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Modern electrical grids incorporate redundancy and automated rerouting to maintain a reliable electrical supply during equipment failures or natural disasters. Plants have coping mechanisms to deal with adverse conditions like drought. Similarly, it is important that IT systems, including applications, infrastructure, platforms, and services, can withstand or recover quickly from difficulties. Resilient technology helps companies thrive, and when disruptions occur, enables them to emerge stronger than before.

In 2024, Intel IT launched our OneIT Resiliency Program, geared to instill resiliency across technologies, operations, and culture and organizational entities. In addition to defining these three pillars, we codified resiliency through a unique combination of framework, principles, standards, maturity model, and roadmaps. We focused first on applications, closing 100% of all resiliency gaps in Tier 1 applications by the end of the year. We also successfully weathered a major incident caused by one of our security software vendors: Despite widespread PC failure across the company, we restored functionality in just a few hours for Tier 1 and Tier 2 applications and under 1.5 days for all applications at Intel.

During 2025, we will extend our efforts to infrastructure and data centers. We hope that our resiliency framework, standards, blueprints, recipes, and maturity model will inspire other IT departments to pursue a similar path to IT—and enterprise—resilience.

Intel’s manufacturing landscape is evolving rapidly, driven by digital transformation and automation. Intel’s manufacturing facilities generate vast amounts of data that must be efficiently processed, analyzed, and secured to optimize operations and maintain competitiveness.

In 2013, we deployed an “ultra” data warehouse that replaced our older fragmented and siloed databases. It has stood the test of time, easily scaling as we added yet more data and data domains. As Intel transitions to a foundry model, its factories will become more complex and will generate even more data than before. Plus, security is becoming even more critical—Intel must not only protect its own manufacturing data from unauthorized access; it must also ensure that Intel Foundry customers have confidence that their data is in safe hands and guaranteed to be separate from Intel’s data. We have implemented process-specific security (PSS), which helps prevent unauthorized data access while still delivering efficient query execution.

Our highly secure and high-performance data warehouse, running on modern Intel architecture, helps unlock important insights hidden in massive manufacturing data volumes. This solution has shown that it can reliably scale to handle not only today’s data challenges but also grow with Intel as it expands its factories to support its foundry model and IDM 2.0.

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.

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.

Can music really change our brains and boost our well-being? Archaeological evidence shows humans have made music for over 50,000 years, and today, science is revealing just how deeply sound shapes our emotions and mental health. In this episode, neurobiologist Dr. Daniel Bowling highlights the therapeutic potential of music and sound for mental health conditions like depression, anxiety and ADHD.

Guest: Dr. Daniel Bowling, Neurobiologist, Stanford Medicine

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.

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.

Health and well-being can be improved by medicine, but can purpose and personal connection achieve similar effects? With the advent of social prescribing, a model of care delivery in which a health care professional formally prescribes non-clinical community activities, including artistic and active endeavors, medical professionals are testing a new way to improve employee well-being.

In this episode, journalist Julia Hotz, author of The Connection Cure, explores social prescribing and shares how it can improve health outcomes for patients with depression, anxiety, diabetes, stress, and more.

Guest: Julia Hotz, Journalist and author of The Connection Cure

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.

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.

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.

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.

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.

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.

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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.

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.

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.

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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.

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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.