were better at knowing how to implement systems. Once you figured out what you have and the actual amount of data science work was small, the computer’s pretty good at this. In fact, you’ve got some computer systems now that will sort of basically be automated data scientists. What they can’t automate is the business understanding that you get from human beings, and they obviously are not going to be able to fully handle the implementation with human beings to make this stuff come to life in your company. This field of AI is moving very fast, and every problem you read about, there’s a bunch of smart people working on fixing it. Sooner or later they will. In fact, there’s a story that came out written by a CIO and he said Everything we thought was hard turned out to be easy. Referring to the conversational ability that you have with Gen ai, which was something the year before it came out, people would’ve said, oh yeah, we’ll get that 2030, maybe 2035. And here we are.

Jason Lopez: Norm Jacknis is Professor of Practice Innovation and Entrepreneurship at Northeastern University. This is the Tech Barometer podcast. I’m Jason Lopez. We have another podcast in this series featuring Professor Jacknis and in that one, and check it out on forecast by nutanix.com. Check out the changing role of the CIO. Again, this is the Tech Barometer Podcast. You can find more stories on technology and the people in tech at the forecast by nutanix.com. That’s all one word, the forecast by nutanix.com.

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In this video interview, Nutanix cloud native technology expert Dan Ciruli describes the trends and technologies powering an explosion in new applications, particularly those with AI capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Dan Ciruli: The more we realize the benefits, apps can do this, apps can do that, and the more use cases that over time we have more data, we’re gathering more video, audio data, they just enable more things. And so, I have no doubt that as we, you know, data centers become more powerful, the things in our pockets become more powerful, the computers at the edge become more powerful, people have more and more ideas, we’ll continue to write more applications than ever, you know, ten years from now we’ll be blown away at where, you know, container-based applications are running and what those things are accomplishing, and all of that means, yes, more apps.

[Related: AI and Cloud Native Spark Explosion of New Apps]

I’m not the first person to make this analogy, but watching AI right now reminds me of late 90s, mid to late 90s internet, and just kind of realizing we don’t know all the ways this technology is going to change everything, but it’s clear it’s going to change everything, right? And that’s where AI is right now. It is, you know, let’s say it’s mid 90s, because it’s in that time when you just knew it was going to be incredible, e-commerce hadn’t been invented yet, I mean, companies really weren’t even figuring out how to market on the internet yet, they were barely putting up websites and taking out, you know, radio ads to tell you to go visit their website, but you just knew it was going to transform, it’s going to transform technology and in turn society. AI is at that, you know, it’s very early on, but you can already tell there is no doubt, and that in itself will mean lots more apps, right, and AI will be, it’ll be in the data center, it’ll be in the cloud, it’ll be at the edge, it’ll be on your phone, it’ll be on your watch, it’s going to be everywhere, and all of that will be transformative.

[Related: AI, Cloud Native and Hybrid Cloud Fuse to Run Apps and Data Anywhere]

Virtually all apps that are being written today are running on cloud native, that’s true of all the AI apps, right? All of the big companies, they’re running all of that AI stuff on Kubernetes, for sure, you can’t, I don’t know of anybody who’s really writing new AI based apps, running those models in VMs, that’s all happening in Kubernetes. So in that sense, AI is built on cloud native and is an example of, it’s a new application, by the way, and it’s one that companies are starting to run in the cloud and just like their other workloads, they’re going to want to run on prem, for sure. Your provisioning timelines are different, you’ve got to buy that hardware and it’s expensive, however, that’s where your data is, you don’t want to send all of your most important data that you need to train models on up into the cloud, so it’s another one of those. On the other hand, in a sense they’re orthogonal too, because ultimately when you’re consuming an application, you don’t care if it’s running on cloud native, you don’t know if it’s running on cloud native, when I open up my bank app and I check my balance, that might be running Kubernetes on the background, I don’t know, right? Eventually it might be using AI in the background, I don’t necessarily know. So in a sense they’re orthogonal. One other thing is I think that when we talk about gen AI, it is one of the things that is going to solve the usability problems. We hear a lot about skills gaps and cloud native, and I can’t hire people who understand Kubernetes well enough to run it and then be asked to do that. To me that’s not a skills gap, it’s a technology gap. The problem there is that Kubernetes is too hard to use, and AI is going to be one of the things that solves that. And when we get to what we call intelligent infrastructure, or invisible infrastructure, infrastructure that is using ML to understand, oh, here’s a problem that maybe hasn’t happened yet, but is about to happen, and here’s what we do to solve it. And you can imagine a day, and we’re already seeing this, we’ve already got AI chatbots built into our software that allow you to say, in plain English, why am I seeing this crash loop error, and what can I do about it? And have AI help to solve that problem. So AI will help us bridge that skills gap by making the technology easier to use.

[Related: Making IT Infrastructure AI Ready]

Almost all application development now is happening in containers, in Kubernetes. What happened over the last 10 years, Kubernetes is having its 10 year anniversary, but the first three or four years was very experimental. So more over the last five years was, yes, organizations were adopting it, but they were adopting it mostly in the cloud. Because the cloud vendors, Google, where I used to work, and Amazon, and Azure, were all making it very easy to get a Kubernetes cluster. And so for many organizations, for the last five years, new development efforts were happening in the cloud, and they were happening in Kubernetes, everything on-prem was still running in VM. There’s two things that are happening. One is that now even some of the stuff that’s being written and run on-prem, they’re saying, well, this is new development now, we want to do it in this modern way. And two, companies are rethinking the economics of cloud computing, and realizing that essentially renting is more expensive than buying, and doing all of your stuff in the cloud is very expensive. So they’re saying, hey, we like these benefits of running in the cloud, we like this idea of velocity, we can get changes into production faster, but we need to be able to do that on-prem too. And so increasingly, IT departments are being asked, hey, how can you give us Kubernetes clusters? You’re the one who can give us VMs on-prem, how can you give us Kubernetes on-prem?

[Related: Building a GenAI App to Improve Customer Support]

Cloud Native started, I’ll say it started at Google, although that’s not entirely fair. There was a bunch of people at a bunch of different companies that were trying to figure out how to ship software faster, how to make it from the time someone had an idea, put that in code, put that into production, could reduce the amount of time to do that. And at the same time, also make sure that when you did, it would be scalable enough to use at some of these big companies. And Cloud Native is what we use now to describe what they were developing back then, which was a, it’s essentially, it’s more layers of abstraction between the developer and the machine that the code is running on. Because traditionally, getting a code to a piece of machine involved server installs, and server installs are notoriously complicated and complex, and it would take a long time to get something just installed and running. And with Cloud Native-style architectures, you could put together a pipeline where a developer wrote some code, they checked it into source code, pushed it to the source code repository, and then it kind of automatically and automagically got deployed to a server somewhere, no one cares where, no one’s ever going to SSH into that server. So Cloud, and then over time, a bunch of this technology got open sourced or created again in open source, and now when we refer to Cloud Native, we refer to these projects like Kubernetes, that ultimately are aimed at this, and aimed at how can we make it faster for developers to get code into production.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

In the beginning of cloud computing, we would talk about hybrid, we would talk about hybrid as a temporary state, and we really thought that hybrid was going to be how people ran until all of their stuff was in the cloud. And I think now where we are as an industry has realized that hybrid is not a temporary state, it’s not a transitional state, this is how most organizations will be running for the next decade, right, for the rest of my career probably, is that, oh no, we will be absolutely using the cloud appropriately, but we are still, we’re investing in data centers, we’re realizing for reasons of cost efficiency, for sometimes data sovereignty, sometimes just control, there’s stuff we’re going to want to run here. And so solving hybrid, not as how do we have this temporary bridge, but how do we put in place something that permanently lets us use the cloud and the data center appropriately, taking into account costs, taking into account where the data is, and taking into account sovereignty rules. So that’s what’s different about hybrid and multi-cloud today, is that companies are realizing this is a permanent state, and how do we make sure that we’re putting in place the right structure so that we can take advantage of each of them for what they’re good for.

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In this video interview, NAND Research Chief Analyst Steve McDowell explains how IT decision makers assess strategies and infrastructure needed to run artificial intelligence capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Steve McDowell: I think we’re all getting AI fatigue. Every product briefing I go to, if it doesn’t have the word AI in it, I’m in the wrong room. That’s all anybody wants to, whether it’s relevant or not. And I think a lot of these technology transitions, whether it’s the internet, whether it’s the smartphone, whether it’s the PC, going way back, there’s a brief burst of time where we’re very excited about the technology pieces, but the value of technology comes in how I use it. So have we hit a wall on AI? No, but I think it’s time to change the conversation. Let’s stop talking about GPUs. We’ll stop talking about who the providers are, and let’s start talking about what are we going to do with it and how’s it going to change my business? Because that’s the interesting conversation. The way I think about AI and we talk about AI, now I’m going to talk about generative AI, but I’ll just call it AI. What’s driving the current moment? I mean, we’ve had AI for a decade, kind of the modern form of AI for a decade. We use it. We use it for predictive analytics, image recognition, retail, whatever. But what’s really impacting what’s about to impact enterprise is generative AI.

We’re seeing a couple of things, and I think 2023 was really the year we figured out how to make these models. And right now we’re going through a kind of rapid phase of how do we make it safe for enterprises? And I look forward over the next 18, 24, even 36 months, and it’s really how do we deploy that in the enterprise from an IT perspective? That means a couple of things. One, I need to pick a partner who’s going to be my generative AI provider. I have a core set of functionality. There’s only a handful of companies in the world that could train these models. It’s OpenAI, it’s meta with Llama, it’s Anthropic, just a handful of these companies. And the way it deploys in the enterprise is I take this large language model and I fine tune it with my own data.

So if I’m an IT guy right now, I’m going down the path of how do I, I’m just trying to deploy. I’m trying to enable my enterprise to use it. And then when it comes to using it, there’s really two pieces. There’s the piece that’s very business focused. How am I going to use AI to enable the next or the next iteration of digital transformation? It’s going to change all our lives, but it’s also, how do I use it to make my own IT operations more efficient? I’ve been to 15 conferences this year, and the theme for 2024 is AIOps, right? Even Nutanix is announcing capabilities around AIOps.

[Related: Pivot Past the Enterprise AI and Cloud Native Hype]

Do I trust it? How do I trust it, how do I deploy it? Where do I use it? A lot of decisions are happening for the poor enterprise IT architect. So what’s happening with ai, we’re trying to figure it out. We’re at the phase where we’ve invented the technology right now, we’re in the enablement phase, then we’re going to start to use it, and then it’s going to drive the transformation. And I look at every kind of big technology, every transformational technology kind of follows this path. Although the timelines are getting much shorter, right? It’s understand it, it’s enable it, it’s play with it, and then it’s going to drive change.

It doesn’t have to be expensive to get into AI as a user and consumer of the technology. And I think where it’s disrupting the tech industry and driving and forcing a lot of this conversation about the technology is it’s very expensive. It’s very complex. It doesn’t look like anything I’ve touched before as an IT guy, or it looks like scientific computing. The cloud guys are solving this for me. They’re managing the infrastructure. They’re buying these expensive GPUs. They’re amortizing the cost over multiple users. Things that I don’t have a budget or capability to do as an enterprise, where that’s causing disruption in the industry is, well, if AI is driving the industry right now and cloud is taking all those dollars, and my company’s name is Dell or HPE or Lenovo, where am I getting my revenue? I mean, the server market was already down. And if those dollars now are being prioritized to cloud, that creates a real dilemma.

[Related: Cloud Vendor Shakeup Puts Focus on IT Resilience]

If I’m looking at this as an IT practitioner saying, where’s the value of AI to my enterprise? I don’t care about hardware. That’s why I like GPT-in-a-Box that the Nutanix is delivering because that’s a software set of capabilities. I can deploy that if that makes sense, right? I can deploy that at the edge, if that makes sense, or I can roll that out in the cloud, if that makes sense. So I think a lot of tech companies are trying to prove their relevance around AI. And I’m not saying they’re irrelevant. It’s just causing a lot of disruption. It’s going to change the way that we think about infrastructure.

There are two pieces of AI. There’s training and there’s inference, right? As a business user, the value is on the inference. An inference is when I take an AI model and I throw some of my own data against it, and it gives me back results, the speed, the time to value, the time to decision is the AI. I say, the closer to your data that the AI is, the faster I’m going to get time to value. And where we look at where, and this is not even a generative AI thing so much when we talk about AI, the most prevalent use of AI is image processing, whether it’s for manufacturing, whether it’s for retail, whether it’s for automotive. If you have a car made in the last 10 years, you have so many sensors in your car, it’s not efficient to take the cameras in your car or the cameras at the seven 11, send those up to the cloud to be processed and send them back down.

[Related: Living Workflows of AI at the Edge]

If I’m running a retail establishment, I might not have internet. It may go down, there may be a storm. I can’t shut my business down when I lose the internet. So by moving those inference functions to the edge, I get all the value of that AI where it makes the most sense. And there’s a couple of things that came together to make this the moment in time where that happens. One is we’ve been talking about the value 5G is going to bring to the world. I get all of this high bandwidth wireless everywhere. We rolled that out without really a killer application. And then we started all this generative AI stuff, and we got really good at AI. And now part of what that did was all the focus on high-end ai, kind of the natural curve of technology is all of the AI inference capabilities required for mid-range, low level ai, dirt sheet, dirt sheet, right? The camera you recorded me on right now can probably track my face. It’s doing inference and facial recognition on a $30 processor. It’s AI at the edge, it’s practical, and it brings true business value. Now, where it gets complicated when we talk about edge, edge is anything outside of the data center where there’s no IT guy, let’s call it that.

[Related: Building a GenAI App to Improve Customer Support]

It’s a couple of edge segments. There’s robo, remote office branch office. To me, those are extensions of your data center. They’re well controlled environments where it becomes really interesting or kind of mass deployments, whether it’s a convenience store, whether it’s factories, whether it’s smart cities, and I have sensors on the lights for traffic control. Managing all of those becomes a challenge. So I’m deploying AI at the edge for these kinds of use cases, but I also have to manage security patches, updates. I got to push new models down. Sometimes I got to bring data back. So what’s the framework look like for that? Well, guess what it, it’s not that different from how I’m doing hybrid multicloud. The boxes are just a lot smaller. So we did this paper and it’s like we’re driving AI to the edge for all the reasons I just said. But then it was really more about, okay, we’re pushing AI to the edge. How do we manage it and how do we make it efficient to manage? And that requires, again, it looks a lot like multicloud, but it may be a more unconstrained environment.

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In this Tech Barometer podcast segment, Nutanix President and CEO Rajiv Ramaswami talks to tech reporters about why IT leaders turn to Nutanix to counter changes they face after Broadcom acquired VMware.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
Rajiv Ramaswami: We’ve been competing against VMware. Yeah, it’s a good company. We competed some. We won some. We lost some. Obviously, you can’t foresee what’s going to happen. Could we have foreseen that Broadcom was going to acquire VMware? No, I don’t think so. But now that it’s happened, there is an unexpected opportunity. Our view is to continuously evolve as a company by investing in innovation and being where our customers need to be over time and not just stay still. And if you stay still at some point end up having to be optimized because there is no more growth left.

Jason Lopez: In this story, you’re going to hear the voice of Nutanix CEO, Rajiv Ramaswami recorded in a conversation with journalists. This was not the typical press briefing of the speaker at a podium with prepared remarks, but rather he sat at a table with tech reporters in an open-ended back and forth. This is the Tech Barometer podcast. I’m Jason Lopez. The journalists led the discussion, which zoomed in on Nutanix’s view of Broadcom’s acquisition of VMware.

Rajiv Ramaswami: They acquire companies that are fairly mature, that they have good market share, that have good products and fairly sticky ones, and then they do what they need to do to maximize the profitability of those companies over the next three years. And their model is one of continuous acquisition. Acquire, optimize, acquire, optimize, right? Because then that’s the model. So you keep acquiring, you keep optimizing. So they’ve done well with that model so far. They’ve done very well for their shareholders.

[Related: Cloud Vendor Shakeup Puts Focus on IT Resilience]

Jason Lopez: Companies go through stages. Ramaswami pointed out such as an incubation period, then scaling, then maturity. He said VMware had arrived at that stage.

Rajiv Ramaswami: Their growth was slowing. They had kind of largely saturated their markets that they were playing in. vSphere had what, 80% plus share in the market? That’s the stage. That’s when you start thinking about optimization. You cannot grow anymore. So if you were to take us and you do the same squeezing what we got, 25,000 customers, we still have huge amount of opportunity to grow and gain share.

[Related: Slew of Changes Drive VMware Customers to Consider Alternatives]

Jason Lopez: For comparison. Ramaswami explained that Nutanix wouldn’t be a good target for an optimization strategy. It still has a high ceiling for growth, meaning it can essentially double its customer base.

Rajiv Ramaswami: That’s what we are out to do. So we are still in the scale phase and we’ve told our investors, we are profitable, we are growing. So it’s a different status.

Jason Lopez: One of the things Nutanix has been able to do and continues to do is to adapt to the market. The company started with a hardware focus and has shifted to software infrastructure,

Rajiv Ramaswami: But that’s not where we want to be tomorrow. But if you look at the future, the future is around being a platform for modern applications. And that’s why we bought D2iQ, for example. We are investing and expanding our platform offerings. We are investing in cloud native AOS for containers. I think as a company we are in a better place to evolve our customers to the next stage building and running modern applications on these platforms.

[Related: AI, Cloud Native and Hybrid Cloud Fuse to Run Apps and Data Anywhere]

Jason Lopez: As the conversation continued, Ramaswami began talking about the realities of customers making the switch from VMware to Nutanix. The bottom line is the rise of VMware pricing.

Rajiv Ramaswami: The vast majority of VMware customers today are running on vSphere only, and those customers mostly are not perpetually (licensed). And so those customers are not being forced to buy just a subscription, but the full bundle. Customers have to buy the full VCF stack.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Jason Lopez: In addition to the issue of being locked into a subscription model with rising costs. There are other questions.

Rajiv Ramaswami: What’s the level of innovation going to be? What’s the level of support going to be like if the model is to double the ebitda, the profits within three years, which is what they’ve said publicly, what would happen there?

Jason Lopez: Another aspect of switching the VMware environment versus the Nutanix environment. An engineer who has worked with VMware tools for 20 years will have to relearn some new ways of doing things. But Ramaswami pointed out, Nutanix is built for adoption. Its processes are easier to learn.

Rajiv Ramaswami: It’s not a huge learning curve, but it is not exactly the same. We do a lot of things differently. In some ways, many of them simpler, and we also have a lot more focus on the data side of the equation compared to VMware.

Jason Lopez: And finally, Ramaswami addressed the question of the VMware stack of the hypervisor connected to three-tier storage.

Rajiv Ramaswami: In the past when people chose Nutanix, they had to adopt hyperconverged (infrastructure or HCI). And a lot of VMware is not hyperconverged. They’re hyperconverged, but the big chunk of their rest estate is really three-tier storage, right? Hypervisor connected, three-tier storage. And so that conversion, we were doing it at a certain pace in terms of converting that three-tier, right? HCI. That’s how we grew as a company. That’s what we did. And now we have an opportunity to go back in and also potentially just replace a hypervisor and get a faster entry. We have seen a huge amount of engagement increase in customers, so lots more customers concerned and upset and talking to us. Many of the customers actually signed multi-year deals with VMware before the acquisition closed. And so that gives them three to five years to do whatever they need to do. And so they’re not in a rush to necessarily do anything at this point.

[Related: Seeing AI’s Impact on Enterprises]

Yes, a lot of them are kicking the tires. They’re testing our product, but to go from there to a sale and actual migration takes time. So they’re not in a rush. The second factor is the hardware, right? At what point in time have they invested in hardware? When does it come for refresh? Because that’s the time at which you can actually make the migration to hyper converge because it’s an architectural choice. And the third point, I’ll tell you, there is much more awareness of what would happen, but it depends on the country and where you’re in. For example, I was in Japan recently, and a lot of Japanese customers were just hoping that everything would be fine because they believe in these long-term relationships and they believe and trust them. And now they’ve seen what’s happening and now they’re concerned. But it took them a year, and they did nothing. Right? And now they’re starting to think about, okay, maybe I should have a plan B. So it takes time. Have we seen a big spike in our business? Immediate business as not yet? We’ve brought on investors that this is going to be a multi-year journey.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

Jason Lopez: The core of Ramasswami’s message is that Nutanix and VMware within Broadcom, though they compete in the same space, have different agendas. Nutanix is aimed at growth. Broadcom is aimed at billing an existing customer base.

Rajiv Ramaswami: That’s their model. So they’re actually going down a true and trusted strategy. Typically, private equity companies do this, but they’re doing this in the public market and doing that very well, I should say, executing on their strategy. I just want to say that’s not our strategy. I mean, our strategy is to grow, continue to drive innovation, and focus on building these long-term relationships with customers.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This was recorded at the 2024 Next conference in Barcelona, Spain. This is the Tech Barometer podcast. I’m Jason Lopez. Thank you for listening. Tech Barometer is produced by The Forecast where you can find more podcasts, articles, and videos about the technology story and the people in tech. It’s at the forecast by nutanix.com. That’s The Forecast by Nutanix, all one word. Dot-com.

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In this Tech Barometer podcast segment, McDowell shares insights from his report Taming the AI-enabled Edge with HCI-based Cloud Architectures and explores the impact of extending IT resources to the edge and the driving force of AI.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcription:

Steve McDowell: The reason we push AI to the edge is because that’s where the data is, you know, we want to do the processing close to where the data is so that we don’t have latency. And in a lot of environments, if we’re ever disconnected, it’s going to shut down my business.

Jason Lopez: The question is, how do you deploy edge resources in real time? In this Tech Barometer podcast, Steve McDowell, Chief analyst at NAND research talks about his paper “Taming the AI-Enabled Edge with HCI-Based Cloud Architectures.” I’m Jason Lopez. Our aim in the next several minutes is to discuss how AI impacts edge computing.

Steve McDowell: We’ve always defined edge as any resources that live outside the confines of your data center. And there’s some definitions that say the extension of data center resources to a location where there are no data center personnel. It’s remote.

Jason Lopez: But AI, of course, adds complexity. One example McDowell cites is automated train car switching. The sides of train cars have bar codes which are scanned, and a local stack of servers processes where the cars are and where they need to be.

Steve McDowell: I can do this in real time. I can partition my workloads so that, you know, computationally expensive stuff or maybe batch stuff can still live in the core. And I don’t have to do that at the edge all the time. So I can really fine tune what it is I’m deploying and managing.

[Related: Slew of Changes Drive VMware Customers to Consider Alternatives]

Jason Lopez: This is important when you consider that AI at the edge differs from traditional edge deployments primarily due to its need for greater computational power.

Steve McDowell: Once we start putting AI in, then suddenly we have to have the ability to process that AI, which often means the use of GPUs or other kinds of AI accelerators. Ten years ago, if we talked about edge, we’re talking largely about embedded systems or compute systems that we treat as embedded. Embedded is a special word in IT. It means it’s fairly locked down. It doesn’t get updated very often. When we look at things like AI, on the other hand, that’s a very living workflow. If I’m doing image processing for manufacturing, for example, for quality assurance, I want to update those models continuously to make sure I’ve got the latest and the greatest.

Jason Lopez: And along with managing fleets of hardware and software in AI deployments at the edge, there’s also the issue of security.

Steve McDowell: By treating edge systems as connected and part of my infrastructure, and not as we historically have treating them as kind of embedded systems, if you will, it also allows me to, in real time, manage patches, look at vulnerabilities, surface alerts back up to my security operations center, my SOC. It makes the edge look like it’s part of my data center.

Jason Lopez: Tools like Nutanix allow for this approach, applying a consistent management practice across both core and edge environments. This involves deciding what tasks to perform at the edge versus the core due to constraints like cost, security, and physical space.

Steve McDowell: A key part of the conversation becomes what lives where? And that’s not a tool problem, right? That’s kind of a system architecture problem. But once you start partitioning your workloads and say, this certain kind of AI really needs to be done in the core, Nutanix gives me that ability and cloud native technologies give me that ability to say, well, I’ll just put this kind of inference in the cloud and I’ll keep this part local.

[Related: Pivot Past the Enterprise AI and Cloud Native Hype]

Jason Lopez: McDowell’s thinking springs from the flexibility afforded by hyper-converged infrastructure. The idea of AI at the edge is part of the whole architecture of storage, network and compute.

Steve McDowell: That can be as disaggregated as it needs to be. So if I need a whole lot of compute in the cloud, I can do that and then put the little bit at the edge and I can manage all of that through that single pane of glass, very, very powerful.

Jason Lopez: Treating edge computing as a part of the data center becomes so interesting because of how the data center itself is being transformed by AI and machine learning.

Steve McDowell: Once we abstract the workload away from the hardware, I’ve broken a key dependency. I don’t have to physically touch a machine to manage it, to update it, to do whatever.

Jason Lopez: The point McDowell makes is how management, not just of the configuration of a node, but across a fleet, is simplified. It enhances efficiency and scalability.

Steve McDowell: We’re taking technology that evolved to solve problems in cloud, but they apply equally to the edge, I think. It turns out, it’s a fantastic way to manage edge.

[Related: More Reasons for HCI at the Edge]

Jason Lopez: AI at the edge is increasingly adopting cloud-native technologies like virtualization and containers. The shift is to container-based deployments for AI models, sharing GPUs and managing them remotely.

Steve McDowell: If you look at how, you know, NVIDIA, for example, suggests pushing out models and managing workloads on GPUs, it’s very container-driven.

Jason Lopez: And McDowell explains why this simplifies edge management.

Steve McDowell: A GPU in a training environment is a very expensive piece of hardware. And giving users bare metal access to that, you know, requires managing that as a separate box. Using Cloud-native technologies, I can now share that GPU among multiple users, very, very simply. That same flexibility now allows me to manage GPUs at the edge with the level of abstraction that works. So I can sit in my data center, push a button and manage that box without actually worrying about what that box looks like necessarily. So I don’t need that expertise kind of onsite, right? Which is a key enabler for edge. If you have to have trained IT specialists wherever you’re deploying, that doesn’t scale. And edge is all about scalability.

[Related: The Future of AI Computing Resides at the Edge]

Jason Lopez: GPUs are typically what power AI, but are are not commonly found at the edge. But inference is a facet of AI that many technologists see value in at the edge. GPUs would be the right fit if at the edge, generative AI is needed. But what’s needed now are inference engines, especially around vision and natural language processing.

Steve McDowell: Take, for example, a retail environment where they have intelligent cameras that are positioned all up and down the aisles of the grocery store. And the only job that these cameras have is to monitor the inventory on the shelf across from the camera. And when they’ve sold out of Chex mix and there’s a gap there, it sends an alert, come restock. I mean, it’s very kind of data intensive and you don’t want to send that to the cloud necessarily.

Jason Lopez: Technology is moving toward managing infrastructure environments seamlessly, such as edge, data centers, and cloud, without changing tools or management models.

Steve McDowell: Nutanix has capabilities for managing AI in your workflow, kind of period, full stop. A good example of this is GPT in a box. Where it’s a technology stack and I plug a GPU in and I can do natural language processing. If I want to push that out to the edge. I don’t have to change my tools. I mean, the beautiful thing, and the reason that we use tools like Nutanix is that it gives me kind of a consistent control plane across my infrastructure. Now, infrastructure used to mean data center, and then it meant data center and cloud. And now with edge, it means data center and cloud and edge. The power of Nutanix though, is it allows me to extend outside of my traditional kind of infrastructure into the edge without changing my management models. So, as AI goes to the edge, I think the things that already make Nutanix great for AI in the data center are equally applicable at the edge.

Jason Lopez: Steve McDowell is founder and chief analyst at NAND research. This is the Tech Barometer podcast, I’m Jason Lopez. Tech Barometer is a production of The Forecast, where you can find more articles, podcasts and video on tech and the people behind the innovations. It’s at theforecastbynutanix dot com.

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In this Tech Barometer podcast, Nick Mahlitz, digital infrastructure manager at Forestry and Land Scotland, takes listeners to his homeland, where he helps the government use data and cloud technologies to manage natural resources and meet sustainability goals.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Nick Mahlitz: I learn new things every day as I talk to our staff members. Just recently, we’re using drones with lasers to map out the land. So the drone footage stuff is really, really, really good. They can then tailor what they do with the land around what they found with these drones and with the lasers, they can go through forest layers, they can analyze the types of land that is, and oh, there’s a ridge there that we perhaps need to avoid, and just making better decisions.

Jason Lopez: We’re starting this podcast interview by parachuting right into the heart of the work of Forestry and Land Scotland, the federal organization that manages the country’s public land and wilderness. A year ago we talked with the manager of the organization’s data centers, Nick Mahlitz and posed the question:

Ken Kaplan: The forest needs technology?

Nick Mahlitz: Yes. The forests do, yes, to manage your forests well and good, to use technology in a challenging environment like Scotland, where it’s very remote and the weather can be quite extreme sometimes. Technology and exploring all realms of technology will only help us better manage Scotland’s forests and our land.

[Related: Forestry and Land Scotland Trailblazes Private-Public Shift to Cloud]

Jason Lopez: So, we circled back to follow up on that interview. Forestry and Land Scotland is utilizing technologies like drones with laser mapping capabilities. As we’ll learn, it gets very data intensive which is Nick’s job to oversee. He supports the organization’s mission to balance natural resources. One of those balancing acts is to better track wildlife, particularly deer, to protect young trees.

Nick Mahlitz: Scotland has a lot of deer, so we have to cull a fair amount of them. And the challenges around Scotland being a very remote piece of land and identifying and culling enough deer in a small frame of time can be very challenging. So with technology of tagging deer and using drones to manage where they are, a ranger can go from culling a couple of deer and say half a day or a day to 8, 9, 10 deer within the same timeframe.

Jason Lopez: Scotland’s public land serves many interests. Trees enhance carbon sequestration. Some of its forests are grown for timber, and some of its land is used for renewable energy projects — such as wind and hydroelectric power. Scotland’s goal is to be net zero by 2045. One of the most important uses of the land is for the public’s enjoyment of the outdoors. He takes note: it’s good for people behind the scenes of his own organization to experience the places their work supports.

Nick Mahlitz: You know, I don’t want to be the person in the basement. You know, the data is there, but it’s also good out and enjoy it. And we try and do that. We encourage non-forester staff to go out with a forester for the day. Pick what you want to do. Do you want to go and see a piece of bog peatland be restored? Do you want to go and plant a tree? Do you want to go and uplift trees? You know, we offer these activities to people like myself or back office staff, HR procurement, except anybody who wants an interest in it, because it’s so important. If you understand that you’ll understand your role in the organization and how better you can play a part.

[Related: How a Top University in Scotland Expanded Remote Teaching Tech During a Crisis]

Jason Lopez: This is why the forest needs technology. The earth’s landscape has been so altered by human development for the past 30,000 years, it requires human conservation to prevent further decline or restore land to a balanced state. Intervention is critical.

Nick Mahlitz: Peatland restorations, the soil gets degraded, you know, so we’re trying to restore the balance in the soil so that it can keep more carbon, et cetera, et cetera. So there’s that ongoing. We’ve got big plans for a big nursery that’s coming up where we can plant 19 million trees a year. We’re planting the trees in a special way. The seeds are planted into biodegradable paper that can just be streamlined, planted, and we can plant far more than we could normally. And then as we cultivate them in a nursery, we have to plant them outside in fields. And we’ve re-engineered equipment to then do that planting for us rather than manually. And, you know, just that kind of approach just means far more efficiencies meeting our targets and making our staff more efficient, which is fantastic.

Jason Lopez: What scientists are learning today about the natural world and what technologists are innovating is accelerating.

Nick Mahlitz: And that is unlocked by cloud, by edge, by modern approach. Lots to be done though. Lots to be done still.

Jason Lopez: Nick’s current goal, managing the data centers of Forestry and Land Scotland, is on integrating AI and automation to improve their operations to better understand their data.

Nick Mahlitz: And that’s what we’re actively working on now. And the reality is I can know about AI and automation, me and my team and others in the digital landscape. We know about that for ourselves, but to translate that into somebody with a chainsaw who’s cutting down trees or somebody who’s managing wildlife management as in deer or our nurseries where we’re planting our seedlings to then grow, what does AI and automation do for these ones? And that’s the core of our business.

[Related: Do Forests Really Need Technology?]

Jason Lopez: He reminds us that AI is not the core of the business. AI is a tool. His team’s core business is about data.

Nick Mahlitz: How much data do we have? How can we better understand it? How can it better help us make more informed decisions on our future for sustainability, for a net zero, for generating revenue, et cetera, that we do? So there is now a big piece to understand how we capture data, how we store that data, how we report on that data, how we integrate that data, how we use AI and automation with that data. So that really is a big focus for our organisation over the next year or two.

Jason Lopez: And this exemplifies how Forestry and Land’s data centers operate, with a passion for sustainability. Migrating to the cloud improved efficiency and prompted a shift towards reducing energy and CPU usage ,as well as modernizing.

Nick Mahlitz: So, you know, we have, we have legacy systems and they have legacy interactions and, but they’re now in the cloud. So we can replace some of those interactions with more modern solutions, which gets rid of, you know, having to spend so much energy or CPU, I get rid of that inefficiency, even in code, even right down to the lower levels. And that really can make a big difference as well. And that’s something that’s, FLS is passionate about sustainability, but even, you know, filtering that down to our digital teams, they appreciate that too. So that’s where we can look at and make those changes that just makes everything run better in a more sustainable way.

Jason Lopez: The transition to a full public cloud makes management easier and has simplified data center operations. And it’s allowed for easy integration of other cloud products and solutions.

Nick Mahlitz: So we have no on-prem environment to administer or manage. So that is quite a unique position that we find ourselves in. And we could not be more delighted with the results. The actual transition to the cloud using Nutanix was an experience that made our journey so much simpler and has bought us the time that we need to modernise and transform our solutions into that next-gen approach. So what we did with Nutanix really was, we saw it as groundbreaking. It’s delivered what we aspire to and it had benefits that perhaps we never really realised at the time that we now enjoy.

Jason Lopez: Nick says the migration has resulted in something else they didn’t originally factor in: time savings. He and his colleagues have the time resources to invest in new technologies, which help in the goal of reaching that net zero target by 2045.

Nick Mahlitz: And comparing our footprint of our on-prem environment compared to what we have now through the metrics that we receive from Microsoft, given that we’re in the Azure platform, it’s really heartening to see that that sustainability piece and net zero is being reached in some way or added to our targets that we have as an organization. And then equally that full cloud integration means that we can now tap into other cloud products, cloud solutions, and very easily integrate them into what we have, which before we didn’t fully appreciate that we could do that. And as Nutanix expand their products and services in the cloud, we’re only going to enjoy that more and more.

[Related: Easy Alternative for Migrating or Extending to Public Cloud]

Jason Lopez: The transition to the cloud, again, enabled the organization to adopt other cloud technologies.

Nick Mahlitz: And now that we’re in there on NC2 in the cloud, what we had to do as part of that journey was unlock other cloud technologies to help us realize that. So for instance, identity management, access management, VPN, et cetera, et cetera. We’re now using all the cloud variants of such so that we have no dependency on really local on-prem infrastructure. So what that entails is a very much a kind of zero trust model that’s highly secure within line with modern approach. So that really can then unlock capabilities that we’re just starting to tap into. But given my team, the ability to now manage that in a completely different way to what we had. But it unlocks really future capabilities at which the IT person’s appetite and desire and helps for recruitment and retain of staff and investment and development on staff when what we’ve done can excite people. It can excite the IT people for sure and other people in the organization. So that’s been really, really a good thing for FLS.

Jason Lopez: Astrophysicists remind us what it is to look back, from space, at planet earth, and viscerally understand how vital earth is to life as we know it, yet how small and fragile. People across political boundaries, economies and cultures are galvanizing efforts to preserve oceans, land and forests… and doing this whether its reducing pollution, conserving farmland topsoil, or establishing more efficient data centers.

Nick Mahlitz: And it really just lines up to understanding where we are in technology and our timelines and our lives, working in that agile manner, having the growth mindset, embracing technology, all the attitudes that really permeate behind a good digital team. We took that novel approach. We did our due diligence, but we did something new and exciting. And really that’s the refreshment I have from working for 20 years in this career, so to speak, that still there’s the ability to do novel, new things. We Scots are a passionate people. And as I meet other digital and IT teams in other government areas, there’s a similar aspiration and enthusiasm for understanding technology in their, in their areas too.

Jason Lopez: Nick Mahlitz is the senior digital infrastructure manager for Forestry and Land, Scotland. This is the check barometer podcast, I’m Jason Lopez. You might want to check out the original video we did with Nick entitled “Do the forests really need technology?” You can find that and more stories and podcasts at the forecastbynutanix dot com.

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In this Tech Barometer podcast segment, NAND Research Chief Analyst Steve McDowell describes how CIOs manage change and mitigate risk in the first year after Broadcom acquired VMware.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Steve McDowell: I think this year is a lot of, you know, resetting how we think about VMware and I think a little bit of resetting everything about Broadcom and what they do. There’s a lot of uncertainty. I don’t think my position is changed.

Jason Lopez: Steve McDowell is the chief analyst of NAND Research. Welcome to another Tech Barometer podcast from The Forecast, I’m Jason Lopez. When McDowell talks about the Broadcom purchase of VMware these days, he’s quick to point out how the discussion has shifted from what’s Broadcom going to do to what customers are going to do.

