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Training models on Intel Gaudi accelerators is helping improve efficiency in Intel's factories.

Intel’s smart manufacturing environment relies on AI to improve yield, perform root-cause analysis, and accelerate anomaly detection. As Intel transforms to an internal foundry model, it is crucial that the Manufacturing Automation team helps improve factory efficiency by enabling faster, more cost-efficient deep-learning (DL) and machine-learning (ML) workloads.

We recently benchmarked a manufacturing object detection DL model training workload. We compared the performance of our legacy GPU-based system, a vendor upgrade to the GPUs, and Intel® Gaudi® accelerators. Our AI-based solution using Intel Gaudi accelerators produced the following benefits:

• A 20% improvement in DL model training time

• Reduced manual processes for employees and faster defect detection

• Streamlined workflows and enhanced manufacturing efficiency

• Improved cost efficiency and better supply chain availability for reduced operational expenses

Intel’s AI hardware and software ecosystem is a competitive and efficient solution for high-volume manufacturing (HVM) use cases. The manufacturing efficiency offered by Intel Gaudi accelerators will help us improve production yields and increase Intel factory revenue.

Individuals with disabilities make up 16% of the world’s population. They face a stark health disparity as their life expectancy is 14 years shorter than those without disabilities. In this episode, Hannah Kuper, epidemiologist and Co-Founder of the Missing Billion Initiative, explores the intersection of disabilities and health, highlighting ways to close the health care gap.

Tune in to learn how social determinants of health impact disability-inclusive health care, ways to improve access including innovative uses of AI and how multinational employers can consider this population when designing health and well-being strategies.

Guest: Hannah Kuper, Co-founder of the Missing Billion Initiative, founder and co-director of the International Centre for Evidence in Disability at the London School of Hygiene and Tropical Medicine

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.

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.

Software-defined networking (SDN) is helping Intel IT keep up with Intel’s business expansion. We regularly experience an annual 25% increase in demand for data center network capacity. Furthermore, business pressures necessitate that new capacity be operational within 24 hours. As early as 2014, we acknowledged the potential of SDN to help address these challenges.

After evaluating the components and architectures of SDN, we opted for an open, standards-based architecture rather than a supplier-centric solution. As our SDN architecture has evolved, we have established a standardized and scalable data center network architecture that extensively uses automation. The open interface provides the flexibility to integrate additional business-driven automation, enabling us to meet our growth objective and timeline requirements.

Our network architecture strategy relies on five pillars:

Over the past five years, we have migrated the majority of our data centers to a new SDN architecture that employs a leaf-spine underlay combined with overlay networks. By utilizing Industry-standard components and protocols, we have significantly improved network delivery times while reducing the need for human resources, thereby enhancing overall efficiency. Additionally, we have increased the stability and reliability of the network and consolidated multiple dedicated customer networks onto a common infrastructure that’s integrated with enhanced security controls.

Intel IT adopted a “right workload, right place” multicloud strategy nearly 10 years ago. This strategy has accelerated application development and deployment agility, achieved cost-responsible scalability, and enabled our business partners across Intel to achieve their business goals.

A decade of validation has underscored that emphasizing business value in cloud hosting decisions is advantageous. However, we continue to adapt our multicloud strategy as both business needs and the IT ecosystem evolve.

Some of those adaptations include the following:

But the cloud journey is never finished. New trends in the enterprise—such as the increasing use of AI, as well as innovations in the IT landscape—mean that we must continue to evaluate and evolve our multicloud strategy to consistently support the business in the most cost-efficient manner possible.

In a 2025 Advisory from the U.S. Surgeon General, alcohol use is highlighted as the leading preventable cause of cancer in the United States contributing to nearly 100,000 cancer cases and 20,000 cancer deaths each year. In this episode of the Business Group on Health podcast, we revisit two insightful conversations about the complex issue of alcohol use.

Listen in as John Kelly from Harvard Medical School and Rachel Sayko Adams of Boston University School of Public Health discuss the cultural and psychological drivers of alcohol consumption, the rise in binge drinking among women, the "sober curious" movement and more. Learn about the optimal pathways to recovery and how employers can help support healthier choices.

Guests:

John Kelly, PhD, Professor of Psychiatry in Addiction Medicine at Harvard Medical School and Founder and Director of the Recovery Research Institute at the Massachusetts General Hospital and

Rachel Sayko Adams, PhD, Research Associate Professor, Boston University School of Public Health

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.

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.

To benchmark the end-user PC experience, Intel IT uses tools that clarify PC power consumption and applications’ effect on battery life. This helps us optimize the IT build and answer one of the most common questions we receive from users: “Why doesn’t my corporate laptop battery last as long as my laptop at home?”

We know employees want to be as productive as possible, and battery life is crucial to Intel’s highly mobile workforce. On the other hand, our IT build includes several components that help keep Intel’s data and intellectual property safe. Necessarily, these additional software components consume their fair share of power. To fully understand what users are experiencing with their laptops—as well as how the Intel IT build introduces power consumption overhead—we rigorously benchmark the corporate PC experience.

In collaboration with Intel’s Client Computing group, we recently overhauled our PC experience benchmarking methodology and toolset. We adopted new benchmarking tools that more accurately represent the applications that Intel employees use. We also now use the Intel® Battery Life Diagnostic Tool (Intel® BLDT) to identify specific background processes and applications that are responsible for excessive power consumption.

We use these tools to reduce power consumption rates on users’ PCs and help the Client Computing group drive improvements to client devices and meet customers’ expectations. By sharing our benchmarking approach and results, we hope to encourage other IT departments to take a similar approach.

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.

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.

Historical biases in medicine have left a lasting imprint on women’s health. In our latest podcast episode, Dr. Elizabeth Comen, oncologist and medical historian, reveals how the systemic devaluation of women’s health—ranging from misdiagnoses to delays in treatment—continues to impact the quality and efficiency of care that women receive.

Dr. Comen’s insights extend to show how these disparities impact the workforce. By more clearly identifying and understanding women’s unique health needs, organizations can better design health benefits and well-being strategies for greater impact and support.

Guest: Dr. Elizabeth Comen, author of All in Her Head: The Truth and Lies Early Medicine Taught Us About Women's Bodies and Why It Matters