In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
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Kyle Leciejewski, Dell
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
play_circle_outlineTransforming AI Infrastructure: Dell's Growth in Cloud Environments and Budget Reassessments at New York Stock Exchange CUBE Studios
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play_circle_outlineCustomers seek integrated, turnkey solutions within complex AI factory architectures.
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play_circle_outlineRapid advancements in AI technology are necessitating evolving infrastructure and modernization efforts.
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play_circle_outlineAI initiatives should focus on key use cases tied to ROI for the enterprise.
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play_circle_outlineDell's holistic approach integrates storage, compute, networking, and AI for optimal customer outcomes.
In this segment from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future, Kyle Leciejewski, senior vice president for North America at Dell, joins theCUBE’s John Furrier at the New York Stock Exchange CUBE Studio to unpack how AI factories are reshaping enterprise infrastructure. Leciejewski explains why the “public vs. private cloud” debate is over and why hybrid models anchored in on-prem data centers and the edge are winning, with 83% of data still on-prem and roughly half of new data created in the real world. He breaks down Dell’s AI factory ...Read more
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What is the current state and growth of AI infrastructure in enterprises and the market, particularly in relation to investment and activity from hyperscalers?add
What are customers looking for in terms of integrating intricate systems, and how does Dell's AI factory address these needs?add
What are the dynamics influencing the deployment of data centers and computing at the edge?add
How is ROI connected to the implementation of use cases in an enterprise?add
What is the significance of the current trends in AI and system integration for the future of data storage and computing?add
>> Hello. I'm John Furrier, host of theCUBE here at our New York Stock Exchange CUBE Studios. This is theCUBE's features on AI factories bringing the next generation of computing, enabling AI. It's really the growth curve in the enterprise and certainly in the clouds and hyperscalers, seeing a lot of activity on the AI infrastructure. Kyle Leciejewski's here, Senior Vice President for North America at Dell. Kyle, great to see you. We're just talking before on camera how the market has changed so much in AI infrastructure, but it's kind of the game it's still the same.
Kyle Leciejewski
>> Yeah.
John Furrier
>> You've got private clouds, you've got public cloud, and the on-prem activity. If you look at all the activity on, even in the news cycle today, it's growth in the data center. CapEx numbers are off the charts on these AI factories, seeing the hyperscalers. They're pouring money in this. Even talk about nuclear, clean nuclear, so clearly we were in a Cambrian explosion of a new infrastructure build out. Okay, that's going to be great, but the enterprises are there too. So you've got the enterprises now retooling. They might not have the hundreds of billions of dollars of CapEx, but they do have budgets and so they're really tooling up. I think they're ready. It's been a year of assessments. You guys have done really well. I saw the server numbers certainly playing well in that AI factory market. Obviously the relationship with NVIDIA, you guys have these systems. Give us the update on the innovation that you're seeing right now with customers because the startup market's blowing up great. You got open source, these new AI stacks are emerging. You guys are powering a lot of this. What's the current state of customer?
Kyle Leciejewski
>> Yeah, it's awesome. Well, first off, it's amazing to be here. I was mentioning to you earlier, I've been a fan since 2010 when we started theCUBE. It's so great to be on it and be on it with you. And to be on it like the most iconic setting here at the New York Stock Exchange. So honor to be here and thank you for the opportunity. Look, I think one of the things that we're seeing is this debate of public or private is over its hybrid. We're seeing a lot of these workloads deployed on-prem. When you look at the data sphere, 83% of the world's data is in an on-prem data center today, and 50% of the new data that's getting created is getting created out at the edge in the real world. I think you layer in some of the sovereign private security elements of this, which the AI models that are most effective are the ones that are being powered by the company's most unique and proprietary data and the desire to keep that secure and sovereign and in a more controlled settings, probably a second interesting driver there. You layer in some of the performance and latency things, the decision making in real time out at the edge. Again, probably another layer there. And then finally look at some of just the financial hardcore TCO-ROI type numbers. The total cost of ownership is typically between 70 to 75% more cost-effective to run it on-prem. So yes, we are seeing this Cambridge explosion. Yes, we are participating in a large meaningful way. We really feel like this is our moment, right? And infrastructure is cool again as we start to drive this amazing adoption of AI.