[Related: Slew of Changes Drive VMware Customers to Consider Alternatives]

Steve McDowell: IT is all about managing risk. As long as there’s uncertainty, as an IT guy, I need a plan. I need to know how to mitigate against that uncertainty. Even if it’s not wholesale replacement, have a plan B. And a big part of this is second source. Start mixing in as new projects come up, other technologies and balance the risk, right? You mitigate risk by balancing the options.

Jason Lopez: He says the way to look at next steps from an IT perspective is that customers have a lot of unplanned stuff on their plates. There’s a challenge to ensure there are no hiccups in the data center, especially if IT has to find alternatives to VMware.

Steve McDowell: There’s nothing that is a hundred percent drop in replacement for all the overlap Nutanix has with VMware, for example. It’s still a big effort. It’s still a big effort. And you’re asking me to do this effort, well, if I’m going to switch, right, while I’m also trying to figure out this AI thing and solve all my cybersecurity problems. If I’m doing a new project, I’m going to look at cloud native, I’m going to look at Nutanix. And it’s really the only two alternatives. I’m either going OpenShift or I’m going AHV.

Jason Lopez: What IT wants is predictability and consistency.

Steve McDowell: That’s all any IT guy wants. He wants not to have to think about this. IT plans way ahead, and there’s so many digital transformation products on their plates. And this is a distraction. And that’s what they hate.

Jason Lopez: And McDowell’s advice to the players who make IT solutions.

Steve McDowell: You know where that pain threshold is, and you need to build your programs around that. You got to make the switching costs come down, whatever that means, rebates, technical assistance, training, whatever, professional services. There’s ways for competitors to come in there and leverage the situation that does bring relief to these IT guys.

Jason Lopez: Steve McDowell is Chief Analyst for NAND Research. This is the Tech Barometer podcast. I’m Jason Lopez. Tech Barometer is produced by The Forecast. You can find us and more tech stories at theforecastbynutanix.com. All one word, theforecastbynutanix.com.

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In this Tech Barometer podcast, Tobi Knaup, general manager for Cloud Native at Nutanix, explains what’s accelerating cloud native application development and how enterprises run these apps across hybrid multicloud IT environments.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (AI generated):

Tobi Knaup: Cloud Native really is a concept that describes how to build and run modern applications. Those applications are typically microservice-oriented, they run in containers, and they’re dynamically managed. On top of a container platform, typically that’s Kubernetes, that’s really become the industry standard. Kubernetes runs anywhere. You can run it on the public cloud. You can run it on a private cloud. You can run it on the edge. So if you’re building applications on top of Kubernetes in containers, that makes them truly portable so you can run them anywhere, hybrid multicloud.

Jason Lopez: That’s the voice of Tobi Knaup, the general manager for Cloud Native at Nutanix. In this short podcast, the editor of The Forecast, Ken Kaplan, chats with Tobi about the integration of cloud-native applications and hybrid multi-cloud environments. In this discussion they touch on cloud-native applications across various cloud environments; the synergy between AI and Kubernetes; and the shift to Kubernetes for consistency and portability.

[Related: Flattening the Cloud Native Learning Curve]

Ken Kaplan: What was the limitation that’s been unlocked with Kubernetes?

Tobi Knaup: The limitation that was there before is there wasn’t a consistent sort of packaging format for applications that made them really easily portable. And containers kind of provide that abstraction. In computer science, we sometimes call it a layer of indirection. So it abstracts applications away from the underlying infrastructure. And containers are a very lightweight way to package an application, so it’s very easy to ship them all around, all over the place.

Ken Kaplan: And their connection to managing data in different ways, is that something that you have to think about?

Tobi Knaup: Yeah, absolutely. So containers, or Kubernetes when it was first launched, actually did not have support for data. It purely ran stateless applications. So only a few years later, the community, it was actually our engineers at Data2IQ together with Google, created what’s called the container storage interface. And so that became the industry standard for attaching storage to containers. But that was still very bare bones, just simple volumes attached to containers. And so what we did here at Nutanix recently with NDK (application-level services for Kubernetes), really takes that to the next level. Really adds capabilities for disaster recovery and resilience. So makes it really, really easy to run these kind of sensitive stateful applications on Kubernetes.

Ken Kaplan: Awesome, and we’ve been hearing the word resilience and it sounds like the hybrid multicloud, building your apps in containers, these kinds of things are bringing some more control and flexibility for things that you need to keep your business running. Talk about hybrid multicloud in the sense that it’s a choice or it’s where people are today. How did we get here?

Tobi Knaup: Yeah, so I think there are many reasons for why people choose hybrid or multicloud. It’s typically not what a lot of people think at first. I think when hybrid cloud or multicloud first became a concept, people thought, people are going to look for the cheapest compute all over the world and that’s where things are running. I talked to some customers that are doing that actually, but they’re kind of rare and they’re typically hedge funds. So of course they look at market prices. But for most people, the constraints are different. It could be regulatory constraints, right? Data needs to reside in a certain geography or frankly just what an organization is comfortable with, where they’re comfortable putting their most sensitive data. Some don’t want to put it on the public cloud, right? But they appreciate the flexibility and the dynamicism of a public cloud for new developments. So they’re sort of running their steady state production workloads on prem, but they’re giving cloud environments to the development teams for building the next generation of apps, which then may move on prem later when they go to production. So I think a lot of organizations are looking at what’s the best environment for each app and that’s how they’re choosing. And now in large organizations, what we see too is that’s where the multicloud strategy is very common. Sometimes also for regulatory reasons, they have to go with a dual vendor strategy. But also large companies, they tend to acquire a lot of other companies and so the cloud that the company uses that they bought may be different from the cloud that corporate uses and so now they have to manage multiple.

[Related: Containers Progress in a World of Data Center Virtualization]

Ken Kaplan: That’s a lot of complexity. Yes. I guess we could finish here with what are some of the things that you’re seeing, you’re excited about, they’re grabbing your attention, are rising, they’re coming up, everyone’s talking about AI. AI and containers and Kubernetes, they are going together, what’s on your mind?

Tobi Knaup: Yeah, 100% they’re going together. So AI is my other passion besides cloud native. Been doing a lot of work there over the years actually. And it’s worth mentioning that Kubernetes came out of Google. It’s sort of built on the same ideas on which Google runs and they’ve been running their own AI workloads on that platform internally for years. And today, the leading AI companies in the world, like OpenAI, they’re running in Kubernetes. So if you’re using chat GPT, that’s running on Kubernetes. And so it’s a really great fit for these AI workloads because AI workloads, they tend to be very dynamic. They need to share resources, very expensive resources in this case. GPUs are very expensive and in short supply. So it’s a great fit. Also organizations want to iterate on AI very quickly. So Kubernetes really enables that, enables people to ship software fast. So with GPT in a box, which runs on Kubernetes, we’re really making it easy for organizations to put these models into production. And we’re also putting AI into our Kubernetes product, into NKP to assist Kubernetes platform engineers. So there’s a co-pilot type chat bot that’s built in that answers their questions, both sort of generic knowledge questions as well as questions about the environment. They can use it to troubleshoot. So, you know, really simplifies their lives.

[Related: Cloud Native Architecture Critical to 5G Success]

Ken Kaplan: Everything’s coming together.

Tobi Knaup: It is indeed.

Jason Lopez: Tobi Knaup is the general manager for Cloud Native at Nutanix. Ken Kaplan is the editor in chief of The Forecast. They spoke in Barcelona, Spain at .NEXT 2024. This is the Tech Barometer podcast, a production of The Forecast. For more podcasts, video and articles about technology and the people in tech, check it out at www.theforecastbynutanix.com.

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In this Tech Barometer podcast, Rene van den Bedem of Microsoft’s Cloud and AI division discusses the future of AI and how cloud computing is evolving to power more aspects of life.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (AI generated):

Rene van de Bedem: When I started, the personal computer was becoming more miniaturized.

Jason Lopez: Rene van den Bedem says when he started his career in computing it was 1994. The trend was smaller and more compact machines. Windows 3, with a more user-friendly, graphical interface was the dominant OS. It was a period of diversification in personal computing. This is the Tech Barometer podcast. Rene van de Bedem is Principal Technical Program Manager at Microsoft, where he does a lot of work in cloud and digital transformation. We asked him about the state of enterprise computing and he ushered us into a sort of timeline, that ends up at AI… but starts in the 90s with computers becoming more miniaturized.

Rene van de Bedem: And then the networking constructs had just come out.

[Related: Focus Shifts to Migration in Wake of Broadcom’s VMware Acquisition}

Jason Lopez: The emergence of ethernet, token ring technology, and TCP/IP, ithelped establish the building blocks for the interconnected world we live in today. It was the beginning of the transition from military and academic use to the public.

Rene van de Bedem: Jump 10 years later, we went from narrowband in telco, so you know, like PSTN, dial-up modems, 64k data circuits.

Jason Lopez: This shift from slow to faster connectivity, laid the groundwork for the high speed internet technologies that would make cloud possible.

Rene van de Bedem: Jump to let’s say 2001-2002, you had the explosion of the internet. The internet really became this mainstream thing.

[Related: IT Leaders Get AI-Ready and Go]

Jason Lopez: This was the dot-com era, with faster chips, advances in hardware. It moved us from dial-up to broadband, It was a time marked by the spread of wi-fi. Mobility was becoming a big deal.

Rene van de Bedem: So you had all of these building blocks coming together to where we are now, with the invention of the cloud back in 2006, I think it was, with AWS.

Jason Lopez: There was a fundamental transformation in information technology, where physical infrastructure was being replaced by the cloud. It gave users unprecedented levels of accessibility, efficiency, and scalability.

Rene van de Bedem: And now in 2024 with AI, we’re now on this cusp of this next rocket launch that’s coming.

Jason Lopez: AI is becoming a tool with a wide range of uses, much the way calculators did back in the 80s and 90s. Microsoft, Rene says, is integrating AI into all its products. That’s called “co-pilot.” This change signals a transformation to the era we’re entering, where AI is a must have technology.

[Related: Creating AI to Give People Superpowers]

Rene van de Bedem: People who work in an industry, if they don’t adopt these new tools, they’re going to be left behind. So in 10 years time, all jobs around the world, most of them will have some type of AI-based co-pilot that you’ll need to use to do your job, and those that don’t, they’ll just be left behind.

Jason Lopez: It’s a continual evolution. And it especially applies to tech companies which must adapt to the changing needs and challenges of storing and processing an ever-increasing volume of data.

Rene van de Bedem: Obviously, having very, very fast, expensive storage, you need that for a part of the workloads, but then the ability to archive petabytes of data so that you can derive business value from your data sets, that’s a necessity. So storage is always evolving. I’m sure it’s similar is going to be true for quantum computing. We’re going to see a shift in the way that we build our traditional computing models so that that can harness and integrate with AI as well as quantum computing.

Jason Lopez: Cloud service providers are beginning to offer quantum products in a limited way, though scalable quantum computers are not yet a reality. Right now, it’s in the realm of researchers and developers to experiment with quantum principles and algorithms.

Rene van de Bedem: Most of the cloud providers have a service that allows customers to play with quantum computing.

Jason Lopez: Unlike traditional computing, quantum computing stores information in a more complex way, with an exponential increase in processing power for certain types of problems. Rene says the future looks like a hybrid.

Rene van de Bedem: Quantum computing is not going to replace traditional computing. Every technology has got pros and cons. Quantum computing, even though the processing is happening, is not really able to maintain its state once the problem is solved. So what happens is you’ll have your quantum computing model that’s running, and then you’ll have traditional computing services wrapped around that, and all of the data, once it’s solved, goes into traditional computing software constructs, I suppose you would say, to maintain the results of that data and the history and the archives and all the reporting and everything. It’s a hybrid technology where the two need to work together.

Jason Lopez: With the rise of the cloud, many businesses rushed in. There were and are all sorts of very good reasons: reduced IT costs, scalability and flexibility and agility, access to big data analytics, access to AI. And then, simply, it was a trend. There was a sort of peer pressure to move to the cloud.

Rene van de Bedem: When they got there, they realized, “Oh, the business goals that we’ve been trying to achieve are actually not being met,” or they weren’t considered. Typically, cost and operational complexity, because there’s a level of skill that you need to have to work in a hyperscaler correctly, regardless of whether it’s Google, Azure, or AWS. It may turn out that the laws of the land, the laws of physics, or the laws of economics are very, very important to them. So they’re constrained as part of their business goals that they’re trying to achieve. And it turns out, “Okay, running in the cloud is not such a good idea. And then they’re forced to go back. So I have seen that a few times. Because obviously, you’ve got people, process, technology, and financials are the four major domains. And if one of those is weak, then you’re probably not going to be successful.

Jason Lopez: This is what hyperconverged infrastructure was born to do: to consolidate storage, compute, and networking resources into a single, easily managed platform which is software defined, and helps reduce capital and operational expenditures. Nutanix launched its first HCI product in 2011, focusing on making data center infrastructure invisible. Rene says that when it comes to virtualization he’s seen a variety of platforms such as VMware and HyperV, though the Nutanix platform offers a more user-friendly experience. Nutanix Cloud Clusters, also known as NC2, is aimed at more easily managing workloads on hybrid clouds. Rene’s job is to make sure these platforms work inside Azure.

Rene van de Bedem: The beauty of running NC2 on Azure or Azure VMWare solution, to use Microsoft as an example, because that’s all being extrapolated in the back end and the customer doesn’t see it, it’s a lot more easy for them to consume, because really the main requirement is “do you have an Azure landing zone” and then you can build whatever service that you want on it.
So, I’ve been working with Nutanix since 2014, and what I always respected about Nutanix was the fact that Nutanix was the company that invented hyperconverged infrastructure and it was really about the customer experience. And even with the CSAT scores and support, there is really no better support than Nutanix. So customers that buy into the Nutanix ecosystem, it’s similar to Apple fanboys and VMware fanboys, Nutanix customers are very passionate about the infrastructure and the solutions that Nutanix brings to market. And when you look at the evolution of NC2 on Azure, NC2 on AWS, that’s really just an extension of the things that the customer is asking for. They want to get out of the data center business, they want to move into the cloud, and that’s what Nutanix is doing with this multi-cloud strategy. And obviously VMware is going down a similar path as well. What makes Nutanix more interesting is that focus on customer experience and then also you have this Broadcom acquisition of VMware. At the moment, the market is very much in a state of flux and it’s not clear where the chips are going to fall. What does that mean for Nutanix? I think it puts Nutnaix in a very very interesting position. Because customers that don’t have visibility on where the platform that their mission critical and business critical apps are running, that’s a problem. If you introduce risk to the story, you’re going to have a lot of customers that are going to be looking to shift and change to mitigate that risk.

Jason Lopez: Rene van den Bedem is Principal Technical Program Manager at Microsoft. This is the Tech Barometer podcast, thanks for listening, I’m Jason Lopez. Tech Barometer is produced by The Forecast, where you can find more stories on technology. Check out Jason Johnson’s article which profile’s Rene, entitled, “Simplifying Hybrid Cloud and Migrations to Azure Public Cloud.” Just go to theforecastbynutanix.com. That’s theforecastbynutanix, all one word, dot com.

Editor’s note: Learn about Nutanix’s hybrid multicloud capabilities, compare offerings from VMware by Broadcom and Nutanix, see how to migrate to Nutanix then explore the VMware to Nutanix Migration Promotion.

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In this Tech Barometer podcast, Red Hat’s Bev Gunn and Richard Harmon take listeners into the collaborative world of open source software and explain why it can lead to responsible AI.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript (AI generated):

Richard Harmon: The fundamental principle that’s first is around a global community trying to innovate. So it’s not a single firm, but the global community collaborating. You have people with very different backgrounds, different experiences that are heavily driving innovation overall, and much of the innovation is coming from the open source world.

Bev Gunn: We have strong partnerships with Intel, a lot of the hardware manufacturers as well from the IBM systems groups to NetApp and Dell and HP, and all of the main hardware firms, plus the APIs and chip organizations. So again, just in terms of how open source and our technologies can actually enhance what they’re doing, aligned to what Richard has said is very key to moving forward.

[Related: Can Open Source Software Help Resolve AI Trust Issues?]

Jason Lopez: This is the Tech Barometer podcast. I’m Jason Lopez on today’s edition of Tech Barometer, a look at open Source. Open Source was in the headlines quite a bit during the.com boom. And rather than develop things in the seclusion of their organizations, the idea was to collaborate across company boundaries. This model has evolved over the past two decades. There’s still a bit of controversy over it versus building software in proprietary ways, but open source proponents like the speakers you’re about to hear from Red Hat, see open source as a crucial factor in developing AI in a responsible way In this era of virtualization, hybrid multi-cloud ai, edge quantum computing, it has a vital role to enable new capabilities and engineering business and governance.

[Relate: Open Source Software Powering Business Application Boom]

Richard Harmon: The open source community also has, in some cases, it’s not always profit focused. It’s about building something that’s completely new or different. It’s also about making it accessible to the global community.

Jason Lopez: Richard Harmon is the vice president for the Global Financial Services Industry at Red Hat.

Richard Harmon: But I think, for example, AI governance, there’s a lot of effort about how to evaluate the accuracy of whatever algo that you want to utilize. It’s important not to have black boxes so you can explain what the model is doing, why is it doing and what the answers are. It’s also making sure that things are done in a fair way so that you can monitor that. There’s no bias in other things that could come from the data, come from the algo, come from all different sources.

Jason Lopez: Harmon adds that the ability to audit as well as the security and privacy of applications for consumers, businesses, and governments, is paramount. This is where open source plays a role in accountability, such as in AI responsibility.

Richard Harmon: And I think that that’s something we see with many, many of the projects, the AI based projects in the open source space. Now, I’m not saying if you go into a project that’s done by a single firm, they would also require to instill those principles, but there’s also in many cases, a profit motive behind it. So sometimes when it has a wider open source view, you have people in the community that really focus on these types of things that make AI responsible versus just making AI work.

[Relate: The Race to Hybrid Multicloud Interoperability]

Jason Lopez: Companies like Red Hat provide open source tools that enable developers to innovate across various platforms while ensuring code security and privacy, including practices like confidential computing. We

Richard Harmon: Allow developers to build and innovate on any platform they want to run on basically. So it’s that kind of enablement.

Bev Gunn: It allows those developers with different skill sets, with different experiences and backgrounds and what have you to help to contribute around new technologies linked into AI.

Jason Lopez: Bev Gunn, who works in Red Hat’s global financial services, refers to what’s happening with the rise of AI in terms of speed and momentum. It’s a matter of how to develop and deploy technology smarter, quicker, and doing more with less.

Bev Gunn: In the old days, there was a certain way and methodology of working and partnering and what have you now, I said every day of the week where we have organizations who want to be part of that cultural movements of open source, of transparency, of contribution.

Jason Lopez: Open source tools like Python make advanced algorithms accessible through freely available libraries and development tools. As Richard points out, these are provided not just by Red Hat, but other companies in the space.

[Relate: IT Leaders Get AI-Ready and Go]

Richard Harmon: And eventually we’re going to have more and more of things like generative AI that will actually make learning of how to code much easier and quicker and make things much more efficient. But I think the secret, which you hit on precisely, is that this ability of democratizing access, and I think that’s the most compelling part about open source, is that it’s accessible to anyone. And I think that also is a lot about the culture of Red Hat and the culture of any company that’s in open source. It’s really a culture of sharing. It’s a culture of openness and transparency, but it’s also a culture of trying to strive to really build something and develop something that people can use and benefit from.

Jason Lopez: There’s a role for open source AI and transforming enterprise operations within the hybrid cloud environment. It allows for experimentation and it benefits from improving hardware performance, especially in automation, which is foundational to AI and makes processes smarter and more efficient.

Richard Harmon: I’ll give one example Swift right? It’s the global payment system. It’s owned by more than 11,000 banks globally, and it is really largely the global international payment rails for almost all countries. And what Swift has done is they’ve taken an open source approach, and they’re focused on building out sort of the next generation of AI platform with particular focus on trying to build out what they call financial transaction intelligence at scale. And their initial focus has been in financial crime. They’re building out a high performance AI environment, leveraging the unique data set they have, which is pretty much a very, very large percentage of all transactions around the world. That’s unique. No individual institution, or bank commercial provider has that kind of data set. They’re in a unique position to potentially build tools that the 11,000 plus members can utilize to help address financial crime aspects.

[Related: Birth of a Hypervisor That Unleashed the Hybrid Multicloud Era]

Jason Lopez: This is where AI is being enabled to allow Swift to securely innovate and information and insights can be shared with other institutions, which can build whatever models they want.

Bev Gunn: And so banks had to become very innovative in terms of the service offerings they had and have today because a lot of the new and neo banks or digital banks are spinning up with a lot of innovation. A lot of our banks are now offering almost concierge services with different capabilities and innovations. And so how do you differentiate yourself in the market, but stay ahead of the competition by still being profitable? Coming up with the example that Richard gave in one of the big banks, saving costs is going to be imperative because those costs can then be diverted and redistributed to create more innovation and more differentiated services to your customers and to the public.

[Relate: IT Team Modifies Open Source NetBox to Help Manage Hybrid Multicloud]

Jason Lopez: When we began this story, one of the facets of open source was about its effect on creating responsible ai. Open source is an enabler of transparency. It helps to maintain flexibility, allowing organizations to switch platforms. The approach enhances resiliency by mitigating risks associated with over-reliance on a single provider.

Richard Harmon: Yeah, I mean, in Europe we have the DORA, the Digital Operation Resiliency Act, and it’s around mission-critical applications, but it’s around third-party dependencies for, it’s mostly in financial service for banks. In this case DORA was the first time that the cloud providers are included in this because they are a critical third-party platform. And so the regulators are concerned that there is a concentration risk about that, and at the same time, they want to ensure that firms are doing the right things to ensure that they have an exit strategy. At Red Hat, we like to talk about portability, about moving across, basically running your application anywhere on a hybrid multi-cloud environment. But it doesn’t mean that you have to run on many clouds. It means if you’re running in one cloud and you need to demonstrate that you can run an application like core banking or payments or something in another cloud, you can demonstrate that, but it doesn’t mean you have to be running in many, many clouds. It’s the ability that allows you to avoid that lock-in effect,

Jason Lopez: Regulators aren’t just concerned about one bank, but about the entire system of banks. It’s one thing for one bank to go down, but the platform issue it’s entirely another. When you’re talking about five or six major banks,

Richard Harmon: The analogy I always make is if you look at Lehman Brothers, Lehman Brothers went down, the problem is no one knew who else was impacted, and there’s spillover effects. And so we had the commercial paper market crash. We had financial a disaster that many, many governments had to bail out the financial system to put it back into a stable environment. And it’s that kind of systemic risk that regulators are really, really worried about. So obviously they don’t want any individual bank to get into trouble or have big issues, but their big picture is the overall financial system.

[Related: Unlocking Benefits of Hybrid Multicloud IT Automation]

Jason Lopez: Today, there are millions of open source projects which reflect contributions from a diverse global community, including government, corporate sectors, academia, and students. In the last decade, global adoption of open source has significantly increased, and Red Hat has played a major role as a leading open source company.

Richard Harmon: It’s a global community from government entities, corporate, academics, and students of all walks of life that in some cases contribute to these projects. So it’s typically projects people are passionate about. Obviously at Red Hat, we have a lot of very passionate engineers, but it’s also very focused on addressing challenges that various industries have, society has. So it’s not just about building a piece of software, it’s typically about trying to make something. And even when you get into, for example, climate, there’s a project called OS Climate. I’m on the governing board of it, and Red Hat is a major participant with many other institutions, but that’s an example of a good number of institutions getting together to try to help the wider industry efforts and make things like data and analytics available to anyone who wants to access it.

Bev Gunn: And just on that collaborative piece, when we think about what we’re doing with Microsoft and AWS around managed OpenShift that’s taken place over the last five years because they’ve understood as well from an open source perspective, the strength of open source capabilities, even though they’re contributors to community as well, but actually building out managed service offerings within their own organizations with our technologies is kudos to the value that they see that this brings to the markets and to their own customers and prospects. So feeding of what Richard said and then looping in some of that activity definitely showcases that collaboration piece in the market with some of the big cloud providers, which there’s a lot.

Jason Lopez: Bev Gunn is an executive in Red Hat’s Global Financial Services. Richard Harmon is the Vice President for Global Financial Services Industry at Red Hat. This is the Tech Barometer podcast. I’m Jason Lopez. Thanks for listening. For more stories and more insights into technology and the people in tech, check out the forecast which produces this podcast. You can find the forecast at the forecast by nutanix.com.

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In this video, Harmail Chatha shares how he helped build a state-of-the-art, efficient data center using software-defined capabilities from Nutanix.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Ken Kaplan: When you started building these early on, sustainability was definitely a part of it. You want to make them efficient, but it’s shifted.

Harmail Chatha: So when I first started, sustainability wasn’t even a thing in the industry, right? You heard about sustainability farms and food production and stuff like that, but you didn’t hear about it too much in the tech sector. And of course, larger companies like HPE, and Cisco, have been at it for a long time, but customers really, really never understood it and it wasn’t very top of mind. The way that I got involved in sustainability really started in 2018, not knowing that I was trying to build a sustainable data center, but I’ve actually built one of the most sustainable data centers. And what the premise of that build-out, the architecture then was, we wanted it optimized. We wanted to ensure the amount of power coming into a rack was exactly the amount of power we’re using within the rack, and that the rack wasn’t under provision.

So we’re only using half of the rack, but we’re maximizing full utilization of the rack itself. As I started getting more involved with ESG and specifically the E portion of it, IT consumes a major part of total power in any organization and IT manages the data centers. And we started to realize, wow, we have some really efficient data centers that we architected back in 2018, and we got some not-so-efficient data centers around the world that are more colo retail, a couple of racks here and there.

[Related: Building Scalable, Sustainable Data Centers]

So for us, our multi-megawatt data centers are the most sustainable data centers. We sought out partners that were focused on minimizing their environmental impact by having a lower PUE, and by having innovative strategies around water reclamation. And that really offered us solutions and options around renewable energy as well. So that baseline with that premise set, and now we’re in 2023 and sustainability is top of mind for everybody. For example, we conducted an annual ECI, Enterprise Cloud Index, survey with 1500 global IT leaders, and 92% of the respondents said that sustainability is more top of mind this year than it was last year. With that sustainability starts at the data center. I mean, there are obviously different scopes of sustainability, but IT being the largest consumer makes the biggest impact and can have the most impact on reductions as well.

Ken Kaplan: And this experience that you’re gaining, you’ve done the work that was very successful.

Harmail Chatha: So it was an epiphany to be involved in ESG, specifically the E, and to understand that data centers I built in 2018 are a core part of Nutanix’s environmental impact and the fact that they’re so sustainable. We don’t have to go back and reinvent the wheel because our data centers are already hyper-dense. We’re already maximizing the utilization of power and real estate within the data center. On top of that, we’re already gaining more impact by having renewable energy options and being able to offset or be carbon neutral in our data centers as well. So we got lucky. We’re ahead of the game. It was very smart and intuitive for us to kind of have that focus of optimization, which has ultimately led to more efficiency in the environmental impact.

[Related: Experts Discuss Top IT Sustainability Challenges]

Ken Kaplan: Tell me about your journey, Nutanix’s journey towards sustainable IT.

Harmail Chatha: So Nutanix’s journey really started when the company started itself, right? A step in the right direction, whether they knew it then or not, with hybrid converged infrastructure was this very sustainable approach. So from the get-go we’ve always been a sustainable company per se. But really in 2018 when we revamped our data center strategy, when we went to hyperdense rack design, we even got more sustainable. We only got the amount of power that we needed for our consumption. We only got the right amount of real estate that we needed for our hyperdense racks as well. So those two steps were key in our sustainability journey. But ultimately what we focus on is reducing our infrastructure footprint. We try to consolidate as much as we can. Our standard architecture in the data center is a four-node block, so that way we’re maximizing the power in the space within the rack in itself.

So that was really the third step we took is the consolidation. And then within our platform, we ensured that we weren’t over-provisioning and underutilizing, meaning that we only provisioned infrastructure that we needed for a specific workload or a data set without an overkill of infrastructure in itself as well. We avoided VM sprawl problems as well, ensuring that we’re only running the VMs that we need to run and then hibernating or deleting the rest of the VMs as well. And then ultimately what that led to is a carbon reduction on the back end of it, right? Because our environment is so highly efficient from the data center provider to our power consumption and real estate utilization to our consolidation of infrastructure, eliminating waste of over provisioning, and underutilizing has really led to a much more sustainable carbon-neutral footprint for us in the data center environment.

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Experts in modern data center technologies and sustainability provide valuable insights for building IT sustainability strategies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Mat Brown: There is a lot of regulation coming, a lot of legislation.

Andrea Osika: Between 2021 and 2022 there was an 80% increase in regs that encompass ESG.

Harmail Chatha: If you’re going to build a sustainability strategy, budget should be the starting point. By consolidating the hardware, you’re reducing your power footprint by 35%.

Jason Lopez: What you’re about to hear are the highlights from a breakout session for IT pros who attended the Nutanix .NEXT conference in Chicago. The speakers are from Nutanix, Andrea Osika, who is laser-focused on sustainability in ESG, Mat Brown, technical marketing manager, along with Harmel Chatha, who leads global hybrid multi-cloud operations. They work on sustainability issues, which are reported in Nutanix’s annual Environmental, Social, and Governance report. This is the Tech Barometer Podcast. If your IT organization is trying to navigate ESG, this segment sheds light on IT sustainability trends and insights Nutanix has gained from evolving its own sustainability efforts. They reveal their experiences in adapting to various waves of change, including uptime and costs, security and regulations, particularly around carbon compliance and the management of greenhouse emissions through FinOps and AIOps. Andrea Osika starts off with this: as sustainability becomes increasingly central to business operations and strategy, IT professionals and executives will be expected to adapt to, in fact, their performance often measured by, a focus on ESG.

[Related: Data Center Decisions Draw on Sustainability Strategies]

Andrea Osika: When I started in this space, a lot of very large companies mostly had initiatives around this. And it was kind of seen as a nice-to-have. But if you think about the definition of sustainability and balancing today versus tomorrow, there’s business value that’s involved. And it turns out that companies who set short-term goals and balance those with sustainability metrics have actually outperformed the market in the last 10 years. So this has made investors take notice. Depending on what app you use to look at your publicly traded stocks, you might see now a sustainability tab. And we’re going to add on top of that an energy crisis and more environmentally-minded customers and even talent that your organization wants to attract and hire and then even retain. They’re all paying attention to what different organizations are doing in this space, particularly in Europe, in the UK, over in Asia. There are all kinds of pending and imposed regulations that are coming. I have been attending so many webinars and working with consultants to try and keep up. It seems like the goalpost is almost moving as we try to find a way to standardize and communicate all of these metrics. And one way is through an ESG report. I know I throw acronyms around. I’ve said it a couple of times, but environmental, social, and governance.

Jason Lopez: The 2022 Enterprise Cloud Index Report, or ECI, showed that 95% of IT professionals saw sustainability becoming more crucial in business decisions. The more recent 2024 ECI report shows IT pros are actively integrating sustainability strategies with their IT modernization efforts. Many organizations have become more data-driven about sustainability. 51% of organizations say they improved their ability to identify areas for reducing waste. 44% indicate they improved their ability to monitor and measure greenhouse gas emissions, as well as their carbon footprint. In the breakout session, Nutanix experts explained how the environmental impact of technology goes deeper than power usage and metrics. There’s the lifecycle of IT hardware, starting with the mining of metals and minerals, the manufacturing process, packaging, shipping, the management of devices in an IT setting, and the eventual disposal of the devices. Mat Brown identified a starting point for a business case. Dive into getting educated about sustainability facts, build relationships with like-minded individuals and organizations, plan and implement, identify the metrics relevant to the organization’s operations, and automate the measurement of those metrics.

[Related: 4 Steps for Building an IT Sustainability Strategy]

Mat Brown: IT is both a big part of the problem, particularly for companies that aren’t involved in manufacturing. They are often emitting the most carbon emissions through the use of their data center and other IT services. But technology is also, of course, a massive part of the solution, potentially. Whether it’s digital transformation, removing paper processes, automating lots of operations, smart building management to be more energy efficient, improving logistics and mapping out routes and things like that. On the one hand, we’re this big consumer of energy that’s being asked to consume less. And on the other hand, we’re also the platform where they want to do all of this smart, intelligent AI and ML stuff to improve the world around us. And what we’re starting to learn is that everything has a footprint and everything adds up.

Jason Lopez: As Andrea Osika talks about frameworks used for measuring power consumption and measuring carbon emissions, she introduces the concept of scope. Each scope, from Scope 1 to Scope 3, classifies emissions based on their source and whether emissions are direct or indirect.

Andrea Osika: Scope comes from the greenhouse gas protocol, which is the basis for all carbon emissions reporting worldwide. It’s essentially the gold standard for how companies report their emissions. And it’s broken down into three categories. They are in the order of descending control of the reporting organization. So if we start at the top, those are the direct emissions. That’s scope 1. And these are the emissions that are released into the air, typically from combustion, from burning something. And if you think about what that means in real terms, that’s like if your organization has a fleet of vehicles or the natural gas that’s used to heat your facilities. Those are two kinds of concrete examples. Scope 2 are indirect emissions. And they are a result of energy that is used to produce power. So typically this is in the form of steam or electricity. So electricity in your organization’s facilities and your data centers. And then scope 3 is the easiest one for me to explain. But it’s the most challenging. Even the greenhouse gas protocol tells us that this is something that they need to continue to work on. But it is literally everything else. This is your value chain. This is your hardware, your OPEX, your CAPEX. This all falls into scope 3. This is how your organization will report your emissions. And if you need an easy way to remember scopes 1, 2, and 3, here’s how I’ve learned. The energy that you burn. The energy that you buy. Everything else.

[Related: Experts Discuss Top IT Sustainability Challenges]

Jason Lopez: Focusing on scope 2 and scope 3 emissions is where Nutanix and other tech companies see the most opportunity to influence their overall carbon footprint, either through improving energy efficiency or by addressing the broader impacts of their products and services on the environment.

Mat Brown: How far does the rabbit hole go? Well, it certainly goes to embedded emissions in the hardware that are built and shipped to a facility. That’s one thing. Any cables you buy to plug in. It also includes any cloud services you may use, SAS services, bare metal services, and things like that. Those are probably a lot more tangible that you can get a feel of. Some providers also will be able to feed back to you the emissions associated with those services provided. Others less so. How their methodologies work probably are on an ongoing basis, but often they’re estimated using monetary values or something like that. Scope 2 is really where we talk a lot more to our customers about. So when a customer has their own data center, the electricity used to power that IT in the rack, that is a scope 2 emission. Somebody somewhere is burning fuel, gas, coal, or even renewable energy comes with a carbon footprint. That all adds up to power the IT equipment that we have running in our data centers.

Jason Lopez: This is where Harmel Chatha comes in. He’s responsible for running Nutanix’s hybrid multi-cloud IT operations. He explains how Nutanix and its customers leverage software-defined hyper-converged infrastructure, which brings operational efficiencies and helps manage and scale resources wisely. Chatha talks about using fewer resources, generating less waste, and avoiding unnecessary complexity by relying on modern IT solutions.

Harmail Chatha: We reduced our infrastructure footprint. We consolidated where we could. We got away, not that we ever had, three-tier architecture as well. We only deploy hardware that’s required. It’s not deployed in a safe manner on the over-provision just to be safe that I have enough capacity. Step two of it is really eliminating waste and over-provisioning. You only buy what you need, you only deploy what you need to deploy, and you only run the services that need to be run. And step three is really the benefits of step one and two, which leads to carbon reduction. That means you can deploy your applications anywhere you like across any region, wherever the power is cheapest at that given point in time. Now that we’ve got some context around what can be done, let’s apply to what we have done. So Nutanix is about 12 years old. We inherently are sustainable from the HCI perspective because we integrate, converge, compute, storage, and networking into our stack. Since 2010 when Nutanix started, we already had a sustainable footprint, whether we knew it or we didn’t. Nobody was really measuring sustainability at that point. But then in 2018, we took a bigger step. We revamped our data center strategy from our data center where we initially started to a new strategy where we consolidated our racks. We’re populating our racks with 92 nodes. That is 23 blocks in a single rack. And what we gain from that is 68% more efficiency from an OpEx, CapEx perspective, and consolidation perspective. If we are already an HCI stack, we’re already 100% virtualized in our environments. There is no bare metal one-to-one environment in any of our data centers. The last couple of years, we voluntarily started reporting our ESG reporting. And this is so complex. So for us, sustainability has been and will continue to be a journey, not just a one-off set destination that we can check off now that we’ve done our ESG report. We’re super sustainable. We’re not by any means. That first year, we only focused on Scope One, Scope Two, and Scope Three related to our data centers and related to our offices’ total power consumption.

[Related: Building Scalable, Sustainable Data Centers]

Jason Lopez: Understanding what to measure means identifying the relevant metrics that would accurately reflect the company’s impact on the environment. Then it’s determining how to measure. No small feat given the number of locations and data centers. To manage and analyze this vast amount of data, his team consolidated the information into Google Sheets as a starting point. Partners with the expertise to measure emissions helped. Chatha says it seemed they had a baseline.

Harmail Chatha: But no, we didn’t. The second year, we learned more. We gained a better understanding of how to measure. The data was more accurate, so hence our emission reporting was much more accurate. But in year three of our ESG report, we implemented a SaaS platform that actually aggregates in real time all of our power consumption from our leased offices and our data centers. So we can go to the platform and actually check it out in real time which location is utilizing how much power and what the footprint of that is. We got away from the Google Sheets. We’ve got a platform now. Data is more accurate. Our emissions calculation is more accurate.

