Tech Barometer – From The Forecast by Nutanix

In this video interview, Nutanix CTO Daemon Behr explains how platform engineering has grown out of DevOps and DevSecOps into a platform engineering practice that now incorporates ChatOps, AI agents and infrastructure automation.

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Video transcript:

Daemon Behr: One thing that’s happened in the software industry is going from the traditional way of software or development to talking to agents and saying, “This is what I would like to achieve. Tell me how I can build a requirements document or a PRD in order to do that. Help me create a spec for this application.” And then once you create that spec, you can feed it into a team of agents or a swarm of agents, and they can do the front end development, the backend development, the testing, the evaluation. And at the end of it, you end up with a fully baked application that can go into production. So I’ve been working with agents that do that, that leverage the Nutanix infrastructure that can connect through MCP servers to our Prism environment, create VMs, to connect your Kubernetes environment, create pods, connect databases in the backend, and be able to do rapid provisioning of entire infrastructures with a single prompt.

Ken Kaplan: How does it make you feel when you can do that? And what do you think is possible because you can do this?

Daemon Behr: Yeah, I think that the technical limitations that may have been there in the past are gone now. So the real limitations now are the constraints are creativity. What can you think is possible and what do you think is not possible that you can still strive for and learn things along the way. So it’s an innovator’s paradise.

Ken Kaplan: Let’s talk about platform engineering. I talked to you about that maybe a year and a half ago, and it’s an important role, relatively new for the IT industry, but it’s been evolving. Can you talk about that role and how it’s evolved, where it’s going?

Daemon Behr: Sure. So platform engineering has evolved from what was initially DevOps, which turned into DevSecOps, which started incorporating other portions of the operations that were missing and that evolved into what we now have as platform engineering that’s all encompassing of those different roles. Some interesting things that have also evolved along with that are our chat ops. So being able to talk to your infrastructure through Slack or Teams or another channel like that. Now that has been a bit of a segue into talking with agents. So using that chat ops mentality and method to do the things that I was talking about, create new infrastructure, but also get a better understanding of your existing infrastructure. So that’s some things that I’ve seen evolve in terms of platform engineering and the way that you interact with it. The other thing that I’m seeing is from an AI side, you had prompt engineering where you would initially guide the way that you engage with an LLM or an agent. Then from that it expand into context engineering where you would frame the working space of the conversation from various sources, whether it’s retrieve augmented generation or have a multi-step process. There was many different strategies in order to do that. Now, the next thing that it’s really evolved into now is what’s called harness engineering. And that’s being able to take the context engineering and then providing skills and workflows and being able to use other tools so that you can have specific outcomes. And there’s many different harnesses that are out there. Some of the common ones are cloud code, codex, there’s open code, but we’re seeing more and more open source ones proliferate that allow you to use different large language models that are not just the closed models, but also the open models. So that lends itself to be very, very interesting to organizations that want to have a more secure ecosystem and run everything internally and get the benefits of using frontier models and agentic harnesses within their infrastructure. And that’s something that we can do.

Ken Kaplan: At some point, do we see agents and when there are millions of them out there, do we just see I have to build an agent or I have to employ an agent or I’m using agents. Do we just see it as we see software today? What’s your perspective on that?

Daemon Behr: Well, I think that agents will be a lot quicker to spin up and destroy. It’ll be more ephemeral. So what I foresee is that you’ll have different types of persistent memory that are associated with that. But when you need to use an agent, you may have a management HR master agent that will then create an instant swarm of different agents that can do specific tasks and they all report over to that manager agent to complete that task once they’re done. That way you’re able to paralyze the workflows and make things or complete things a lot quicker by going through that paralyzation process as opposed to a serial process. So the management agent could instantiate 50 different agents for a couple minutes and then they would collapse after that. But the various teams of agents would have access to a shared memory space that they’re able to understand what their tasks are, what they’re supposed to do, the specific knowledge or data that they need to have access to to complete the job, but the agents themselves are ephemeral.

Ken Kaplan: So that doesn’t feel like how we look at software today.

Daemon Behr: Not at all.

Ken Kaplan: It’s like a sci-fi movie versus, I don’t know, a sketch.

Daemon Behr: Yeah. It’s very different from the way that we’ve approached where right now you have a software that you install, you update on a regular basis or use SaaS software and then they’ll update on a regular basis to have certain rollouts of functionality to specific users. That’s really gone away with agents and we’re seeing agents being able to create on-demand software as well. So if you need to have a specific function or an outcome, you could have a team build a dashboard for you, connect to your existing data, create a UI that can have specific functionality that you need. And then once you’re no longer using that, it just destroys the ephemeral application that you created. So very, very different than software that we’re seeing.

Veteran analyst Michael Warrilow warns enterprise AI disruption will reshape every industry and profession. CIOs who delay risk falling behind as AGI approaches.

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Video transcript:

Michael Warrilow: It builds on disruption from cloud and internet and going back all the way to the mainframe, back to the churing machine for the computer scientist in me in the 1930s and 40s. This is the most profound change and they need to get onto it. And I think all of society needs to prepare because it’s coming for all of us, whether you’re an actor, whether you’re an artist, whether you’re a musician, whether you’re a professional, it’s just starting across all of those. And I would say to CIOs that are still burying their head in the sand, this is going to disrupt their careers, their children, their friends, and they need to be the leaders that help their employer get through this journey that’s just about to accelerate. Let’s talk about what’s going on now and how you’re getting up to speed and staying on top of things.

Yeah. So I sometimes joke I’m a 30-year industry expert in AI. I studied it in the early 1990s at university, Macquarie University in Australia. I’m based in Australia, great university. And I did a bit of it. There was actually some technology I used in the late 1990s from a company called Tivoli, which became part of IBM. And that was pretty much my exposure to it until maybe two to three years ago. I played around with ChatGPT-2 and stuff like that. And I was working at Gartner and I used to joke that it was too profound for me to cover. I couldn’t dedicate time to it. So when I left Gartner, I spent the first month just going back to university and studying, doing all these online courses. Along with becoming a student again, I became a practitioner and I learned, as Australian saying, maybe I learnt AI, modern generative AI on the tools.

So I’ve run over 180,000 prompts across to automate some research I’m doing. So I’ve learned that practically and I think we are at the absolute beginning of this. And with our industry hat on, the most impacted so far at the very early stage is the programming. And it helped me. It’s not a great programmer, probably I’d still be programming if I was, but in terms of practicality, it’s helped with the deficiencies I have. I didn’t have decades of coding experience, but I’ve created industrial scale code for my research purposes just by having terms like meta prompting where you ask AI to help you use AI better. And this is just the beginning. I make the joke that AI is nothing but the new Excel, right? Excel is the world’s best integration tool. You copy and paste into Excel, you make a formula, you’re the business.