John Furrier
>> Jeff Clark, I think four years ago we had this hardware is cool kind of wave hitting and he's like, "Yes, it's cool." At that time, everyone was talking solution, solution, solutions. It was the speeds and feeds got leveled up and it was mature. Now we're in a whole other step function, change of speeds and feeds call. If you look at the chip evolution, there's still debates on how people are going to reuse it and we're seeing the software stacks evolve. It's not like yesterday's buy some servers rack and stack them, load your workloads, that was IT 1.0. Now you have IT in every aspect, AIs infused in all applications, all verticals, and so you're starting to see that. That changes the hardware. I mean, we're talking about memory now like HBM, I remember in the '90s we talked about memory, so the PC. So we're seeing this new system, AI factory conversation. What are your customers telling you? Because you guys are in all the big accounts, Dell has the number one share in these large enterprises. You're servicing the customers. What are they telling you? How are you engaging with them and what are their core things that they need?
Kyle Leciejewski
>> Yeah, so first it starts with, it really takes a portfolio to make these successful. Certainly origins are in the compute conversation, both air and liquid cooled, and you look at X86 and ARM-based architectures, but then you very quickly move into a networking conversation. Is it InfiniBand or is it Ethernet? Is it this variety or that? Then you quickly get into, "How do I feed those hungry GPUs?" Which is a storage conversation, file object, unstructured storage contexts, and then you get into the thermal conversation into the cabling and how you rack these things. So customers are looking for the easy button. They're looking for the way to integrate these intricate systems in a turnkey way. That is the AI factory from Dell. We have a leading position in every one of those categories. Our ability to put those together for customers is step one. Then the second thing is like, "How do I deliver those outcomes predictably?" It starts with the predictability of supply and the relationship in the supply chain. That relationship goes back to 41 years when Michael founded the company, the 38 years that Jeff has been here, those relationships are deep. Then as you get the supply, you got to put it together. We're learning amazing engineering lessons as we deploy 200 racks a day for some of the largest deployments or we're going from our shipping dock to production in 36 hours. Those lessons learned in the engineering are getting packaged and then scaled as we get to the next customer and the next customer, and then ultimately they want the stuff up and running. So it's time to first token, and then it's the reliability and the resiliency of the infrastructure once it's up and running. So it's portfolio plus the way that we deliver it. That's what customers are looking for. I think that's the gap we're filling, and I think that's fueling a lot of our success in this part of the market.
John Furrier
>> Yeah. And the relationships you guys had with NVIDIA and also the chip, all that chip experience. It's funny, you guys have made some great bets and I talked to both Michael Dell and Jeff Clark about this as some videos. I'll find the videos, but I'll start with Jeff Clark first. He said, I think it was last year or the year before, the bet they made on some of the engineering decisions was significantly a great bet that's paying off. You mentioned some of the engineering marvels going on at Dell liquid cooling, others. And Michael go back six years ago. He was the first CEO to talk end-to-end. Remember the whole end-to-end. He really talked about the edge too. So those are two bets that I think are going to pay out. I think the one that's not talked about much is the end-to-end piece. I think that's going to come with the hybrid. So I want to get your thoughts on one, the speeds and fees, the engineering bets, you mentioned some of that, and then holistically, the end-to-end now is its core-cloud-edge.
Kyle Leciejewski
>> Yes.
John Furrier
>> Because the edge is going to transform once the factories get built, you're going to have users, devices, phones collapsing in. So I think the end-to-end's going to shine probably in about a year more heavily. But talk about those two dynamics.
Kyle Leciejewski
>> Yeah, look, I think it starts with some of these large-scale data center deployments, but then I think what's interesting is that I had mentioned that the majority of the world's data is not going to get created inside a data center or inside a cloud. It's created out in the real world, that is the edge, right? And it's a lot easier to bring compute to the data than it is to move the data back into some centralized compute. So if you start to think about Dell's portfolio and some of the elements here, this extends beyond the core data center. It started a couple years ago with the AI-enabled PC and moving small language models out to the edge. You then heard us announce the GB10, which is this little desktop NVIDIA-enabled GPU box to begin to run even larger models out at the edge.
John Furrier
>> How big is this? Like this big?
Kyle Leciejewski
>> It's like about the size of that cup and you're running multi-multi-billion parameter models out at the edge, and then-
John Furrier
>> Jensen calls that the AI factory in the hand.
Kyle Leciejewski
>> I love it. I love it.
John Furrier
>> He's holding an AI factory. I mean basically it's a small box.
Kyle Leciejewski
>> Oh, no, no, no, no, it's exactly right. And then you start to think about what it takes to operate. Supply chain becomes very important because I've got a very broad distributed elements, the breadth of the portfolio, whether it's server-based or PC-based or gateway-based technology. So the breadth of the portfolio becomes important. A global services organization to service and support tens of thousands, potentially millions of devices out at the edge becomes important. These are all core competencies for Dell, how you supply, how you deliver, how you service a broad distributed network out to the edge. AI is going there. Our portfolio is shifting there, and then we're doing the software investments with some technologies around NativeEdge and some of our other technologies to automate lifecycle patch and make that a more streamlined operating environment out at the edge. We love the data center opportunity. We love the AI enable PC and we love the explosion of data out at the edge. We think that's going to be the next interesting turn of the crank here as the innovation continues.