Jason Lopez: A strategic approach to sustainable IT begins with the optimization of the infrastructure platform. As Mat Brown describes, it offers the potential for significant environmental benefits and supports things like the deployment of sustainable applications and better management of hardware life cycles.

Mat Brown: If you can use less hardware, then that obviously is a good thing for embodied emissions. There’s less power supply potentially in that hardware. That’s another good thing for reducing power consumption. But if you think about how you can reuse or repurpose IT equipment, there’s recycling programs in place. You have the choice of hardware vendor, even down to choice of components, whether that’s an Intel CPU or an AMD CPU. You can look at their various properties, features, et cetera, and see what fits your business requirements around your hardware life cycle and circularity the best. So take up that choice where you can.

[Related: Green Data Centers: Designing a Sustainable IT Future]

Jason Lopez: Different electricity grids in different countries or regions have different carbon intensity. Carbon intensity refers to the amount of CO2 produced per unit of electricity generated. Grids that rely heavily on coal or oil have a much higher carbon intensity compared to renewable sources like wind, solar, or hydro.

Mat Brown: For example, if you had a data center in Poland and you were able to magically shift it up to Sweden where there’s a lot more renewable energy with a lot lower carbon intensity, that could be a 30 times drop in the use phase emissions for that platform. In order to work out how much IT platforms emit or are responsible for in terms of carbon emissions, you’ll need to know that carbon intensity grid factor, which you can get from a variety of sources. And you’ll also want to know something called the power usage effectiveness of your facility. Those tend down towards one, the lower being the more efficient. But it’s not just all about the PUE. Reducing power at the rack can be much more effective than just shrinking a few percentage points on the power usage effectiveness of the data center. But being able to place your workload potentially where it’s going to run in the most efficient way is very key to this whole physical location, power, and environment.

Jason Lopez: He said Nutanix emphasizes the critical dynamic between infrastructure and power usage. Modernizing data centers increases flexibility, scalability, and encourages more innovation in IT while consuming less power.

Mat Brown: Because that platform’s all integrated in a single pane of glass, you can then start working on your operational efficiency more and more, optimizing the actual demand, optimizing what consumers of IT are asking for, doing right sizing, using AIOps for improvement, reducing waste, oversized VM everywhere, implementing show back, charge back, or even recently I heard the term at a FinOps discussion, shame back, to shame people who weren’t implementing their optimizations effectively. You can put the impact in front of the user, the application owner, and show them what they’re responsible for and feed back to them, then they can take on more responsibility for driving optimization.Jason Lopez: Mat Brown is Senior Technical Marketing Engineer at Nutanix. Harmail Chatha is Nutanix’s Senior Director of Global Cloud Operations. Andrea Osika is Marketing Manager of Sustainability at Nutanix. What you heard were the highlights from a stage presentation on sustainability given during the .NEXT conference in Chicago in 2023. This is the Tech Barometer podcast. I’m Jason Lopez. Tech Barometer is produced by The Forecast. For more podcasts and stories about technology and the people in tech, you can find it at theforecastbynutanix.com.

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In this video, learn how a cloud operations expert thinks about power efficiency, software-defined infrastructure and how to get more with less from data center innovation.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Ken Kaplan: What does the data center signify to you? What’s the role?

Harmail Chatha: So whether people know it or not, data centers are a very crucial part of everyone’s daily lives, right? All the applications that we have on our mobile phones and our desktops, all the websites that we visit come from a data center. A public cloud is actually a large scale data center in itself as well. So anything that’s tech related goes back to a data house where it’s hosted and hence the services are provided.

Ken Kaplan: Everything we do from health to going to the grocery store increasingly touches the data center.

Harmail Chatha: Absolutely. Health for sure. All the images, all the applications that are used within the hospitals as well. The POS systems and restaurants and grocery stores for sure. Airline industry, when you’re booking your tickets online, that’s all being served by a data center. So really just about anything digital has something to do with the data center.

Ken Kaplan: Part of building the data centers that you’ve helped build: a critical part is the hardware that needs to go in and the power consumption that needs to run it. How is that changing?

Harmail Chatha: So I’ve been in the data center industry since 2005. Back in the day it was small, 42 U racks, maybe three to four kilowatts per rack of power utilization. Maybe you half fill those racks and you had multiple power coming into the rack from a redundancy perspective. But to the point we’re at now, we’re pushing multi kilowatts into a rack. Specifically at Nutanix, we’re between 17.3 to 34 kilowatts per rack. Racks have gone very high density in the sense that we’re able to deploy a lot more compute, especially with hyperconverged infrastructure where you’re able to consolidate storage, networking, and compute into a single server. You’re able to really impact and pack these racks up with compute, which ultimately requires a lot of power and therefore requires a lot of cooling from the data center provider as well. So we call it a hyperdense rack design.

Ken Kaplan: You’re talking a lot about hardware, but software’s playing a key role here, just capabilities you’re being able to take on because of the advancements in software.

Harmail Chatha: Yeah, so software obviously is a huge part of it. One, starting at the lower tier infrastructure consolidation is a first step in the right direction, but ultimately it comes down to the software. Our data centers are software defined from a networking perspective. It’s all white box switches and we control the entire network through software itself. Our infrastructure is all controlled through clusters that are provisioned through AOS, AHV and Prism Central. And what that’s leading us to is infrastructure has a three to five year lifecycle, but we’re actually pushing that lifecycle longer now through our software. Because the software is getting more efficient, it’s better able to utilize the underlying hardware, therefore we’re able to use that hardware on a five to seven, eight year cycle in itself. So software really is becoming the core. Infrastructure is agnostic at the lower tier and software is dictating the lifecycle.

Ken Kaplan: How did you connect with your data center most recently to check on it and see how it’s doing?

Harmail Chatha: Yeah, so our data centers are lights out data centers for the most part. Our bigger data centers, we do have some folks in them, but because they’re so software defined, we don’t have to go to the data center every day. The ones that are lights out through hardware and making sure we have enough redundancy. Then the software being able to manage the VMs. If we do have a hardware failure, there’s really no need for us to go to the data center. And because we got a blueprint for scaling out our data center when we are at a capacity that’s really simple for us to expand. Example would be during the pandemic, we expanded one of our data centers by two megawatts during the pandemic. And that’s no easy feat, right? That’s a lot of physical build out. But because I’ve got such a great team, we got such amazing architecture that was ahead of its time, we’re able to leverage that in addition to the partnerships that we built as well. So there really isn’t a need for me to go to the data center anymore. I think ever since the pandemic started, I’ve probably been once and that was more to check in on the team, make sure they’re doing good in our larger data center. But the remote ones I haven’t been to in several years.

Ken Kaplan: Now, how about virtually? Are you checking it two or three times a day? Are you looking at through your laptop on your phone? What is that part like?

Harmail Chatha: So virtually, we’ve got a lot of alerts set up for the data center as well. So if something does go wrong, a power PDU goes out, cooling is inefficient, we’ve got systems in place where we get alerts that’s at the infrastructure tier from a data center perspective. But for our infrastructure of Nutanix and the cluster that we manage, they’re all software defined as well. We get alerts through Prism Central. We’re able to act on those alerts remotely. Even if there’s a failure of a hard drive or a power supply, we’re able to call some people in and just get that fixed. But the data center interaction is really mostly at the software level now and not at the physical level.

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In this video interview, Mat Brown, senior technical marketing engineer at Nutanix, discusses the increasing importance of data center sustainability strategies and the challenges of managing carbon emissions from IT operations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcription:

Mat Brown: Increasingly, our customers are asking us about sustainability issues and about ESG. They want to know about the carbon emissions related to their IT solutions, whether they’re running them in their own data center at a co-location or in public cloud. These are becoming top of mind for more and more customers all the time.

It’s especially grown up over the last couple of years. What’s really kicked that on has been the energy crisis that’s built up in Europe especially. But then more and more regulation is coming out of the EU and in other places around the world, and that’s driving these sustainability challenges for businesses everywhere.

[Related: Three Trends Defining IT Sustainability in 2024]

Eventually it’s going to be have to embedded into every part of the business they do. I’d expect people will be doing sustainability training, like they do security training, and I expect that people will be reporting on their organization’s and missions like they currently report on their financial practice.

Yeah, so we’ve kind of mapped out four steps on the road to progress, as it were for organizations. Of course, the fundamental basis for everything is a better understanding of impact, but also an understanding of what your business goals are. Who are your allies? Who can you talk to and what resources are at your disposal? From there, you can understand what you need to measure, start implementing the processes to make those measurements, and then on that basis you can then plan and transform and build a business case for future activity as well.

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In this Tech Barometer podcast, explore how data management and security innovations are bringing resilience to escalating threats of data destruction and exfiltration.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

John Dodds: Data governance and regulatory frameworks are creating the impetus to involve ransomware. It’s like creating the external factor.

Tuhina Goel: Today it has really evolved into pure data destruction, data exfiltration. So that’s one major difference we’ve seen in the last couple of years.

Jason Lopez: On this edition of Tech Barometer, ransomware. The latest trends and developments from two people behind the scenes working on technologies to keep data protected.

John Dodds: Every new cool technology comes out and solves an amazing problem immediately makes security more difficult because it’s new.

Jason Lopez: In 2023, the costs of cybersecurity and the payouts to ransomware criminal organizations rose to unprecedented levels. Businesses lost over a billion dollars in payouts. Hospitals, school systems, public utilities have been prime targets.

John Dodds: How can we go from detecting as fast as possible to closing the risk holes before they even become exploitable? That’s where AI is going to be a big deal because we have the power at the edge now to make it a reality.

John Dodds: The privacy thing has really created a big problem because it’s not just a matter of, oh, I can ignore the ransom and maybe I had good backup hygiene and things like that.

Jason Lopez: John Dodds is a cybersecurity expert who’s part of the product management team at Nutanix. In the interview we did with him, he cited multiple factors which explain the rise of ransomware.

John Dodds: The data itself creates a problem because with all the different privacy regulations and data sovereignty rules and regulatory frameworks, it started with GDPR, moved into California Consumer Privacy Act. And now we have regulations in the European Union like DORA. All of these regulations are putting an emphasis on the data custodian’s responsibility to protect that data. Everyone knows that these companies could be held liable.

Jason Lopez: One kind of organization is especially vulnerable… health care companies. And they are increasingly vulnerable when hackers don’t just lock down data but steal it.

John Dodds: That information is so sensitive and so protected. When they get attacked and there’s any chance of exfiltration, they almost have to pay the ransom in some cases because penalties for violating HIPAA and violating all these other things, not just monetary, but also the human damage is getting very, very, very severe.

Jason Lopez: Many organizations are legally responsible for keeping the data on their platforms secure. But this exists, he says, within a compute environment that gives hackers numerous entryways. Dodds points out that, as users, we have more data freedom than ever before.

John Dodds: We have hybrid workforces coming in and VPN aggregators. We have people allowed to access corporate data on mobile devices like iPhones and Android devices. Security teams 20 years ago would have never have let that unrestricted, multidimensional access happen on corporate data that was considered sensitive. The reason why we have this power and flexibility of the modern hybrid workforce and the hybrid cloud environments out there is because these technologies have gotten much more seamless and much better.

Jason Lopez: In the past many organizations might ignore a ransomware attack. But lately, more are paying the ransom. When you add up all the variables from sensitive information, data freedom and liability, the world which organizations operate in has become far more complicated.

John Dodds: Complicated is the right word. The ransomware attacks aren’t going to stop, but how we react to them is continuously complicated by everything that’s going on in the data governance world at the same time.

Jason Lopez: In this simple hypothetical, Dodds illustrates how, along with threats from hackers, IT departments have to deal with regulators.

John Dodds: There’s one thing that I could say that could probably scare you. Do you process credit card data? If we were in a legal case and someone comes up and says, show me every single file that this person touched because I need to know if they had access to insider data or something like that, we were finding a lot of IT administrators sitting around with those kind of, I don’t know what to say because you’re not going to like the answer type of face.

Jason Lopez: Increased entry points for hackers, more data governance… those help explain the rise of ransomware attacks. There’s also been a change in the extortion itself.

Tuhina Goel: There are chances that you will be asked for additional ransom before you can eventually get access to your data.

Jason Lopez: Tuhina Goel is the director of product marketing at Nutanix

Tuhina Goel: So there has been instances where corporations, they were asked to pay ransom, then they were asked to pay additional ransom. And in some cases, they still did not get back access to the data.

John Dodds: That secondary ask is absolutely diabolical because once you’ve made that threshold where you said your best business decision is to pay the ransom and you have that sunk cost, I hope I never end in a situation where I have to make that sort of decision.

Jason Lopez: If Dodds is right, if ransomware isn’t going to be stopped in its tracks anytime soon, what can an organization do?

John Dodds: The best way to protect yourself is to assume you’re going to get attacked and make sure that you’re doing everything to prevent it at the very beginning on the clients and on the front end, and then ensuring that you have those immutable air-gapped backup copies and snapshots somewhere so that if it does happen to you, it’s an inconvenience that you can recover from. That’s the best way to make it worthless to a hacker.

Jason Lopez: He says practices like using strong algorithms and salting of keys helps to make data useless.

John Dodds: The way that you devalue the data to a hacker is by proper cyber resiliency built in with many layers of defense, including air-gapped backup copies. So the reason why that data will no longer be valuable to the hacker is if they can’t get into it and it’s properly encrypted, it’s only valuable to a hacker if they have your only good copy of the data.

Jason Lopez: Tech innovation since the rise of the Internet, such as mobility or the cloud, have certainly given companies more tools. But with that, more to protect.

John Dodds: It’s a cloud adoption question. Every new cool technology comes out and solves an amazing problem immediately makes security more difficult because it’s new. I hate that that’s the way it usually ebbs and flows. But generally, there’s a reason why cloud was slowly adopted by financial services companies and healthcare companies. They were very conservative on the security side, and they were like, let’s wait and see before we bless this stuff.

Tuhina Goel: We talk about cloud native applications and how everything’s going to be born in the cloud. And we ourselves as Nutanix, we support hybrid cloud, right? So we are more than an on-prem company. But there are industries whose first preference is to keep their most coveted asset on-prem because the control measures are much more in control, right? So it’s just easier to manage that.

Jason Lopez: Since the advent of the Internet, and by extension the cloud, it’s not as if cybersecurity has been after thought. Technologists have been working on it all along. Nutanix’s cybersecurity journey began with storage.

John Dodds: As storage got bigger, as audit logs became bigger, as threats became more diverse, permission structures are inherently super complex, we found out that we needed to move to cloud scale. So that was kind of the evolution of data lens, which was let’s do everything we can to make NUS as easy to use to its highest degree for our customers as possible. Because we didn’t just want NUS to be a great tool, we wanted our customers to be able to use what made it great. Then we ran into some scale limits, and that’s kind of how data lens was evolved.

Jason Lopez: Data Lens is designed to give ransomware resilience to unstructured data on the Nutanix Cloud Platform. It focuses on proactive detection and rapid containment to prevent security breaches before they can cause significant harm. It uses one click recovery and offers tools for compliance and risk analysis. The precursor to Data Lens was a free tool called file analytics, which was essential intelligence for managing data and storage.

John Dodds: Everyone was playing catch up on ransomware on files-based storage systems. We’re trying to learn from that and stay ahead of ransomware on object storage systems.

Tuhina Goel: One of their recent emerging technologies is cyber storage, where the expectation is the storage systems will be able to defend themselves against ransomware, especially the unstructured storage, which is files and objects. So the idea is that inherently the storage system should have capabilities to defend itself against ransomware.

John Dodds: It was much more complex to defend security threats on objects because they’re the current state of the art in terms of sophistication in a lot of cases, and there’s no established kit for object-based ransomware. So we saw the benefit that we had on files, and we didn’t want object storage systems to be left out. They are a core source of unstructured data now. So at the same time we did this ransomware containment window, we added support for NUS objects auditing and data lifecycle reporting and anomaly detection.

John Dodds: We’re legitimately investigating uses for real AI models now. We’re starting to see that the chips have become powerful enough. The models have become sophisticated enough for these multi-ontology LLMs to really start to do some really cool things for security and we’re actively researching, how can we take our ML-ish types of things that are more statistical? How can we use AI to push that to the appliances themselves? Security is actually a really good place for it because it is a complex layer of permission structures that were human designed constructs that were put on top of a file system, constructs that were put on top of a directory services infrastructure, data that’s being managed by thousands of individual little unique humans. We can’t set one policy that everyone will adhere to. That’s where we’re actually pushing the boundaries with AI. We’re taking a lot of our machine learning and basic AI stuff and saying, we have the data, we have to compute. We can now not just proactively monitor for threats, we can proactively identify the risk before it becomes exploited.

Jason Lopez: Dodds essentially says that everything is on the table to not only help protect against ransomware, but to stay ahead of it. So, AI is one tool. What are the others?.

John Dodds: The real answer is we need to already be thinking about adopting the post-quantum cryptography.

Jason Lopez: His team is already looking at three algorithms approved by the National Institute of Standards and Technology.

John Dodds: We’re already evaluating how we can implement Crystals Kyber and Crystals Dilithium, which are two of the NIST approved quantum resistant algorithms. The problem is always going to be, it’s all theoretical.

Jason Lopez: That’s a key concept in the discussion. As much as technologies and cutting edge advancements have a major role to play in protecting against ransomware, both Dodds and Goel place the factor of human nature prominently in the conversation about cybersecurity.

John Dodds: You want to know why I love hackers? Because I feel like they’re my people. If there’s something that I can find that defines a hacker, the attribute that first comes to mind is laziness. The laziness is winning because there’s easier targets. Ransomware hackers can go out there and try to reverse engineer and circumvent all these protections. But why do that when it’s still easy to check out someone’s LinkedIn profile and find someone at the end of the day who’s thinking about what they’re going to make for dinner that night, call them up on a phone and try to socially engineer them?

Tuhina Goel: There’s no silver bullet when it comes to ransomware or protection against ransomware. People and processes play an as important role as technology does. It’s a people problem. It’s a people loophole. So it’s very important to also pay attention and time and energy budget towards prioritizing employee training and awareness, because that goes hand in hand with what technology can do.

Jason Lopez: Tuhina Goel is director of product marketing at Nutanix. John Dodds is a cybersecurity technologist at Nutanix working in security and data governance product management. This episode of Tech Barometer is produced by The Forecast. I’m Jason Lopez. You’ll find more podcasts and articles like this one at theforecastbynutanix.com. That’s www.the forecastbynutanix.com.

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In this Tech Barometer podcast, four IT industry experts discuss trends and early best practices shaping IT sustainability strategies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Jason Lopez: The sound you’re hearing is in the room at a press conference on IT sustainability, which was done at .NEXT in Chicago in 2023. You’re listening to the Tech Barometer podcast. This was a gathering of journalists in a round table setting, sitting elbow to elbow with subject matter experts, Chris Kanaracus, Research Director of Cloud and Edge Services at IDC, Steve McDowell, Principal Analyst with NAND Research, Harmail Chatha, who oversees data centers at Nutanix, and Steen Dalgas, who at the time of this recording was a London-based cloud economist for Nutanix. This session wasn’t necessarily closed door, but it wasn’t being recorded, and we thought we’d step in just as it started with a Zoom recorder to capture it. It starts off with Steen talking about the focus on frequent hardware upgrades to boost business productivity. He says it’s short-sighted. Instead, he sees a major shift toward software-driven innovation, which extends the life of hardware and scales to meet growing demands and growing sustainability strategies.

Steen Dalgas: I looked at the car industry. They’re much more mature in terms of their sustainability dialogue than we are in the IT industry. So the topic there was embedded emissions, which is the emissions in the manufacturing process. This whole area in the IT industry is not being actively considered. I mean, I used to work at IBM. So you think about the business model of all the big vendors, their hardware vendors. They have this sort of strict end of life, and it’s all about trying to effectively force customers, if you want innovation, to buy new hardware. And the Nutanix approach is completely different. So we come up with the approach where actually the hardware piece is commodity, and all the innovation should come from the software layer. And that’s more sustainable by design. So what that means is every six months, our customers get new features to deliver through the software, and we make the upgrade process really simple. And actually what happens is when we deliver a new upgrade, often the hardware performance improves as a result of the software design. I personally don’t believe the numbers I’m seeing from the hardware vendors in terms of embedded emissions. They’re saying it’s like 10% of the life cycle. I don’t believe those numbers because I’ve seen other evidence that it’s actually a lot higher.

[Related: AI Reorients IT Operations]

So the embedded emissions are the emissions from manufacturing, assembly, and then shipping. And that can often happen. The assembly can be in a different country. So you’re shipping to two different countries. And if you’re talking about three-tier architecture, the server is made in one country, the storage, the networking, and they all have to be assembled together. You’ve got emissions every time you refresh.So the obvious answer is to extend out the life of hardware and deliver innovation through software. So this wasn’t a Nutanix designing this to be sustainable. This was a smarter way, because we want to get away from this forcing customers to buy new hardware every five years and actually extending out the life of hardware. And we know that the hyperscalers are going down the same route. They’re looking to extend out the life of assets up to six, seven, eight years. And I’m working hard with our internal folks. The one issue that we have in Nutanix is you’ve got to test that the software works on the hardware. So we’re actually going beyond end of life. And we can see, so the future is that hardware life will be extended out. And the hardware vendors don’t really want to hear that, but I think that’s, I think, where the industry should be going.

Steve McDowell: Yeah, so I agree with everything you said, right? And I look at it from the bottom up. Technology is intrinsically dirty. Data centers consume something like one and a half percent of the world’s energy. I saw a statistic when I was preparing for this that it generates almost 1% of the CO2 every year in modern times. We’re not going to get off that train, right? We need this technology. We just need less of it. And the way you get less of it is to optimize its usage, with cloud I’m sharing a CPU among X number of instances. With technologies like Nutanix delivers, I’m optimizing my workloads and consolidating them. And that’s a sustainability play. And it’s not a save the planet play necessarily, because industry’s motivated by profit. But there’s regulation in the EU, there’s regulation emerging in the US. 89% of institutional investors look at ESG stories when they invest in technology companies. Yes, they’ll save the environment, but there’s also a huge profit motive to do the right thing. And the way you do the right thing is to efficiently manage your resources.

Harmail Chatha: Well, I feel like we’ve been trying to tackle edge community for like 10 plus years now. That’s still a very hot topic. Everyone’s creating these edge data centers and what does that really mean?

[Related: Building Scalable, Sustainable Data Centers]

Jason Lopez: Harmal Chautha illustrates that the world of IT is evolving rapidly. As data center hubs connect with more dispersed computer resources, such as branch offices, franchise stores, warehouses, or manufacturing plants powered with robots and thousands of connected devices.

Harmail Chatha: Prior to the last couple of years, it was a few hundred kilowatts was your edge. And now we’re pushing it to a megawatt plus is becoming your edge. We conducted our enterprise cloud index study with over 1500 IT decision makers globally and 92% are saying their top of my initiative is corporate sustainability and how do they tackle that? And I think the same conversation holds up. How do you optimize your infrastructure, reduce your footprint, even if it’s at the edge or it’s at your core data center? How do you get away from this three-tier architecture? Consolidate the infrastructure, eliminate the waste and the over-provisioning that happens in a typical three-tier architecture. You’re definitely going to over-provision on compute, you’ll over-provision on network and storage. So how do you eliminate that? You should only be deploying what you need, not over-extending. With the Nutanix infrastructure, even if it’s at the edges, you buy what you initially need, then you scale out. And you maximize that utilization of it and you can build metrics at one point, do you expand the cluster or not? And that inherently will reduce your carbon footprint as well because you’ll be consuming less power and less resources. So wherever the play is, whether it’s in the edge or primary or robo-offices, just this notion of consolidation is tremendous. And going to, again, to Steve’s point is, optimizing at the software tier and then extending the life of the hardware.

Jason Lopez: It’s becoming more important, the shift to software-defined IT strategies, which leverage hybrid multi-cloud resources. Chatha says the mindset today is centered around maximizing resource efficiency in the face of growing demands for more powerful computing resources.

Harmail Chatha: We’re testing GPUs in our environment with our software. And of course, they’re very power-hungry, they’re very intensive. But it goes back to that same point again, is how do you make use of consolidation versus just expanding horizontally? We have this notion in our data centers when we designed them in 2018, they’re optimized for vertical growth rather than horizontal growth. We recognize a 68% reduction in OpEx, CapEx by optimizing vertically versus horizontally. The IT initiative has to be optimization rather than playing it safe and buying so much hardware, so much compute, storage and network, and over-provisioning. And that inherently has been the go-to model in IT versus now IT is having to refocus and say, okay, how do we optimize within the stack rather than horizontally at the hardware tier?

[Related: Making the Future of Streaming Media More Sustainable]

Steen Dalgas: I’ve just been working on a case for the last three weeks on an edge case in partnership with a data center company. And what that highlights is Nutanix doesn’t have all the answers, the data center companies don’t have all the answers. It’s all about how you can leverage the best team and each individual player brings their best foot forward. So on the data center side, we’ve started working with a, in the UK, a company that’s come up with a pod-like design. So traditionally, if you go to a co-location data center, you’re limited to five KBAs, that’s pretty industry standard. What that does is it limits the design because you only fill the rack a quarter full. But this company’s come up with a design of 20, so 20 KBAs, so we can actually fill out the whole rack. It’s a two-part design, so you have a rack of servers, HCI servers, and then a rack of all the data center infrastructure. That would be really well-designed for edge AI-type cases. And it can just sit in a car park. And from a cooling perspective, it’s incredibly efficient. So with air cooling, they’re at 1.3 PUE, but you could potentially put liquid cooling into this, which would bring it down further. So this is the type of innovation partnerships where the HCI form factor of our software, our design, plus a really good data center, innovative partner, could create a brilliant solution. And if you compare that to traditionally, you’re going to be looking at racks and racks to fit within the normal rules. So this is how we need to go out to the market, find the best partners, and then bring these solutions to our customers.

Steve McDowell: And the hardware guys are not ignoring this either. As Steve just said, we’re pushing liquid cooling to the edge. I’m involved in a project now that’s using two-phase emerging cooling, where we’re putting a megawatt and a half into a rack about the size of a freezer in your garage. So we’re driving that down. And to put a number on it, 20 to 25% of data center IT is for heating and cooling. So while we’re driving consolidation with intelligence software, we also need to solve the cooling problem. And we’re pushing on that from a different direction.

[Related: The Amalgamation of AI and Hybrid Cloud]

Steen Dalgas: We have a huge problem globally with legacy IT. It’s incredibly inefficient. So if you went from a legacy infrastructure to an HCI in a new data sensor, you’re going to see a 80% reduction is pretty normal. Can we get companies out there to make that plunge?

Harmail Chatha: Well, I think the other thing is, hardware providers, software companies like us can continue to push the envelope, but are the data centers keeping up well? Some of these legacy data centers don’t have enough power to support vertical growth in the rack, and don’t have enough cooling to support that. So then you’re stuck at single-digit KBAs, whereas companies now are definitely going to liquid cooling and pushing the envelope even further, but then data center providers have to do their part.

Jason Lopez: The conversation moved to environmental responsibility, and how it’s becoming a crucial component of business strategies. There’s the rising cost of energy. There’s the increasing cost of maintaining legacy hardware inside data centers, which can hamper sustainability efforts.

Harmail Chatha: All of the European data center providers are definitely pushing the envelope on this. They’re a lot more ahead of the game, I feel, than the U.S. data centers, as far as just innovative technologies, innovative design. For them, sustainability is very top of mind, versus in the U.S., we personally seek out data center providers and partners that want to minimize the environmental impact, that have innovative solutions like water reclamation, that are offering renewable energy options. And I feel like the U.S. market just hasn’t caught up, besides a couple of top-tier providers that we work with, versus the rest of the, well, especially Europe. I don’t think AsiaPAC is definitely there.

[Related: Rapid Spread of Hybrid Multicloud IT Drives Need for Simplification]

Steen Dalgas: We’ve had the energy crisis in Europe, so we’ve seen the cost of energy go up almost five times. Really focuses the mind to suddenly, so I do total cost of ownership models. Energy is now the single biggest expense item now in the IT budget when we’re looking at infrastructure. So it’s cost-driven, yeah. So another thing that we’ve seen in Europe, which is all around the same area, so climate risks are now becoming a business resilience issue. So in the U.K., you’re designed to run at a certain temperature range, based on the sort of standardized models of climate. And those climate models don’t apply anymore. So it’s been broken in the U.K. So we had a 42-degree day. The impact of that was, so Google’s data center in the south of England went down, which took down one of the Challenger banks. It also had impacts in other regions as well. And one of the big hospitals in London had a failure of its production and DR data centers. And the impact for them was, they were on paper records for two months. So you can imagine the stress it puts on the staff and the impact on the patients. And there was a big cost impact as well. And that’s because that’s a legacy design data center. And the more infrastructure you have, the bigger the physical footprint you have, the more cooling you need. Those are the types of data centers that are more susceptible. So this whole sustainability piece I’m trying to highlight is a business resiliency issue.

Jason Lopez: Next, transforming IT infrastructure with a Nutanix customer in India. Harmail Chatha outlines how HCI technology reduces costs, emissions, and complexity.

Harmail Chatha: In working with Nutanix, they’re able to reduce their cost by 43% over five years versus traditional three-tier. They’re able to reduce their data center emissions by 80%. And they’re able to reduce their data backup management by 100%. So what’s happening is, IT just needs to rethink and refocus and modernize of getting off the traditional three-tier and moving to something that’s more efficient like HCI models. And with HCI, you don’t have just the option to reduce your physical footprint on-prem, but you have so many options, whether it’s your private data center or your colo or even public clouds. For us, I mean, the ecosystem has evolved so much now. You can get away from that traditional DR strategy of having something remote in a different data center versus now we have a product called NC2. You can offload your critical VMs right back to the public cloud and have them in a hybrid mode there, sitting there versus just running infrastructure that’s doing nothing, literally sitting idle on-prem. So I think it just needs to be a refocus of not focusing too much on, this is the way we’ve always done things and this is the way we’re going to continue to do things and really go out and seek innovation and opportunities to improve. Sustainability is a great starting point, but really it’s just our entire world runs on apps and data. And when you have issues like Steve was describing in the UK where the hospital’s offline for two months, that’s very critical.

Steen Dalgas: There’s a story just to illustrate that. So in the UK, one of our customers is the 999 service. So if you have an emergency, you ring them up. And they did things the wrong way around. So they decided to modernize their data center and then once they did that, they then decided to choose their IT partner. They then built this great, huge new data center room, went to Nutanix and we were a one rack solution. So you have a huge data center with a one rack. And this is what I mean is that you’re bringing a legacy mindset to solve a problem that you’re not taking advantage of the modern innovation. You have to do it in reverse. So think about what you’re going to have in a rack and then the data center and do that together, not separately.

[Related: Creating AI to Give People Superpowers]

Steve McDowell: But there’s also a trend, too, of thinking less about the infrastructure as an IT guy. Cloud is driving this, right? We all think about cloud as someplace we can park resources. But we’re also seeing the emergence of things like HP GreenLake and Lenovo TrueScale, consumption based, where it’s in their interest, the technology providers to keep your technology current.

Chris Kanaracus: Cloud cost optimization and sustainability are linked topics, but I kind of look at it from the optimization side a little bit differently. I look at more from the IT buyer perspective of cloud services they’re buying and how they can buy them and where they can buy them. It’s a reward in a really new era. I think if the first era of cloud was about speed of adoption and using that scale and so forth, then you had all the waste and you had people finding out, ooh, I’m not using the cloud efficiently. I really think we’re in an era of cloud cost optimization really in the forefront. There’s a lot going on and I’ll give a couple of examples. One of the big pushbacks that customers have made, and we always hear from them, is from egress fees from the cloud providers. They hate paying data egress fees. Every single customer call I take, if this comes up, and have you guys heard of the Bandwidth Alliance? This is a group of companies that work with CDN providers and they kind of agree to waive or deeply discount egress fees to their joint customers, and that’s really good, and AWS is holding out. They kind of have not gone that direction yet. They need their margins to stay high and so forth, but that’s one example, but a more recent example is just more density in options for running your workloads around the world. It isn’t the big three anymore. I don’t know, Equinix on bare metal with VMC, that’s one example. Oracle’s trying to do, and it’s very new with Alloy, where they’re licensing their cloud whole stack to partners around the world, and they can rebrand it, white label it, and you own the customer relationship. Is it going to be cheaper? Possibly, because when you have more options to run your workloads, like any market, things have to go down. There’s much more work on portability. It’s just we’re in a good place for that. The problem isn’t solved, but we’re in a good era where we’ve built out this universe of cloud computing, but now people are really starting to and are able to optimize it for real.

Jason Lopez: Chris Kanaracus is Research Director of Cloud and Edge Services at IDC. Harmail Chatha is Senior Director of Global Cloud Operations at Nutanix. Steve McDowell is Principal Analyst with NAND Research, and Steen Dalgas is with Nutanix, and at the time of this recording, he served as Cloud Economist for the company. This is the Tech Barometer Podcast, produced by The Forecast. I’m Jason Lopez, thank you for listening. For more stories on sustainability or on technology in general, you can check us out at theforecastbynutanix.com. That’s www.theforecastbynutanix.com.

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In this Tech Barometer podcast segment, Debojyoti “Debo” Dutta, vice president of engineering, AI at Nutanix shares his passion for computational biology and how AI will dramatically change enterprises.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Debo Dutta: If you look at what’s happening today, it’s not just because of AI, but in general because of high throughput data that can be generated out of a biological system. And the amount of data that you can generate was unimaginable even a few years ago.

Jason Lopez: In our series on MedPerf we wanted to introduce you to a computer scientist at the forefront of AI and machine learning development. Debojyoti Dutta, who goes by the nickname Debo. His work is bringing AI to IT leaders across many industries, including healthcare. This is the Tech Barometer podcast, I’m Jason Lopez.

[Related: Exploring Uses for AI in Healthcare]

Jason Lopez: When you hear about AI and how great it will be for healthcare, you might wonder, well who are the people making it happen? Debo is one of those technologists who’s working to connect the dots of computer science and medicine.

Debo Dutta: When coupled with AI, it’s going to completely change the way we design drugs, how we design antibodies, new therapeutics, I can’t even imagine all the things that we will be able to do using this iteration of AI coupled with the amount of data that we can generate out of living beings.

Jason Lopez: One area Debo thinks AI will have a profound effect is in examining medical images. For example, people with diabetes are at risk for loss of eyesight. With AI assistance, images of the inside of the eye could give doctors far earlier detection of diabetic retinopathy. Other imaging examples include detection of tumors and cancers. But another area where AI could bring a transformation…immunotherapy.

Debo Dutta: Immunotherapy in a nutshell is to program your immune system to fight bad actors in your body. The bad actors could be viruses, it could be cancer cells.

[Related: Building Infrastructure to Unlock AI’s Next Big Leap]

Jason Lopez: Our immune system can detect viruses, but sometimes cancer cells are invisible. Cancer cells can appear normal and the immune system allows them to go about wreaking havoc. In immunotherapy, the DNA of the immune system’s T-cells are changed to recognize the genetic strands of the tumor cell and destroy them.

Debo Dutta: Let’s see how AI gets into this space. So the programming, the existing T-cell can be done with gene therapy, but how do you know what to program? That’s where AI comes in. Using AI, you can rapidly design sequences. People are today looking at a lot of sequence data as well as literature, feeding them into large language models. And they are actually trying to generate candidate sequences for lab experiments.

Jason Lopez: Advances in large language models as well as computer infrastructure are cutting development times dramatically. Debo says what would have taken years might take a 10th or even a hundredth of the time.

Debo Dutta: I’m not an oncologist. I’m not a gene therapy researcher or an immunotherapy researcher, but I do believe that as a systems person, if I can help the lifecycle of the machine learning go faster, I can help them to make immunotherapy get better and better. We can cure more types of cancers and more diseases.

Jason Lopez: Debo was born in India. His mother is a retired doctor and his father a retired engineer. Those two professions are some of the most sought-after in the country and getting into a good university to pursue those professions is very competitive, and he was able to enroll in one of the top colleges in engineering where he opted to study computer science.

Debo Dutta: But I kept thinking about biology, but I didn’t do much about it.

Jason Lopez: After graduating, he came to the US to study for a PhD at the University of Southern California, and that’s when he saw another dimension to computer science.

Debo Dutta: It looks like pure science in many ways because it’s a lot of math, applied math and discrete math, and a lot of engineering too. But it can be applied to biology.

Jason Lopez: At USC he encountered a large team doing computational biology. And after his PhD, he had this thought:

Debo Dutta: No matter what I do in life, I need to spend some time in computational biology at USC before I actually get into my career and just be a researcher and learn how computer science can be applied. And that’s when I first picked up what is now known as machine learning.

Jason Lopez: What do you think about the AI boom?

Debo Dutta: Yeah, that’s a very interesting and loaded question. I could spend the entire session just talking about that.

Jason Lopez: Models are at the heart of machine learning. Data is used to train models. Which are deployed to infer from new data. And in the ChatGPT and the generative AI space, models are getting bigger.

Debo Dutta: These models can be as large as 1 trillion parameters. And typically when you see new data, you have to run this data through the model. Big models take longer time, more compute resources. And so measuring the performance will lead to more innovations in the infrastructure space.

Jason Lopez: That will make models cheaper to develop and cheaper to deploy. But, Debo points out the quality of the model still matters.

Debo Dutta: When you’re generating text, how good is the text? Is it hallucinating? Is this text believable? How was this model developed? Was it developed on open data? Was it developed on somebody’s private data? And if that is the case, what’s the liability of the model if I use that model?