You may be good at writing formulas, you may not be consistently applying them. That’s all AI is in terms of a simple analogy that people relate to. So much of businesses decided on Excel formulas for right and wrong. AI could be that or it could be the thing that powers society, civilization, frankly. So what I encourage people to do off those simple analogies, and they need to find it for their job, even for their friends. Where this is going, I see in the medium term, and Google have said this, they’re far smarter than me, they’re DeepMind team, that we’ll get to artificial general intelligence somewhere in the next five years. They’re actually a bit more bullish. They expect it probably sooner. And these are the guys that invented the term. That takes us so far beyond generative AI. It’s the next step on this evolution that ends up potentially with super intelligence, artificial super intelligence.

And so I encourage people to learn today’s, to find ways to articulate it today, to help set up that this is going to accelerate and also to lean in and find even for just a survival perspective, take that in any context you want from your job to your children’s future, you got to keep leaning in to how this is disrupting. And it’s not just going to be in business and IT, it’s going to be across the whole of society. So I guess that’s something I leave you with is for those watching this, get on board, find the strengths, the weaknesses, the opportunities and the threats, stealing from that kind of analogy and keep on the journey. There’ll be highs and lows like we’ve never seen before.

AI infrastructure is straining under memory shortages and supply chain constraints, forcing IT teams to rethink how they manage workloads across cloud and on-prem.

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Video transcript:

Steve McDowell: This is probably the most exciting time in technology since the dot-com era, but in good and bad ways. I mean, the things we’re looking at right now is certainly you can’t talk about technology without talking about AI, whether that’s the impact of AI on IT operations or coding or just societal. I mean, it’s a big topic and it’s impacting everything that I cover. That’s causing some rippling effects. There’s supply chain problems with memory and flash memory. So there’s a scarcity that’s impacting how IT operates. That’s the bad side. And then there’s just the usual, how do I manage my workloads across cloud, on-prem, and with AI, these neo clouds? So IT infrastructure is getting very complicated and you really have to step back and think about what’s the best way to tackle these problems if I’m an IT guy. And this comes after years, I don’t want to say it’s been stagnant in IT world, but kind of natural evolution. And now we’re in kind of a revolution period.

Ken Kaplan: How do you perceive agents and what that’s doing to the mentality of the IT folks who have to be ready and manage these things?

Steve McDowell: Sure. So it’s all an extension of how do I use AI? And all an agent is simply is a small version of an AI someplace. So I have agents talking to agents. I have AI talking to AI. If I’m a coder, maybe I have an agent that’s writing code. I have another agent that’s testing it and I have another agent that’s validating the security. So it’s a multiplier. It’s a force multiplier for AI. And I think where we are right now is there’s so much that we can do with that technology. What are the high value plays? Certainly as an IT guy, because my business as an IT practitioner is to enable the business, not play with technology. Now technology is how I get there, but what’s the right set of agents? What are the right providers? What’s the cost look like? There’s so many things, but a genic AI, that’s the accelerator for how I’m going to use this.

Ken Kaplan: You get into the mind of the IT guys pretty well and you often speak for them when we talk. Just get a sense for how you think they feel and what they’re seeing.

Steve McDowell: I think they’re scared. One of the great ironies is there’s been a lot of worry and speculation around is AI going to put me out of a job? And the irony is the jobs that it’s displacing right now are the coders. I mean, AI is doing the technology jobs, that’s where we’re seeing displacement more than others. So if an AI can do my job, so there’s a lot of fear and some’s founded, some is not. I think you have to look at AI as a tool. It’s an enabler. It’s an accelerator. It helps you do more with less. It’s not that I need less engineers, but it changes the way they operate. And as humans, we like to live in a comfort zone and it’s very hard, but once you push out of that comfort zone and grow, if you will, it becomes more natural. But no, I mean you have to put yourself in the mind of the IT guy because the other side of me, my whole life I’ve worked for tech companies. It is so easy as a technologist to get wrapped up in the bright, shiny object, the sexy, I want to go solve these hard problems. But at the end of the day, why are we solving these problems? It’s to help somebody else do something that’s not technology related.

Ken Kaplan: Let’s talk about the supply chain constraints and what’s happening there. Have you seen anything like it before?

Steve McDowell: No. So I alluded earlier. The downside of AI is it’s consuming a lot of physical resources. It’s very memory intensive and there’s only a handful of factories in the world that can build memory. And with memory, I put NAND flash, so flash storage and memory is very similar manufacturing processes and supply chain. And the memory companies are doing what I would do if I owned a memory company. I’m going to focus on these high value AI clusters, this high bandwidth memory DDR5 because it’s high ASP, high margin. So they’re doing their normal business. I am not saying a bad word about the memory guys. They’re doing what I would do. But kind of the unintended consequence of that is mainstream memory, mainstream storage is harder to come by. It’s driving up prices, it’s driving up availability. And I think I saw yesterday that the latest MacBook, it’s got a five month lead time because of memory. Vendors are given price quotes that are only valid for 10 days because the memory market is shifting. So what that does to the IT guy, yes, things are more expensive, but there’s also scarcity, which means as an IT practitioner, I have to do more with less. And how do I do more with less? And then I start looking at solutions that allow me to separate my workload from the hardware. We used to call that virtualization, but the containers, virtualization, number of techniques, but it’s all about managing my workloads now to take advantage of my existing infrastructure. Whereas even three years ago, I might buy a server with a terabyte of memory, that’s impractical now. So now I’m buying a server that’s not as equipped or maybe I’m not buying that server. So it requires me to think differently about how I provision those servers. So maybe I have an old server and that’s a perfect place to go put my mail or my SharePoint. So it forces me to think differently about allocating resources and that’s new. That’s new for IT. I don’t know that in my history in the industry we’ve had a moment quite like this with you have both AI as a disruptor and you have supply constraints. We see supply constraints in bubbles. This is going to last through the end of 2027 and maybe beyond. I think maybe we might even be establishing a new bar for normal for memory prices. So how do I deal with that as an IT guy? I’m going to go look for software solutions, allow me to manage that workload both on-prem so I can put the workload where it belongs, but also offload the cloud. And there’s not a lot of solutions that allow me to bridge on-prem to cloud. And with AI, even Neo-Cloud, so I might have something on CoreWeave and something on Google Cloud and then the core of my data center. And the other thing we haven’t touched on is AI is also driving expansion of edge. So I have this whole new infrastructure expansion and how do I manage that? So that management plane is becoming critical in a way that it never was before. In the days of pure virtualization, I’m just trying to get the most out of a box. Now I’m trying to get the most out of a very broadly distributed infrastructure and that’s a whole different skill set. And that’s where Nutanix I think excels and where you’re finding great success.