John Furrier
>> It's just in theCUBE research guys, Dave Vellante pointed this out too. The growth of the data center is not at the expense of the cloud, because the cloud's still growing. So I think there's a rising tide going on across those cloud, core edge, but the data center's where the crown jewels are. So a lot of the on-premise activity will be because their data's there, they're scaled to the cloud, and then with the Neoclouds coming on board, you're one API away from getting GPUs, so the consumption model changes. So I know you guys power a lot of the Neoclouds as well. Does that tie into the enterprise at all? Do you have a view on that?
Kyle Leciejewski
>> Yeah, I think certainly those are some of the most demanding engineering environments and you learn a lot at the scale delivering the outcomes to the Neoclouds. I think you've then package a lot of those learnings to activate the enterprise. We've talked pretty publicly about 3,000 plus enterprise customers and growing. I think as you work through the enterprise, it really starts with having that conversation earlier in the value chain than we once were. It's like, "What makes you unique as an enterprise?" That's a good place to start with AI 800 use cases is probably not the right approach, three or four, critical core competency use cases make a lot of sense. How are you tying ROI to the use case?
John Furrier
>> Yeah.
Kyle Leciejewski
>> Because ROI is how you get back to the board. It's how you create the flywheel for the next project and the next project in the enterprise, and then you start to see some emerging solutions or outcomes in the enterprise that are portable, right? You see in a lot of these engineering examples, code assistants and you say, "Okay, we believe there's probably a packaged outcome, a solution architecture for a code assistant." You look in some of the marketing and content creation and you look at some of the outcomes created there, like Dell's using a lot of these internally. 42% of our code is being written and driven by an AI code assistant. If I think about content creation, we're deploying this in sales and go-to-market where we're saving our sellers three, four, or five hours a week by curating content via a chatbot we call Dell Sales Chat to drive the outcomes for content creation. And then lastly, you see across industry in services, like we've got 250 million assets at a dial-in home to Dell. You're trying to identify what is the next best action to create a better customer service experience. So those are some of the ones we've deployed, but we see that echoing across the enterprise, those use cases tied to the ROI, create the flywheel to activate the enterprise. That's some of the early observations there.
John Furrier
>> So you clearly have the motion on hitting that low-hanging fruit, get success, build on it. Great. Most you guys have been successful with that. We've documented that too on theCUBE. The question I want to ask you, I'm a sales rep and I work for you, and you're my sales manager, actually you're the big executive, but to say I'm a sales rep. "Oh, I got this new account. I got the banking account." What's the motion? Because one is, "'Hey, here's how Dell we're in the account. We have relates, let's talk about these use cases, these workloads." Then there's the executive kind of the future-proofing motion. Take me through the day in the life of that relationship because there's two things that are going on. You're servicing these accounts, you're building that business, getting new business, but what's that conversation with the leader, the relationship conversation? Is it transformation? Is it how fast can you stand up AI, and then what's the engagement look like? Take me through that.
Kyle Leciejewski
>> Well, first off, I think our customers are looking for fewer but more impactful strategic partners and we're usually engaged in the account on many fronts, right? We're working with them on their commercial PC, display and peripheral project in the modern workplace, or we're working with them in the traditional sense as they modernize their multi-cloud data center and they move to multi-hypervisor and multi-container. And they have a desire to build a disaggregated architecture of server and storage in the VM and container space. Or you're working with them in cybersecurity or data protection to build a mutable, intelligent, and isolated cyber recovery stance. Now, this is a very natural evolution to have a conversation about how we're going to transform their business and be a meaningful participant in helping them form that strategy, identify those use cases, get through the data preparation, optimize the process, pick the tooling, and then deploy the infrastructure. And then I think inevitably you're going to start to see the more traditional infrastructure morph into this accelerated computing infrastructure. And us helping customers build that bridge in the enterprise where we're already well ingrained across a very broad portfolio is typically how those engagements will flow and why I think we've got a really unique advantage to helping customers through that journey.
John Furrier
>> I mean, Dell's been the supplier. You have business, so you're in there. So the question is, "Okay, what's that next conversation?" So okay, here's the question for you on the AI factory. So in all of our conversations on this program and other CUBE interviews, it's always been cost savings is one driver, but majority of people are now looking at it from a revenue.
Kyle Leciejewski
>> Yeah.