Jason Lopez: When Debo did his postdoc in computational biology at USC, it was called statistical algorithms. He says he was on the right track as he explored signals in biological experiments, especially proteomics: the study of proteins.

Debo Dutta: I got to learn about machine learning as a part of understanding how the human body works.

Jason Lopez: Statistical algorithms were one of his tools at his job, later, at Cisco. He did cloud computing for a while. But then decided to investigate a new area.

Debo Dutta: And what struck me is cancer therapeutics in the US was still not advanced enough by a computer scientist standards.

Jason Lopez: One day, he found himself at a meeting at Stanford with some leading machine learning experts. That group became ML Commons. Subsequently, they began discussing what ML Commons ought to do. It wasn’t long before a medical-focused working group was formed.

Debo Dutta: And that’s when the idea of this med perf paper was born.

[Related: Alex Karargyris’s Path to Becoming a Pioneer of Medical AI]

Jason Lopez: The MedPerf paper, entitled Federated Benchmarking of Medical Artificial Intelligence with MedPerf, established the vision for the organization.

Debo Dutta: MedPerf was not even a thing. I mean, we just started Med Perf. We got together a bunch of experts from different walks of life, because problems in medicine are hard. They need expertise from all corners. So we consulted with oncologists, computer scientists, data folks, and a lot of people who knew the ins and outs of the pharmaceutical industry.

Jason Lopez: In our conversation with Debo, we did ask him specifically about Chat GPT. And he relayed what Nutanix customers are saying.

Debo Dutta: I love Chat GPT, it can do amazing things and maybe transform my business, but I have to give away my data to Chat GPT.

[Related: IT Leaders Get AI-Ready and Go]

Jason Lopez: What they want is a Chat GPT equivalent – kind of a GPT-in-a-box running on their own private infrastructure.

Debo Dutta: Nutanix is very customer-focused. So our reaction to our customer’s request is a turnkey solution, which will help our customers run generative AI models, including large language models that fuel applications like Chat GPT, on their private infrastructure.

I joined Nutanix in 2020 during the pandemic. And when I joined Nutanix, I realized that Nutanix had this amazing platform, which could host a lot of these amazing next-gen AI workloads that would change the world in ways that we don’t even know about. For example, new drugs, new antibodies. So I’m really excited by medicine and AI and infrastructure and what it could do to humankind.

Jason Lopez: Debo Dutta is vice president of engineering at Nutanix and a founding member of ML Commons. This is the Tech Barometer podcast, I’m Jason Lopez. If you like this report and want to know more about MedPerf, we’ve got a couple of other stories on the group, one focusing on the paper and another on MedPerf co-founder and the paper’s author, Alex Karargyris. You can find more at the Forecast, which produces this podcast. It’s at www.theforecastbynutanix.com.

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In this Tech Barometer podcast segment, Greg Diamos tells how an early passion for computing led him to a pioneering role Baidu’s Silicon Valley AI lab, where he discovered ways to scale deep learning systems, and went on to co-found MLCommons and AI company Lamini.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Greg Diamos: I think computing is a way to give people magical abilities, like superpowers. I like to live in a world where you can give it to everyone. Like, what if every single person had superpowers? I had basically ten years with nothing to do and just a pile of computers in my garage.

Jason Johnson: Just getting the ball rolling, I was wondering, Greg, could you tell me a little bit about what it is you do?

Greg Diamos: I’m one of the people who builds the software infrastructure that’s needed to run applications like ChatGPT or Copilot. Let’s see, I think it’s helpful to start from the beginning. My last name is Diamos. It’s a Greek name. It’s kind of cut in half. About a hundred years ago, my family immigrated to the U.S. They mostly settled in the West.

Before I was born, my father, he moved to Arizona. And his goal in life was to retire and find someplace relaxing and to go and play golf most of the time. And so he moved to Carefree, Arizona. Most people, when they think of Arizona, they think of Tucson or Sedona or places that normal people would go. You go to the Grand Canyon or something. Carefree is a place where no one ever goes. It’s right in the middle of the desert. There are not a lot of people, but it has a lot of golf courses and it’s very relaxing. It’s like a spa. So it’s a good place for that, but it was a very boring and brutally hot place to be as a 10-year-old boy.

[Related: AI Reorients IT Operations]

So I was bored, bored out of my mind. My mom worked as a database administrator for IBM. IBM at the time, they had a mainframe business. And so they would basically throw away the last generation of stuff. She would just take all the machines that were headed to the dumpster, put them up in the car, drive them out and throw them in our garage because it would be such a waste if such a thing was thrown away. I had basically 10 years with nothing to do and just a pile of computers in my garage. So it took me from when I was 10 years old until when I was about 25 to kind of figure out how the machines worked. It seems, like, magical to me that you could build such a thing. You could do that as a little boy stuck in the desert. I did a PhD in computer engineering. After that, I went to Georgia Tech.

Okay, let me tell the story of this one. I really love telling the story. I actually worked at the Baidu search engine. You could think of it as the Google of China. If you go to the search bar, there’s a little microphone on it. You press the microphone, you can talk to it. It was based on the last generation of machine learning technology. It still used machine learning, but it didn’t use deep learning. This exists right now in all major search engines. Around 2014, 2015, Baidu was in the process of deploying their first deep learning system.

[Related: Alex Karargyris’s Path to Becoming a Pioneer of Medical AI]

One of the projects that we had in the Baidu Silicon Valley AI lab was to apply deep learning, which had just been invented and was just starting to work to improve that particular product. Along the way, we had a number of researchers. One of the researchers was Jesse Engel. He was a material scientist, but he was not a deep learning researcher. Actually, none of us were deep learning researchers because deep learning didn’t exist. One of the things that he did is he performed sweeps over all the parameters in the system.

One of the sweeps produced this plot that seemed to show that there was a relationship between the accuracy of the system or the quality of the system with the amount of data that went into the model and the size of the model. If you think of a neural network as just being a collection of a ton of simulated neurons, not real neurons, but simulated neurons in that system, trained on thousands and thousands of hours of recordings of people talking. It seemed like as you added more neurons or simulated neurons, and you added more recordings of people talking, the system got smarter. It didn’t just happen in an arbitrary way. It happened in a very clear relationship you could fit with a physics equation, E equals mc squared kind of equation, a one parameter equation. It was a very simple, very consistent relationship. We thought that was pretty intriguing. After that project succeeded, the team grew a lot.

[Related: IT Leaders Get AI-Ready and Go]

I inherited a pretty big group of researchers, about a 40-person group of researchers. I had to decide, what should we do? I thought that was the most interesting thing that I’d seen, so let’s see if this actually is a real thing. Let’s try to reproduce this. Let’s try to understand it better. We spent about a year trying to reproduce that experiment, and we absolutely could not break it. It was very repeatable across many different applications.

We tried it for image recognition. We tried it for different types of speech recognition, different speech recognition models. We also tried it for language models. Language models at the time were actually used in speech recognition, basically as a spell check. Essentially what came out of that was this very consistent, very repeatable effect that if you increase the size of the model according to a certain ratio, if you increase the amount of data according to a certain ratio, the quality of the system, like its ability, for example, for a language model to predict the future, like predict the next word, would get better in a very predictable way.

[Related: Seeing AI’s Impact on Enterprises]

We published this as a paper. It was called Deep Learning Scaling is Predictable Empirically. This is, I think, came out in 2016. I just thought it was so weird that you could actually predict intelligence. We tried even more to break it. We went and consulted all sorts of machine learning theory experts, and I actually finally understood why it was happening. There actually is a theoretical explanation of why this is happening. The result of all of this is basically we can’t break this thing. This is actually a real thing. This is a real relationship that’s repeatable.

It’s absolutely the most amazing thing I’ve ever seen in my life because if I try and explain what that means at an application level or a user level, it means that we have a repeatable recipe for intelligence. We have a simple equation that you can write down, and then I can just apply this recipe, and I can create intelligence. The implications of that, we understood what they were. I think, in particular, Dario Amodi, who is also a researcher in our group, really got it, what the implication of that was. He was a biologist who had done very detailed computational biology experiments. He took that and went on to put it into GPT-2. They just kept scaling it, so it kept getting smarter. GPT-2 became GPT-3. You had to have an enormous amount of computation to run this thing.

[Related: Will AI Workloads Overload IT?]

Finally, it seemed to cross a threshold around ChatGPT and GPT-4. It’s not just spell check anymore. Now it’s abilities that we think of as being very uniquely human abilities, like the ability to read and write English, the ability to reason logically, the ability to plan, the ability to write software correctly.

It’s not like these are just being produced in a research lab. We’re actually seeing them integrated into products that are deployed to all users. ChatGPT right now has more traffic than Netflix. It’s not just AI researchers who are talking about it anymore, who are playing around with these things. It’s actually pervasively available. If we project into the future, as long as we can keep feeding it with data and keep feeding it with computation, it’s going to keep getting smarter, and we’re going to see new abilities emerge out of it, many other human abilities and potentially even beyond human abilities.

Jason Lopez: Greg Diamos is co-founder of ML Commons, as well as co-founder of Lamini. He’s also a founding member of the Silicon Valley AI Lab, where he was on a team that helped develop deep speech and deep voice systems. This is the Tech Barometer podcast. Tech Barometer is produced by The Forecast, where you can find more tech stories about topics from enterprise software to the cloud revolution, AI, and digital transformation. Check us out at theforecastbynutanix.com. This is Jason Lopez. Thanks for listening.

View Details

In this Tech Barometer podcast segment, explore how a partnership between data networking giant Cisco and hybrid multicloud software company Nutanix is changing perspectives and opening opportunities for people who build and manage IT operations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Lee Caswell: 750 million new applications in the next three years, more than the past 40 years of computing. How do you assimilate that quickly without overrunning your staff?

Jason Burns: You’re able to roll out these applications and not have to worry about how much time it might take to stand up new servers, stand up the network. All of that piece just can be more seamlessly provisioned so that you have more time to deploy applications.

Lee Caswell: Let’s say it a different way. Infrastructure doesn’t get in the way of supporting new applications. The best partnerships are customer-driven.

Jason Lopez: Those are the voices of Lee Caswell, vice president of Product and Solutions Marketing and Jason Burns, Director of Technical Marketing, both of Nutanix. This is the Tech Barometer Podcast Here, they’re in conversation about the evolving nature of network infrastructure and application deployment in the context of Nutanix’s partnership with Cisco. They chat about different perspectives and understandings that network, storage, and compute teams bring to the table, unified communications and Cisco UCS, rapid application deployment and many other facets of the partnership

[Related:Focus Shifts to Migration in Wake of Broadcom’s VMware Acquisition]

Lee Caswell: Today, for example, I was talking to a customer who’s the first Cisco customer we have in the Americas, not the first we have worldwide. And one of the things that really stuck out for them was the relationship that they have with Cisco, you know, expanding from networking into compute, has really meant that they have a level of trust there and confidence. And now that Cisco’s reselling the Nutanix offer, it’s really bringing together the networking and the, call it the infrastructure teams, in a way that we brought in the past, the storage and the compute teams together. So I’m curious on your perspective on that, Jason.

Jason Burns: From my perspective, I’ve seen the same thing. When customers are deploying a solution from Cisco, it is exactly that. Oftentimes you’ll see something with the unified communications on top of Cisco UCS and the Cisco networking piece as well. And so the combination there of being able to bring in the entire application on top of the computing system and then the storage as well is really appealing to the customers. And then having that delivered as a solution rather than something that they’re piecing together part by part.

Lee Caswell: You know, as I’m watching and talking to some of the Cisco folks, there’s some really interesting things that Nutanix does that they didn’t have access to before and now make it just easier to sell anHCI-based solution. One is, you know, the ability to have mixed nodes, for example, to be able to have nodes with different capabilities, whether it’s disk and flash or different CPU capabilities. That level of flexibility actually makes it a lot easier to sell this. And now integration, you know, support for ACI, for example, really helps from an integrated management solution. So I’m seeing those two things really set the bar as to how we’re now we’re bridging the network stack along with compute and storage.

Jason Burns: Yeah, the integration ofNutanix AHV and Cisco ACI is something that I’m personally excited about. As a former network engineer, I love to see the automation point where we can make networks inside of the network fabric and have that pushed into the compute fabric. What you get from that action is a system where you’re able to really easily deploy applications because the network substrate is already there for them. That’s part of what Cisco has done to integrate with Nutanix AHV. That’s really complemented by what Nutanix is doing on the virtualization side, both with our flow virtual networking and flow network security for virtual networks and micro-segmentation inside the hypervisor itself. So you have this physical network layer that Cisco is providing, and then you have the virtual network layer that Nutanix provides there, and that’s all being tied together.

Lee Caswell: Yeah, it’s kind of fun. I don’t know if you’re familiar with the concept of parallax, which is basically things, lines move as you move. It looks like things are moving because you’re looking at it from a different vantage point. And so in many cases, the network community is looking at things from one vantage point, where you’ve got now the storage and compute teams coming in and looking at it from a different view. For example, things like capacity utilization and performance from a storage standpoint are around IOPS, consistent low latency during failure conditions. Those are the things that a storage person cares about. And at the same time, now you’ve got all this based on the underlying network infrastructure. So the ability to have both of those lenses, I think, is really an interesting way to think about how to deploy this more seamlessly over time.

[Related:Why Hybrid Multicloud Matters]

Jason Burns: The lenses is a great point of it. The network team, rightly so, controls the network because they need to have the best performing network for their applications. They hold some of that responsibility, and therefore they hold the provisioning control as well. Same thing for the compute and storage side. But going back to that comment around lens, you get visibility from the storage side of what the network is doing. And then from the network side, you get visibility into what the compute is doing, thanks to the integration of ACI and AHV. So the solution works a bit better now that these entities are aware of each other.

Lee Caswell: You’ve also got inter-site integration, looking at how you go and view, manage, and monitor the holistic system. And so, I’m particularly excited about this because there’s over 60,000 Cisco resellers, for example, who are familiar with inter-site. And now to be able to say that inter-site can go and monitor Nutanix environments today, manage shortly, is really an exciting way to go and leverage the experience and expertise of that Cisco reseller base.

Jason Burns: The inter-site piece is also very interesting because I came from a world of deploying Cisco UCS with local UCS managers. So for me, I’m learning more about Cisco through this and learning that they have inter-site as a tool. It’s a tool that I wish I had when I was deploying UCS servers years ago. The ability to look at a fleet of servers and manage them all from that point would have been really helpful to me as an administrator back in the day.

[Related:The Spirit and Hybrid Multicloud Mindset of a Healthcare CIO]

Lee Caswell: Yeah, I was looking at some of the roadmap items we’ve been talking about. One of the things that a lot of customers have been looking for and are excited about what is coming with Nutanix and Cisco together is the ability to run on the latest blade servers that are storage-rich to be able to take that new form factor. Because certainly Cisco is known in the UCS world as having performance-oriented systems with a lot of memory access. That was the early design of UCS. And now the ability to bring storage-rich capabilities and Nutanix’s value on our HCI element makes it possible now to go and run the highest performance applications on this new joint offering.

Jason Burns: Yeah, absolutely. And I’m looking forward to seeing expansion into that blade ecosystem because that’s what I had seen a lot of deployed myself, these high-performance applications running on a blade backed by some sort of storage that might have resided outside the blade. And so looking at what we have as possible future options with Nutanix compute-only accessing the storage remotely, that to me just seems like a win-win for configuration in these blade server deployments.

Lee Caswell: Especially important, by the way, for the highest performance applications because of the licensing model, it turns out. So the ability to have a contained, let’s call it compute-only node, for example, where you can now go and have, let’s say Oracle licensing to pick one running dedicated on that node means that now you’ve got a predictable licensing expense going forward. And so that’s important because, you know, there was a risk in the early days, you could basically have licenses spread across all of the CPUs and the cores. And of course that would be prohibitively expensive. So the idea that you can contain licensing to this actually means now you can take the highest performance applications that are licensed by core and make sure that financial exposure is guarded there.

Jason Burns: Anecdotally, one of our customers who’s a service provider, they deploy a lot of different solutions. I was helping them out with a technical design I said, you know, this is how I would do it. And he said, well, Jason, about 40% of the way I design things is technical and the other 60% is driven by the licensing requirements of all the solutions I’m deploying. One solution, while I might from a technical side think it’s the right answer for licensing or financial reasons, might not be a viable solution for them to deliver that service.

[Related:Ransomware Resilience: Evolving Data Protection]

Lee Caswell: Super interesting. Even as you look at what’s next after databases right now, the number one workload on Nutanix. Analytics now from our own survey data is 49% of our customers are running analytics. And a big part of that, of course, is Splunk. So now acquired by Cisco, I see a pretty interesting opportunity to go bring together these high-performance analytics along with our Cisco partnership. Bring that all together and make that really a solution from a customer standpoint.

Jason Burns: Yeah. I’m also interested to get hands on that in the lab myself. Part of what we do is we, in the tech marketing team, we develop these lab environments for people and we try to demonstrate our own software and try to pull in realistic use cases where we can. One of those use cases is you have this Nutanix software stack. It’s generating alert data, event data. How can you send that into a tool that’s very similar to what the customer has? A lot of our customers ask us, how do I send all this data into Splunk? If I were able to, in the future, have a system that can send all that data into Splunk and then show our customers how to do that, to me, that seems like a great offering and something that customers would benefit from. So I think that’s how they deploy it in the real world.

Lee Caswell: Of course, no conversation would be complete without a discussion of generative AI. Thinking for a moment about how we’re taking GPU-enabled Cisco nodes and now being able to do our GPT in a box, for example, together with Cisco UCS. Pretty interesting, right? As a way to go and say, how can I get started? You could be training a model, probably not a large language model, which is happening in the public cloud, say, but you could certainly be modifying or tuning a model with your Cisco-enabled, GPU-enabled nodes. And then being able to take some of the value that we offer on the software side to help customers get started on their AI journey.

Jason Burns: Yeah, Lee, I see people ask that all the time, which is, how do I get started? How do I deploy this? What’s your best practice guide? And working with Cisco, what they have on their side is the Cisco validated designs. And so what I would love to see is a Cisco validated design for something like generative AI, kind of complementary to our GPT in a box solution for, okay, here is how you get from end to end a deployment with Cisco UCS servers, the Nutanix AHV hypervisor, your GPUs of choice, and how do you build that out from end to end? For me, the validated design is a great starting point because it shows customers that we’ve tested this out in the lab. And if you follow this recipe, you also can have success.

[Related:Will AI Workloads Overload IT?]

Lee Caswell: I guess it strips out both the real risk by doing predetermined testing and then also any perceived risk. So you can say, all right, if I can take the infrastructure piece out, which by the way, is what every data scientist wants. It’s like, how do I make sure the infrastructure is there responsive and fast? So we do that. And by the way, reliable and day-to operations and compliance, all the things that IT brings to the party. And then at the same time, be able to deploy new applications really quickly, whether they’re libraries like PyTorch, for example, or bringing in new applications, bringing in our support for the NVIDIA and VAI libraries. I mean, these are just ways to say, hey, we’ve got all the building blocks and can help you with putting them together.

Jason Burns: And having the validated design or whether that’s a Nutanix validated design or a Cisco validated design means that you’re really speeding up the deployment. You’re cutting out a lot of those investigatory steps and just following along a cookbook rather than inventing the wheel on your own.

Lee Caswell: I think for a lot of customers, particularly, they’re looking now at these new server-based architectures as a way to build out an architecture for the next three to five years, where you’d put all of your new applications. It’s rarely just like rip and replace, take something out. That’s why we still have Unix systems in there. It’s additive in the sense that now you’re going to take these server-based architectures, add that in, but put all the new workloads on there. Certainly that’s what happened with x86 because the economics and the simplicity of moving to a x86 architecture based on Ethernet, which is what we’re doing here. And then deprecating over time, fiber channel, SANs, switches, and even the expertise. So you can basically focus on the new stuff is how I’m viewing this market.

Jason Burns: As a practitioner, what I like is that that gives me the time to focus on the new things. If you’d asked me a few years ago if I was going to be learning about generative AI models, I would have said, no, that’s not really something. But now that the infrastructure side is more taken care of, that’s a place where I can look at these new applications and maybe learn more about how Kubernetes works and how to deploy a modern app in Kubernetes or maybe learn more about an AI application rather than spending all my time just getting the compute and storage and networking infrastructure up and running.

Lee Caswell: So this infrastructure as code transforms into everything as code. If you think about over time, how do we make this easier for you to take and basically treat AI as just the next workload? Many customers from Nutanix started off inVDI, then went todatabases, then went to analytics, and now are looking at AI, generative AI, as the next thing. And if you can assimilate that easily with the same team instead of having a separate architecture, boy, it just makes it a lot easier to get some operational leverage, particularly when you’re having trouble finding talent.

Jason Burns: That is something that I see every day, even in some of the new designs that I’m rolling out. You might do something where you have an engineer walk through and manually set something up as a proof of concept. That’s pretty time-consuming to do. Now, if you have something like infrastructure as code or your application deployment defined as code, the subsequent deployment of that can happen really fast. And so you just cut out a lot of time there for deploying that. That really is moving toward everything as code with the solution we’re showing here. So you have the Cisco ACI portion, where you have these APIs for configuring your network. You have the Nutanix compute and storage part, again, with lots of open APIs to configure. And then these modern applications on top of it, which might be deployed as a series of config files against aKubernetes cluster. You can just keep all of that code for the entire infrastructure and even application configuration in a code repository where someone can just check that in to make changes to it.

Lee Caswell: Yeah, it’s always struck me that ACI was probably one of the best acronyms in the industry, application-centric infrastructure. The idea that you’re thinking, hey, how do I make sure that applications are optimally located? And there’s a really interesting way to think of how we introduce value jointly here, because when you bring these together, these technologies, now all of a sudden you’ve got a full complement of how to make a seamless outcome for optimally locating applications and their associated data over the hybrid multi-cloud universe.

Jason Burns: And as I was seeing adoption of modern applications and containerized applications, that was the one thing that struck me the most, is that all of these apps are going to generate and use incredible amounts of data, and they’re going to need highly available storage that’s also high performance. I didn’t see on the market a dearth of those solutions. I saw that Nutanix really had a great offering in that space, that people were going to be deploying these modern apps and using Nutanix storage to back that app deployment.

Lee Caswell: Both of our companies, I think, have terrific brand loyalty, by the way, in part because of the support experience that customers have gotten. And one of the things that impressed me about the relationship that’s more than just your average partnership is how we’ve integrated the support path so that our support systems and support tickets can be follow-up and make sure that the customer experience is singular in nature. Curious on your thoughts on how you’ve seen that develop, and why is that important?

[Related:How Flash Memory is Reinventing Hybrid Cloud Storage]

Jason Burns: Working at Nutanix these last nine years, I’ve heard consistently again and again that customers value Nutanix support. And in my previous life, I was actually a Cisco support engineer long, long ago, and heard the same thing, that customers valued the support that they got from Cisco. So we have a history of two great support organizations, and being able to support our customers really is what I know that they’re looking for. It keeps them coming back. It keeps them expanding solutions on Nutanix or expanding their solutions on Cisco, because they know that when something goes wrong, they can give us a call and get routed to someone who can help them.

Lee Caswell: I have a theory on why our companies are so well suited together. So my theory is this, that architecturally, from a design point standpoint, network engineers tend to make things fast first and then reliable later, because you can always retransmit. And storage companies come at problems with exactly the opposite lens, which is that they try and make things reliable first, and only then try and make them fast. And you can certainly see that through the enhancements we’ve made over the latest versions of AOS, for example, and moving into newer flash technologies, you know, NVMe and new CPUs and everything. So I’m curious to say, how do you think about it, right, architecturally, isn’t that an interesting way to think about why the engineering teams have so much appreciation for each other?

Jason Burns: That definitely rings true to me. And I’ve seen that growth at Nutanix over time, where all of our subsequent software releases have basically worked on performance over all others, but reliability was always there, because from a storage perspective, you had to keep that data. Data corruption wasn’t really a possibility for consideration, but we really worked hard on increasing performance over time. And it seems like we’ve kind of met in the middle here between Cisco and Nutanix.

Lee Caswell: I think storage people, right, preserving the data is number one and all the data services that we have. And I think that’s a really interesting value also, because for most customers now, this is a really unpredictable environment. Trying to forecast, for example, your mix of blocks, files, objects, data, trying to forecast whether you have VMs or containers three years from now, and start to think about, you know, what you have on-premises versus the cloud. The simplifying design that Nutanix brings is the fact that you’ve got the same data services across all of those endpoints. If you go and buy different unique architectures for each, you’re going to have a different way to go and protect that data, different snapshots, different recovery mechanisms, different disaster recovery. And so it’s a really easy simplifying, you know, way to basically make this easier for customers to get operational leverage and deploy things in an unpredictable environment.

Jason Burns: Nutanix has a lot of that flexibility on the data services side, and I see Cisco having that flexibility on the compute side. The longevity of some of these Cisco UCS chassis, for example, or how long you might have a UCS deployment is pretty incredible. And that’s just something that you can keep as a long-running deployment and take out parts of it, maybe blades, maybe certain rack servers while keeping the whole of the deployment there. You get the ability to upgrade in place and really extend the possible lifetime of a deployment. And for me, that was always the beauty of the Nutanix cluster, because your AOS storage cluster could live for quite a long time, even across multiple generations of Nutanix hardware. If you are adding newer-generation servers and removing older-generation servers, you have a pretty long-lived deployment without a lift and shift.

Lee Caswell: So one way to think of this is in a highly unpredictable environment where applications are changing really fast. You don’t know necessarily if there are going to be block interfaces or file interfaces or object interfaces. By being able to buy one thing, you can actually get started. And then by having seamless licensing that allows you to go and basically deploy at will over time, you’ve got this flexibility now, along with the fact that we’re the only company that gives you a consistent way to do data protection across all three of those protocols. And by the way, across VMs, traditional VMs, and now newer container systems orchestrated by Kubernetes. So it’s a really fantastic way, I think, to go and give that flexibility for customers who actually don’t want to be storage experts, it turns out.

Jason Burns: That would describe me as well, a former network engineer who has come to the storage world, but Nutanix has made that a lot easier. Storage is not as intimidating as I had thought it was after working with Nutanix. Let me say it this way. You know, what I’m hearing from a lot of CIOs now is that they’re interested in consolidating architectures so that they can address the fact that they don’t, let me say that in a different way. Yeah, one of the things I’m hearing from CIOs recently is about trying to consolidate the number of different architectures that they have. So for example, a simple one would be like separate filers and separate SAMs, for example, bringing those together, and a different object store, bringing that all together. And so for CIOs thinking about how they do it, they’ve already done that with Cisco on the networking front. They have a single vendor on the network front that gives them everything across different architectures. And so now one of the opportunities here is by working with Cisco and Nutanix together, now you’ve got the opportunity to basically address any of the degrees of freedom you’re likely to anticipate in the future.

Jason Lopez: Lee Caswell is vice president of Product and Solutions Marketing at Nutanix. Jason Burns is director of Technical Marketing at Nutanix. This is the Tech Barometer Podcast, I’m Jason Lopez. Tech Barometer is produced by the Forecast. We have a treasure trove of tech stories at the forecast from all across the technology landscape. You can find more at:
www.theforecastbynutanix.com.

View Details

In this Tech Barometer podcast segment, IT industry analyst Steve McDowell and Lee Caswell from Nutanix discuss the challenges and risks IT operations leaders face in the wake of Broadcom’s acquisition of VMware.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Steve McDowell: Broadcom announced quite a while ago that they’re acquiring VMware.

Lee Caswell: It’s not a light switch moment.

Steve McDowell: Because of regulatory hurdles and all sorts of other things, it stretched out for multiple quarters. That is where I think the initial wave of concern came in, because now not only do I have to wait and see, I have to wait multiple quarters to see.

Lee Caswell: Very rarely in IT do you just decommission something now instead, what you’re thinking is, all right, how do I make sure that I’m containing the risk, and then taking all the new things and putting that on the new modern infrastructure.

Jason Lopez: In the ever-evolving landscape of IT infrastructure, companies are increasingly finding themselves at a crossroads, particularly in the wake of significant industry acquisitions such as Broadcom’s purchase of VMware. This shift has ushered in concerns for VMWare’s customers.

[Related: Hedging Into Expected Broadcom Acquisition of VMware]

Lee Caswell: What happens if they’re not one of the top 2,000 customers?

Jason Lopez: Lee Caswell, Senior VP of Product and Solutions Marketing at Nutanix, says VMWare customers are worried about three major things: pricing going forward, and we’ll dive deeper into that in a few minutes, as well as support for products and the risk to personal careers.

Lee Caswell: Those customers are worried from a support standpoint. I think those customers are legitimately worried that they’re not going to get a call. Partners are worried, too, because Broadcom doesn’t have a great relationship with partners. So I think that support piece will come to be the thing that really actually unhinges them. And the third one is about personal career risk. If you can’t get support and you’re running something and you didn’t make a change, then all of a sudden it’s going to be back on you.

Steve McDowell: There’s a lot of uncertainty among the IT community about what Broadcom is going to do with VMware.

Jason Lopez: Steve McDowell is principal analyst at NAND Research.

Steve McDowell: Broadcom historically, they’ve largely acquired companies that have largely commoditized and they maintain them forward, but you don’t see a lot of continuing innovation. Are they just going to take the core VMware business and maintain that moving forward? Because the reality is a technology like VMware, or VM in particular, is very sticky, right? It’s hard to kind of rip and replace.

[Related: Broadcom’s Acquisition of VMware Stirs Uncertainty]

Lee Caswell: The way that customers are talking to me about this right now is that what they had with VMware for the past, you know, almost 20 years, right, was a very safe offering with known risks. And now what’s happened is what was the safe bet is now the risky bet. And it’s got unpredictable risks.

Steve McDowell: You know, if you have a VMware license, you’re probably not going anywhere for the life of that infrastructure, right? But where the uncertainty lives is on, you know, when I do come up to a replacement cycle or I have a greenfield project, you know, is VMware going to continue to deliver the level of innovation?

Jason Lopez: This has left many customers contemplating a strategic approach where they maintain their existing VMware infrastructure but refrain from expanding. Instead, they’re considering investing in new applications and technologies on alternative platforms based on newer architectures.

Steve McDowell: From Broadcom’s perspective, I think everything they’re doing makes a lot of sense for their investors and for maybe existing VMware customers. But where there’s, I think, going to continue to be a concern: how does the VMware technology continue to evolve, right? Am I still going to get the level of support I had? And am I still going to be paying the same amount of money? There’s also news out of Broadcom earnings, they’re re-examining kind of their subscription strategy, subscription bundle, and subscription pricing.

[Related: IT Leaders Get AI-Ready and Go]

Lee Caswell: There was a blog published by the leader of all the infrastructure products, saying some very interesting things that people had been expecting, but now they’re in print. So number one was it’s the end of any new perpetual licenses forever. That was interesting because that’s the way that most VMware customers have transacted businesses in the past. Also when those perpetual licenses came up for support and services, you’d have an SNS or support and services element, and there also no new SNS contracts as well. So those two things basically said that, hey, every customer is moving to a subscription model. Now, what you know in your personal life, right, is also true in business. When you move to a subscription model you end up paying more.

Jason Lopez: Caswell cautions that subscriptions, strictly speaking, don’t automatically equate to higher pricing. Subscriptions work when a customer maps out the long-term costs and benefits. But other factors within a subscription matter, such as bundling.

Lee Caswell: Subscription is one way where you would pay more, even if the prices were the same. But now what we’re looking at is that the bundling of products is starting to look like you may have to go and move up into higher level products where the way it’s being described is you’ll get more value. And that could be true, but the question is, it may be value that you wouldn’t have ordinarily bought. Think of it like when you have a cable TV subscription and you get 800 channels and you’re like, okay, I’m gonna watch like 20 of them. Can you give me the 20? And they say, no. And so now you’re stuck with a whole lot of stuff that you may not have originally wanted.

Jason Lopez: The immediate aftermath of the acquisition saw restructuring within VMware, in areas such as partner teams, engineering, and customer relations.

Steve McDowell: Five days after the acquisition closed, the week after Thanksgiving, they laid off 2,500, 3,500 people, I mean, a significant number of VMware employees. And these were in the partner teams, these were in engineering teams, these were in kind of customer relation teams. Not a good signal if you’re trying to build credibility with your customer base.

Lee Caswell: Publicly they just announced they laid off over 300 people in Cork, Ireland, which is the support center. They’re also closing countries that don’t have a sufficient number of customers.

Steve McDowell: First week of December, we saw them announce that they’re spinning out their desktop compute pieces. So they’re already starting to shed some of the things that are not core VMware capabilities.

[Related: How to Secure Modern Apps and Databases for Hybrid Multicloud Operations]

Jason Lopez: Today, one of the driving forces behind the way companies use and pay for enterprise software is an anticipation of a massive influx of new applications over the next few years, far exceeding the growth seen in the past four decades.

Lee Caswell: 750 million new applications coming online over the next three years, more than the past 40 years of computing. So you’re going to have this new wave of applications. And guess what? It’s all unpredictable. If you ask people what they’re going to do three years from now, they can’t tell you which applications they are. They don’t know whether they’re going to be interfacing with blocks, files, or objects, VMs, containers, on-prem, in the public cloud. And so risk right now with VMware is that you’re stalled like a deer in the headlights and you don’t know how to move forward because you don’t know what the risks are going to be over time.

Steve McDowell: Those are the kinds of things that when they’re in the air are going to lead to a certain amount of uncertainty. At the end of the day, being in IT is about de-risking your operations, right? Every business today is a digital business. And if I’m putting a piece of software that’s critical to my infrastructure it just makes sense that I’m going to look for alternatives. Not saying I’m not going to choose VMware, but I’m not going to default to VMware. So I’m going to look at Nutanix. If I’m in more in a cloud native mode, I’m going to look at the various Kubernetes things like OpenShift, Portworx, and things like that.

Steve McDowell: Broadcom’s business model is to take a commodified technology and continue to monetize that over time.

Jason Lopez: Innovation is a cornerstone of success and resilience in the tech industry. McDowell reminds us that it drives competitiveness, customer loyalty, adaptation to market changes, long-term growth, and it influences strategic business decisions.

Steve McDowell: You know, and when you’re talking about a commoditized technology, you’re not talking about a lot of continuing innovation. Now, a lot of the excitement around VMware in recent years was things that were kind of adjacent to their core infrastructure management path, like Carbon Black for endpoint security, for example. Broadcom, two weeks after their acquisition, said “We’re spinning that out.” So they’re kind of validating, I think, some of the initial concerns. That’s not to say that Broadcom’s not going to take care of existing VMware customers. I think they will. They want that revenue stream moving forward. The concern is really around new projects and replacement projects. Is the VMware technology the right technology? And I’m not saying it’s not, I’m saying it’s early to know. So we’re just going to have to wait and see. But, you know, a lot of IT projects can’t wait and see, which is going to force you to de-risk the decision by looking at alternatives.

[Related: Why Hybrid Multicloud Matters]

Jason Lopez: The market has been trying to understand the implications of this acquisition for about a year. And that limited Broadcom’s ability to articulate a strategy. This, sort of, limbo benefited competitors such as Nutanix.

Steve McDowell: Nutanix is not alone. I think everybody who competes directly with VMware in whatever space began benefiting well before the close just because of all of the uncertainty that just continued to linger. Because of where they were in the process, Broadcom, fair or unfair to them, they couldn’t respond to that, right? I can’t talk about a company I don’t own. So they would talk in generalities and that doesn’t do anything to ease concerns. So I think, you know, we’re a year into trying to grasp what this acquisition means.

Jason Lopez: Steve McDowell is principal analyst and co-founder at NAND Research. In his tech experience, he’s been at IBM, Fujitsu, and AMD among other firms. And he’s been a prominent industry analyst since 2017. Lee Caswell is Senior Vice President of Product and Solutions Marketing at Nutanix. He served two stints at VMWare and in his career worked in many roles such as at NetApp and Fusion-io as well as co-founding Pivot3 in Silicon Valley. This is the Tech Barometer podcast I’m Jason Lopez. Tech Barometer is a production of The Forecast. You can find more tech stories at theforecastbynutanix.com.

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In this video, Ryan McBrearty, the senior IT infrastructure engineer at global specialty insurer Markle, tells how his team uses Nutanix software and customer services in their quest to cut costs and simplify IT operations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
Ryan McBrearty: We are an all-Nutanix shop on-prem. We do have some cloud as well. We have over close to 30 different Nutanix clusters with over 400-plus individual nodes within those clusters. And we have a mixture of ESXi as well as AHV. Some of our AHV clusters are running at 32 nodes, so some big boys out there.

We wanted to cut down operational costs and use automation as a key focus in that. An objective was given to everyone to figure out what processes do we use on a regular basis and how can we speed up those processes and really automate them, especially in the server build space and taking a process that takes two months that has as quick as a window as possible, whether that’s two hours or four hours, beginning with we just had, we want to automate server builds so that we have consistency across that platform. So it was really taking that process and putting a workflow around it.

[Related: Forestry and Land Scotland Trailblazes Private-Public Shift to Cloud]

Automation is a key foundation, so we want to automate everything. If there’s a process that is repeated more than twice, then let’s see what we can do to automate it. So that’s how we’re going to continue to leverage Nutanix self-service. So that and the integration pieces that it has with the other tools in our environment, whether it’s networking, whether it’s security, whether it’s cloud, being able to touch all of those things. Our backup team as well, we want to work towards that software-defined data center.

So there was a lot of back and forth and things had to touch hands multiple times. Whereas once somebody puts in a request, now all of that information is gathered at the beginning, it’s put into at launch of the blueprint and then we just hit go and it just builds it. That shrinks down that timeframe from, again, two months to two hours.