Ken Kaplan: It sounds like we’ve arrived at this place where you’re able to store some data here or over here and have this system grab it from there or there. It does feel like there’s this complicated web that maybe wasn’t functioning that way a few years ago, but that’s where it is now and it will probably continue like that.

Steve McDowell: Oh yeah. It’s a new normal, right? I mean, AI is not a bubble. That comes up a lot in these kind of conversations. Now, certain segments of AI may be, right? I mean, how much training capacity do we really need or whatever? And I think where we are right now and we’re trying to figure out is where does inference live? There’s going to be many answers to that, depends on what I’m trying to do. But it is a fundamental shift. It is a fundamental shift. Absolutely.

Agentic AI engineering lets teams orchestrate GPU resources, models and agents. Daemon Behr of Nutanix explains how he uses agent swarms to stay ahead.

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Video transcript:

Daemon Behr: The fact that there’s so many different large language models that are out there have different capabilities, whether it’s multimodal or reasoning or so on, and the frontier models change on a weekly basis in terms of capability. But that impacts organizations as well because maybe they don’t necessarily want to have those changes occur on a regular basis. They want to be able to lock certain models down to get deterministic outputs from their environment. So what we’re able to do is we have our AI gateway that allows you to connect to those frontier models when needed, but you also have the capability to have open models that are running on-prem that you can lock to specific versions. So you have the capability of managing your tokens, managing your spend, have control over the environment, the performance, the availability, and also governance over your MCP servers as well. So having that greater control over where you’re running your inference is a game changer and it’s something that we’re able to do really well.

Ken Kaplan: Teams have to build those capabilities. What do you call that activity or that area?

Daemon Behr: That’s agentic engineering.

Ken Kaplan: So just describe that world a little bit. What is an agentic engineer doing?

Daemon Behr: So with agentic engineering, you’re able to say these GPU resources are assigned over to this model. I want to scale with X number of users and then you’re able to do that. You can increase the number of instances, you can load balance your endpoints, you can use some endpoints that are running in the cloud or on-prem, on bare metal. There’s many different options on how you actually do the inferencing and the orchestration of all those different components. The vector databases, the data sources, the various agents that you create, the frameworks for that, the infrastructure that it’s actually running on as well. That’s all part of agentic engineering.

Ken Kaplan: How do you stay plugged in and moving ahead with so much rapid development around AI and how people are using it and what’s next?

Daemon Behr: So I actually have a bunch of agents myself that I use. I use Nemo Cloth, it’s a fork of open cloth that’s a little bit more secure, in order to do research for me and curate my information on a daily basis. And then it actually sends me messages over Telegram and creates reports which I then study that as well as various YouTube videos from probably about 50 different people on a regular basis. And from that, I’m able to understand the changes that are happening in the industry. I also have a swarm of agents that I use to deploy new and innovative software that I come across on GitHub. So if something is rising up at the GitHub stars, I’ll tell one of my agent teams to go download that, to deploy that in sandbox, tell me how it works, tell me the deficiencies, and then tell me if it’s valuable to me. If it is, then I will actually adopt it. If not, then it’ll tell me that I can just kill it.

Ken Kaplan: How did you get to that mindset and to actually do that?

Daemon Behr: I was doing that on my own and then I found out that I didn’t have enough time to do that. So that’s where I thought, well I could actually lean on the technology that I’m studying and learning to do that for me.

So the combination of traditional AI and generative AI is what we’re calling modern AI. So it’s not a bubble that’s popped per se, it’s an evolution to expand to generative AI. Now in terms of things that you could call bubbles and that are popping, it’s not the same way that we’d have the like the dot-com bubble or busts like that where you have I guess a souring of expectations or the the trowel of disillusionment per se. That’s happening on a much more shorter time span. So what may be at its peak point in terms of expectations, that could pop the next day and then you have a whole new thing which is propped up. So the rotation of innovation is a lot more frequent. So the bubbles are popping on a very regular basis but the bubbles are smaller.

AI agents are transforming database management, automating provisioning, patching and migration while cutting migration time by up to 82% and doubling performance.

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Video transcript:

Ashish Mohindroo: If you think about AI, AI is nothing but running on top of data. So, data I would say is critical to running any successful AI initiative or application. And on the other hand, you also need AI to manage the database. Most of your data or critical data for a company, is stored in your database, a variety of databases. And then how do you really operate those databases seamlessly and at scale? You need AI to do that. So it’s kind of interrelated. You could always run open source small databases on containers in a stateless manner, but now you can run stateful databases on containers. That means you can run more complex and mission-critical applications on it. So that’s the first thing that’s happened. The second thing is, as everybody knows, is the whole excitement around AI and the innovations that’s taking place in AI, and that’s how AI is helping run and manage these databases in a more efficient manner.

So that really is a very exciting area where we can build agents to manage your databases, to run your databases, to optimize your databases and to squeeze out more performance, more scale and reduce costs. So customers are choosing to run databases on multiple public clouds along with on-premises data centers, and they want simplicity in terms of how they manage this entire complex environment. So I would say those are the big three trends that have really emerged and are starting to accelerate in the market today. And we’ve really transformed our entire development lifecycle process to adapt to using AI so that we can actually build product faster and deliver more functionality to our customers. So that is happening there. The second aspect is how do you actually use AI within the platform that is exposed to our customers? So how can our customers now take advantage of AI and really benefit from productivity gains, cost savings, or whichever benefit they’re looking for?

[Related: How Database Automation Is Redefining the DBA Role]

And in that case, we are looking at exposing new capabilities within the platform that makes our customers’ lives easier. So where you needed a lot of manual intervention, we are trying to see if we can automate a lot of that or give you an assistant. So this would be more like, I would say a driving assistant to say, look, I need to understand why my database is not performing up to par. What could be slowing it down? What are the threat vectors that I should be looking out for?

How do I automate the lifecycle management of certain databases? How do I create a quick clone of my database based on certain triggers that may be happening in my business? So all of those things can be, we can create agents for that and then give our customers the ability to deploy those agents and to really benefit from the agentic architecture. Today, as we are kind of experimenting with this technology and we are developing that in our own labs, what we are seeing is you can actually run agents to do any kind of tasks for these databases. You can provision your databases, you can back up, recover your databases, you can actually apply patches to it, you can do cloning, you can set those up. So agents pretty much can do a majority of the operations of a database today. In addition to that, you get some really interesting insights.

You can monitor these databases more efficiently. You can know, hey, which database is not performing up to your target spec or what is really slowing it down? Is it an indexing issue? Is it a query optimization issue? You can actually make the databases more secure. What are the vulnerabilities that are going to be exposed around this database? So a lot of this interesting stuff, now the agents can really enable you to do that. And as we are building these technology, we’ve seen that within our own lab environments that you can become a lot more efficient with agents. What would take you, I would say months can now be done in a matter of days or weeks overall. So that is the kind of efficiency gains that we are seeing by moving to an agentic architecture. An application can be broken down into components and each one of these components can be built independently.

That’s what typically is called a microservice. Now each one of these microservices can run on its own set of databases, for example. And what you really want, and those microservices typically you want to bring them up and down really quickly. This is where containers really come in very handy and you want to reuse them as well. So the idea is if you actually have a microservices that typically is deployed on a container environment, right? So if that’s the case, you want your databases to be very efficiently run on containers and that’s what we’re making it possible. The other thing is you want the ability to spin them up and down really quickly. And in a VM environment, it takes a little bit longer to do that. In a container, you can do that very efficiently. Also, container nicely packages that entire microservice in a self-contained environment in some ways that makes it very easy to become portable.

So you can port that, you want to run this on premises today, you want to run it on the cloud tomorrow or run it another different server on premises. It’s very easy to just take that container and deploy it somewhere else versus you having to kind of move the entire VM. So it becomes bulky, containers are lightweight. So all these things really make containers more efficient and makes it really possible to build and assemble applications quickly, run these applications very quickly and to manage them more efficiently. So that’s the reason why customers are choosing to deploy applications on a containerized environment for their different kinds of workloads. If you’re thinking about migrating, let’s say from a legacy VM environment to a more modern environment, typically what’s the number one workload that’s running on that legacy VM environment? It tends to be a database. It may be an application on top of it, but databases are your number one or I would say heavy duty workloads and the most mission critical workloads.

So when you’re thinking about moving from legacy environment to a modern environment, there are multiple ways you can do that. The one way that customers gravitate towards typically is lift and shift. So I take the VM and I just lift it and shift it to the modern VM, modern platform. Now what are the advantages? You can do that in certain cases, but in other cases where you have databases running and these databases are configured in, let’s say high availability cluster or a disaster recovery cluster or failover in a setup, what happens is when you do a lift and shift, you lose context of these VMs, the database VMs. So if you’re in a cluster, it doesn’t recognize that it was in a cluster. So it’ll take the first VM, move it to the modern environment, it’ll take the second VM of the cluster and move it to the modern environment, but doesn’t know how the first one and the current VM are interconnected.

This is called application consistent movements or backups and restores. You can’t do that. So customers spend a considerable amount of time once they migrated individual VMs to now kind of correlate them to recreate that previous environment of a high availability cluster or a disaster recovery cluster or failover. And that takes a lot of time and effort to do that because now you’re stitching together all these VMs without really having context. Now we have a better solution. We say with Nutanix database service, you can actually connect to your databases on the legacy VM, in whatever configuration they’re set up, single instance, high availability, disaster recovery, failover. It backs up those environments maintaining context. So it does application consistent snapshots. And then you can create a replica or you can create a standby on the modern environment like Nutanix and then easily be able to replicate the entire database as it was without losing a beat.

So without having significant downtime, you moved your most sophisticated database configurations and environments onto Nutanix without disruption to your business. That is huge. And you can be confident that when you have cut over to Nutanix, those applications will work as they were intended, which is a big deal. This typically takes a very long time. With NDB, we are able to reduce that time by up to 82%. So it’s a significant acceleration in terms of migrations, giving you the confidence as well that the new environment will work for you as intended. And the majority of these cases as well, we see a performance improvement of 2X on these databases. So without paying anything extra, doing anything extra, what you’re getting is 2X performance improvement, 82% faster acceleration, up to 82% faster acceleration, and all the benefits of a modern platform that Nutanix delivers to you. So it’s a huge, huge value add for our customers.

And you can do this whether you’re moving it from an on-premise to an on-premise environment or from on-premises to a cloud, or if you’re running it in the cloud to another cloud or back to on-premises. So we support all these environments with the same APIs and the same process that you will use in any kind of system overall. If you look at a NeoCloud, why would I use a NeoCloud? I’m using it for running my AI workloads. So I need access to GPUs. I need an AI stack to build my next generation AI application. And one of the things as I’m building my AI workload is I need access to vector databases and data that’s stored in a vector table. So what does NeoCloud need to offer? They need to offer choice to their customers to use from a variety of vector databases so the customer can pick and choose what they want.

If they want Postgres for vector capabilities or Mongo or a pure play like Pinecone or Milvis, NeoCloud should be able to offer that in the backend. Now, each one of these databases require lifecycle management. They need to be provisioned, they need to be scaled, backed up. It gives them recovery capabilities, patched, cloned, upgrade. So either NeoClouds have to build this on their own or they need to automate this using a platform like NDB where regardless of what database they run, they get all the capabilities and operations taken care of by a single platform. And that’s what NDB offers. So that’s why there’s a powerful combination of NeoCloud plus NDB. And I would say this is the reason Chronoscale has partnered with Nutanix is our entire stack that we offer for them to give them the ability to give them a modern platform for their end users along with all the data capabilities.

Expedient’s Anthony Jackman says cloud service providers can turn VMware, AI and cloud repatriation pressures into opportunity by selling managed outcomes, not one-off science projects.

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Podcast transcript:

Anthony Jackman: 2025 was viewed as like the year of the frozen deal that just never happened. The customer didn’t decide I’m not doing this, they just were waiting. I think what that gave everybody was a year to really think through what they wanted and to strategize. This year everything seems to have picked back up even in the face of the rapidly increasing costs. I do see customers finally making a change and I think that that’s going to continue.

Jason Lopez: Anthony Jackman is the Chief Innovation Officer of Expedient, an IT provider running 15 data centers in the U.S. This is the Tech Barometer Podcast. I’m Jason Lopez.

Anthony Jackman: It has been a very great year for us so far, but it’s also a very challenging time because a lot of change is happening.

Jason Lopez: For years, the question of how a ran its data center had one default answer, VMware. It integrated with everything and IT departments built their entire operating model around it. Jackman has watched that certainty unravel from the inside.

Anthony Jackman: The thing that was consistent is that we never had problems.

Jason Lopez: He’s talking about Nutanix, which he first brought into Expedient a decade ago, not as a replacement for VMware, but as a storage layer underneath it. The reliability record built over those years would matter later.