The beauty of self-service is that it will bend to your whims. So if you are comfortable with writing code in a certain language, whether it’s Python or PowerShell, it will allow you to do that the way that you want. The different pieces were very complicated and very challenging to learn and manipulate, and it took a lot of time to go through and really understand how to use those tools where I think the tools today from Nutanix are a lot easier, a little bit more straightforward.

Support with Nutanix has always been great. NCM [Nutanix Cloud Manager] self-service has really been the easy button for us when it comes to automation. It’s been a great tool that has endless possibilities that we have barely begun to scratch the surface on how to use it and how to leverage it. The deeper we dive into it, the more things that we realize that we can do, and I’m excited for the future of what that looks like because again, the possibilities are endless.

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In this Tech Barometer podcast segment, CIO of Delaware Valley Community Health, Isaiah Nathaniel describes his spiritual and purposeful approach to helping the healthcare provider leverage all the innovation IT can bring.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Isaiah Nathaniel: Never let anyone beat you giving. Never let anyone beat you loving. Never let anyone beat you at believing. And finally, never let anyone beat you serving.

Jason Lopez: That’s obviously a spiritual insight. But Isaiah Nathaniel has a double meaning here. It’s also about the role of IT.

Isaiah Nathaniel: You’re hitting a nerve. I come from a long line of preachers as I told you my first time, but here we are.

I am an on-prem guy.

Jason Lopez: Isaiah Nathaniel is a CIO. He works for, or as he would mean it, serves Delaware Valley Community Health, a hospital that offers medical, dental and behavioral health services in nine locations around Philadelphia. They’re noted for focusing on accessibility and affordability, regardless of a patient’s ability to pay. And that’s something which informs Nathaniel’s work leading his IT organization. He may be an on-prem guy.

Isaiah Nathaniel: But there is flexibility in the cloud if you do it the proper way. So for us, it’s really being able to be flexible to scale at the drop of a dime based upon the business vision or the business need, but then also scale back.

[Related: Why Validating Medical AI is Crucial to Improving Healthcare Outcomes]

Jason Lopez: So he says if you can afford it, you can throw things into a public cloud. But his experience has shown him, it’s not fiscally responsible to operate with a blank check.

Isaiah Nathaniel: Any fiscally responsible CIO would say that’s not the way that we want to conduct our hierarchy of our business unit. And so for me, it’s okay, let’s dip my toe in the water, but let’s also make sure it’s fiscally responsible because waste is something that I don’t like because waste takes away from patient care. It takes away from staff retention. It takes away from the ability to grow. And particularly in the non-profit space, you’re looking at that bottom line to be able to say any extra spend really does take away from truly good quality patient care, patient experience, and then staff retention, relationship, and ability to serve who we need to serve.

Isaiah Nathaniel: Nutanix saved my organization by having that infrastructure.

Jason Lopez: The pandemic forced some profound changes in the way many businesses run. Spectator sports and movie theaters basically had to shut down. Restaurants had to drop everything and shift to takeout. And it seems everyone else met online via video. And in that idea, just think of how the demand for digital services affected IT. There was an accelerated shift to remote work, forcing IT departments to quickly adapt their infrastructure. Companies on a 5 year digital transformation plan suddenly recalibrated to do it in a year or two, and budgeting went back to the drawing board. Cybersecurity and disaster recovery efforts got amped up, and automation rollouts got pushed forward. Vendors like Nutanix were key, Nathaniel says, because he had to rely more on cloud services, needing more IT agility and scalability in the Delaware Valley Community Health IT environment.

Isaiah Nathaniel: Before, we couldn’t control a lot of the things that were happening. You just had this sense of angst because you did not know what was coming next.

[Related: Alex Karargyris’s Path to Becoming a Pioneer of Medical AI]

Jason Lopez: But in looking back at the years of the pandemic, he says a lot of things he and his team launched worked and much has stayed.

Isaiah Nathaniel: Now we can say, all right, we know where we are. We know where everything can possibly be, not only from just a personnel perspective but also from an infrastructure perspective. So we can say, hey, I know where this workload is. I know if I need to migrate this workload, I can do the same thing with one simple click. But then also from a security perspective, using products like Nutanix Flow that we instituted after the pandemic started and saying, “hey, now that we’re stable, let’s get more stable. Let’s take our scalability and be able to deliver workloads where we want them to be, but also secure them so that they aren’t impacting other workloads that need the productivity that we needed to have.”

Jason Lopez: There’s a balancing act he needs to do, between on-prem and the cloud, hands on and automation, and maintaining flexibility… which he refers to as enabling.

Isaiah Nathaniel: Enabling means actually not being the no people, saying, here’s a great idea. Give me a good use case. Make sure it’s secure. Bring it in the environment. We have the scalability because of our infrastructure to be able to test things out from a development perspective. And if it goes to market, so be it. And then we’ll go back to the whole wheel of innovation from there. I call it the scalability because I can pick my flavor of the day or flavor of the week because there’s a new app popping up literally every minute. And it’s so fun because I like the development that’s happening. I like the ingenuity. I like the innovation that’s coming out of these things. It’s about where’s the business case? Where’s my business need? And then we’ll throw an application potentially into it or add to one that already exists.

[Related: University-based Medical System Innovates Healthcare with Hybrid Cloud IT]

Jason Lopez: We’ve been talking a lot about healthcare IT here on Tech Barometer over the past year and privacy and security just can’t be emphasized enough. If you ever find yourself working in healthcare IT, it’s going to be ever present at the top of your to-do list.

Isaiah Nathaniel: When we look at legislation like the 21st Century Cures Act and how that impacts data at the point of care for the patients being really truly in charge of their information, my data is no longer four walls sustainable. It actually is beyond my four walls. And so for me, it’s make sure that, one, at rest it’s properly secured. But then in transmission, it’s extra secure so that when the patient gets it or the referring hospital and or specialty gets it, they can also say at that point it was secure. Your infrastructure internal to that, under that firewall, under that brick, is where the special sauce is. But I can at least say, CIO to CIO, “hey, you can trust my data.”

Jason Lopez: We started this report with Isaiah Nathaniel recounting a spiritual guide. It’s how he thinks about his role as the CIO of Delaware Valley Community Health. But it’s a lesson he learned from his uncle, Reverend Herbert Lusk II, who died in 2022. In the 1970s, Lusk was a professional football player who played for the Philadelphia Eagles under coach Dick Vermeil.

Isaiah Nathaniel: He was part of the team that went to the Super Bowl against the Raiders. He told Dick Vermeil, I’m only going to play three years, and then I’m going to go into the ministry. I’ve got a calling on my life. He was getting ready to be the lead running back, brand new contract, and he followed his calling. And that calling took him to North Philadelphia. North Philadelphia was depleted in the 80s. It was one of the worst urban areas in the country. And he took a church that had less than $5 in the bank account and over $500,000 in structural damage. Forty two years later, he rebuilt the church, over 1,000 members, welcomed three presidents, built the first welfare to work program in the country, and he left a lasting legacy. And to make this even more poignant, where the church is, is actually across the street from my home office. So his words to all of us as his children, his nephews, nieces, were these four principles. Never let anyone beat you giving. Never let anyone beat you loving. Never let anyone beat you believing. And finally, never let anyone beat you serving. When you talk about moments like that, when you lose such a powerful figure, I get up every day with those four principles. And my job is to serve my patients through access of technology and give them what they deserve.

Jason Lopez: Isaiah Nathaniel is the Vice President and Chief Information Officer for Delaware Valley Community Health in Philadelphia. This is the Tech Barometer podcast and we’re delighted to bring you these profiles of people in tech who are doing remarkable things. If you like what we do here, check out more stories both podcast and text pieces at the theforecastbynutanix.com. I’m Jason Lopez, thank you for listening.

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In this Tech Barometer podcast, Nutanix President and CEO Rajiv Ramaswami explains the challenges his hybrid multicloud customers are facing as they move their organizations into a future increasingly powered by artificial intelligence.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Rajiv Ramaswami: I am optimistic about technology and its role in the economy because regardless of where I think the economy is, I think companies are going to invest in technology. They’re going to invest in software. If you look at the rates, I mean whatever GDP growth, I think tech spending will be growing faster than that. And then software spending within tech is probably going to grow faster than that just because of the nature of how companies are going digital. It’s a business imperative. Companies struggle with getting good software talent. There isn’t enough to go around. AI talent is even harder to get. The set of tools that you need to put together AI applications and get them going to market, that’s not easy either. And then finally, on top of that, there is a shortage of hardware – GPUs – today in the market. Even if you want to do it, you’ve got everything to do it, you can’t get a hold of the hardware.

Jason Lopez: You might be wondering, didn’t the speaker start by saying he’s optimistic about technology? This is the Tech Barometer podcast. I’m Jason Lopez. What follows are comments from Rajiv Ramaswami, CEO of Nutanix. These come from a conversation captured by an audio recorder in the room. So we thought it would be interesting to whittle this down into a short podcast to hear him essentially riffing on the topic of AI in the enterprise, which has become essential for companies trying to empower workers or streamline or automate.

Rajiv Ramaswami: Whether there is automation of business processes, whether it’s customer support, whether it’s document analysis and search or processing credit card applications or market applications or what may be, or making software developers more productive through co-piloting or enabling more automated capabilities like better fraud detection, et cetera. So these are all big things that can move the needle in a very significant way.

[Related: 9 Predictions for IT in Age of AI]

Jason Lopez: Rajiv sees momentum building, at least on the development side, in the next year or two. There’s an expectation for the emergence of the first wave of generative AI applications, building on the ecosystem. But the challenge will be to overcome a few things like GPU storage, cost benefit analysis and getting higher quality results.

Rajiv Ramaswami: If you’re, for example, using some sort of a copilot to generate snippets of code automated, for example, how good is the code? Can you trust it? Can you go send it out? Whatever use case may a customer service use case or where you’re looking for document search and retrieval. So getting the accuracy to a reasonable level where you feel comfortable. That’s another thing that you have to do and you have to keep training until you get to that level before you can actually put it into production and do inferencing.

Jason Lopez: AI implementation isn’t inexpensive. The cost of compute clusters for training can be very high, and Rajiv sees companies having to really clarify the ROI and make a business case for AI adoption.

Rajiv Ramaswami: And so I think people are also going to go through that life cycle of, okay, there’s a lot of interest in AI, and then they’re going to go implement and they’re going to say it’s expensive to implement and therefore they’re going to be, again, looking at business cases like everything else. I think we’ll go through that phase as we move forward here with AI, I think there’s a lot of potential and to realize the potential, you also have to pay attention to what it’s going to take to get it done and how much it’s going to cost and make sure you’re getting a benefit. And then you’ve got to go get it done. You have to go implement it, which means putting together the team of people, figuring out how to get your data in order, how to choose the right large language model, how to train it properly, how to reduce the size of the model to what you need, and then of course, train it and get into production, get the fidelity of the data results and then put it into production.

[Related: Generative AI Propels IT Modernization]

Jason Lopez: And this presumes you have the talent on board to make it happen.

Rajiv Ramaswami: It takes data scientists, it takes AI engineers and machine learning operational engineers, and then it takes good infrastructure people along with the developers who build the app. So it takes all of these skill sets to go bring this to life.

Jason Lopez: The other challenges of AI implementation revolve mainly around data management. And as we’ve already heard, it requires high fidelity data in the right place for effective algorithm functioning.

Rajiv Ramaswami: Getting the data together in the right place itself is a massive task for many customers. And the second is you have to run the AI algorithms where your data is because data has gravity and you need to protect that data. You don’t want to give up your IP when you run a general purpose AI LLM, for example, on your data. So you have to protect that as well. So that’s the other set of considerations that emerge when people are running these AI applications. And then we hear about large language models that are in the trillions of parameters and they can do great things, but they’re also very expensive. And so the question you have to understand is, what is it that I need from my application? Do I need that big a model? Can it do a smaller model? And I think there’s a lot of optimization that has to happen there also.

[Related: Developing in the Age of AI and Multicloud]

Jason Lopez: Nutanix has gone through a process of AI implementation and uses a cloud platform known as GPT in a box. It provides an integrated solution with storage and machine learning toolkits.

Rajiv Ramaswami: So we can help our customers run their AI applications using a platform that’s close to being a turnkey platform, and it can be used wherever their data is sent. And so this is a platform that we call GPT-in-a-Box. It’s our usual cloud platform. It’s what we all use it for, running every other application. In this case, we also include files and object storage with our unified storage because all these applications need a lot of storage for the data. And then we include some commonly needed machine learning and operational toolkits as part of this model, because again, customers don’t have the where with all to go integrate everything that’s needed to run an AI application. So we try to integrate everything that’s under the covers needed to run an AI application so the customers can focus on the application itself, and they can rely on our platform to go run it wherever they’d like to run, wherever the data is present.

[Related: Generative AI Moves Beyond Hype for IT Operations]

Jason Lopez: Twenty-twenty-three was the year of large language models.

Rajiv Ramaswami: Twenty-four, I think, is going to be the year where it becomes more real. People start building applications, especially not consumer applications like chat and writing poems to your friends using ChatGPT, but real enterprise business applications that could be running. So this is probably going to be the year where people start using these for good business use cases. I think that’s the case there.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This is the Tech Barometer podcast. I’m Jason Lopez. Check out another Tech Barometer podcast featuring Rajiv’s comments on the Nutanix roadmap in the story why hybrid multi-cloud matters. You can find it and other tech stories at theforecastbynutanix.com. Tech Barometer is a production of The Forecast. Thanks for listening.

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In this Tech Barometer podcast segment, Nutanix President and CEO Rajiv Ramaswami discusses the challenges driving more IT leaders to develop hybrid multicloud strategies for managing across private data centers, public cloud services, edge computing sites while enabling new application development and AI capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Rajiv Ramaswami: As customers here, all of you expect to have solutions that meet your business outcomes. We solve some portions of that, but we don’t claim to solve everything. So clearly that’s why we are investing in the ecosystem here around us, the ecosystem at every level, the ecosystem in terms of our OEM partners, our cloud partnerships, our best-of-breed partners in areas that we don’t play in like security for example, in cloud native Kubernetes and public cloud providers. We all realize that we operate in this broad ecosystem and we have to come together to help you get that solution. So that’s what we are committed to doing, we’ll continue down that path and we hope to create deeper and deeper relationships with our ecosystem.

Jason Lopez: What you’re hearing is a talk Rajiv Ramaswami, CEO of Nutanix, gave to Nutanix customers at the Nutanix dot NEXT conference in 2023. He highlighted several partners, such as OEMs like HP, Dell, Lenovo, and Super Micro, a security partnership with Palo Alto Networks, cloud-native services with Red Hat, collaboration with major cloud providers like Azure and AWS, and with Citrix, particularly in VDI workloads which represents about 20% of Nutanix’s platform usage. This is the Tech Barometer podcast, I’m Jason Lopez. In this talk, Rajiv addresses hybrid multicloud, public cloud, edge technology, platform services, and a brief picture of the Nutanix roadmap. But to get back into the talk, here Rajiv touches on the company’s support of AI application development and its integration into services.

Rajiv Ramaswami: There are two parts to that role. The first part is many of you are developing new applications that do use AI, and we want to be the platform that you can run those applications on. We are also committed to open source on this. There’s a lot of open source work being done on AI. We are also working closely with Nvidia because a lot of these use GPUs and we expose DPU through our platform and help you use them in an efficient way. Clearly one whole approach around making these AI workloads work well on our platform. And some of these tend to be very industry-specific workloads like retail and manufacturing as examples that I mentioned. So that’s part one. Part two is using AI internally within our company’s offerings in terms of the products and services that you consume from us. So in fact, our Prism Pro, by the way, the R & D team for Prism Pro is actually called AI Ops because we truly believe that we can actually make operations more efficient by using machine learning and deep learning. That’s just one example of where we are starting to use some ai. Another example is we all, at least a good chunk of you actually provide telemetry data for us. We’d like to do more with the telemetry data. Of course, protecting identities, protecting privacy, of course, is very important there. But being able to look at the telemetry data, we could potentially be doing things like predictive analytics and helping you understand what needs to be changed. And so there are potentially new use cases there. And then of course when it comes to support, I want to make sure we retain our NPS there and we continue to do a great job supporting you, but there’s also AI that can be used inside to help make that process even more efficient.

Jason Lopez: Now imagine, if you will, a digital landscape, with applications and data freely roaming across multiple domains. In the middle of this, there’s the home base, the on-prem setup. There’s the edge, on the outskirts. Then there are the public clouds. In this next part of Rajiv’s talk, he hones in on creating a single, unified platform that bridges these diverse places..

Rajiv Ramaswami: The reality is yes, most of you are actually hybrid multi-cloud in the sense that you have these applications and data in multiple places. You have your on-prem, you might have your edge, you have public cloud, a, public cloud B for the most part. Each of these is different. They operate as silos. You have a different stack, you have different processes, tooling, and security to manage each of these. That’s actually what hybrid multi-cloud is today. And what we aim to do is to simplify that for you with a single platform at the infrastructure level that cuts across all of these and gives you that same experience, same tools, same processes, and complete flexibility to run your applications wherever you’d like across these environments and manage them all with a single team. That’s really, I think what we are aspiring to do, and we’ve got a lot of proof points along the way. We’ve got our offerings today on AWS on Azure with a number of managed service providers, and we are seeing an increasing number of edge opportunities in very specific verticals. Manufacturing, and retail, for example, just to name a few. And of course, defense.

Jason Lopez: Attitudes toward using public cloud services are changing. Nutanix initially embraced the cloud for certain services but eventually had to reconsider and move some of their operations back to a private data center to control costs more effectively. Here, Rajiv addresses this.

Rajiv Ramaswami: We are certainly seeing people being a lot more careful about what to do in the public cloud or what not to do because if you had asked this question three, four years ago, a lot of people in the room would just say, I’m going to the public cloud unless I’m a dark site. I’ve got some specific requirements. A lot of people would say, I can go to the public cloud. And then a couple of things happened over the last few years for those of you who have gone, I think you’ve realized that it isn’t as easy to take everything you want to the public cloud. It can be very painful for lots of applications and will need refactoring and potentially re-platforming. So not easy to take your existing apps. And for those new apps that you’re building, it’s great. It’s an easy on-ramp. But once you start running it at scale, and once you start running it continuously, the cost starts Up. And we’ve experienced that even at Nutanix. My cloud bill keeps going up every year and we keep trying to look at what we can do to optimize this. And I’ll give you one example of what we have done, on a much smaller scale. One, during COVID, we started a program called test drive. Test Drive ran on Google Cloud. Actually, it’s a nested hypervisor for those of you who want to be technical, but they didn’t have a bare metal offering at that time. And it proved to be quite popular. We didn’t charge for it because it was just an online proof of concept. And the use has continued to grow like crazy for us. And our cloud bill, therefore from Google, keeps going up every year. We said, okay, well we’ve got to now it’s starting to get serious. This is here with us to stay. We have to treat this properly and figure out how to optimize it. And so what did we do? Well, we moved back a chunk of what we call the steady state workloads back to our data center. We run that very cost-efficiently. Then, for burst capacity, we use the cloud, and it’s working out well. It’s certainly saving us some dollars when it comes to cloud costs. And what you’re going to see is a lot of companies realizing going through the same journey that we did, which is there’s an easy on-ramp to the public cloud. It’s an easy way for you to get a new app out there quickly, and if you don’t do it right, you’re going to be locked into the public cloud. At least in our case, it was portable. We could move it. It’s tempting to go use all the services, get there quickly, and build this app and run it. But then you realize that as you start scaling, at the end of the day, they have to make their margin. So you end up spending more and you can certainly, I always say a well-run private cloud infrastructure can certainly beat the public cloud in terms of pure cost. There’s no doubt about that. And I think lots of you probably have experience with that and you’ve done that work already. So we are certainly seeing that as, I don’t know if it’s maybe a little too early to call a trend, but I think what certainly we’re seeing as a trend is people being much more circumspect about what’s going to go in the public cloud. And I can’t say I’ve seen a trend in terms of everybody moving back from the public cloud.

Jason Lopez: Edge means different things to different people. But Rajiv points out its evolution has paved the way for a new generation of edge applications. New use cases demand advanced computing power, containerized applications and centralized automated management systems.

Rajiv Ramaswami: The way I would think of edges would be, let’s call it reasonably high compute edges. Some people call it near edges. And then other people say, I call it far edges where you have much lower cost, very simple solutions that are more OT type deployments. So for us, we are more focused on the compute edges. In fact, to put this in perspective, what you are deploying as a server in the data center three years ago, you can actually get more computing in a single server sitting in the edge today than you probably got in your data center a few years ago. So imagine tons and tons of compute potential available in the edge by deploying very few numbers of servers and a small number of nodes, one-node deployments, and three-node deployments. And so there’s a lot you can do with it. But more importantly for many of you, there’s a whole new set of applications that are emerging at the edge. If you’re in retail, we are talking to people about fraud detection. We are talking to people about automatic checkout manufacturing. We talked to car manufacturers who are saying, I want to have completely automated visual inspection of defects through machine learning and ai. So just to name a few here. And so there’s just a range of these new edge applications coming in. There’s a lot of data being generated at the edge as well, and you might be able to do training for some of these AI applications in the cloud, but inferencing will likely be done locally. And as you look at this, these edge use cases actually starting to get fairly compute-heavy, and we are engaged with many customers today in terms of helping our solution optimize for those specific use cases. Now, what’s common in addition to meeting these specific application needs is that most of the new applications are containerized. There’s a lot of AI being used and data management being needed, and there’s also a need for clearly centralized management, provisioning, upgrades, everything because you’re not going to be able to go out there and have people manage your edges. It has to be automated. So that’s what we are seeing today, and I’m sure this is going to continue to evolve over the next few years.

Jason Lopez: Rajiv’s talk moved on to a vision for simplifying the management of applications across platforms. He says it comes down to a platform service vision.

Rajiv Ramaswami: If you look at applications today, modern applications are being built with containers and Kubernetes, and that Kubernetes substrate is available for you everywhere. I would almost go so far as to say it’s starting to be commoditized. It’s everywhere. You can get it on-prem, you can get it in AWS, they have EKS, and you can get it in Azure. They have AKS. You can get it everywhere. Google has GKE. So that the compute substrate is available everywhere. In addition to the compute substrate, all apps also need a set of data services. Most apps, almost all apps will need databases. They’ll need messaging and streaming. They’ll need caching, they’ll need search. And this is a set of services, most of them related to data that’s available today in the public clouds. The only issue there are the public clouds, so that they tend to be siloed. It’s not easy for you to go from one to the other. It’s not easy for you to have flexibility in terms of avoiding locking in. And so what we are looking to do is to provide you with a consistent set of these data services across everywhere, right? Across all native substrates. You can run it on top of a Nutanix infrastructure, of course, wherever that’s available. But we are also going to enable these services to be available natively on AWS, Azure, and other native cloud substrates, so that you can truly then think about building an app once using this set of services. You could see how easy it is to be able to deploy that app anywhere and also not be locked in.

Jason Lopez: In this last section of Rajiv’s chat with customers, he touches on the company’s plan for enhancing their data services, especially in the context of Kubernetes and containerized applications.

Rajiv Ramaswami: So really you’ll see a range of data services for Kubernetes and containers that will essentially help you to run these modern applications and deal with containers much the same way as you deal with a VM. We always do snapshots, but now we are going to be doing snapshots and being able to store the snapshots in low-cost public cloud object stores or any SC-compatible object store for that matter that has a lot of use cases and implications. They’re going to provide you with a single console for you to look at your entire estate and manage your entire estate across on-prem public clouds and edges. And the fourth is more of this foundational vision around our future, how we can help you build these applications, modern applications in a portable way, using a consistent set of data services and being able to run them anywhere. This vision is for us to help you build portable applications. This is a five, 10 year journey for us and we are just getting started with our Nutanix database service. So I’m excited about the future of the company. I’m excited about how we can continue to meet your needs, not just for today, but also as you go forward.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This was a talk he gave before customers at the Nutanix dot NEXT conference in Chicago in 2023. In May of 2024 the conference moves to Barcelona, Spain. This is the Tech Barometer podcast, I’m Jason Lopez. Tech Barometer is a production of the forecast. If you like what we do here check out more stories at theforecastbynutanix.com.

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Alex Karargyris is helping a nonprofit called ML Commons develop open-source technologies that bring global standards to medical AI/ML initiatives.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
Alex Karargyris:
Healthcare technology, what it used to be like 50 years ago, has changed dramatically. You go right now to some operating room or even a radiology department, and there’s so many sensors and gadgets everywhere. It’s natural for machines to be able to process this information and funnel it to something that is meaningful to the human and let the human make the final decision. Right?

Jason Lopez:
That’s Alex Karargyris, co-chair for the Medical Working Group, which is a part of a large nonprofit organization called ML Commons. He’s also the co-author of the paper, “Federated Benchmarking of Medical Artificial Intelligence With Med Perf.” AI models have become an integral part of our lives, whether it’s your smartphone, recognizing your voice or a healthcare facility using a model to detect cancer and X-rays. That’s AI at work. This is the Tech Barometer Podcast, I’m Jason Lopez. AI models perform specific tasks. It’s like an expert who’s had extensive training and learned from vast amounts of information to become great at their assigned job.

Alex Karargyris:
So all these models are developed by different groups. It could be academics, it could be vendors like companies that develop AI systems. It could be internal researchers at the hospitals. It could be a kid on the blog that writes some code in Python and writes a model using cargo, open source datasets. So all these models, they do some specific task and they’re trained on data that you find either internally or you acquire, you paid for them or in the public domain. So once you build these models, then you have to deploy them. You have to take them to the hospital to do a specific task like organizing cancer or an x-rays.

Jason Lopez:
AI models start off as a blank slate. They have to be fed data and to improve, have to continually be fed. Deep learning models have an insatiable appetite for data, and the more they consume, the better they perform.

Alex Karargyris:
Similar to a doctor that is in 1 million, let’s say patients, right? And once you train this model, you need to test it. You need to validate it, and you need to validate it in a diverse selection of data out there in the real world. These were, the testing comes, it’s called benchmarking.

Jason Lopez:
This piece of the story is really important. Alex and his colleagues are focusing on getting AI models to perform as accurately as possible, which means reducing the bias that an AI model might produce. So let’s go back to the blank slate.

Alex Karargyris:
So you have a model that knows nothing. You have a machine. If you give ’em an X-ray, it doesn’t tell you if it’s cancer or not cancer. So you have to train it. So you need to keep all this data. You have privacy concerns, so you need be careful with that when it comes to healthcare,

Jason Lopez:
And that carefulness means a hypersensitivity to security concerns. Centralizing the data of several or several hundred healthcare facilities in order to train AI models will not work. That’s where federated learning comes in, deploying an AI model to the hospital where it learns on site, the place where the data securely resides.

Alex Karargyris:
But when you take that model, and let’s say you train until, let’s say in Indiana, and you see population data from that particular area, and you take that model and you deploy, let’s say in California, the population are different, right? So your model, which was trained in Indiana population data might be biased to that particular population. So when you move to California and then maybe in the Bay Area, which a large Asian descent population, it might fail. So this is where before you deploy it in the clinical workflow, you need to validate it. You need to make sure that you test it thoroughly in a diverse population.

Jason Lopez:
The med perf idea is to test AI models on real world data from diverse locations, ensuring the models meet high quality standards and maintain security and privacy.

Alex Karargyris:
We wanted to contribute to this effort to build an open platform that allows you to facilitate this testing. How can you test models that were already trained in a limited dataset or whatever dataset? How can you test them out in real world data from multiple diverse locations so that you know can feel confident that these particular models or any model are meeting high quality requirements? This is where we are.

Jason Lopez:
Alex Karargyris is from Greece in the late eighties and nineties. His father brought in some of the first computers to his hometown and started a school of computer science.

Alex Karargyris:
We used to get this expensive monochrome, green color screen computers at home, and also he had plenty of books like CBA and Assembly and me and my brother. We would spend a lot of time hitting some programming back in the days, print asterisk and stuff if then statements, and eventually we moved on to have a little bit more serious machines in the early nineties with the Intel pendulum. And of course, like any other kids, we got into the commuter games, video games, games, so a lot of video gaming, but eventually decided to go to electrical engineering and computer engineering in Greece. And during the last two years of the school, you have to choose which direction you want to go to. I was always intrigued with medical. My brother was also in the medical school back then. At some point I was considering switching to biology, so quite earlier.

Jason Lopez:
Alex’s mother was a biologist, and that’s one factor in his future at the time. And as a student being pulled in two directions, it was later that he discovered he could sort of do both.

Alex Karargyris:
Right now, we are living in a world that computers are everywhere, and AI right now is one of the major forces around us, and it’s becoming mainstream. And I’m glad I’m working in a field which is intersection of improving healthcare, but also the scientific approaches like artificial intelligence.

Jason Lopez:
One side note Alex gave us when we chatted with him about his computer science background was his introduction to remote computing through learning about programs like the search for Extraterrestrial intelligence or seti. That was an early example of what we now think of as cloud storage and resources.

Alex Karargyris:
When it comes to healthcare, I would say the tensions are stronger there. Sometimes, yes, there’s a tendency to go to move to the cloud, but there’s also this tendency of for protecting privacy to keep it locally. But I think healthcare systems and organizations understand that it’s hard to maintain local resources, and that’s why many of these laws that were written back in the nineties HIP and all this, that forced privacy, they cannot take into consideration right now to allow basically the migration to the cloud.

Jason Lopez:
AI’s increasing presence with the rise of cloud services is overall a good thing for healthcare. Despite the extra effort needed to satisfy security and privacy requirements, anyone with knowledge to develop AI systems from anywhere in the world can contribute.

Alex Karargyris:
Healthcare goes, skyrockets in the United States, I think it’s like, I don’t know how many trillion right now. There’s a shortage of experts to support these tasks, and it’s natural that people are looking into AI to help fill in the gaps and also to reduce the overload that maybe clinicians have into this field.

Jason Lopez:
As AI’s capabilities expand, especially with innovations like ChatGPT, it’s critical to understand its role as an assistant, not a substitute. Over time, as with any new technology, apprehensions will settle and AI will seamlessly integrate into clinicians’ daily tasks.

Alex Karargyris:
We are witnessing how AI can be transformative to many industries lately. And similarly, I think AI has impacted multiple facets of healthcare. I mean, from drug discovery, diagnosing therapeutics, it can contribute to reduction in healthcare costs as well as the improvement of healthcare delivery quality. More and more people need to be taken care of as well as the limited amount of experts are being around to support this delivery. There’s a gap that needs to be filled, and I think that’s the opportunity for that AI can offer.

Jason Lopez:
Alex Karargyris is a member of the nonprofit organization, ML Commons, where he’s co-chair for the medical working group. He’s also the co-author of the paper, federated Benchmarking of Medical Artificial Intelligence with Med Perf. There are two other stories in our series on Med Perf, one on the Med perf paper, and another on Alex’s colleague Debo Dutta, another pioneer in the intersection of AI in healthcare. This is the Tech Barometer podcast produced by the Forecast. You can find more reporting on technology at theforecastbynutanix.com.

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In this video, members from the IT team at Organic Valley explain the cost, time and performance benefits of modernizing their data operations with Nutanix.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Joshua Krzych VP of IT (00:00):
I am Joshua Krzych. I’m Vice President of IT operations at Organic Valley, so we have about 2000 small family farms at ebbs and flows from year to year. When I’m making a big decision, such as when we purchase Nutanix, one of the first decisions I have in my head is, what would a farmer think if they saw that this is what I’m spending their dollars on? Because in the end, we’re trying to make them the most profitable small family farmers out there in the world. That’s what we want to do to make sure they’re sustainable and can continue supporting their families and their livestock.

Nick Korte, Dir of Tech Ops (00:30):
My name is Nick Korte and I’m the Director of Technology Operations. When we went to Nutanix, it was a decision around, we went from a traditional three-tier infrastructure, and it worked really well for us for a long time, but we realized that this was a lot less management, a lot less backend nights of doing firmware updates, so simplifying it for our folks and giving them more opportunities to kind of stretch into what could be with Nutanix, whether it’s files or some sort of database as a service operation. It just opens up the door for us.

Joshua Krzych VP of IT (01:13):
Our current ERP solution is there, our warehouse management systems, our supply chain analysis.

Nick Korte, Dir of Tech Ops (01:22):
I look at it from the people perspective of we were spending our network teams and our storage teams and our compute teams we’re all working weekends and just doing updates. There was a fear of breaking something. You have a much more complex infrastructure. When we went to Nutanix, it was like, Hey, we have an opportunity to simplify and we have an opportunity to take that time back for ourselves. If I remember right,

Joshua Krzych VP of IT (01:48):
There was some DBA database maintenance activities that happen nightly that usually took several hours to complete before and after we moved over to Nutanix. It took minutes, maybe not five minutes, but it was way under an hour. Very impressive performance boost there. Moving to the Nutanix platform has really allowed our techs to focus more in engineers, to focus more broadly without having to deeply specialize in an area and then create themselves a backup as well with someone else on their team so they can take a vacation.

Nick Korte, Dir of Tech Ops (02:24):
Really, support was the main thing that we were looking at was, and that’s what really showed Nutanix rose to the top because of the support they work with the hardware, they’re really close with their vendors, so it just was a much better experience, and to me that’s an easy investment for our employees. Again, getting back to how they live their lives. I don’t want them sitting on the phones every night or coming in on a weekend because they can’t figure it out. I want ’em to be at home with their families and enjoying life. I think the main goal will be to get onto the platform a hundred percent, get rid of some of that technical debt that we’ve had and utilized in the past to try and save a couple bucks, and now that we’ve seen the value and being on Nutanix, I think that that’s a great opportunity for us to pivot towards that.

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In this video interview, Steve McDowell, principal analyst at NAND Research, talks about how the 2023 economy is impacting IT decision makers.

Find more enterprise cloud news, features stories and profiles at The Forecast.

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Transcript:

Ken Kaplan: It is a very strange economy. You know, there’s all these different indicators and mis-indicators, but in general, because you’ve been following this for a while, there’s a bit of, I don’t know if you want to call it a pendulum or these things come around where IT folks have to make it through these times that are tough, and then there are times when it’s go and grow. Can you just describe where we are today? What’s your take on the economy in these cycles?

Steve McDowell: Yeah, so the macro economy is impacting IT decisions, right? I don’t think there’s a problem with the industry delivering solutions that IT wants to consume.

The challenge is there’s so much uncertainty, whether it’s, you know, what’s happening in Europe or, or, or the continuing saga with China. the, and, and, you know, the interest rates. CIOs are deferring decisions.

Well, CFOs are asking CIOs to defer decisions, and that trickles down. And what that really means practically is the IT guys have to do more with less oftentimes.

And it comes back to, you know, we talk about sustainability and getting the most bang for my buck outta my resources. You know, if I’m not allowed to buy new resources, I really have to do that, sustainability aside. So anything that helps an IT practitioner, you know, better utilize his servers, his storage, his networking, his cloud usage. Man, that’s goodness.

And that’s all because of the economy because, you know, we’re deferring purchasing decisions. And you look at the earnings of HPE of Dell, of all the tier one enterprise OEMs, they’re all talking about this. They’re all saying at some point, second half, maybe, we’ll, we’ll start to see some recovery.

But there’s pent-up demand, but sales cycles are much elongated. And, and, you know, we’re not delivering servers as fast as IT guys want them or that we can do that, and it’s because of the economy.

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In this special Tech Barometer podcast series, Alex Karagyris and Debojyoti “Debo” Dutta explain how the MedPerfs platform paves the way for artificial intelligence and machine learning applications in healthcare.

Find more enterprise cloud news, features stories and profiles at The Forecast.

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Transcript:

Alex Karargyris: MedPerf is a platform that evaluates medical AI models on diverse real-world medical data.

Jason Lopez: In the public conversation about artificial intelligence, one thing just about everyone agrees on is that healthcare will be a big beneficiary of AI. This is the Tech Barometer podcast. I’m Jason Lopez. In this story, we’re going to explore how AI can impact healthcare, how testing in the real world can improve trust and how it can quantify medical AI performance on a platform called MedPerf.

Alex Karargyris: MedPerf can securely distribute the AI models to healthcare organizations and test those models on their local data. No data leaves the premises of the healthcare organization. It states within the private network and only high-level aggregated results are shared among the participating organizations.

Jason Lopez: Alex Karargyris is co-chair for the medical working group within the nonprofit ML Commons, and he’s one of Med Perfs co-founders and developers.

Alex Karargyris: The platform has been designed with patient privacy protection at its very core.

Jason Lopez: During our conversation, Alex circled back to the issue of security and privacy a number of times to emphasize how sensitive the healthcare industry is to the risks of deploying new technologies, especially in the light of HIPAA security rules.

Alex Karargyris: We aim for patient privacy first and foremost, and one more thing because we are developing source code, met Pref is open source so that everyone can contribute and reuse freely, right?

Jason Lopez: The high-level description of Med Perf you’ve heard so far was outlined in a 2023 paper Carris authored, entitled, announcing Med Perf Open Benchmarking platform for Medical AI. It lays down what the organization is, what it does, and how to get involved.

Debo Dutta: The reactions after the paper came out were different from different walks of life.

Jason Lopez: Debo Dutta is vice President of Engineering at Nutanix, and he was integral to starting the group ML Commons.

Debo Dutta: When I talked to technologists, they say, wow, this is a cool system. The ML folks were saying, oh, that’s a very good application for federated learning. Awesome. Some of my colleagues in computer science, they were quite amazed at the amount of effort it took to get so many institutions together and do clinical trials. I think people were just amazed by the sheer amount of effort and passion that this whole group took to get to this point. Then there are doctors.