Anthony Jackman: We just never had that with Nutanix. That’s what gave us the confidence to move forward with AHV as a hypervisor and we did that a number of years ago and now it’s growing very fast. We do a lot of AHV private cloud at this point.

Jason Lopez: That growth has accelerated, not because Expedient went looking for it, but because the VMware world changed and clients came looking for them.

Anthony Jackman: I don’t know what I would compare it to because it was a lot of things at once. It was first and foremost a business challenge, but it was a business challenge that brought technical challenges along with it.

Jason Lopez: Jackman says the clients who came to Expedient weren’t necessarily angry at VMware’s technology. The technology, he says, is still good. What changed was how it gets sold and what customers are now required to buy.

Anthony Jackman: A lot of people saw what were perceived to be very large price increases. In reality, it wasn’t necessarily a price increase for a given thing. It was that we don’t sell that thing anymore. We sell this big package and at least within our client base, the bulk of them don’t use most of those features. Now, when they come run on our cloud, they do because we’ve baked all those things in to just work well.

Jason Lopez: That distinction between buying a package and consuming a service is central to how Expedient positions itself. It’s an ROI proposition.

Anthony Jackman: We’re a full service provider, so everything we do is very integrated. It’s the compute, backups, disaster recovery, networking, and security services, and then the human team that goes along with it so that our customers are buying an outcome and not buying a science project.

Jason Lopez: For clients who wanted to stay with VMware but needed a different operating model, Expedient offers that. For clients who wanted something else entirely, Expedient offers that too through its partnership with Nutanix.

Anthony Jackman: They’re the ones that come knocking on our door saying, I like the cloud operating model. However, I don’t want to stay with VMware. I want something else. Fortunately for us, we have a very strong partnership with Nutanix. We’re very good at it. We support the entire stack and we do it at large scale. We’ve been doing it for years. The confidence level is there for those clients.

Jason Lopez: But the decision between platforms is never simple. For most clients, Jackman says, what they’re really evaluating is how much change they can absorb and what capabilities they can’t live without.

Anthony Jackman: The truth is most of these clients have to learn something new regardless because some solutions just aren’t even possible anymore. For the ones that want to look at something else, we can offer them the integrated Nutanix platform, which is very easy to use. And they get the advantage of a lot of the newer capabilities with a relatively small uplift in how much they have to learn. And obviously, we’re there to help them. We are a full service provider. So the ones that really don’t want to learn much new, they can just rely on us. But I think most customers like to have a little bit of control over what’s happening in their environment.

Jason Lopez: That shift away from open integrations and closed ecosystems is pushing clients to reconsider the whole stack, and AI is pressing on that reconsideration even harder.

Anthony Jackman: I think one that’s kind of high up on the list is network security, which historically was difficult to do, required a lot of products, required a lot of thought. And that’s something that they could take advantage of in the Nutanix platform because it’s very integrated to do micro-segmentation and to get visualization and observability into what’s happening in your network. And security is just becoming more and more important. Every year, security becomes more important than it was before. And AI is definitely pressing on that even harder.

Jason Lopez: Meanwhile, Expedient itself has been rebuilding its own infrastructure for what’s coming. 15 data centers standardized to look identical from a network perspective and a shift in how Nutanix fits into the picture.

Anthony Jackman: It’s funny, we started with Nutanix as a storage platform. Now what we’re primarily driving is Nutanix as a compute platform using external storage. And it’s not because we don’t like the Nutanix storage, it’s great. But as a provider at our scale, hyper-converged storage can come with some challenges because we don’t operate 10 or 20 hosts, we operate thousands of hosts. So now we have a unified storage layer and I can buy one type of host and I can use it for VMware or Nutanix and we’re not moving things around racks.

Jason Lopez: And then there’s the edge. Manufacturing plants, remote facilities, anywhere that IT going down for five minutes could shut down a plant for a day.

Anthony Jackman: We have manufacturing clients that if IT goes down for five minutes, the plant could be down for a day or multiple days and cost them millions of dollars. So it’s very mission critical and it has to live in their four walls. So we basically package up an expedient private cloud in a small, resilient, rugged box, drop it in their data center. And to us, it looks like it’s in our four walls. So we manage it the same way we manage everything else. It just happens to be living somewhere else.

Jason Lopez: The year of uncertainty that froze so many decisions in 2025 has given way to something different. Jackman says clients who spent that year thinking are now moving.

Anthony Jackman: The customer didn’t decide I’m not doing this. They just were waiting. I think what that gave everybody was a year to really think through what they wanted and to strategize. And then this year, everything seems to have picked back up, even in the face of the rapidly increasing costs, specifically hardware. I do see customers finally making a change, and I think that that’s going to continue.

Jason Lopez: Anthony Jackman is the chief innovation officer at Expedient. This is the Tech Barometer podcast, a production of The Forecast by Nutanix. I’m Jason Lopez. For more stories on technology and the people in tech, visit theforecastbynutanix.com.

In this Tech Barometer podcast interview, Insight CTO Juan Orlandini describes IT modernization challenges hitting simultaneously with the rise of AI. This interview was recorded before CRN reported that Broadcom had deauthorized Insight to sell VMware licenses and Broadcom-funded VCF Entitlement Services in North America.

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Transcript:

Jason Lopez: This is the Tech Barometer podcast, I’m Jason Lopez. When thousands of VMware resellers were deauthorized  last year, many customers needed a new provider of VMWare licenses. One of those resellers was Insight Enterprises. It was reported by CRN in September of 2026 that Insight would no longer be authorized to resell VMware licenses in North America. But in an earlier interview prior to that CRN report we talked with Insight’s CTO for North America, Juan Orlandini, who says this is the kind disruption they are built to handle.

Juan Orlandini: These are all knobs, and every knob is connected by a piece of string. And if you turn one knob, the other knob goes in the direction that you probably didn’t want it to go. The job becomes, how do I adjust all these knobs that are linked together to find the optimal solution for each client? And that’s not always obvious or easy or even clear until you’re well into the process.

Jason Lopez: He mapped out the some of the big factors that make this so complex: dozen of decisions, tangled together, and none of them are happening on their own.

Juan Orlandini: We’re going through a number of inflection points simultaneously. It wouldn’t be a conversation without AI, obviously, so that’s a big one. The other one is this post-Broadcom world that we’re living in.

Jason Lopez: In the post-Broadcom world, how companies think about how they’re running most of their data centers is being rewritten. When Broadcom bought VMware in 2023, it didn’t just change who owned the company, it changed how the product got sold.

Juan Orlandini: The dynamics that Broadcom has introduced is that they want to bundle their products so that customers buy into more of the ecosystem that is included in the bundled package. Now, the challenge is that many customers either can’t or don’t want to do all of that deployment. They don’t want to deploy all of the capabilities that are included in VCF. It is a tall order. It’s a big shift. If they don’t, well, then the value proposition that Broadcom is bringing to the table goes out the window. It’s too much, and you have consternation. There’s also arguments to be made that this was played a little bit heavy-handed by Broadcom, so that’s caused some ill will. For some customers, it’s not a matter of technology. It’s just a matter of, like, hey, I’m going to find something else, flat out.

Jason Lopez: VCF is VMware Cloud Foundation, the bundle that Broadcom wants everyone to buy. But Orlandini’s read on this moment goes past pricing and packaging. He sees it as the end of an era when VMware simply didn’t have to compete for anyone’s business.

Juan Orlandini: Right or wrong, while we were living in the pre-Broadcom days, VMware dominated the market like almost nothing else has ever before. In a large sense, a lot of innovation had stagnated because there was no competition. We were literally at the behest of what VMware thought was going to be next. Well, now that there is a healthier market, innovation has really started to creep up, and we’re seeing some amazing technology and stuff that I don’t think we would have ever seen before coming out from people like Nutanix and some competitors that are really pushing operations, optimization, performance, and all these other dynamics in ways that are just extraordinary. In the long term, I think as an industry, we’re going to be in a much healthier spot. But until we get there, this is really, really painful.

Jason Lopez: Painful, yet Juan says it’s also forcing a conversation a lot of IT departments had been putting off about what a modern data center is actually supposed to look like.

Juan Orlandini: For many organizations that wanted to transition from running that highly automated IT infrastructure to a private cloud, there is a big operational change that needed to be done. And without a forcing function, there was no impetus to change that because of that inertia that was there. And now this broadcom change has actually created a forcing function that is actually allowing IT organizations to relook at how they’ve built their processes and their tools and the services that they provide, and then start reconsidering this to really build a true private cloud that happens to include virtual machines and containers as well.

Jason Lopez: That was two, maybe three years ago. He says the mood has shifted since.

Juan Orlandini: Right now, the angry exits, the knee-jerk reactions have largely started to disappear. We’re two, three years into this transition, and most people have already been through one refresh of their licensing model, at least, so they know what’s coming. So it’s not an angry decision anymore.

Jason Lopez: The exodus has not stopped, but it has moderated.

Juan Orlandini: You’re probably going to see a smaller amount of exits, but the ones that do are going to be very public. And you can do your own research. There’s tons of companies that are very large that have very publicly said, I’m out. But there’s also very large ones that have not said. That doesn’t make the news, but it’s there. It’s happened, If you go below the global 1,000, the number is probably going to be higher for exits because broadcom has very clearly said, hey, I’m really particularly interested in those really larger organizations. And for the smaller ones, it’s actually much harder for them to adopt all of the capabilities that are built into the broadcom platform. So for them, it makes more sense to exit into something alternative that is a little bit more bespoke or tailor suited for their environment. So you’re going to see a higher number for those.

Jason Lopez: Every one of those exits is a client sitting across the table from someone like Juan asking what comes next.

Juan Orlandini: For the customers that are choosing to exit from broadcom, the VMware technology stack, then for us, it’s really an opportunity. And what we work with our clients is to understand what are their workloads really doing? What are the kinds of capabilities that are needed in order to support those workloads?

Jason Lopez: And where is the best place to put those? And what those clients want from him is not a list.

Juan Orlandini: What customers are not looking for is a catalog of things to buy. They’re looking for an opinion. They’re looking for somebody to come in and understand their business, become a true partner to them and come with an informed opinion that says, hey, I have access to all of those technologies. But here’s my opinion as to which one actually fits you best. And it’s not an agnostic approach. It’s an informed opinionated approach that clients are looking for.

Jason Lopez: Juan Orlandini is the CTO for North America at Insight Enterprises, an Arizona-based IT firm providing services like cloud migration, cybersecurity and digital transformation. This is the Tech Barometer podcast, I’m Jason Lopez. Tech Barometer is a production of The Forecast. For more podcasts and stories on technology and also the people in tech, you can find them at theforecastbynutanix.com. That’s all one word, theforecastbynutanix.com.

Healthcare IT veteran Benjamin Urquhart says AI can transform patient care, but only if it fits clinician workflows and tames shadow AI before it scales.

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Video transcript:

Benjamin Urquhart: AI, if done and implemented correctly, and I believe the healthcare environment, it has to be done thoughtfully. It can be amazing. It can be. I don’t want to get on the hype train and go, “It’s going to solve everything,” because we’ve had those solutions in the past. It’s going to solve everything and it never does. It might come close, but it’s going to miss some pieces that we just don’t realize. And AI has rapidly evolved and it’s been adopted at such a rate that in the 18 years I’ve been in the industry, I don’t think I’ve seen anything like it. In a sense, it’s scary. Because of that in healthcare, all the regulational pieces we have and understanding how our client data is kept, where it’s kept, what can access it, east-west traffic right inside with an AI agent can be extremely scary, very vulnerable in that aspect.

And so when we’re looking at it from AI and healthcare, in my mind, the biggest thing becomes doing it thoughtfully and then doing it in a way that works within the end user’s workflow already. We give them all these things all the time. We tool them to death, right? I’m sure you’ve heard that phrase. And tools and solutions are not always the way to do it. You can look at their best thing about AI and healthcare, I think we can get rid of a lot of the administrative burden. We can utilize it within their workflows to remove some charting capabilities, do the reporting, take care of these things, but then at the same time, also use it to help analyze that data that’s coming into these EMRs and everything like that to where, because it is complex data. Health data is much more complex than I think everyone doesn’t always understand.

But we can use AI to better analyze that, giving us better outcomes at the end of the day for our patient care. Because if we’re using it in a thoughtful manner to go, we’re going to remove some administrative burden and we’re also going to utilize AI to help assist you diagnose a client or work through in determination, well, that provider themselves now has a better understanding of what’s going on. They’re able to spend more time with the client, which gives us the outcome we want and that is helping people. That’s the way I view AI in our world right now in healthcare. It’s got to be done in their workflow. It has to be done very thoughfully.

Ken Kaplan: With the foundation that you’ve built and are growing, you could take on new capabilities.