Jason Lopez: Debo says, in the area of cancer, some oncologists who’ve been a bit skeptical about AI say the paper changes things.

Debo Dutta: I have had oncologists come and tell me this, that this is awesome. Oncologists who were skeptical about ai, one of my collaborators on this paper, told me, now that this is done, we can move on to doing even more amazing things that have a bigger clinical impact.

Jason Lopez: The story of the Med Perf platform essentially started in 2020 with the medical AI benchmark project that was done through ML Perf, an organization that benchmarks computing platforms for machine learning workloads, initially focused on brain tumor detection and M R I scans. People from organizations such as Nutanix, Intel, I B M, Google, and the Dana-Farber Cancer Institute wondered if they could replicate the success of it benchmarking in healthcare, they aim to build a platform that could test machine learning models on real-world medical data while fostering trust in AI for healthcare. Over time, the group’s membership grew to include people from over 20 organizations across five continents with full-time engineers and volunteers working together to develop the platform and establish best practices within the medical AI community.

Alex Karargyris: We were discussing with other people how the same benchmark philosophy could be applied to medical AI and how to expand the capabilities of the whole community. Some questions we’re asking are, how can we validate medical AI efficiently, and mutually, and make sure this privacy is guaranteed? So we set out to test this hypothesis three years ago, and we found out there were more and more people that did the same questions. The group grew from seven people to right now we have people from 20 organizations across five different continents, and we have full-time engineers in the group and also a lot of volunteers contributing to the development of the platform as well as best practices framework around the platform.

Jason Lopez: The Med Perf framework is a system designed to coordinate the evaluation of medical AI models. It consists of two main components, the Med Perf server and the Med Perf client, a lightweight software installed in hospitals and healthcare organizations. The framework allows the initiation of model evaluations on the med perf server, download the model weights, and push them to the client nodes.

Alex Karargyris: And then the clients can execute this particular model against the data by executing the tests.

Jason Lopez: This is the basic workflow that enables medical research using AI at hospitals, universities, and labs around the world. The Med Perf platform makes it as seamless as possible, but arguably the highest priority is security and privacy.

Alex Karargyris: So we did observe during the development and prototyping of MET PERF with our partners that this is the number one. Number two, major concerns, security and privacy in this context with a regulatory framework that has been developed around the world will force even stricter and deeper security considerations. I think that IT teams will have to look for professional solutions that offer tight and holistic security that cover AI as well, because it comes back to resource management. How do you handle heavy workloads?

Jason Lopez: This is one of the challenges. Med Perf is figuring out how to run things in the cloud because many healthcare organizations are not necessarily heavily IT-oriented.

Alex Karargyris: They don’t have the infrastructure to run this, but they want to join this AI ecosystem, right? So this is the way to go to be able to execute in a very trusted, secure way on the cloud. All these workloads from training all the way to validation that Met PERF does.

Jason Lopez: Alex says there’s a broad diversity of workloads in healthcare, and there’s not necessarily a right computing platform, but there is a common thread from an IT perspective,

Alex Karargyris: Better application management on the available resources. These AI systems could be very resource-hungry, GPU to be used accelerators to run these things. I think this is the value that many of the cloud providers can bring because they support this, right,

Jason Lopez: And always a high priority in the health IT conversation, security and privacy

Alex Karargyris: Major major importance like Nutanix, there is secure trust execution as well is important to be able to close the space. You can run these workloads. Depo has supported the group from its inception, and he has been a provider of wisdom, I would say, a strong believer in AI for healthcare, and she has been supported in open efforts like ours. Without email, it would be impossible to do this.

Debo Dutta: I was one of the founding members of the ML Commons organization. It was not called ML Commons before. It was just called ml perf.org. It was just a small organization of mostly AI infrastructure vendors and a bunch of academics who wanted to redefine how machine learning performance would be evaluated because we saw the tide of ML coming very soon, and we said, okay, we need to evaluate performance in a meaningful way, in a repeatable way, and we want to be a nonprofit so that people trust us. And then over time, ML perf evolved into ML Commons. The goal of this organization, it’s an amazing organization by the way, is to basically improve the state of the art and accelerate the AI transformation via open artifacts like performance benchmarks, data sets, and best practices.

Alex Karargyris: What we try to achieve with this, with MedPerf, if you’re an AI researcher and you run our study, MedPerf can help you collaborate with many institutions and evaluate your model on a much wider, and I would say diverse patient population than you could do possibly right now. Similar to what Mel Commons has been doing with its other benchmarks in other fields such as computing power platforms, we are creating benchmarks that are neutral an reproducible and can help improve the effectiveness of medical AI.

Debo Dutta: I’m elated because this has been a long journey to get the leadership on board and then getting hold of all the top-tier oncologists and medical researchers, AI researchers, and companies who do AI to converge on the one cause. That’s let us actually build clinically impactful ML platform that respects data privacy and patient privacy. Frankly, to evaluate the next-gen AI models that could change the world of therapeutics, cancer therapeutics in particular. So I’m super excited.

Jason Lopez: Debo Dutta is Vice President of Engineering at Nutanix and a founding member of ML Commons. Alex Karargyris, who led the effort with his collaborators to write the MedPerf paper, is the co-chair of the medical working group in ML Commons. You can find the paper “Federated Benchmarking of Medical Artificial Intelligence with MedPerf” at mlcommons.org. This is the Tech Barometer podcast, produced by the Forecast. I’m Jason Lopez. We’ll be doing some deeper dives into the MedPerf platform – a profile of Alex and one of Debo in future podcasts. You’ll be able to find those at the forecastbynutanix.com.

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In this video interview, Steve McDowell, principal analyst at NAND Research explains the challenges of onboarding artificial intelligence capabilities that require robust IT operations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

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Transcript:

Ken Kaplan: Any other trends that you see as we’re going forward that are going to be momentous, especially the, especially during the economy that we have?

Steve McDowell: The biggest, the thing I’m asking most about right now is the impact of AI on IT operations. And it ranges from, you know, how many GPUs can I put in a rack and how do I heat and cool those? How do I power those? Because GPUs are very power hungry and heat-generating more so than CPUs by far. But on the other end, it’s how do I use AI and specifically generative AI to, you know, is it a threat or is it a tool that helps me do my job better? And you know, like any tool it comes down to how you use it. but you know, again, we’re still very early stages of how we’re going to use this in IT operations. But we’re using it for cybersecurity, look for anomaly patterns you know, pattern detection to tell me when there might be trouble. we’re using it for compliance to read, you know, specifications across a number of industries and, and consolidate that into, you know, a set of actions that I need to take. you know, we’re using it to write code, you know, simple pieces of code. Generative AI’s pretty good at writing it if you ask it correctly, right? And that’s kind of the double-edged sword, is asking it correctly and, and ensuring that it’s trained on, on the right knowledge to really be a benefit.

Ken Kaplan: There’s a lot of hype around AI and ChatGPT.

Steve McDowell: There’s a lot of hype around AI, and that impacts it in a couple of ways. One is I need to figure out how to build the infrastructure to support that, because traditional compute does not account for it. So we’re seeing hundreds of experiments, thousands of experiments across organizations, and it has to step up and support those often in short order. so it’s a challenge operationally. The bigger question and where I think a lot of the hype lives is what’s really the power of AI to help me do my job better. You know, generative AI and chat GPT and things like that have a lot of promise, you know, as they exist today. I think they’re interesting tools, but I don’t think anybody’s solving real production problems in it, with those tools, right? What chat G P T does very well is, you know, you train it on a set of patterns, whether that’s code or language or whatever and then it processes those in a way that it can, it can talk back to you, right? Essentially. So I can say, you know, train it on Python code and say, gimme an algorithm that sorts the problem with as they exist today is the training field is very broad. So we don’t know the quality of the data, right? Those of us old in the industry, you know, there used to be a, a garbage in, garbage out saying, and that’s really true of generative ai. So technology has a lot of promise, and I think it’s going to end up being extremely impactful to it. I think it’s just very, very early and, and, you know, I would advise any IT practitioner to well, the IT practitioner already has his expectations set. You have to manage up the chain and set expectations that this is not going to solve all the world’s problems. I can’t go fire all my tech support guys because I have, you know, ChatGPT answering the call. It’s not that simple. So, early days, but let’s keep watching it.

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In this video interview, Harmail Chatha, senior director of cloud computing operations at Nutanix talks about sustainability factors and automation capabilities of AI shaping IT strategies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Related stories:

• The Role of AI in Cloud Computing
• Building Scalable, Sustainable Data Centers
• Tool Estimates Data Center Carbon Emissions and Power Consumption
• Green Data Centers: Designing an Eco-Smart Future
• Solving Generative AI’s IP Problem
• Who Owns AI?: The Rise of Artificial Intelligence Patent Law
• Artificial Intelligence is Safeguarding Society One Network at a Time

Transcript:

Ken Kaplan: What’s your view on AI from a data center mindset?

Harmail Chatha: So AI is going to require a lot of compute, right? It’s a heavy algorithm base, which requires a lot of GPUs, high processing computers. So that’s going to require a lot more power. It’s going to require a lot more real estate, and AI is useful in so many verticals, but ultimately it’s going to be useful in the data center as well. Through AI we’ll be able to self-heal a lot of the problems at the software tier as well. We’re planning to deploy robots in our data centers to do troubleshooting as well, and that’s going to be based on a lot of AI in itself to try to tell the robot what to do, how to fix it, and ultimately we want to be able to plug into the robot Prism Central. Anytime a system does have an issue, the robot already knows and goes and tries to address it itself. So AI, obviously, is going to be tremendous, a vertical that’s going to grow and continue to grow, and it’s going to have a huge impact on data centers and sustainability in itself.

Ken Kaplan: How does it make you feel when you see this wave of AI coming?

Harmail Chatha: So the wave of AI from an infrastructure perspective is going to be tremendous. Right now we’re at a chasm where we’re doing some AI/ML for small level data sets, but now what we’re seeing in the industry, for example, with ChatGPT, that infrastructure is heavy for them to even start the company. They have to deploy so much gear so the systems could learn what to do, how to do, they literally took the entire internet and dumped it into Chat GPT. So the entire internet is run across thousands of data centers. So much power consumption. Data centers consume about 1.5% of global power. And if ChatGPT took the internet into its set and we’re building on AI, that number’s probably going to double in the future as well.

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In this video interview, learn how Forestry and Land Scotland discovered Nutanix Cloud Clusters (NC2) as their ticket to moving their IT operations to public cloud.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Ken Kaplan: The forest needs technology?

Nick Mahlitz: Yes. The forests do yes, to manage your forests well and good, to use technology in a challenging environment like Scotland, where it’s very remote and the weather can be quite extreme sometimes. Technology and exploring all realms of technology will only help us better manage Scotland’s forests and our land.

Nick Mahlitz: I manage a team of engineers and technicians and manage our data center where all the magic happens of technology or files or applications and data, et cetera. So we manage that on a day-to-day basis. And then I’m responsible for helping lead and establish our strategic decisions in technology and our future.

Ken Kaplan: And where might technology be used in Scotland?

Nick Mahlitz: Sure. I mean, think of managing and looking after forests where some trees have diseases. The ability to use drones to scour around a forest and use AI and pattern matching algorithms and software to then find the trees that are diseased and stop that spreading is a wonderful use of technology in the realms of a tree in the middle of nowhere in Scotland.

Ken Kaplan: How did you get to this spot in your life, working at this place? How did you get here?

Nick Mahlitz: I was born in the highlands of Scotland and lived there all my life, albeit, I spent a year in Taiwan for quite a while. But I’ve always been interested in technology and for the last 20 years managing and building data centers has been what I do and trying to do it well. And that’s what I now do for Forestry and Land as well.

Ken Kaplan: That world has changed dramatically in the past, especially in 10 years. How do you feel in your career now about that world of IT? How do you feel about it?

Nick Mahlitz: You could feel many feelings about it. It’s challenging, it’s exciting. It’s unknowns. There’s so much happening that we don’t know yet. If we think about the technology over the last 20 years and how all the power was centralized and then the power of technology spread out to the edge, then it came back again into the center, and now we’re seeing this pattern repeat with edge technology and cloud computing, et cetera. So it’s an exciting world, exciting challenge ahead of us. And with the advent of cloud computing, we’ve got some real opportunities here.

Ken Kaplan: Let’s go into the data center and talk about the big challenges that you’re overcoming.

Nick Mahlitz: Yes. So Forestry and Land Scotland are only four years old, so we’ve always been forward-thinking. We have a lot of evangelists of technology in our organization. And so whilst we have our own data center the goal is always to migrate to a full public cloud and enjoy all the benefits that brings us. So one of the projects that we’re working on now is to migrate our entire data center to the public cloud. And that’s where Nutanix are really helping us. They helped us realize with NC2, their product, that migration path can be less challenging than what many other private and public sector organizations are experiencing. To re-engineer all their workloads into cloud takes significant money takes significant resources in terms of skills, which is a big problem in the world, finding and keeping good cloud-skilled people. So yeah, NC2 gives us the ability to migrate our data center to the public cloud using tools that we’re very familiar with. Because Nutanix offers that single management plane to manage both your private and your public instances. So that’s a very exciting project for us, and particularly because we in Scotland have high aspirations for sustainability for our net zero targets for CO emission, CO2 two emissions et cetera. So NC2 really does lend its weight into that area, and that’s very important, very important for us. So an exciting time where we can migrate our data center to the cloud and free up our digital technologists and our IT skilled people to perhaps concentrate on other things like AI and automation and other new exciting technologies.

Ken Kaplan: Let’s talk about sustainability and NC2. Tell me how it’s helping you particularly achieve some goals or set some new strategies for sustainability.

Nick Mahlitz: Yes. So we’re actively looking at the metrics and analytics behind what we currently have in our private data center. And then with Nutanix’s assistance, we know what that will look like with NC2 in the cloud. And there could potentially could be up to a 40% reduction in our footprint, which is a big savings. So whilst we don’t have the analytics yet, it’s a report I’m looking to create in June this year. NC2 really does help with that. And as we try to meet Scottish government targets for sustainability and we’ll do that via our vehicle management and our buildings management. But our data center is another big area where we can reduce that by using public cloud and NC2.

Ken Kaplan: Do you, do you ever get a sense that what you’re doing can also help other government agencies?

Nick Mahlitz: Yeah, yeah, very much so. We meet frequently with other agencies and the Scottish government in cloud communities where we share what each of us are doing in that area. And there’s been so much good work done by the Scottish government and realizing all the technologies to migrate to the public cloud, but what we are doing, no one else has done. And so using NC2 to trailblaze in this and show the benefits to my peers in our government organization, I think it’s key for me if I can help reduce costs and manage those costs in a fixed way. And if I can avoid the challenges around recruitment, which is a particularly challenging thing in the UK to reduce our footprint, then I think other organizations in the government and public sector should also look to and analyze both your native cloud approach and using technology like NC2.

Ken Kaplan: I mostly want to just capture what it is that you do. It’s interesting because we don’t think about, you know, the forest and technology and that you’re actually trailblazing. You said that those great words.

Nick Mahlitz: If you think about what’s important to people nowadays, you know physical and mental health and wellbeing are very much on the forefront of the organization. You’ve got to look after your people. If you don’t invest in your people and lose them, then you know, the organization suffers. So, Forestry and Land Scotland wants to invest in their people. We want to look after the people of Scotland, too. So, you know, there are areas that we do in terms of creating visitor centers and tourist places, and those green spaces where the family can go out for a day and enjoy a wonderful day with a picnic and enjoy the beautiful scenery that Scotland has. That’s very much also in our hearts. And again, technology is playing a part in that because we can deliver services to these rural areas that are in isolation in a better way with technology, which is a fantastic thing to do, to look after your people.

Ken Kaplan: Stepping back. The land of Scotland is the treasure, it’s precious. What’s your mission with all this?

Nick Mahlitz: Yeah, our mission is to look after our land and enhance the management of the land and forest for the people of Scotland and manage it on their behalf. Really, we’re just custodians of this. And give them the beautiful scenery that Scotland has and give it to the people to enjoy. But we also have the responsibility to not only manage that but to manage that in a sustainable way. So managing our forests in the best way possible, managing our land and creating renewable energy to help that sustainability piece is very important to us. So that’s our mission, to really provide what Scotland is to its people, but also really help our government and the world for climate control because it’s one of our big, big strategies that we take.

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In this video, see an early version of a modeling tool that estimates carbon emissions and power consumption of data center operations. This data can help shape strategies and inform decisions around IT sustainability.

Related: Building Scalable, Sustainable Data Centers

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Mat Brown: Cheers, Ken. I’m Mat Brown. I’m the technical lead for data center and Sustainability at Nutanix. We’re here today in Chicago for Nutanix dot X 2023, and I’m gonna show you a little bit about the Nutanix Carbon and Power Estimator tool, which is an educational tool we’ve been developing and currently have it in early access. We’re previewing it here and it’s to help our customers better understand their environmental impact of their IT systems.

Ken Kaplan: Okay, so what do, what do we see on the screen?

Mat Brown: So on the screen, we’ve simplified down these four inputs. On the left-hand side here we have a type solution type. We have the sort of workload for that solution type in terms of the number of virtual machines. We have the p e that’s the power usage effectiveness or how efficient the data center is in terms of delivering power to workloads. And then we have the location here at the bottom, so the location of where that workload’s running. And that’s really important because of the carbon intensity related to the electricity grid that the data center would pull its power from.

Ken Kaplan: Just describe to me again, why would people want to use this or why do they need it?

Mat Brown: This is mainly an educational tool so that people can better understand the different aspects of their workload, where it is, how efficient their data center is and where it’s located and what that overall adds up to.

Ken Kaplan: Okay. Can you show me what kinds of systems you have in here, and what you can analyze? Yeah,

Mat Brown: Sure. So we’ve got a few different solutions. These are all based on Nutanix-validated designs. No, we’ve generalized and simplified things here, a great deal. But because of the way that Nutanix scales really linearly and because of the great work our teams have been doing around the validated designs, we’ve been able to base them on these core validated designs here. So we’ve got generally virtualized applications, databases using NDB (Nutanix Database Services) and end user computing, virtual desktops, and cloud native Kubernetes as well. So we’ll just pick one of these and then in those validated designs there is a config for a bm, a typical CPU and RAM. And from that, we can then decide how many we want to apply to a workload. So here I’m giving a 4,000 virtual machine workload that’s based on a certain configuration that’s documented in our validated designs. We can then have a look at the data center, p u e.

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As Broadcom’s acquisition of VMware nears completion, IT leaders are hedging their investments, according to Steve McDowell, principal analyst at RAND Research.

Related:
Hedging Into Expected Broadcom Acquisition of VMware
Three Strategies to Help Manage Risk Arising from Broadcom’s VMware Acquisition
Assessing Broadcom’s Acquisition of VMware
Broadcom’s Acquisition of VMware Stirs Uncertainty

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Ken Kaplan: Last time we talked to you in an interview, we had talked about Broadcom.

Steve McDowell: I know we’re at a Nutanix event, but I’m going to say I’m a fan of VMware and I think the products they bring to market add value to IT organizations and, and, and help simplify their life. Um, but at the same time, I think there’s a question as this acquisition drags out a number of questions around, you know, how safe my investment in VMware is. We don’t know what Broadcom is going to do with that asset when they have it. Broadcom has a long history of acquiring companies that they feel have reached kind of commodity level, right? What concerns me when I look at VMware and the Broadcom acquisition is, is there going to be a drain on innovation, right? We already see people leaving VMware. You know, what’s going to happen with pricing? you know, this is Broadcom is going to take VMware from, you know, an innovative kind of leading player in the industry and try to fit that into a broader portfolio. And that’s causing a lot of confusion. That’s causing a lot of uncertainty, not just among IT guys, but about, you know, those of us who track the industry and those around the industry. Um, you know, so there’s questions in my mind if I’m deploying VMware, is that a safe bet moving forward? I’m not saying don’t do it, but I’m saying as you do, you know, make sure you hedge a little and make sure that you’re really safety proofing your investment in those assets. Right?

Ken Kaplan: Has anything changed in your thinking about it now?

Steve McDowell: Well, here’s what I’ll say. So Broadcom is saying all of the right things, um, about their acquisition of VMware. The challenge is that this thing continues to drag out and drag out, and the uncertainty within VMware itself is causing a little bit of a brain drain, right? We’re seeing an exodus of VM employees into other organizations. That concerns me a little bit. So even if they continue to invest in r and d, there’s going to be a bump. There’s going to be a bump in their roadmap until this acquisition closes, I don’t expect we’re going to see, you know, significant new innovation out of VMware. And then post-acquisition, there’s always a period, right? So, you know, there’s fear, there’s uncertainty, there’s doubt about the future of VMware under Broadcom. I think that’s well-founded now. I don’t think VMware’s going anywhere, right? But are they going to be the company that we thought they were two years ago? I’m not so sure.

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If cloud computing is the biggest infrastructure ever built by human hands, then data centers are the engines and building blocks that make it run. Building data centers is increasingly challenging as space, power, scalability and sustainability intertwine into priority number one. No one knows this better than Harmail Singh Chatha, senior director of hybrid cloud operations and ESG at Nutanix. In this third of a three-part podcast series, Chatha talks about building one of his company’s modern data centers, which powers the company’s critical aspects of the business. Adding to his list of challenges was timing: during the COVID-19 pandemic.

Related:
Architecting Sustainable Data Centers
Building Scalable, Sustainable Data Centers
IT Sustainability Becomes Business Imperative
Attention Turns to IT Sustainability and ESG
Green Data Centers: Designing an Eco-Smart Future
Inside a Hyper-Dense Data Center
2023 Enterprise Cloud Index

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
You’re standing in a data center. The biggest infrastructure ever created by humans – the cloud – lives here. Building an environment like this is a feat of engineering. It powers AI, IoT, virtualization, crypto, the metaverse to name a few technologies. Some of the solutions to the most urgent problems of our time – like climate change, disease, and feeding the world – are being mapped out here. It powers the apps on your phone, online banking, and businesses large and small globally. It’s enormously complex.

This is the Tech Barometer Podcast, I’m Jason Lopez with another in our 3 part series on data centers as seen through eyes of Harmail Chatha, Director of Global Datacenter Operations for Nutanix. In this episode, the challenges of building a data center during the pandemic.

Harmail Chatha (01:18):
We run one of the largest private clouds based off of Nutanix and that’s our entire software development platform.

Jason Lopez
Before there was any notion of the threat of a new virus, Harmail’s team was planning a couple of data center buildouts. The existing data center environment had to continue. Still, in one of his larger data centers where the company’s critical development platform ran, they managed to build out a megawatt of power space cooling each year during the challenges of a pandemic.

Harmail Chatha
You can’t travel, you can’t be hands-on because anytime you actually do a data center build out, it’s very physical. For a guy like me that’s been in the industry for quite some time, I like to see it getting built out. I like to be hands-on. I like to course correct if anything doesn’t look right, but in this situation, obviously I couldn’t do that.

Jason Lopez
In Harmail’s words:

Harmail Chatha
Trust the process. I had to trust my team on the ground. I had to trust the relationships I had with the data center provider and with the contractors and the cabling companies and the entire supply chain. Trust them to like order the right parts, deliver the right parts, install it the way I want it installed and get it done in a timely fashion.

Jason Lopez
Let’s say you were going to build a data center. What are some things you have to think about.

Harmail Chatha
You’d have to go find the data center to host your gear. That would be step number one in a traditional process, if I was doing this from scratch.

Jason Lopez
It’s often not enough to know the equipment and how to hook up. Building a data center requires coordination with the data center provider. He knew the facility and knew the people who were managing the space. And he had already been there and seen the environment first hand, so he didn‘t have to meet and see it again. So again, he trusted the process.

Harmail Chatha
Trust the data center provider you already have experience with, you already have a relationship with. That kind of takes care of your power, your cooling your space, your physical security. The next is really standing up the environment and by the environment, I mean cabinets. In-Rack PDUs, your containment, your wire cabinets.

Jason Lopez
When the truck backs up and delivers the gear, you now have a floor full of boxes of servers, cables, switches, and racks. You can connect everything out of the box, but Harmail says his environments are custom designed.

Harmail Chatha
We call it a hyperdense data center environment that’s little bit custom tailored to our HCI platform. Having already done that, I didn’t want to change up the model. There’s always 2.0, 3.0. We went with a model here that absolutely worked. So for us to stand up one megawatt… Space was already vetted, locations already vetted. And then it was just a matter of how many racks we can order, how many PDs we can order the containment, how we do hot al containment, how do we take care of that, the wire racks and then cabling itself. Cabling means structure, cabling from your core networking components to each of your top racks and then from top racks connecting into the servers.

Jason Lopez
In the first buildout, in 2020, the pandemic hadn’t been going on long enough to affect the supply chain. All the gear showed up without any problems.

Harmail Chatcha
Then came the issues as far as the buildout goes, because in the data center you have a couple of different teams that come in and do the work for you. One is the team that mounts the racks, does all the physical build out of installing the power busway, installing the racks, installing the hot out containment for you and the basket tray. So that phase was, you know, it took a little bit longer due to resource constraints.

Jason Lopez
They needed to connect the electrical distribution system, known as a busway, to the facility’s uninterruptible power supply. But they couldn’t find electricians.

Harmail Chatha
They weren’t taking work that they didn’t want to take. They could pick and choose. I want this job, I don’t want that job. That was a challenging factor, working and trying to find electricians. But we were able to, you know, work with our data center provider. And these data center providers are not small. I mean they’ve got massive campuses essentially with lots and lots of power. Mega multi digit megawatts of power.

Jason Lopez
In addition to the challenge of getting electricians there was the matter of cable management. Anyone who’s ever connected a large home theater, multi-room, audio and video setup will know that organizing cable makes the difference between a crisis or an easy upgrade down the road. In a data center, that’s fantastically magnified.

Harmail Chatha
This is a art form. Like you don’t just plug one cable to one top of rack switch. It’s highly designed. Every port, every rack is connected into a specific port and it’s all based off automation and we want consistency in that. We want it to look nice and beautiful. So if we ever have to troubleshoot something, it’s easy for us to identify which cable to rip and replace.

Jason Lopez
The second data center came a year later in 2021. It was based on the same architecture… one megawatt, 60 racks, four pods of 30 rack pods, and containment. But now, Harmail’s team was up against a big supply chain problem. A three month project was stretched to nearly seven months and some gear didn’t arrive until 2022.

Harmail Chatha
What it taught me was you had to have the experience in the industry to be able to do this. You couldn’t be just a newcomer and say, “Hey, I’m going to go and build out a one megawatt data center.” If you didn’t know how to do it, if you didn’t have the industry contacts, if you didn’t have a vetted architecture already, I don’t think there is much success for anybody. So for me it was trusting the process. It wasn’t time for me to go out and vet out new technologies, new schemes on how to do a power bus way or how I should change my cabling or try out new vendors potentially where I can, you know, save a few bucks.

Jason Lopez
He also points to large providers like Google, Microsoft, Facebook, Amazon which highly architect their spaces for efficiency and sustainability, saving space and power at every turn.

Harmail Chatha
That’s the model we are trying to follow. You know, we’re, we’re a mid-tier data center customer. We didn’t want to do everything the traditional route. We kind of challenged ourselves back in 2018 when we re-architected our data center strategy and how we’re going to do our deployments to really do a different scale it out. The work that we put in then to design the data center environments now is what led us to succeed during the pandemic. Because of that hyperdense design, we had some leeway to go more dense versus just continually having to deploy more racks and going horizontal versus us focusing on going vertical.

Jason Lopez
Harmail Chatha Is the global lead of nutanix’s data centers. This is one of three reports we’ve done with Harmail. In our other reports ee profile him and his role as the data center manager, and we also take a look at data center sustainability. you can find those at theforecastbynutanix.com. I’m Jason Lopez thanks for listening.

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Explore the joys and challenges of leading data center operations with a sustainability mindset. In part two of a three-part podcast series, Harmail Singh Chatha explains what it’s like being director of Global Data Center Operations at Nutanix as the company embraces IT sustainability best practices. “The mindset is changing,” he said. “Everything is becoming more efficient. Compute is becoming more efficient. Data center providers are becoming more efficient. Everything is going to a software-defined network. The conversation started 10 years ago, but it’s finally starting to come to fruition where networking is deployed as code servers are provisioned, as code instances are spun up as code. That’s the mindset that everybody has to go into as they’re going into this industry.”

Related:
Architecting Sustainable Data Centers
Building Scalable, Sustainable Data Centers
IT Sustainability Becomes Business Imperative
Attention Turns to IT Sustainability and ESG
Green Data Centers: Designing an Eco-Smart Future
Inside a Hyper-Dense Data Center
2023 Enterprise Cloud Index

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
Harmail Singh Chatha
I call one of our data centers, my baby. We’ve heard about building data centers submerged in the ocean. You know, data centers traditionally are super boring. Let’s just have an honest conversation.

Jason Lopez
If you’re following along in our series of three podcasts with Harmail Chatha, Director of Global Data Center Operations for Nutanix, you know his vision is of a software defined world. A world where efficiency is a key factor in how people are thinking about data centers these days. This is the Tech Barometer podcast, I’m Jason Lopez. As the work goes into data centers to make them more sustainable, they also have to do more. Just about everything we do compute wise from surfing the Internet to the cloud to AI comes from a data center.

Harmail Singh Chatha
Back in the data center. In the 2000 fives or earlier, it was that cookie cutter approach of, you know, just give me a 42 rack. People used to push three four kilowatts per rack. They didn’t really challenge the process. They just wanted a fail safe environment to go into.

Jason Lopez
If you’ve ever been in a data center unless you’re an IT engineer there’s really not a lot to see.

Harmail Singh Chatha
You walk in, there’s just rows and rows of servers and lots of cables and you know, back in the day, cable management wasn’t a thing. You just connected it, get it online, and the servers are gray, the racks are black and you have white light in the data center.

Jason Lopez
Harmail said the bland look of data centers inspired his team to build the Nutanix data center with a bit of flair.

Harmail Singh Chatha
When you walk into our data center, you’re going to see a lot of LED lights. You’re going to see hologens of the Nutanix logo, you’re going to see blue and green inside the hot out containment. It’s rows and rows of Nutanix gear. But we have a cool LED wall on one side just to brighten up the space and a little bit more engagement and not have it so boring.

Jason Lopez
But there are data centers out there Which have a fantasy golf course feel if you will, not so much because of what they look like on the inside but rather the environments they’re in.

Harmail Singh Chatha
There’s a cool one in the Nordic track up north. It’s built inside of a hill. It’s very small, but it’s very exotic. Haven’t been to that one yet, but it’s definitely on the list. That one is pretty cool.

Jason Lopez
And what sounds like out of a James Bond movie, you can even find data centers that are underwater or on boats.

Harmail Singh Chatha
Can that really scale out? Like look at the total megawatts of data center power being consumed or built total square footage worldwide of data centers. You know, the folks that are hosting on barges, I would think are just like a small mini subset of services that live there that are highly critical in the grand scheme of things. I don’t think those small data centers is going to hold up because of the demand. But if there was ever a vent of some sort of environmental impact, the barge would just take off with your environment. It’s fully connected, it’s powered up, and it’ll stay on.

Jason Lopez
We wondered if there were any movies where Harmail felt they got data centers right… something that made him say, “that’s a data center.” Iron Man, Transcendence, The Matrix?

Harmail Singh Chatha
No, obviously it’s dramatized. The closest that I feel like anybody’s come to those Hollywood data centers is probably our data center that we purpose built for customers engagement. It hasn’t been used in the movie yet. We’ve done some videos on it just to highlight it, but we’ve had a lot of customers walk through there and just absolutely be floored. And what it does is just creates a memorable moment when you walk in, you’re like, “Oh, this is a data center.” And not every data center’s like that.

Harmail Singh Chatha
It’s all about customization.

Jason Lopez
During the pandemic Harmail and his team built a data center amid the challenges of staffing and the world’s supply chain issues. They didn’t build it out-of-the-box but custom designed it in the way they wanted, fully planning for it to scale out.

Harmail Singh Chatha
We faced no issues, fortunately, no power constraints. Nothing went down. And it’s really just having that vetted design that you know is going to work for you. But also at the same time, like, you know, what we’re doing in that data center, the traditional route was a 400 amp, 208 volt busway, a power busway, and people would do two per row A and B versus we have two rows. We went with the 800 amp busway and just two bars across the top and then crossing AB power and going 415 volt. And of course going higher density on the racks is. And it’s hot out containment we’re pushing anywhere from like 110 to 125 degrees hot air in the pods themselves. So you can just imagine the density of that.

Jason Lopez
One of the things Harmail watches as a date center goes online is whether the plan he had, which is the architecture, is robust enough. Can it hold up to the demands put on it. For his team, that’s a global effort.

Harmail Singh Chatha
Under my umbrella, we’ve got about a dozen or so data centers around the world that my team and I are responsible for. We’re working with all the major providers. Some of them are partners in what we do. And a lot of them are just, you know, where we’re leveraging their, their real estate, their power efficiency, cooling efficiency to host our gear.

Jason Lopez
He began working in data centers in 2005 at a couple of ecommerce platforms and it turned out not to be just work… he had found his calling.

Harmail Singh Chatha
I started in my IT career just working in IT and just, you know, helping, helping people troubleshoot issues and stuff like that. And I got to go to my first data center by Mission College there was a data center there. I just fell in love with them. It was just like a very ah moment.

Jason Lopez
When he told us about cool data centers he’d like to visit we asked, well, how many have you walked through? 30?

Harmail Singh Chatha
Way more than that.

Jason Lopez
75?

Harmail Singh Chatha
I think I’ve been in probably 100 plus data centers.

Jason Lopez
If you think about how many technologies since the 1990s have fired people up, not only in Silicon Valley but around the world: smartphones, IoT, peer-to-peer, cloud, virtualization, artificial intelligence, and the list could go on, they’re brought to you by data centers.

Harmail Singh Chatha
I love the challenge of it, you know, to build these data centers out, to maintain these data centers and do some unique work that not everybody gets an opportunity to do.There’s a limited number of football players and basketball players, there’s a very limited number of people that actually operate data centers and get to be hands on data centers and that’s what really excites.

Jason Lopez
We’re in an era when data centers are becoming even more central to how the world works. Perhaps Hollywood goes overboard and doesn’t get it right technically, but the movies are on to one thing: today’s data centers are more than just a repository of rack mounted gray server computers in the basement with a secure always on mission.

Harmail Singh Chatha
That mindset is changing. Everything else is becoming more efficient. Compute is becoming more efficient. Data center providers are becoming more efficient. Everything is going to a software defined network. The conversation started 10 years ago, but it’s finally starting to come to fruition where networking is deployed as code servers are provisioned, as code instances are spun up as code. That’s the mindset that everybody has to go into as they’re going into this industry.

Harmail Chatha is the Director of global data center operations for Nutanix. This is one of three podcasts about him and his work at Nutanix. Check back at theforecastbynutanix.com for stories on data center sustainability as well as trends. This is the Tech Barometer podcast I’m Jason Lopez, thanks for listening. We’ve got an extensive library of written stories as well as podcasts, again, at theforecastbynutanix.com

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In the first of a three-part Tech Barometer podcast series, go inside the mind of a data center architect as he helps lead Nutanix IT into a sustainable hybrid multicloud future. “We’re constantly and consistently using electricity, so the power and cooling have to be always available, every second,” said Harmail Singh Chatha, director of Global Data Center Operations for Nutanix. Server racks, networking cables, cooling systems and power sources all interconnect in a highly strategic and systematic way. Chatha explains how hyper-dense data centers power a software-defined world of applications and data, which dramatically grow and evolve over time. The infrastructure and services he pulls together must be reliable, secure and easily scalable while limiting the impact on Mother Earth.

Related:
Building Scalable, Sustainable Data Centers
IT Sustainability Becomes Business Imperative
Attention Turns to IT Sustainability and ESG
Green Data Centers: Designing an Eco-Smart Future
Inside a Hyper-Dense Data Center
2023 Enterprise Cloud Index

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Harmail Chatha
The conversation is really changing around sustainability. What are the data center providers doing to be more efficient, lower their power utilization efficiency or effectiveness, and their water utilization as well.

Jason Lopez
Harmail Chatha is the Director of Global Datacenter Operations for Nutanix. This is the Tech Barometer podcast, I’m Jason Lopez. What you’re about to hear is an insider’s perspective on how people who run data centers think. In this podcast Harmail gives us his insights on the role data centers play in dealing with climate change. Data centers run the digital world. According to an Ernst and Young sustainability report, if you add up the power used by all the digital devices on earth, it accounts for 4 percent of global greenhouse gas emissions. Data centers contribute to almost half of that.

Harmail Chatha
Customers are getting into it and us as Nutanix, we have a number of customers reaching out to our sales folks saying, what are you guys doing from a sustainability standpoint?

Jason Lopez
One thing Nutanix has done in it’s data centers is shift from a physical orientation to infrastructure as code wherever it can. Whenever possible, workloads run on virtual servers rather than on dedicated hardware.

Harmail Chatha
Anything and everything is pushed out through code versus having to manually go into operating system and do anything like that. And that in itself has been talked about probably for a decade plus and is finally coming to fruition with heavy usage of public clouds and heavy usage of private clouds like Nutanix. That provides some efficiency gains because you’re able to better utilize the hardware if you’re able to load it up with multiple VMs on it or workloads on it is if we get more people to do that, get off of dedicated bare metal horizontal buildouts, go more virtual, increase your utilization where you know, the average utilization of a server on a bare metal is 30 40%, with Nutanix depending on the nature of the environment and the workloads we’re running, we can push that up to 70, 80%. That’s where you gain sustainability. Being more efficient on how you utilize your hardware, increasing the utilization of your space, you’re increasing the utilization of your power that you allocated for in the data center. And of course the cooling that the data center is providing as well, that’s going to move us forward into that sustainability conversation.