Benjamin Urquhart: Yeah. Anytime companies grow or you have to evolve, it’s always kind of a mental check aspect and you kind of go, “Are we really going all the way in? Are we only going halfway in?” But being afraid of the technology as it’s evolved, if you are, we might want to look elsewhere. And I don’t mean to be mean or so blunt about that, but because tech, my dad, I’ve grown up around tech. My dad was a mainframe programmer in the banking industry when I was a kid. Going to the Saster test at midnight when I was like seven or eight, no idea what’s going on.

And now it’s great. The tech has evolved my entire life and everything. One of the things though too, people have evolved and our end users today, they grew up with smartphones. We maybe didn’t, but they did. And so they, for an end user and a lot of this younger generation I feel that are coming in, we need to change maybe how we communicate with them a little bit. It’s not just a technical solution to them. We need to prove that in value to their life because that’s the tech that they’ve had their whole life. It added a value. Our legacy work environment does not really add a value. We add burden, just the way that systems are designed. So adjusting that and making them work within that is an evolutional piece I think from just being in tech in general. We just have to kind of adapt how we’re explaining, how we’re talking and communicating more so than just about anything else.

Ken Kaplan: How comfortable are you and your team to say, “This is the year that we’re going to do these three things with AI.” How do you feel you’re able to move into the future?

Benjamin Urquhart: I feel cautious about it. It’s one of the things that I know we’re going to have to do. Every company is doing it. We have shadow AI now. They were talking about that, one of the keynotes today. And in the past we had shadow IT where people were just. Now we have agents where they’re going out and they’re, “Oh, hey, because we didn’t have something maybe locked down.” Well, now there’s this agent that no one knows about. So I think when we look at having to do AI, we’re going to have to do it. I think it’s the way of the world. I think if, like we said earlier, thoughtfully done, it can make massive improvements. But at the same time, we need to ensure that’s structured correctly for being in your industry, whether it’s healthcare, manufacturing, whatever, you structure it right before you just say, “Hey, we’re doing all of this.” Standardization.

You might have someone who goes, “I really like Claude. I’ve got someone over here, ChatGPT, someone who says they don’t like Copilot, but maybe Copilot’s part of your 365 ecosystem. Why would you come over here unless there’s an obvious reason to do so?” So right now it’s really more around trying to figure out in a strategic aspect, look at where we are, look at what fits our ecosphere within our organization and making those decisions on how to move forward and where to implement first.

Related:

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In a video interview, Five Horizons CTO Benjamin Urquhart explains how hyperconverged infrastructure let a nonprofit healthcare provider scale from zero servers to over 50 virtual machines.

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Video transcript:

Benjamin Urquhart: Nutanix for us was honestly a little bit of an easy call. Organizationally, we had no infrastructure in place as far as server backends, everything like that. Being in the nonprofit sector where we are, we’re a specialty healthcare provider, so we have a lot of grant limitations on how we can spend our resources. Because of that, we can’t really go, let’s go throw them in the cloud, let’s do this, let’s do that. We had to really look at it from a strategical aspect and go, well, what’s my ROI coming back in on this? What type of solutions are we trying to do that’s still going to allow us to be scalable and grow? We’ve opened up four new facilities in the last 18 months between Mississippi and Alabama right now. So being able to have something in house with Nutanix for us was great. It was HCI platform, something I’d never done, but let’s go.

It gives us our hardware, it gives us a hypervisor. It’s different, but let’s give it a shot. Licensing became much more easier or simplified. There we go. Nutanix allowed us to go from really not having an internal infrastructure to running over 53 VMs right now internally from different internal resources, whether they’re part of our research environment or our true day-to-day activity. So it’s been a great marriage from that aspect, I would say, like partnership. It’s allowed us to grow and be scalable, but also secure and understanding where our data is and how it lives within our ecosystem.

Ken Kaplan: From that time that you’ve got things going to the 57 VMs out there, what was that time span and how did things change?

Benjamin Urquhart: So it was a very unique instance actually. I was working for another organization very similar to five Horizons Self-Services and that particular organization was failing. Five Horizons and their board and our CEO, they came in. We had over 3000 clients in our coverage area for that particular organization. They bought all of our assets, everything like that. We retained certain staff members and we didn’t close the doors once in the fall of 2022. So from that angle, it was great because we have this infrastructure here that we had already started building with this company. Now we’re going to Five Horizons because they’ve acquired our assets and being able to keep the doors open for patient care.

It was almost like having where the starting line was on your infrastructure instead of completely starting over again like I had the three years before that. Oh hey guys, we’re halfway there. We have the cores. We’re changing domains. We were changing simplified things. So for us it’s just allowed that ability. The biggest push because we already had it was now we’re at the point where we’re looking at additional blocks and clusters because we continue to expand. Not necessarily a bad thing, but it allows us that ability to keep everything in a manner that we know we can do. We have a very lean team that really have to support not just our Nutanix environment, but our backup environment and everything else on top of it. And Nutanix has really simplified that. I mean they can spin up a new VM now in about 15 minutes from them, which is fantastic. There’s a lot less time and delay because of how HP operates.

Ken Kaplan: Now you’re like a veteran almost with HCI and what have you learned and what do you want to try next?

Benjamin Urquhart: So some of those things are coming from here right now this week and some of that is kind of going more into the VDI world maybe on top of the Nutanix platform running into HV, doing some more virtualizational pieces around our security and compliance that would help dealing with the industry we’re in. Looking at things like that, the containerization aspect, allowing us to spin up more things from an application aspect and having a better use case of our hardware equipment or allocation of whether we’re talking storage or our RAM compute, allowing us to trim line those into things like that is really, I guess evolutionary. It’s like we build an infrastructure to get our foundation and make sure it’s scalable. We build for today and tomorrow where we’re going, make sure it’s a foundational piece. Well now you also have to look at it and go, I have this foundation.

I need to kind of break it up though and let’s evolve it. Let’s allow that foundation to evolve and now we’re just building buildings out of our infrastructure stacks.

Related: 

In this video interview, Liqid Founder and CTO Sumit Puri argues the path to affordable inference runs through composable AI infrastructure that dynamically taps into pools of GPUs, CPUs and DRAM while optimizing usage to manage costs. He says Liqid allows a single server to access a pool of 30 AMD GPUs to meet performance-hungy AI needs.

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Video transcript:

Sumit Puri, Founder & CTO, Liqid: Since we spoke to you guys last, one of the major trends that we’re seeing is the enterprises are finally past the initial exploration phase. Now they’re in the adoption and deployment phase of their journey of AI. And I think a lot of them are trying to figure out how exactly they are going to do this journey. And the first crossroads that they’re up against is, am I going to take all of my AI capability and move it into the cloud? Or am I going to own the infrastructure on-prem or in a colo that’s required to do AI? And what it all comes down to is tokenomics. At the end of the day, these companies have a limited amount of dollars and a limited amount of power in many cases that they can deploy. And what they’re trying to figure out is how do I get the most amount of tokens out of that very, very scarce resource?