Jason Lopez
Part of the conversation isn’t just about CO2. It’s about water which is used to cool data centers. Lots of water. This is a big deal, especially in the western United States which has experienced below average precipitation since the late 1990s. And despite some intervals of rainy seasons, climate scientists warn not to count on it. As innovative as the computer industry has been at developing networking, IoT, AI and the cloud, Harmail says they need to get more efficient in how they cool operations.

Harmail Chatha
They can’t be using very traditional old school mechanical. They should be trying to go into environments where they can use outside air to cool hence leading to better sustainability.

Jason Lopez
Or move to where the water is.

Harmail Chatha
People just need to give up the notion that I need to touch my gear. You don’t need to touch your gear.

Jason Lopez
And another factor is locating data centers is putting them in the wrong place… literally in the same building the company is in.

Harmail Chatha
Your cost to operate within office building versus a true and traditional data center is just so much higher.

Jason Lopez
He cites other factors in location such as the General Data Protection Regulation, which has sparked more localization in the countries where it’s required.

Harmail Chatha
And then what you do have is least, you know, multi databases or multi-data center strategy, whether it’s two data centers, three data centers for high availability, that distribution of compute networking and storage. Whether that happens in that traditional three-tier sense of big sands versus the Nutanix hyperconverged infrastructure. That’s kind of the trend.

Jason Lopez
Sustainability in data centers is about using less power, less water, producing less CO2. This, in the face of more. More apps, more storage, more demand. One of the industry’s recent challenges was a moment of learning. That was during COVID. There was a lot of stress put on Internet providers, especially services like Zoom.

Harmail Chatha
Their demand increased so much by everybody working from home that they couldn’t even predict that. So as much as we wanted to talk about sustainability and how we can be efficient and the impact of data centers, I think it’s greater now than it was pre pandemic. So what’s going to continue to happen is data centers obviously with smart cars and smart streets and smart cities and everything else, like smart homes for example, your home network in itself is becoming a data center. So the demand on data centers, it’s going to continue to grow. There’s a lot of edge connectivity going on, like edge data centers. As the car is driving it needs constant connectivity to offload data, pull data back X, Y, Z. The entire world already relies on data centers quite a bit. The reliance is going to continue to grow as everything gets more connected. I think providers are going to have to change their mindset. They’ve been talking about it for a number of years of how we’re sustainable. We’re doing this, we’re doing that, but that’s a small set of data center providers doing it right now versus the broader set isn’t focused on that and they’re still operating inefficiently. So the industry as a whole has to start changing to combat climate change. Customers like us, we are getting asked by our customers about what’s our carbon intensity, what’s our carbon footprint, what are we doing about sustainability. And ultimately we’re going to be starting to focus or we have been focused on and we’ll continue to focus on only partnering with data center providers that have the same mindset about sustainability. If providers aren’t focused on that, they’re ultimately going to get weaved out of it.

Jason Lopez
In the post pandemic world there’s one major change in how we live our lives… which is due to data centers. As much as some are trying to put the genie back in the bottle, working from home has been established not as a perk, but a real way of operating. Harmail says a similar wake up call is happening around sustainability.

Harmail Chatha
I think there just needs to be a lot more knowledge within the data center industry and folks that are getting into becoming data center operators like myself. If I can go back in 15 years and tell myself, “how do you operate this efficiently?” go out and really do the homework on it. Understand what your business initiatives are, understand what your business goals are and really customize a data center footprint that encompasses, you know, cost efficiencies, high availability ultimately gets you to your sustainability goals. You know, waste is just not welcomed should be the mindset, and partner with data centers that meet your requirements.

Jason Lopez
This kind of demand for sustainable data center services is just starting to be baked into the industry. It’s become a part of the conversation that he says he has with customers all the time.

Harmail Chatha
We’re trying to be as efficient, utilize the power in a most effective way, not having any waste, reduce our carbon intensity and where we can go as much renewable energy as we can and partner with data center providers that utilize the renewable energy. We’re going to continue to get a lot more efficient just because CEOs, customers are asking providers like us or customers to be more sustainability efficient.

Jason Lopez
Harmail Chatha is the Director of Global Datacenter Operations for Nutanix. This is the Tech Barometer podcast, I’m Jason Lopez. This is one of three stories in a series on Harmail and the data centers he oversees for Nutanix. Check back at theforecastbynutanix.com for the other reports we have as we talk with hiom more about his journey as a data center engineer and the challenges of building a data center when the supply chain dried up.

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Innovations in public cloud services and private cloud technologies driving more organizations to build hybrid cloud operations that fit their particular business needs. Hybrid cloud strategies are allowing IT leaders to optimize the total cost of ownership (TCO) while meeting present and future business needs, according to Brett Tanzer, vice president of product management at Microsoft.

In this Tech Barometer podcast, Tanzer talks about how Azure’s customer needs have changed as they move to hybrid cloud IT operations, blending owned and operated data centers with Azure cloud services.

“The infrastructure that Azure provides, really makes it possible for customers to reach new levels of TCO using their Nutanix solutions,” said Tanzer.

“You get to take your existing solutions that have been highly tuned and optimized, you get to bring them to the cloud for further optimization, better TCO, more expanded reach, and then you get to count on the Nutanix roadmap for perpetuity. So all of the work that my friends at Nutanix are doing to make Nutanix Cloud Clusters (NC2) better, you get to count on that. And you know, we have a lot of plans with our friends at Nutanix to drive more integration with Azure to continue to lower the cost and expand the scenarios.”

Related:
2023 Enterprise Cloud Index
Finding Economic Efficiency in the Hybrid Multicloud
Validated Way for Moving Between Private Data Centers and Public Cloud
The True Hybrid Cloud Benefit: Bridging the Public and Private Cloud Gap
Nutanix Clusters: Smoothing the Pathway to Hybrid and Multicloud Deployment
Enterprise Computing Lab Boosts Hybrid Cloud Data Center Innovation
3 Key Cost Benefits of Deploying a Hybrid Cloud
Raising the Bar for Hybrid Work Technologies
Redefining Workloads in Cloud Environments

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Brett Tanzer
The infrastructure that Azure provides, really makes it possible for customers to reach new levels of TCO using their Nutanix solutions. You get to take your existing solutions that have been highly tuned and optimized, you get to bring them to the cloud for further optimization, better TCO, more expanded reach, and then you get to count on the Nutanix roadmap for perpetuity. So all of the work that my friends at Nutanix are doing to make Nutanix clusters or NC2 better, you get to count on that. And you know, we have a lot of plans with our friends at Nutanix to drive more integration with Azure to continue to lower the cost and expand the scenarios.

Jason Lopez
Brett Tanzer is the VP of product management at Microsoft. He’s been at the company since the 1990s and has sort been in the position of seen-it-all done-it-all throughout his career. Today he’s helping Microsoft’s Azure customers more easily build out their hybrid cloud IT operations through partnerships like the one they have with Nutanix. In late 2022, the two companies released NC2 on Azure, with allows an IT team to emulate a Nutanix-powered datacenter in Azure. This gives them the best balance of private and public cloud infrastructure. For this podcast we recorded a conversation with Brett, Ken Kaplan who’s the Editor in Chief of The Forecast, a Nutanix publication, and Kanchan Mirani, director of product marketing at Nutanix. What you’re about to hear is a curated version of the conversation. We pick up where Brett is talking about how Azure’s customer needs have changed and what’s happening today.

Brett Tanzer
Azure in general has a very diverse set of customers and we’re seeing customers come from all industries across all walks of life and all geos. We benefited from that. We certainly started with a focus on the enterprise customer and we continue to maintain that, but that’s our sweet spot. I would say now, particularly in the area where I’m in, we’re just focused on more mission critical workloads with higher SLAs. Whether you’re looking at our SAP services or you’re looking at our Nutanix services or you’re looking at others, we’re really focused on enabling that. And these are the crown jewels of the organization. Their most important workloads run on them. And so that’s where our focus has expanded, not just from understanding IT and kind of the enterprise, but into mission critical enterprise workloads that enable people to modernize their applications.

Ken Kaplan
One of the things you talked about, your passions or interests, was, say, helping a developing country or an organization really modernize, I guess.

Brett Tanzer
You know, I think it’s very rudimentary things. Providing access to technology in a timely manner in a region that doesn’t have it enables a lot. Provides the ability for people to get access to future generations of technologies from ISVs like Nutanix and others. And then combining that with the breakthrough technologies that we’re working on in Azure around AI and analytics and so on and so forth, that enables those scenarios because you know that developer with a team of two may have to deliver solutions as complex as an organization that used to have a team of 50, and the only way that they’re going to get to do it is if we bring simpler solutions and give them access to these technologies that make it easier to build things at lower cost with more reliability without in some sense having to train a massive workforce.

Ken Kaplan
Yeah, and it kind of leads me back to the partnership and the ecosystem where you’re working in now. In this time is it driving the need for these partnerships more than ever because there’s just so much going on out there and does that help the IT professionals?

Brett Tanzer
Well, I think it has a lot of trust in their ISVs like Nutanix and others. And so partnering with folks like Nutanix or others helps bring both trust and skills to these solutions in the cloud. I mean, imagine if Microsoft had to relearn every technology that was out there to operate it by itself, that would be very hard and customers would find themselves with many fewer options than the ecosystem model we have today. The reason why Microsoft partnered with Nutanix to bring NC2 to the cloud is really because of Azure and Microsoft strategy to meet the customer where they are. There are a lot of customers using Nutanix on premises today. In order for them to bring those solutions to Azure, they would’ve had to completely rewrite them from scratch versus us enabling them to go bring those solutions as they are into the cloud and start to take advantage of this. And so this is part of a strategy that Nutanix and Microsoft really share, which is meaning the customers where they are helping them address what are the biggest problems they have today, things around TCO, security, reliability, global reach, all of those things that the cloud can enable for them in the next few years by making it easier for them to take advantage of it. And so we’re really focused on helping customers right now with the set of challenges they have today and over the next 12 to 24 months. And that’s what NC2 enables us to do very easily for customers.

Ken Kaplan
What does the Nutanix Microsoft Azure partnership mean for developers, like app developers? Is this a good thing for developers because…?

Brett Tanzer
We provide more access to technology, we provide lower friction and we provide them a way that the legacy investments they’ve already made in their solutions can still become viable and future-proofed. So it helps them on a bunch of dimensions. If you’re a developer who’s looking to build the next generation application around data, you really have a lot of benefits and a lot of access to technology, whether it’s through Microsoft tools, Nutanix systems, Microsoft services, or the combination of all of them together.

Kanchan Mirani
So in addition to the developer, it’s the infrastructure architect who actually benefits probably the most from it because the idea is what it gives the infrastructure architect the ability to do is locate parts of the infrastructure where it makes sense to locate them, make it all work together. You know, there’s the developer. Sure. And the developer usually relies on the infrastructure architect for things like that. And that’s where the biggest impact comes in to be able to create almost that distributed infrastructure so that, you know, it performs in the most efficient manner, both in terms of TCO and performance.

Brett Tanzer
So then to your question on what’s in it for developers, what I would say is for both developers and infrastructure architects, it makes it easy for them to access technologies to go build more efficient applications and continue to do that in the cloud as the technology stack evolves and the infrastructure offerings we bring with Nutanix to market continue to expand.

Jason Lopez
The world of IT has changed dramatically since the 1990s when Brett Tanzer started at Microsoft. He thinks back to on prem systems were built around local control with capacity being a major issue and managing efficiency being one, as well, and, always planning around future development. There was a cost curve, Tanzer says, where an organization had to learn how run the operation before understanding how to make it efficient. But when he talks about trends, while the role of IT is being re-defined with new apps and platforms, some of the challenges remain the same.

Brett Tanzer
There’s a lot of trends around the cloud. I think the ones we hear most about of late are the desire to really improve TCO in the face of the current climate. And so that’s certainly something we’re spending a lot on. And how to rewrite applications with an eye on automation. We have great infrastructure at Microsoft that enables AI developers to go build their own models and train them for bespoke scenarios, but we also have a lot of systems and tools that make it possible for someone to take advantage of AI models that have been created and optimize them for their domain. And so we can embrace roughly all tiers of AI development in Azure. Like everything else, customers have to figure out how it brings value to their stakeholders and then how to apply the tool.

Ken Kaplan
You’re able to do that based on some other customers that might have a similar challenge.

Brett Tanzer
We provide infrastructure, we provide guidance, we provide tools, and we do try to provide thought leadership. You know, we have a lot of partnerships in the AI arena that really make it helpful for us to go bring that next generation technology to customers who might be all along the spectrum.

Kanchan Mirani
So yeah, we talked about AI and we hear a lot about generative AI and new kinds of technologies like the metaverse and there’s obviously lots of excitement around blockchain. And sustainability is becoming a big, big topic. Look at all these interesting trends, which excite me, I’d love to know what excites you, Brett, just in general, not necessarily related to Azure. What is it that’s becoming something that’s exciting for you personally?

Brett Tanzer
I think for me personally, probably given kind of where I am in my career and where I am in my family, it’s just the opportunities that a young person has trying to build something with technology today. Whether it’s one of my children who wants to be a broadcaster who is able to use technology to provide access to data that he didn’t have before. On the other side, my son who is trying to become a singer on Broadway and now has the opportunity to really debut his craft virtually online to audiences and he has access to communities long before he is ever successfully landed a role. You know, you just see such a broadening of opportunities that are available to everybody, given what technology can do. And I like being on the very bleeding edge of that and helping customers who’ve got these big investments, take them to the cloud as their first step so they can make the next step. I think there’s another 20 or 30 years on it.

Ken Kaplan
At least.

Kanchan Mirani
Yeah, there’s a kind of democratization of technology and you know, the way it’s kind of impacting not just businesses for individuals that is leading to a lot of opportunity to meet those needs really. So it’s, it’s a little bit of chaos, but it’s full of opportunity I’d say.

Brett Tanzer
It’s really an exciting time. Like the work that we’re doing today will have societal benefits for generations to come. The technology barriers have been knocked down. You have access to compute that you didn’t have before. You have access to scientists and data. You have access to open source solutions where you can take the expertise of others and apply them to your domain. A lot of those barriers that existed that just prevented people from doing it, they get knocked down. And so the cloud and next generation technologies are helping to enable that. What we do with it as a society though, is still to be determined, but we certainly have a lot more enablement and power to go solve those problems today than we did when I started my job 30 years ago.

Jason Lopez
Brett Tanzer, is the Vice President of product management at Microsoft. He chatted with Ken Kaplan the Editor-in-Chief of The Forecast and Kanchan Mirani, director of product marketing at Nutanix. I’m Jason Lopez. This is the Tech Barometer podcast produced by The Forecast. We invite you to listen or read up on other stories about technology and the people in tech at theforecastbynutanix.com.

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One of the underlying technologies making remote and hybrid work possible is virtual desktop infrastructure (VDI), which allows organizations to centrally manage essential business applications and stream them over the internet to employee computers. While this technology has been around for decades, it keeps evolving and improving, according to Sridhar Mullapudi, general manager of the Citrix business unit in the Cloud Software Group.

In this Tech Barometer podcast, Mullapudi talks about how end user technologies have evolved and adapted to IT infrastructure trends around private data centers, public cloud services and now hybrid multicloud IT operations. He explains how recent work with hybrid multicloud software company Nutanix is allowing IT teams to run VDI and DaaS from private and multiple public clouds.

During his more than 20 years at Citrix, Mullapudi has been on and led teams that built end user computing technologies around the notion that “work is not a place; it’s what people do,” he said.

In 2021, Citrix and Nutanix announced a joint development effort to integrate Nutanix HCI with Citrix DaaS and Virtual Apps and Desktops. Now with NC2 (Nutanix Cloud Clusters), Citrix customers can securely serve business apps from their Nutanix Cloud Platform, whether it’s powered by a private data center or public cloud service, including AWS and Microsoft Azure.

He said these kinds of innovations are enabling remote and hybrid work, but there’s still lots of work to do.

“I’m sure there are 10 years of innovation ahead of us to really make the hybrid work experiences feel like we’re in person,” said Mullapudi.

Related:
Raising the Bar for Hybrid Work Technologies
Explosion of Hybrid Work Brings Tech Providers Together
How End-User Computing Leaders Make IT Work for People
Vodafone Built a Hybrid Cloud to Power End User Computing
Validated Way for Moving Between Private Data Centers and Public Cloud

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Sridhar Mullapudi:
If Covid happened 20 years ago we all probably would’ve been screwed because, you know, there was not a lot of tech stack to actually help us collaborate. Citrix always talked about this notion of work is not a place, work is what you do. And we believe providing those tool sets and infrastructure so people can work from anywhere.

Jason Lopez:
Sridhar Mullapudi is the general manager of Citrix’s Citrix Business Unit, which operates under the newly formed company, the Cloud Software Group. This is the Tech Barometer podcast. I’m Jason Lopez. We chatted with him about what goes on behind the scenes to empower people to work from anywhere. Citrix and Nutanix have worked together to expand the capabilities of enterprise computing. If you’re a Nutanix customer and you use Citrix, you can do something you couldn’t do before. Take what you run from your private data center, replicate it and run it from Azure or AWS in a public cloud. It can manage this stuff in the backend. They can more easily move data and do it securely and remotely.

Sridhar Mullapudi:
We’ve done the initial work of understanding the stack really well and creating those abstractions and other stuff. And Nutanix does a great job abstracting that across multi-cloud and there’s always nuances in understanding how the Nutanix clusters on Azure works so it can be validated and be tested. But it’s been a great standing partnership and a platform that we understand well, they understand as well and so it’s much easier to keep building it.

Jason Lopez:
And the NC two cluster that runs on Azure, is that, is that like a natural progression since the partnership started, what, a decade ago or so?

Sridhar Mullapudi:
It is. It is. I mean, when we really started, if you think of the evolution of the partnership, it was kind of Nutanix hyperconverged software running on V sphere. So that was the first thing we supported. Later when Nutanix evolved to KVM and or HV as we call it, you know, we were the first to partner with that complete Nutanix stack all the way from software to hypervisor and then Xi I think at that time, the first kind of cloud version of it, we were the first to kind of support it. And so this is really a natural extension which makes it really easy for us to kind of work and and collaborate.

Jason Lopez:
Yeah, and I imagine, you know, we often hear that these kinds of collaborations are driven by customer needs, you know, the opportunities that arise from working with customers. What’s the story there?

Sridhar Mullapudi:
When Nutanix really started, they were the pioneers in the hyperconverged infrastructure, right? How do we bring compute storage, all networking, everything all across, start small and kind of expand without expensive storage? So one of the first workloads they looked at and said, what’s the best workload, right? For, for any new platform? It’s like you need a killer app. And VDI turned out to be a killer app for hyperconverged infrastructure. So our customers are also were looking at scaling VDI and they didn’t want to pay the V Tax or you know, the whole high expensive storage networks and everything else. So I think the Citrix Nutanix stack and partnership was so critical for them to actually scale VDI and make it more efficient, you know, cheaper to operate in a long way and that’s always good for the customers. So this is a longstanding relationship between Nutanix and Citrix. So not just technology and integrations, but also relationships.

Jason Lopez:
Can you take us back a bit to virtual desktop infrastructure, you know, that being the bread and butter app for many Citrix customers. How has that evolved?

Sridhar Mullapudi:
You know, I would say over the past few years the industry as it’s been transforming more into cloud and as a service VDI has been transformed more like Daas, desktop as a service, the core of what it does is still the same. Where you want to be able to securely connect your desktops and applications wherever they’re sitting: private cloud, public cloud hybrid where you’re not managing all the infrastructure yourself. It can be offered as a Saas service. So that’s been the evolution. While the core, what it does, what value it does has not changed, but the flexibility, the manageability, the cost of it have all greatly improved.

Jason Lopez:
Well, on this as a service theme, it seems like, you know, there’s a big demand for hybrid multi-cloud. And in the sense of, uh, cloud as a service, but wanting the tools to achieve that scale of hybrid multi-cloud, is that a trend that you’re also seeing?

Sridhar Mullapudi:
Yeah, we’ve, we’ve started seeing this trend, I would say seven, eight years since AWS started. But what happened was the pandemic happened so suddenly everybody had to work from home and there was just not enough time or capacity for them to build this infrastructure by themselves or hire people or buy hardware, anything else. So they’re like, “Hey look, I want to tap into cloud for flexibility and other stuff.” So now customers are like, “look, I never want to be in that situation.” So now they’re investing in infrastructure, they want to have hybrid market by cloud. I think some of the recent trends where customers are saying like, “I also want to use my spend on cloud just because the market conditions are changing, the economic times are changing.” So I think we’ll come back to an equilibrium where they want that flexibility. “Hey, for certain use cases and workloads, I want to put it in my data centers or private clouds. I think that’s most, and some I want to use public and I want that flexibility back and forth.” I think now there’s a little bit normalization and people understand, but I don’t want to get locked into any single cloud. I want to have that flexibility. So now they’re coming to their partners like Citrix and Nutanix as well to say like, “Hey, how do you provide that hybrid multi-cloud solution? Give optionality, don’t lock me into a single cloud provider and have that flexibility and choice.” So customers want that choice more.

Jason Lopez:
Right. And so choice, on that idea of having options if they’re running in the public cloud, are you seeing them move from one public cloud to the next?

Sridhar Mullapudi:
Yeah, I think a few things are happening there. One, if they’re a pure data center, kind of a private cloud type of a customer doing it themselves and their data centers or maybe cos their strengths where like, “look, I don’t want to be in the data center business and I want to use uh, somebody else data center, public cloud, kind of a data center.” So that trend has been going on for a while. I think second, they also understand the power of these clouds and relationships they have whether it’s, you know, Azure or Google Cloud or AWS or others and they don’t want to get locked in. The move away from a data center I think is driving the hybrid discussion. The move to adopt multiple clouds for flexibility, best breed solutions, is driving a multi-cloud discussion. And I think that’s creating opportunities for vendors like Citrix and Nutanix because they look at it and say, “Hey look, I don’t want to triple my operational complexity if I have one here, another cloud and others. So like I would rather have that flexibility without paying the cost.” So that’s where they look at hybrid multi-cloud vendors and how they can help.

Jason Lopez:
Well, just one more question and this one looking to the future. On that idea of being able to work from anywhere, you know, where we started our discussion, what are the challenges that still remain in your estimation?

Sridhar Mullapudi:
If there was one thing the pandemic kind of showed us that I think we made, you know, quite a big progress in technology evolution, whether it’s bandwidth service, availability device and everything else. For us to actually collaborate sometimes jokingly say “if covid happened 20 years ago, we all probably would’ve been screwed because, you know, there was not a lot of tech stack to actually help us collaborate.” Citrix always talked about this notion of work is not a place, work is what you do. And we believe providing those tool sets and infrastructure so people can work from anywhere, right? I mean, you know, we pioneered the delivery of, you know, applications way back in the day we did Go to Meeting, I mean now we have Zoom, you know, and a bunch of other tools as well. So we always believed in that model. I think both in terms of infrastructure, whether it’s device or network or applications and everything else. Now we are a little bit more hybrid. I do believe innovation drives growth and value and sometimes you’ve got to figure out how to bring people together and, and so I think hybrid becomes very important. What does that hybrid experience look like, right? I think past two years we are all remote. If you’re all remote, I think you all feel like equal when you’re in the office, when you’re remote, you know, you might feel collaboration is challenging. So I think there’s some both technical hurdles there, probably social, personal and those kind of things as well. So I’m sure there’s the next 10 years of innovation ahead of us to really make the hybrid work like you are in person.

Jason Lopez:
Sridhar Mullapudi is the general manager of the Citrix Business Unit in the Cloud Software Group. He’s been with Citrix since 2001. This is the Tech Barometer podcast. I’m Jason Lopez. Tech Barometer is produced by the Forecast, a publication of Nutanix. Check us out for other stories on technology and the people in tech at theforecastbynutanix.com.

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In this Tech Barometer podcast, Nutanix Chief Marketing Officer Mandy Dhaliwal explains why people-focused and data-driven marketing of hybrid multicloud software will help enterprises build their future on applications and data. Digital transformation is a business imperative, forcing enterprises to focus on a growing number of complexities, including application development, data management, security and sustainability. Dhaliwal is stepping in to clear the air and help IT leaders choose the right technologies at the right time. “This is our time as marketers,” she said. “I’m not here to put our name on socks. I’m here to help move the needle in this business.”

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Transcript:

Mandy Dhaliwal:
I am not here to put our name on socks. I’m here to help move the needle in this business.

Jason Lopez:
Marketing is changing, but that’s no news flash. In the 1960s, McKinsey published a report that could have been written last week talking about the shift to the customer, the value of renting over owning, and the importance of digital information. But in the case of Nutanix’s Mandy Dhaliwal the change at hand is a historic opportunity. This is the Tech Barometer Podcast. In this episode, we look at marketing today through the profile of Nutanix CMO, Mandy Dhaliwal. Let’s set up the context. Nutanix provides tools and apps that leverage the cloud. It’s a cloud company. The cloud is perhaps the greatest growth market the world has ever known, according to industry analyst Bob Evans. Or from a technical angle, the cloud is the biggest infrastructure that humanity has ever built. Northwestern University professor and researcher, Mark Mills, cites that in his book The Cloud Revolution. So with that in mind, think about Mandy’s statement. I’m not here to put our name on sox.

Mandy Dhaliwal:
I’m Chief Marketing Officer, which connotes tactical stuff. We’re the party planners, we set up the booths, we deliver swag, et cetera, et cetera. I think that reputation of marketing is changing. When I started my career, we were the ad people, not quite mad men, but that was our world. And now you look at how data-driven we’ve become and all the tools and how much of an impact we have on revenue.

Jason Lopez:
Though the media itself, as far back as the Gutenberg press, was technically considered marketing. The discipline we know essentially started in the early 19 hundreds. Since the 1980s, a lot has happened to the marketing departments of businesses: the rise of information technologies, the emergence of the internet, the adoption of integrated marketing communications. Technologies like AI and new marketing approaches frame the data-driven practices. Mandy employs in her role at Nutanix, a company that itself is helping to reshape computing in terms of the cloud. Yet another new force affecting how marketers deliver the right message to the right people at the right time. There’s a lot of attention, innovation and investment in this rapidly evolving world of information technology, which is helping businesses and governments digitally transform how they operate. It takes a big thinker and doer to raise global market awareness of Nutanix. The blueprint for it hasn’t been written yet.

Mandy Dhaliwal:
This is our time as marketers. This next iteration of the business world really is heavily dependent on marketing. The winners and losers have to obviously have the right technology, but at the end of the day, it’s how you get that message into market. How much dollar do you deploy to go drive that message into market in an efficient way to go get the outcome that you need. So being a problem solver, that’s what I am looking to unravel for this business and get to a clear go to market motion in terms of who we go after, when and how do we get them a message that’s compelling for them to give us a try and get them into our technology orbit.

Jason Lopez:
Mandy’s goal is to establish a revenue marketing operation that effectively and efficiently feeds a pipeline of new business and existing customer opportunities for a subscription oriented model. Finding new customers and keeping them coming back for more is essential to Nutanix delivering what it lays out for investors each quarter.

Mandy Dhaliwal:
I’m the eldest firstborn grandchild on both sides of the family. When I was born, my initials were deliberately set to be MD because my Indian born parents wanted me to be a doctor,

Jason Lopez:
But med school wasn’t in her vision. She enrolled in business school at Simon Frazier University in British Columbia, signing up for an internship program that landed her at Nortel Networks.

Mandy Dhaliwal:
That was pivotal for me in my career. And, I was one of the, the only actually intern that was chosen to go ride along, if you will, on this customer event that they were hosting. I got to go be a fly on the wall and watch them interact with their top telco customers that were buying hundreds of millions of dollars of gear from them regularly. And that to me was a moment I knew that I wanted to be in B2B tech, working in marketing because I just loved everything about that event, the whole relationship building with the customer, the ability to educate them on what was coming from a roadmap perspective. Marketing put the event together, so really showcasing how marketing could be very strategic to drive the growth of the business.

Jason Lopez:
After graduating, Mandy worked in a couple of marketing positions and found her way on the B2B side at a British Columbia based telecom company. While working in tech, she realized she needed to be in Silicon Valley.

Mandy Dhaliwal:
Ended up joining a company called Legato Systems, which was known for disaster recovery and storage. Ran alliances. Marketing, knew nothing about alliances. But learned that entire business worked with seven different alliance VPs supporting their businesses. We had partnerships with the likes of Dell, Microsoft, Sun, Oracle, you know, you name it. Probably the best education I could have had in tech.

Jason Lopez:
Not only did she have to learn Legatos business, but also the partnerships with clients to drive joint value propositions into the market. But this is where the story takes a turn. Lagado was acquired by EMC where she stayed for a while, but then dropped out to raise a family,

Mandy Dhaliwal:
Took seven years off and did it right, did not want anybody raising my child and did not wanna pay a nanny to raise my kid. We did it ourselves. As he went into kindergarten, I decided I was going to do something for myself. Went to wine school.

Jason Lopez:
She earned a sommelier certificate, but she says that wasn’t her motivation. She wanted to learn about wine in all its facets. Without becoming a snob.

Mandy Dhaliwal:
I’ll go to a friend’s place and they’ll be like, I’ll bring a bottle of wine, and they’ll be like, well, let’s open yours because ours is probably not good enough. And I’m like, no, no, no, stop right there. It’s all good. If you like it, I’d love to try it. It’s not about judging, it’s more about what are you pairing it with, what climate, what time of year, et cetera, et cetera, and being able to enjoy the experience. So I think people have a lot of baggage. There’s no wrong answer. If you like something, drink it.

Jason Lopez:
While a career in wine could be down the road. She says her wine interest is for fun and is separate from her role in the tech world.

Mandy Dhaliwal:
I relish the opportunity to step away from my phone and be in a tasting room or be walking through a vineyard to get away from my day-to-day.

Jason Lopez:
But wine and tech aren’t mutually exclusive. As a marketer, she appreciates the unique challenges winemakers face. Many exemplary wineries have small marketing budgets. And having grown up on a farm, she recognizes the tech that goes into agriculture.

Mandy Dhaliwal:
When you walk into a, a winery that state-of-the-art drip irrigation sensors in the vineyards for temperature, zero gravity, no pump overs, like really keeping the authenticity of the wine and the integrity of the juice and not manipulating it, that gets me super excited. So how do you use tech to preserve what really should go into your glass in its most original form? I nerd out on stuff like that.

Jason Lopez:
Mandy Dhaliwal, the eldest of four kids, grew up in a small farming region in British Columbia on the US Canada border. Her family came from India, arriving in Canada in fits and starts. In the 1800s her great-grandfather journeyed to Vancouver only to arrive in the harbor, forbidden from stepping on land. The boat anchored in the harbor where he stayed for more than three months with little food or supplies before being sent back to India.

Mandy Dhaliwal:
So that was the first migration attempt into Canada. My grandfather on my mom’s side, his older brother came across to work in the lumber mills. We were landowners in India in the Punjab, but the family had the means to be able to send their sons to a foreign land. So once my grandfather’s brother arrived, he sent for his other relatives and my grandfather came across. My mother was two years old at that time, so her father left to go make a better life. And then about 10 years later my grandmother and my uncle came across.

Jason Lopez:
Her mother made the trip at 19 for an arranged marriage with her dad, who was a teacher and then became a realtor.

Mandy Dhaliwal:
We started buying up land and then one of my uncles decided that he wanted to get into farming, so he bought a strawberry farm, and then it was just this whole gravitational pull. Every family member all of a sudden got into land, so that’s where the farming came in. And we were cheap labor as kids.

Jason Lopez:
But thanks to agriculture technologies, which were becoming more affordable. The family farm at this point, producing raspberries, was able to bring in automation.

Mandy Dhaliwal:
We had hundreds of workers come to our fields every summer. The harvest was six to eight weeks and they would be out there picking the raspberries and they’re very delicate and you have to be very careful. Just in my childhood, we went from that to the harvester.

Jason Lopez:
The world of AgTech is quietly a vibrant area for investment. As technologists envision the connected farm with cloud, IOT, robotics, and AI making precision ag a reality. Berry farmers, for example, can better time harvests, manage responses to weather, make better environmental choices as far as applying fertilizer or keeping the weeds down and simply getting better fruit to market.

Mandy Dhaliwal:
We were on the precipice of getting to this watershed moment where you can now genetically engineer the plants. You can start to get regulation and predictability, your not getting the swings in the weather because you got irrigation. You can tent on the supply side for the farmers. There’s the John Deeres of the world becoming tech companies. No longer are they a lawnmower and a tractor business. So I think there’s innovation happening throughout the entire ecosystem, not just on the production side. So all of these things, it’s the evolution of mankind, right? You think of the iPhone, you think of all the things that we take for granted. I still remember the days I started my career. I had a pager, I worked for the phone company.

Mandy Dhaliwal:
I’ve done every single functional role within marketing, and I understand what the people that work for me in the orgs are grappling with. So I can go very deep with them and be able to really be a business partner to them to help drive our business.

Jason Lopez:
One of the latest tools for marketers in the technology space has been subscriptions. Not necessarily a new invention, but scaling a subscription business is one of those practices which is wide open for innovation.

Mandy Dhaliwal:
I saw a lot of opportunity here to come in and make a difference, to get that narrative right to the market, really be more customer focused and really start to focus on the growth aspect of this business and really unlock the partner ecosystem.

Mandy Dhaliwal:
As I look at dashboards, as I look at data across my peer sets as well, I’m looking for patterns in terms of pipeline, for example. Where are we driving pipeline, which segments, which regions, what verticals, what’s happening? What’s the story that’s resonating? If I can get to that level of specificity, I know what’s working and I know what I need to improve.

Jason Lopez:
But she says data by itself is nothing without strategy. The question is how to harness data to get the right insights to move a business forward. Those patterns of data help companies create their formula.

Mandy Dhaliwal:
It’s a highly complex algorithm that we have to codify ourselves and every business has to do this, and companies that are really good at it are the ones that are scaling quickly, that’s the secret sauce.

Jason Lopez:
Mandy Dal is the Chief Marketing Officer of Nutanix. This is the Tech Barometer podcast. I’m Jason Lopez. Thank you for listening. Tech Barometer is produced by The Forecast. If you’d like more stories on tech and the people in technology, check out more stories at the forecastbynutanix.com.

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At the end of each year and the beginning of every new one, experts gaze into their crystal balls and share visions and predictions for the future. While some things may never change, this exercize becomes a zeitgeist for a world of iterative advancements. Sometimes a crystal clear vision of the future makes people realize they’re in the midst of a radical revolution, part of a pivotal time in history. To understand what cloud computing-related technology trends will drive business success in 2023, The Tech Barometer turned to business futurist and author Scott Steinberg and IT industry veteran Lee Caswell, senior vice president of product andn solutions marketinig at Nutanix. In this podcast segment, Steinberg and Caswell provide a high-level view of cloud technology trends and how they’re keeping businesses competitive and innovative. They dive into how technologies are evolving, their dimensions and how they’re impacting businesses, customers and consumers.

Related:
6 IT Trends That Will Shape 2023
Businesses Turn to Data Strategies for Competitive Edge
Calculating the Business Value of HCI-Powered Cloud Platform

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Jason Lopez:
When we started discussing a story about technology trends here at the Forecast, a story to be produced as a podcast for Tech Barometer, we wanted to avoid the typical listical of tech predictions like, what’s going to happen with 5G or IoT, or the cloud? We all know what’s going to happen. There will be more adoption as technologies are developed and they mature. The question is, what are the dimensions? What are businesses, customers, and users facing? This is the baseline for this podcast. This is Tech Barometer. I’m Jason Lopez. We spoke with experts, Lee Caswell of Nutanix and Scott Steinberg, a world renowned futurist and consultant who’s authored books on digital transformation and cybersecurity

Scott Steinberg:
Cloud everywhere, anywhere, virtually anytime on demand.

Jason Lopez:
If there’s one technology insight we’ve heard from many interviewees over the past 12 months, Scott Steinberg says it here.

Scott Steinberg:
Everything’s going to be connected and talking to one another going forward. You’re talking billions and billions of devices, and by the way, the cloud brains behind them are going to be essential to help these devices all coordinate, share information and really become more self-sufficient.

Jason Lopez:
The data collection is overwhelming. Take the case of autonomous vehicles. Each car produces millions of data points, and you have to feed in all sorts of sources from the road in real time, the terrain ahead, pedestrians in the road, stop lights and stop sign data, not to mention traffic.

Scott Steinberg:
All of that is going to be powered by the cloud. And that’s before you start to think about the impact of 5G and other technologies.

Jason Lopez:
Steinberg isn’t making a prediction about autonomous vehicles per se. He’s talking about what’s happening under the hood, how cars are emblematic of a world that is rapidly becoming a different place, different because of how much data the coming world will operate on, and how much data the world will produce. Autonomous vehicles are an immediate example of this. Another of his examples is VR.

Scott Steinberg:
Whether you believe that the metaverse is going to be a thing or the next big thing or the future of the internet, it’s too big to ignore at this point. So, so many companies are investing in it. So many technology players, and even so many everyday brick and mortar businesses at this point, from the Walmarts to the CVSs of the world, that we’re going to see much more happening with mixed reality, artificial intelligence, augmented reality, VR, virtual reality, and the like.

Jason Lopez:
A metaverse with billions of people participating. How to manage all that data, not just from VR and cars, but in manufacturing, medicine, education and so on. It would be nice to think there will be an army of engineers to handle it all, but Steinberg says the reality is there will be overworked IT teams and the need for tools that don’t require high level tech skills, that means no-code and low-code solutions.

Scott Steinberg:
So one of the big tech trends I think you’re going to see is more solutions being put into the hands of everyday folks like you and me who maybe aren’t software engineers that allows us to create our own solutions on the fly.

Jason Lopez:
The future will be less about building from scratch and more about smaller teams leveraging open source and other tools like advanced AI routines that companies can use to adapt or create their own custom solutions.

Scott Steinberg:
One of the other things I think that you’re going to look at going forward, we know that cybersecurity is going to be a rising topic of interest, but maybe what people haven’t grasped so much is that your digital ID, your identity online really is the new security perimeter. Because we’re dealing with issues of security versus friction, aka user experience, making sure that things are fast, fun and simple to utilize.

Jason Lopez:
That means you’ll have an online identity which ports rapidly between clouds, platforms and apps. It’s part of the coming solutions to data complexity and volume. He says, we’re grappling with 10,000 times more information we’re collecting than we can analyze.

Scott Steinberg:
And at the same time, you have to think about who in the organization could benefit from having access to that data. What potential sources could you tap into? How are you going to manage the sheer volume of information that’s being shared, and what do you need to do to safely protect and store it all while trying to be efficient and insightful about the ways in which you use data? Because data-driven experiences are going to be the way forward. It’s really going to give you the real-time insights and updates that you need to make, workflows, processes, and applications much more capable and much more self-aware.

Jason Lopez:
But inside and outside organizations, people are trying to respond to the pace of application growth. It used to be that applications didn’t change that rapidly.

Lee Caswell:
Technology advances, particularly in hardware, have gotten so fast in terms of compute and flash memory, for example, and even network speeds that all of a sudden, what’s possible now is to have infrastructure that can go and respond to changing application needs really fast, and that’s server-based systems.

Jason Lopez:
That’s leak haswell, and he points out that’s what the cloud deploys today. Modern infrastructures based on servers.

Lee Caswell:
Now, I have these almost seamlessly scalable systems that can be managed by generalists. Super important in these days because you’re worried about how do I get people to actually be able to manage systems and the concept of infrastructure is changing from an infrastructure piece to a platform.

Jason Lopez:
And that begs the question, what exactly is a platform?

Lee Caswell:
Well, a platform is something that can go and extend to places. It means it can extend out to the edge, for example, to retail environments or a wind farm or an oil rig. It could be in your data center, it could be in public cloud presence. But in any event, right? What’s the solve for this added complexity of new locations and new applications? Is a platform that’s consistent across those and that platform, the way started thinking about it is it’s delivered as a service. It’s got APIs, so I can go on write to it and extend to it across different environments. As new applications are coming in, you’re able to go and leverage one server-based platform for any applications, any location. That platform concept, I think is one that’s gaining traction.

Jason Lopez:
Caswell says this about development teams in the past: when applications came to life more slowly, they were in the business of saying, no, but the cloud has changed this. New server-based systems have turned development teams into saying:

Lee Caswell:
Yes you can. And you get it with security and with compliance and with the opportunity to manage it over time. I bring these two together and you start thinking that the pace at which applications were being developed didn’t lead them to be put in the optimal place.

Jason Lopez:
Caswell foresees apps in the future being developed with this kind of optionality, you might place things one way to get going and then come back later and place them differently.

Lee Caswell:
You’ll move them around for three important reasons. Performance is one. I need to go and locate applications and data, and applications and data together, and I need to go and make sure they’re close to my eventual customers. Or I’m ingesting data right from the edge and I want it to be locally contained and processed. There’s also the idea of data of sovereignty. Who can subpoena that data? Who has access to it? Where is it replicated to? Does it cross geographic borders? Do I know where my data is?

Jason Lopez:
You might not know, but this is the beauty he says of platforms. Caswell predicts this opens the door for the practice of tagging data and providing attributes.

Lee Caswell:
Not only do you know where your data is, but you know who can access it. You want to have attributes where I can now start thinking about what does it mean to move data across the multi-cloud environment? When you start thinking about multi-cloud, you’re thinking about multiple hyperscaler locations. I’m thinking about files, blocks, and objects. However, I want to write data, it’s got to be accessible on any of those protocols. And I started thinking about snapshots because snapshots are a way of thinking about where can I restore my data. People talk about protecting data a lot, but really it’s about restoring data. When you lose something, what you care about is, did I get it back? Not how I lost it. And the fact that we have snapshots that can be shared across the hybrid cloud, the multi-cloud, and back to the edge means you can start having attributes for these, right? And that is like a cloud data structure. It’s a data-centric model and now you can start thinking about, yeah, I’ll co-locate applications and data and move them over time so they’re optimally located. I’ve got the controls that infrastructure people have built their careers on. I don’t give those up just because I’ve got this spread of possible points of presence.

Jason Lopez:
Caswell says, as businesses rely more on cloud infrastructures and technologies to stay competitive… lowering costs, increasing capabilities, maintaining compliance requirements, just to name a few, there’s another aspect of operations sustainability. Steinberg agrees and says, businesses are looking to the cloud, AI and other information technologies to reach their sustainability goals.

Scott Steinberg:
People are going to be more focused on the impact that they’re having on the planet, their technology footprint, applying more green standards to the technologies that they’re choosing to utilize, and their partners as well. So they’re going to be holding external vendors to these standards as well. And of course, we’re going to see more office buildings and more data centers. They’re going to focus on, how can we self-manage energy use? What is our actual impact on the planet in terms of processing power and the amount of carbon that we’re generating? So I think what you’re going to see is more and more of the number crunching and the like is going to move to virtual versus physical locations.

Jason Lopez:
Scott Steinberg is a futurist who’s written extensively about innovation and technology. Lee Caswell is the Senior Vice President of Product and Solutions Marketing at Nutanix. This is Tech Barometer produced by the Forecast. For more stories about technology and the people in tech, visit us at theforecastbynutanix.com

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In this final segment of a five-part Tech Barometer podcast series on hybrid work, Nutanix CIO Wendy M. Pfeiffer talks about what inspired her to think differently about enabling worker productivity, using consumer technologies and experiences that allow employees to be their authentic selves.

Related:
Shift to Hybrid Work: How to Manage Constant Change
Shift to Hybrid Work: Asynchronous Productivity
Shift to Hybrid Work: Reduce Context Switching
Shift to Hybrid Work: Automation and Self Service
Hybrid Work and the CIO’s Role in Enterprise Transformation
Open Source Tool Automates IT Inventory
Cloud Infrastructure Powering Shift to Hybrid Work
IT Leaders Bust Myths About Productivity in Hybrid Workplaces
The Metaverse is Coming: Are IT Leaders Ready for It?
How Hybrid Multicloud Unleashes Human and Business Potential
The CIO Who Drinks Her Own Champagne
Blended Roles and Hybrid Thinking Reset IT
10 Essential Steps to Hybrid Multicloud IT
Essential Steps To IT Organizational Change Management
Hyperconverged Infrastructure Gives IT a Grip on Constant Change
In the Digital Age, It’s Still All About People

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:
Wendy Pfeiffer
One of the contexts that our employees are living in now is home. And home is the ultimate individual consumer environment.

Jason Lopez
Wendy Pfeiffer is the CIO of Nutanix. In this podcast series on the shift to hybrid work at the company, she talks about bringing the consumer experience into the workplace.

Wendy Pfeiffer
At home I got to pick my desk and my chair and my lighting and my pink color on my walls. My alarm clock wakes me up at a certain time and I drink the brand of coffee I want. I am speaking to you while looking into the display of my gaming computer because it’s a great display. Would you ever choose non 4K video if you had the choice of 4K video? I’m embracing the consumer tool. I have a USB microphone that comes from a not enterprise IT context. Let’s think about this a little bit.

[music]

Jason Lopez
Digital has democratized many things. For example, 25 years ago, if you wanted to write music for an orchestra and record it on your own dime, it would cost you tens of thousands, perhaps more than a hundred grand, to rent a studio, hire 80 musicians, the audio engineers and the producer, and pay for music mastering. But with today’s apps you have access to astonishing music sampling of the BBC Symphony for 299 dollars. Wendy has a similar take on the apps her team has standardized on.

Wendy Pfeiffer
I used to have to pay either Cisco or Crestron or Zoom a hundred thousand dollars a conference room illustratively in order to have a camera and microphones in there connected to the computer. That’s enterprise conference room technology. Now as one of our principles and enabling hybrid, first, we’ve brought a consumer tech device into our physical conference rooms. It’s from a company called Owl Labs, o-w-l Labs, and it’s called an owl. It’s called an owl because it looks like an owl. I t’s got an 18 foot range in all directions. And we’ve just put one of those in our board room because our board of directors was having issues using all the expensive devices in the room to enable hybrid board meetings. This thing costs $900 and the audio and the video integrates seamlessly with Zoom. So I don’t have to set up a device, I don’t have to turn on the camera, I don’t need to go find the HDMI cable. I didn’t have to do anything at all. Just works in the room. Data isn’t stored anywhere. Super cool, super integrated, not expensive. Latest technology, very, very clear. Audio and video. Kodak, just a microphone. And some cameras on a device comes from the sports camera space. I used to work at GoPro. It’s just a gadget. But this gadget is enabling hybrid meetings for 1% of the price of the typical meeting room setup. We’re doing that everywhere we can because frankly, the consumer tech is more performant and more cost effective than the enterprise tech. Same with tools for curating personal presence.

Jason Lopez
Here, Wendy is talking about what you look like on video in a Zoom meeting, what your background looks like. It borders on meta-versian in that there are consumer tools for augmenting what you present on screen

Wendy Pfeiffer
If I know that I’m not going to show up on the wavy, crappy video with my face in the dark and people not able to see me, like maybe I won’t spend three out of the eight hours tomorrow that I’ve allocated for work to drive to. And from a location, maybe I’ll show via Zoom and give three more hours to the company for other tasks. There’s this technology, if you’ve ever used Snapchat, very consumer app where like I can take a photo of myself, but I can enable all these filters, so I’ll look funnier, whatever, or like way better that technology’s now available in Zoom. So I can click on the setup icon and I can choose touch up my appearance and I have all kinds of options. I can appear like I’m wearing makeup, I can change the lighting. I can have my face look thinner. I can change even my background, my skin tone, et cetera. And so that I’m able to curate my experience.

Jason Lopez
These features on Zoom fall under categories like Since the features have been available on Zoom, Wendy has gotten a lot employee feedback.

I can’t tell you how much feedback we get from employees on this. How I feel much more comfortable. I feel better in these meetings with prospects when I’m selling that ability to curate how I show up. That principle is in consumer tech everywhere. It’s in Discord. I build my reputation differently there. It’s on my mobile phone, it’s in technologies like Zoom. I just had a conversation with Eric, the CEO of Zoom, and he said that’s their most popular, most enabled feature is that.

[music]

Wendy Pfeiffer
We just lost our minds virtually. And that’s the only place we want to curate how we show up. No, think about how you dress to go into the office. You make sure that you, I don’t know, shine your shoes. You, you know, I wear a longer jacket, so like my, my fat butt doesn’t show, you know, I put a little extra makeup on, I do my hair. Like all of that is like you, you’re already doing that. But we give people the, the ability to do that everywhere.

Jason Lopez
Wendy Pfeiffer is the CIO of Nutanix. In this brief series on hybrid work: the initiative by Nutanix IT on the future of how IT teams work. This is the Tech Barometer podcast, produced by The Forecast. Look us up for more in this series with Wendy at theforecastbynutanix.com.

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Nutanix CIO Wendy M. Pfeiffer explains how easy-to-create automation tools help hybrid workforces stay productive across different time zones and...[…]

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When people switch from one task to another unrelated task, it can have jarring, disruptive effects, resulting in wasted time,...[…]

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Working remotely is liberating for many, but enabling a hybrid workforce to be as productive in the office as they...[…]

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The word hybrid is proliferating across industries and people’s lives. There are more hybrid engine-powered cars. More companies are turning...[…]

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In this Tech Barometer podcast segment with Christian Aboujaoude, CTO of Keck Medicine at USC, learn how hyperconverged infrastructure and...[…]

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Many braced for what might happen next after Broadcom announced in May that it would acquire VMware for $61 billion....[…]

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Tobias Ternström leads a team that helps enterprises manage databases with less effort and risk across hybrid multicloud environments. In...[…]

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To show artificial intelligence can be a powerful tool for humankind, Rutgers University professor Ahmed Elgammal explains how it helped...[…]

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In this Tech Barometer podcast segment, IT analyst Holger Mueller explains how new hybrid cloud platforms are opening the door for faster, easier and more agile IT operations that work across private data centers and public cloud services. He shares findings from his Constellation Research report about the Nutanix Cloud Platform, software that allows workloads […]

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In this podcast, author Mark P. Mills says, “history doesn’t repeat, it rhymes.” Consider what happened a century ago, when the world witnessed the convergence of the greatest technological leap in human history with innovations such as the automobile, airplane and radio; groundbreaking scientific discoveries in physics, chemistry and medicine; and a major jump in […]

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Electric vehicle (EV) adoption will take more than tax credits. Because of so-called “range anxiety” – motorists’ worry that EVs will leave them stranded when their batteries run out – it also will require a marked increase in the number of EV charging stations. Specifically, cloud-enabled smart charging stations. In this story by The Forecast […]

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In this podcast segment, Tony Palmer, principal validation analyst at research firm ESG, goes beyond the metrics of testing and validating IT technologies, sharing insights on the emerging business model of a data-driven organization. In an earlier segment with Palmer, it was evident he’s more than a numbers guy – doing analysis and validation of […]

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In this podcast segment, The Forecast’s editor Ken Kaplan talks to Tony Palmer, principal validation analyst at research firm ESG, who tested Nutanix Cloud Clusters on AWS, designed to reduce the operational complexity of migrating, extending or bursting business applications and data between on-premises and clouds. Perhaps at the top of IT’s wish list is […]

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U.S. healthcare expenditures are expected to reach $6.2 trillion by 2028. Still, many CIOs can’t shift their healthcare IT systems fast enough to modern technologies that are proving to benefit providers and patients, according to Geoffrey Bakeman, vice president of Healthcare Solutions at Comport Consulting Corp.  His firm works with providers to move the patient […]

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In this video, explore a state-of-the-art Nutanix data center built with highly integrated hardware and hyperconverged infrastructure (HCI) software. Harmail Singh Chatha, senior director of global cloud operations at Nutanix, explains what went into building the data center. His team wanted to build a state-of-the-art data center that could scale sustainably to meet present and […]

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The pace of innovation and change has affected not only technology development but the business models that make those technologies available. The subscription model – which has been around since 17th century Britain when it was used to sell books and even products from traders like the East India company – is spreading quickly across […]

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In this podcast, David Hake, UC Berkeley lecturer and co-founder of Resilience Insurance, talks about the rise and spread of cyberthreats facing governments, enterprises and individuals. “Every company has a digital footprint, every company – even Bob’s pizza around the corner – relies on the internet to enable their business,” Hake said. “And so, digital […]

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The world’s farmland is in crisis. The earth is not creating enough new fertile soil to mitigate what our agricultural activity is losing. According to the U.N.’s Food and Agricultural Organization, the world’s topsoil could be depleted by 2080.   Scientists are urgently studying how to preserve the topsoil. They’re delving deep into the microscopics of […]

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Humans are depleting the earth’s topsoil at an alarming rate. Scientists say this is not speculation but a measurable fact. If the world continues with current agricultural techniques the farms of the world will lose its topsoil due to erosion in 60 years. With humanity on track to grow to 9 billion people by 2050, […]

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Interoperability across owned and rented IT systems is essential and a major challenge for IT departments evolving their hybrid multicloud operations. As more IT leaders ask for tighter integration between Red Hat OpenShift and Nutanix software, the two companies joined forces in 2021 to help customers move more quickly and confidently to what Ron Pacheco […]

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Rick Vanover handles product strategy at VEEAM, a backup and replication services firm. In this segment, learn about the service most companies hope they’ll never need. But Vanover says a good backup service is the best thing ever after a data breach or failed IT system. He shares a story about an emergency flight he […]

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As the pandemic exposes the weakness in the global supply chain, companies are building a new one with cloud and AI. The effects of the COVID pandemic have slowed supply chains in many verticals from agriculture to musical instrument makers. But technologies such as the cloud, AI, video and drones are helping manufacturers, distributors and […]

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Medicine has eluded the tech disruptions which seemingly overnight rewrote business models in media, telecommunications, transportation and other industries. That doesn’t mean healthcare is untouched by technology. Hospitals are keen on innovations for diagnosis and medical procedures such as AI doing predictive analysis of patients with COVID-19 or natural language processing algorithms reviewing consumer claims […]

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BAE Systems contracts with the U.S. Department of Defense. It’s in the difficult business of handling military and intelligence secrets. And while there is a lot the company can’t talk about, there are plenty of useful insights they can share, which will help leaders across different industries. In this podcast segment with Dr. Nandish Mattikalli, […]

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In the widely circulated article “The Cost of Cloud: A Trillion-Dollar Paradox,” venture capital firm Andreessen Horowitz showed how the rush to public cloud IT services can eat away business valuation over time. In this Tech Barometer podcast segment, explore the real cost of running a business on public cloud with Martin Casado, general partner […]

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One of the world’s biggest defense contractors, BAE Systems, has to navigate the world of cloud computing just like everyone else. But the stakes are much higher. The company not only provides weapons systems to the U.S. military but cybersecurity protecting highly classified intelligence. In this podcast segment, Dr. Nandish Mattikalli, the chief engineer for […]

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If a child, family member or friend was in an accident tomorrow or had an organ failure, there’s a good chance United Network for Organ Sharing (UNOS) can help find a donor with the necessary attributes. Donated organs are managed nationally by UNOS. Their system for finding and tracking available donor organs and matching them […]

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Restaurant owners and experts explain how the industry responded to the COVID-19 lockdown with technologies that keep them connected to customers. Two sisters, Shanny Covey and Deborah Mok, tell how they evolved their central California coast restaurants through the pandemic. Find more enterprise cloud news, features stories and profiles at The Forecast. Transcript: Perry Quinn: […]

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In this special full-length Tech Barometer segment, Nutanix CIO Wendy M. Pfeiffer shares lessons learned from leading a significant shift to next-generation cloud technologies. Her team transformed IT operations by fusing hybrid multicloud capabilities, including hyperconverged infrastructure (HCI), with automation powered by machine learning and artificial intelligence.  Below are individual podcast segments for each of […]

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New innovations can quickly become IT sweethearts, but what’s best for the business remains the overriding factor for deployment. The emergence of cloud native was born from developers needing to flexibly deploy resources and reconfigure them as needed. “That’s why we invented containers,” said Steve McDowell, senior analyst of data and storage at Moor Insights […]

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In the real world, infrastructure is railroads, highways and power grids. In the digital world, infrastructure is servers, networks and data centers. Rajiv Ramaswami is a man of both worlds. He grew up on India’s national railway system, which employed his father and mother. As a graduate researcher, he envisioned optical networking tools for the […]

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IT leaders are hungry for cloud native technologies but struggle to find expertise because Cloud Native is nascent. SUSE and Udacity team up to feed the need by creating courses to train the next wave of engineers. In this Cloud Coverage segment by The Forecast, meet Sarah Whitlock, the global head of the SUSE and […]

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The COVID-19 pandemic proved that video conferencing could sustain businesses even if workers couldn’t work from their company’s office. It also peaked interest in VR, AR and other technologies that will shape the future of work. In this Tech Barometer report, explore the future of workspaces with Nicole Peterson, professor of interior design at Iowa […]

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Get beyond the buzzwords and break down what cloud-native technologies are and what they promise. In this Tech Barometer Cloud Coverage segment, data center technology analyst Steve McDowell of Moore Insights and Strategy. “Cloud native is one of those terms that’s becoming more overloaded daily,” he said. “It means different things to different people. It’s […]

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Tech innovations aren’t enough to keep companies ahead of the curve. Their stakeholders need to buy in to new approaches and skills, according to Brent Schroeder, CTO of SUSE. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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Automation technologies have evolved rapidly in recent years and they’re helping IT pros manage more things across their data centers. In this tech Barometer podcast interview, Rahul Kelkar, Global Head of Product Management at Digitate and Tarak Parekh, director of product management at Nutanix, explain AI and automation trends, how these technologies are evolving and […]

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Partly as a result of cost-cutting necessities, but also to demonstrate how Nutanix software can anchor a global hybrid cloud, Wendy M. Pfeiffer, CIO at Nutanix, led her team on a transformative journey. The goal was to make IT easier, better automated, and more readily accessible to the user community so employees could focus on […]

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The pandemic wreaked havoc but it also revealed that investments in digital technologies helped keep many aspects going during lockdown and social distancing. In a Tech Barometer podcast segment, Professor Art Langer says technology will only become more important in the future. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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Companies that don’t innovate risk losing to others that learn, adapt to and adopt new data technologies, according to Columbia University professor and author Dr. Art Langer in the first of a two part Tech Barometer podcast series on leadership through transformative times. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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Closing out the 10-part Tech Barometer podcast series Journey to Cloud, Nutanix CIO Wendy Pfeiffer talks about step 10, having a hunger to progress. With so much pressure to perform everything right the first time requires teamwork set on continuous improvement. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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Almost every organization making headway with cloud computing struggles to mix different types of IT services in a way that effortlessly meets user needs. In this Tech Barometer segment, Wendy Pfeiffer, CIO of Nutanix, talks about how automation and continuous monitoring of success metrics helps her IT team keep customers (employees) satisfied. Follow the enterprise […]

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The ninth step in her journey to cloud at Nutanix, CIO Wendy M. Pfeiffer focused her IT team on saving space and money. She explains how moving to hybrid cloud helped Nutanix reduce electrical consumption and footprint. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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As the Nutanix leadership torch changed hands in late 2020, co-founder and departing CEO Dheeraj Pandey interviewed his successor Rajiv Ramaswami in a podcast for company employees. In this version edited for the Tech Barometer, the two tech leaders discuss the future of hybrid cloud, focusing on customer needs, IT automation and more. Follow the […]

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In the seventh segment of a 10-part Tech Barometer podcast series “Journey to Cloud,” Nutanix CIO Wendy Pfeiffer explains why it’s critical to measure and optimize performance in order to delight IT users. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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In this 6th segment in a 10-part Tech Barometer podcast series, Nutanix CIO Wendy M. Pfeiffer talks about augmenting IT skills with artificial intelligence and machine learning to help bring efficiency and grow IT team capabilities. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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In this 5th segment in a 10-part Tech Barometer podcast series, Nutanix CIO Wendy M. Pfeiffer talks about why creating a common, flexible IT infrastructure foundation – based on a hypervisor to virtualize and hyperconverged infrastructure to scale – is the second step toward a hybrid cloud IT operation.? Follow the enterprise cloud tech revolution […]

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Hybrid cloud technologies are about bringing better user experiences across teams to every employee. In the 4th of a 10-part Tech Barometer podcast series, Nutanix CIO Wendy Pfeiffer explains how she empowered her IT and every employee to work securely and effectively from anywhere, on almost any device. Follow the enterprise cloud tech revolution at […]

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Nutanix CIO Wendy M. Pfeiffer talks about how a purpose-built platform reduces operating expenses by simplifying monitoring and maintenance tasks, in the 3rd of a 10-part Tech Barometer podcast series. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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The story of LEGO has a charming beginning. The small Danish toymaker created simple building blocks that could fit together to make almost anything imaginable. Then the company realized their toys tapped into the human desire to create. They weren’t just plastic building blocks. They were platforms for innovation. In a Tech Barometer podcast interview, […]

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In this second segment in a 10-part Tech Barometer podcast series, Nutanix CIO Wendy M. Pfeiffer explains why creating a common, flexible IT infrastructure foundation – based on a hypervisor to virtualize and hyperconverged infrastructure to scale – is the second step toward a hybrid cloud IT operation. Follow the enterprise cloud tech revolution at […]

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In the first of a 10-part Tech Barometer podcast series, Nutanix CIO Wendy M. Pfeiffer explains why creating a common foundation – based on a hypervisor to virtualize and hyperconverged infrastructure to scale – is the first step toward a hybrid cloud IT operation. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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Increasingly, employees and students reply on streamed applications rather than having apps run directly from their computer’s hard drive. In this video animation, learn how end user computing, a catch-all phrase, is changing as technologies like desktop as a service (DaaS) and virtual desktop infrastructure (VDI) stream experiences from the cloud or a data center. […]

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Rapid iteration that has become essential to technology innovation is something well defined in the Manifesto for Agile Software Development. We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value: individuals and interactions over processes and tools; working software over comprehensive […]

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The effects of the coronavirus on technology will be studied for years to come. The ways people responded and new directions companies took are creating a new normal. IT leaders were forced to build more robust, resilient, secure data systems that support remote working. Global IT service providers were on the front lines to help […]

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The ideas behind 5G got their start at NASA, which was seeking to improve space communication for satellites. As telcos roll out 5G networks with speeds of up to 10 gigs, the new wireless technology is poised to bring powerful internet connections to IoT, VR, AR, AI, edge computing and enterprise applications. In this interview […]

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To meet the relentless demand for digital services, data centers will need to become decentralized and more autonomous, according to Ray Wang. He’s the Founder, Chairman and Principal Analyst of Silicon Valley-based Constellation Research Inc., a research and advisory firm which studies disruptive business and exponential technology trends. In this segment, Wang talks about the […]

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Digital transformation isn’t just about technology. It’s also about building new business models that leverage technologies like mobile, cloud computing and IoT. Ray Wang is Founder, Chairman and Principal Analyst of Silicon Valley-based Constellation Research Inc., a research and advisory firm which studies disruptive business and exponential technology trends. In this segment, he explains how […]

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Immersive technologies present an interesting conundrum for users, who are enthusiastic about the possibilities of AR, VR and the next era of audio/visual media. While everyone knows it will become an important part of the fabric of life, few know just what that will look like. One of those is Michael Hoffman, who started Object […]

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Lopez: I still can’t get past the idea that I’m talking to a toaster. Scoble: Yeah. You’re talking to millions of toasters. That’s the breakthrough, right? When you’re talking to a Tesla, you’re not talking just to one chip, you’re talking to 800,000 cars that are driving around with cameras on them, and all of […]

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The key idea behind data center virtualization has been ”no change to the application, no change to the operating system,” according to Nutanix CEO Dheeraj Pandey, in an investor call with Bank of America Securities. This has been the philosophy behind the shift to virtual machines and as well as in storage, Pandey said. “I […]

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There’s practically no debate that some new ways of living and working will be with us long after the pandemic has ended. This is the mindset of the one the world’s biggest IT services companies, Tata Consultancy Services also known as TCS. The company has nearly half a million workers worldwide. Before COVID-19, 20 percent […]

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Flavio Mancini is a IT project manager in Italy’s National Institute for Insurance against Accidents at Work also known as INAIL. Before COVID-19 hit the country hard in March 2020, Mancini and his team helped INAIL move to private cloud. Having that on-premises, scalable IT system allowed the agency to scale its remote worker capabilities […]

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Maron Kristófferson is the CEO of the Icelandic company aha, an online service for restaurant food and groceries. The company delivers food to customers by car, but had an idea for a project that would show how drones can help business stay competitive. Kristófersson talks about the weather and technical challenges and how the project […]

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China’s economic transformation has been among the most significant events in our lifetime, studied in-depth within and outside of China by economists, analysts, sociologists, historians and experts across different fields. In part three of our series with managing director of IDC China, Kitty Fok, we explore how technologies and consumer behaviors are shaping the future. […]

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Technology innovation has been spreading across Italy for years, but the mandated lockdown during the rise of COVID-19 in March and April 2020 proved that Italy’s IT infrastructure can handle a nation of smart, remote workers. Will this change the future of work? Guido Ingenito, IT manager for TXT eSolutions, says while their IT roadmap […]

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For decades “Made in China” has generally meant, to the Western world at least, mass produced cheap goods. But the strategic plan “Made in China 2025” established by Premier Li Qekiang aims to move the country into making high value, high tech products in fields like robotics and automation, computer chips, aerospace and biomedical research. […]

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The coronavirus pandemic jolted economies and businesses around the world. Immediately experts reported on signs of destruction and offered examples of recovery. In a three-part series, Kitty Fok, Managing Director of IDC, China, explains findings from her annual report, which showed China’s economy in the first quarter of 2020 fell 6.8% compared to Q1 2019. […]

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During this shelter-in-place, there have been many feel-good moments of people collaborating with each other whether it’s working from home or personal stuff like having a Zoom party on doing online wine tastings. One of the unexpected story gems was the National Football League draft, in which general managers and coaches—who normally put in insane […]

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Ten years ago Jeanne Meister co-authored a book entitled, “The 2020 Workplace“. In it, she didn’t anticipate a pandemic causing us to work from home. But the foundational ideas of remote working still apply and have been brought to the top of our consciousness by many of us being forced to work from home due […]

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Autodesk University is the flagship conference of the design software maker Autodesk. One of its most famous products is Auto-CAD, which is so data intensive it requires a workstation to run. The company used to set up a fleet of hundreds of these machines for Autodesk University, held each year in Las Vegas, to train […]

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The coronavirus pandemic has modified the way the world works in what could be called the biggest ever work-from-home experiment. Ruben Spruijt, a senior technologist at Nutanix, explains how virtual desktop and desktop-as-a-service technologies are allowing IT departments to stream business applications to remote workers. He says interest in these technologies during the COVID-19 pandemic […]

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Leaders are made, not born… and the era when the boss was always right and barked orders to the company’s worker bees has become obsolete. The work of Professor Jim Kouzes, and his collaborator Barry Posner, in the field of leadership studies has been one reason why people are becoming better leaders. On this podcast […]

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Global semiconductor orders are one of the barometers of how the the computer industry is doing. Analysts look at the breadth of the market, including everything from the chips in IoT devices, automobiles, industrial appliclations like robotics, servers in data centers and of course in our PCs and phjones. Amid the COVID-19 pandemic, there’s the […]

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Now that the world has been essentially ushered indoors, the integrity of internet and cloud to continue to serve all users is a question. Ray Wang, Principal Analyst, Founder, and Chairman of Silicon Valley-based Constellation Research joins us to talk about this, as well as about companies that have updated their IT with hybrid cloud […]

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As the world retreats inside, it’s easy to wonder if the public’s adoption or consumption of immersive experiences and technologies could rise. Formats that are accessible on a smartphone, like 360 video and AR, could take on more importance in our lives, making the shut in experience more bearable. But VR video, which is often […]

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Diversity in employment has been an ongoing issue in the tech industry. According to Bureau of Labor statistics, women make up 26 percent of the computer industry workforce, down from 35 percent in 1990. Nutanix executive, Monica Kumar (SVP Marketing), began her course toward a career in high tech when she took math classes in […]

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It’s hard to tell how the cloud, machine learning, AI, etc., are having an effect in the fight against the coronavirus. In this segment, we chat with Dr. David Shaywitz, a Harvard-trained MD and research scientist in Silicon Valley who’s an expert on medicine and drug development, especially in relation to digital health and IT. […]

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It takes many skills and mental dexterity to manage business across Europe, the Middle East and Africa. Known as EMEA, the vast region is rich with diversity and history, made up of multiple time zones, languages and cultures. Sammy Zoghlami, senior VP for Nutanix in EMEA, is based in Paris, but spends a lot of […]

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To most people outside the country, Italy evokes the delight of pizza and mandolins. But for Alberto Fiisetti and Christian Turcati, their country, which is steeped in tradition with a diverse mix of regional dialects and cultures, is a place where change doesn’t come easy. Yet in just a few short years, the Nutanix Italy […]

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For some people a job or a profession takes on a greater significance than just career. It becomes a life’s work. Bala Kutchibhotla has spent his life working, developing and innovating database technology. Follow the enterprise cloud tech revolution at The Forecast by Nutanix. Transcript

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DevOps typically refers to removing friction between developers (‘Dev’) who build software and operations people (‘Ops’) who have to run it. Mark Lavi’s definition goes much further. “It’s about removing the friction between developers and the customer,” said Lavi, Principal DevOps Advocate for Nutanix. In this Tech Barometer segment, Lavi explains how a DevOps approach […]

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People used to spend a lot of time finding answers, even to the toughest problems like the Theory of Relativity. But today, people put more focus on asking questions and turn to powerful software and cloud computing to find the right answers. Scott Reese, senior vice president (or SVP) of manufacturing, cloud and production products […]

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In Copenhagen last fall, Nutanix held it’s European dot-NEXT conference. On day one at the first keynote, CEO Dheeraj Pandey talked about a concept with a lot of history at the company… 6 years ago his idea was: make infrastructure invisible. But the world of information technology has changed since 2012. Follow the enterprise cloud […]

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“When you can’t count it, and you can consume it as much as you need it for your business, it becomes practically infinite,” said Holger Mueller, vice president and principal analyst of Constellation Research. In this Tech Barometer segment, Mueller talks about the five layers of infinite computing and what it means for businesses to […]

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One way to move IT from a cost to a profit center is to examine the total cost of ownership (TCO), return on investment (ROI) and everything that it takes to run business solutions, according to Steven Kaplan, author of The ROI Story: A Guide for IT Leaders. He shares his journey to becoming ROI […]

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What makes a better leader? It starts with being a better person, according to Ben Ravani, former Senior Vice President of Reliability Engineering at Nutanix. He talks about how mindfulness and the quest for authenticity are critical behaviors for a leader today, especially in a time of tremendous technology disruption. Follow the enterprise cloud tech […]

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Customer centricity becomes front and center when a company goes from the building to the scaling stage, according to Indir Sidhu, Executive Vice President of Global Customer Success and Business Operations at Nutanix. He’s leading the company’s customer service as it evolves from a licensing to a subscription model. “If your product isn’t good — […]

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Jessica Groopman, technology analyst and founder of Kaleido Research, and Satyam Vaghani, VP IoT and AI for Nutanix discuss how the Internet of Things is growing beyond its infancy and into a phase that’s driving a whole new wave of computing just to feed all of those connected machine. Follow the enterprise cloud tech revolution […]

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With the average annual cost of a data breach for a company about to exceed $4 million and the emergence of regulations like GDPR and the CCPA, businesses more than ever must prioritize data safeguards. “If you look at enterprises, the majority of them haven’t moved their entire workloads to the cloud because it’s hard […]

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We have never seen a time like this, where IT is being besieged on multiple fronts, according to Steven Kaplan, author of The ROI Story: A Guide for IT Leaders. On one side is the public cloud and on the other side is software-defined infrastructure known as hyperconverged infrastructure (HCI), allowing private data centers to […]

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“We’re reinventing how we do IT right now,” said Steve McDowell, senior tech analyst at Moor Insights & Strategy. He reads signs from technology’s past and sees a future that is increasingly powered by software. What was once difficult, costly and time-consuming for businesses is quickly becoming more accessible and affordable due to the wonders […]

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With the average cost of a data breach for a company now at $3.8 million and the emergence of regulations like GDPR and the CCPA, businesses have more than customer distrust to contend with if their data is not properly safeguarded. “Without security, you have nothing,” said Ben Ravani, Senior Vice President of Reliability Engineering […]

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Today’s monolithic mainframe approach to computing will soon be shattered and dispersed because that’s the nature of computing, according to Nutanix CEO Dheeraj Pandey. This animation brings to life a talk Pandey gave to technology analysts at .NEXT in Anaheim, California in 2019. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.

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“In the early days, people had no idea what hyperconvergence was,” recalled Steven Poitras, principal solutions architect at enterprise software company Nutani. Hyperconverged (HCI) infrastructure combines compute, storage and networking in a single location, vastly simplifying data center operations that conventionally house racks of servers, network switches and other hardware. HCI takes advantage of innovations […]

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It is important for developers to build up their own empathy in order to design APIs with a UX mindset. That makes APIs easier for other engineers to use more effectively, according to Kollivakkam Raghavan, Director of Engineering for Prism at Nutanix. “When I’m building an API for you, I will put forth the best […]

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As a professional ballplayer, he became fascinated with the power of team chemistry. His next career in business showed him that team chemistry was confined to sports. “At my very first job I realized, ‘Oh, that’s not a sports thing…that’s a human thing,” said Mike Robbins. In his series of books, Robbins explores what leaders […]

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Zach Hilliard always loved cars and started his career as a diesel mechanic. Today he’s senior director of site reliability engineering for Cyxtera Technologies, a Texas-based provider of data centers around the globe. He sees the Uberization of data centers, where companies tap unused computing resources much like how Uber puts ride hailers into empty […]

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In what he calls one of the most volatile regions in the world, Matt Young is helping companies modernize their IT. In this segment, Nutanix senior vice president of sales for Asia-Pacific-Japan region describes how he deals with demanding managers and cautious executives from cultures dating to ancient times as they grapple with 21st Century […]

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With the proliferation of Internet of Things (IoT), IT departments are facing a significant shift as human demand for data is surpassed by machines. Machine-to-machine communications ­from robots, retail store sensors and other connected devices are forcing IT departments to put more computing resources to support IoT. This is driving the technology industry to rethink […]

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Many say DevOps makes companies more agile and focused on business value. Gene Kim sees the practice helping developers and operations professionals work together faster while maintaining secure and reliable IT systems. In this interview, the co-author of The Phoenix Project and the new The Unicorn Project, talks about the feelings and motivations that drive […]