And so those are the discussions that customers are having now. And the cost of these tokens is front in mind for them. If we notice what’s happening in the industry, large organizations are now saying, “Hey, we’re going to limit the amount of tokens that you as an engineer inside of our organization can consume because the cost of that is becoming very high.” And so the one great way to address that increasing cost is to own your own token machine. And so that’s one of the conversations that we are having with our customers is one way to reduce the cost of those tokens is bring the infrastructure, bring the GPUs on-prem or in a colo so it’s a one-time expense and you can consume all the tokens that you want out of that investment that you make. It is about the model that you are looking to deploy and you’re going to, in any environment, you’re going to have a variety of models.

[Related: Rise of AI Agents Forges IT Industry Partnerships]

You’re going to have big models, you’re going to have small models, you’re going to have medium sized models. The model actually, the size of the model will dictate the type of infrastructure that you need. If I’m running a very, very large model, I’m going to need a large quantity of GPUs in order to run that model. And so you have to figure out if I’m going to build a very big system, how do I get those large quantity of GPUs into play? One way to do it is independent scaling of resources. We come in and let customers say, “I don’t want to scale my compute as I’m scaling my GPUs. Allow me to just scale GPUs.” As an example, today we’re at the AMD show. The announcement that we’re making today is our ability to take a single server and scale up to 30 AMD GPUs to that single machine.

And the benefit of that is we can now start to run these very large frontier models on a single server infrastructure. These models are so big. Previously you had to run them in a cluster. Now we come in and say we can take those models that were previously clustered and consolidate them down to a single server, a single instance, back to that tokenomic story. That’s how we reduce the cost of deploying these models. Models will change over time. And so some cases you might want a very small model that will require, let’s say, a single GPU. Sometimes you will want a medium sized model that will require eight. And sometimes you will require a massively large frontier model, which will require dozens. You as a customer, it’s impossible for you to know ahead of time what size model I will need when. And so our vision is let’s take the guesswork out of that.

Let’s have a pool of servers, a pool of GPUs, and dynamically spin up GPU quantity to server based upon models. And the reality is the way that’ll be done is something called Kubernetes orchestration will be the way that they do it. Nutanix has a phenomenal way of going off and doing that through their NKP platform. We’re a big believer in NKP. And so we integrate closely with NKP. And so we say, let’s allow people to deploy these models, the variety of models that are supported with the perfect set of infrastructure so we can have this matching of infrastructure to model to not have any wasted resources, improve the tokenomics. In a resource constrained environment, utilization is critical. You cannot afford to have your GPUs at 30, 40% utilization because you can’t get enough. And so if we can implement technologies that drive utilization to 100%, we can do more with the same amount of infrastructure.

[Related: Demand Shifts to CPUs to Power Agentic AI]

So that becomes critically important and we do a lot of that today. We look at an environment and we say, “Hey, listen, this workload is only using those GPUs 30% of the time. Let’s set up policies that allow you to move those GPUs to different parts of the infrastructure to raise the utilization.” The second thing is Brownfield. We support brownfield environments. So imagine an environment where I can’t buy servers because memory’s limited, CPUs are limited, servers are very expensive. We come in with our solution, which is disaggregated pools of GPUs, and we attach them to existing infrastructure. And so now we’re giving new life, new capability to the infrastructure that customers already own. As inference becomes a thing in places like the enterprise, it will only be done in containerized environments. NVIDIA has created something called NIMS, NVIDIA Inference Microservices. AMD has created something called AIMS, AMD Inference Microservices.

And the reason is both of these companies realize the complexity related to kernel revisions, operating system revisions, library revisions is too complex for average enterprise customers to debug and solve, and it slows the rate of AI deployment. So if we can containerize all of these models, put them into a store, allow the enterprise customer to bring whatever model he wants, a vision model, a speech model, a text model, bring it down in whatever size of model that they need, 7B, 70B, 405B, and make all of these available containers that the customer can deploy and implement immediately, that’s how we’re going to accelerate the pace of deploying AI. The way that we approach that is we have a plugin for Kubernetes that when you say, “Hey, give me Llama 7B as an example,” we’ll take the container, we’ll bring it down, we’ll crack open the container, we’ll determine the exact amount of physical resources that that container needs.

Well, this needs two GPUs and we want to put it on server number four. We’ll pause that container, we’ll go into the background infrastructure, we’ll put two GPUs on server number four, and then we’ll take that container and we’ll launch it onto that server. We automate the entire process of deploying models down to a two minute instance. So you say, “Give me Llama 7B.” Two minutes later, you’re speaking to a chatbot with the exact amount of hardware resources required for that instance. And the moment that that server doesn’t need that GPU anymore, we can delete that instance, remove those GPUs, put them back into a free pool so your next model that comes along always has a pool of GPUs to draw from. Liquid started, the way we started was around GPU pooling. That was the first thing that we went off and we accelerated and we composed and we pooled and we shared.

Now the next thing that we have just announced is memory pooling. So we are the first company to provide an end-to-end solution around DRAM pooling built around CXL. So now I can have my pools of GPUs and now I can have my pool of DRAM. The two most expensive things in the data center can now be dynamically allocated by workload. The days of over-provisioning your server with excess memory, those days are done now. And so we must find methods to precisely provision the exact amount of memory the application needs so we can reduce our memory costs inside the data center. And by doing that, it’s back to that tokenomics. Yeah. Well, the company is surely in its growth phase right now. I’m a technologist at heart. I’m super lucky to have built out an amazing team. We’re very lucky to have a new CEO on the team.

A gentleman named Rick Hagberg, who’s just an industry veteran who’s been phenomenal. We’re raising additional capital. We’re growing the team. We’re bringing new executives on board. We are having our moment right now and we’re super excited and blessed to be part of this industry. The way I feel about it is when we go around and we tell customers about our vision, which is stop putting static resources inside of a server. Let’s take all of the GPUs, pull them inside of the rack, add a push of a button, dynamically allocate any GPU to any server that you want. You won’t lose any performance. You’ll scale up as big as you want. We’re a driverless solution. We can drive to 100% utilization. When we tell this story, no one ever says, “Well, that’s a terrible idea.” That’s not the reaction that we get, right? The reaction we get is, “Wow, this is incredible. This is obvious. Obvious is the one that we get more than anything else. This is obviously the right answer of how this should be done. Why isn’t everybody doing it this way?” And me, that’s my mission as the founder here is to make sure eventually everybody is doing it this way.