John Furrier
>> Actually conversation has shifted in the past year to this is going to drive revenue, okay? So there's now a revenue. This is for your customer, your customer's customer, they want more revenue. So now the frame, you have some TCO in there, payback, all the normal stuff that you have to do when you purchase computing, accelerated computing. But what's your feeling on that and what is that like for you and Dell right now? Because if that's true, they're going to want to see revenue.
Kyle Leciejewski
>> Yeah. It's funny you used to always frame this up as you're going to help a customer, they make money, save money, or avoid risk. Right now, the make money part of this equation is much more front and center that maybe it has been.
John Furrier
>> You're seeing that too.
Kyle Leciejewski
>> Those opportunities are how they drive more revenue, more top line, and more productivity. So it's also not just the dollars and the cents, there's a bunch of human impact to this stuff too. I think about one of our examples with Northwestern where we're enabling radiologists to do a higher quality digital read at a higher volume and velocity with better quality outcomes. That is a great quality of care outcome. I think about a public sector example like in City of Amarillo where we built a digital human. That digital human speaks 50 some odd languages. By the way they speak 50 some odd languages in the county. When citizens are coming to the city to identify Evacuation routes and how they get emergency response and how they identify need, that's a impact on citizen services. So whether it's in the revenue driving in the private sector or whether it's the improving the quality of care in the healthcare sector or whether it's a citizen service outcome in public sector, AI is touching all of these elements and I think understanding what problem you're trying to solve is the first step in the process. And then sequencing the technology, the skill, the capability, and the process work required to make it successful is what naturally states behind it.
John Furrier
>> On the customer side, what's your advice for an AI initiatives? Is there a common pattern you're seeing? Some clients, we've talked to some of your customers and John Rosen, I talked about this as well about some having AI councils and a=AI czar and AI leader. Some take different approaches. Obviously there's groups getting forming together. Is there a playbook for rolling out this accelerated computing infrastructure, the new kind of AI factor?
Kyle Leciejewski
>> Yeah, look, I think this is a forming space. I think people are building their org structures and decision processes to be most optimized here. I think we've learned a lot of things that probably aren't as effective, right? Grabbing 800 projects and trying to make them work tougher, finding your core competencies, applying AI there first, and then getting to the rest of the use cases later, probably a better approach. Finding a single clearinghouse for like, what are we going to do? How are we going to deploy the resources? What is the ROI? And then being able to come back and say, "We delivered on that ROI," is probably another interesting best practice that's emerging. Whether you do that through a chief AI officer or a council of executives in the company is probably one. What I will say is this is the worst AI is ever going to be. You think about it only gets better every week that goes by. You look at a hundred X increase in tokens over the next couple years. You think of the data sphere today is 138 zettabytes growing to 538 zettabytes, zettabyte being a million petabytes. The data growth is going to be exponential. But I think the one maybe thing here is we got to demystify this a little bit for the enterprise. We don't need to build planetary scale data centers to achieve this outcome. In those examples, I was using a Dell, we're doing this with dozens of GPUs, not tens of thousands. We are doing this with air cooled systems, not advanced liquid cooled systems, and we are doing this in our existing data centers. Part of this is getting started, right? It's a bias to action, and I think the ones that move first with those use cases in ROI in mind that probably recognize the barrier to entry is not as tall as maybe you once thought are the ones that'll create the competitive advantage. And you look two, four, eight years from today, they'll be the ones that are major disruptors in their industry. And creating the crisis a little bit within the company to compel action is another interesting trend of how do we get moving faster because this is a major, major disruptive wave.
John Furrier
>> Kyle, that's a great point. Don't stand still. And your point about the GPUs not having a lot of GPUs is interesting because the successful AI factory folks that we see have the similar pattern of they're using GPUs and some systems to accelerate, get that advanced functionality, the tokens and leveraging the tokens, first token out, and then reasoning and as the third scaling laws think deep thinking, but also the leveraging existing scale of compute. And so you have the famous comment is, "I don't need ChatGPT to tell me what one plus one is," but you're using a GPU for that if you're going to use open AI. So you might not have to your point about getting the wins, so one, get some wins, get the muscle, and then agentic systems are now ramping up. They're not going to get worse either, by the way. So the course it's ever going to be now, same with agents. So agents is where the rubber meets the road. What's your thoughts on that? Because that's also the fast path from experimenting, laying the workflows out. Now I got agents that play that gets, again, known workloads, don't try to boil the ocean over, pick something you can win on, it could be a use case that drives revenue.
Kyle Leciejewski
>> That's right. And agent, multi-agent, multi-agent, multi-modal, I mean these models continue to get better and better and better. I think one of the interesting connection points that we talked about just a moment ago too is like customers are also figuring out how they modernize their traditional infrastructure to make room for the AI work they're going to need to go do. So you think about taking traditional core power edge and modernizing to 17G, right? You get a four or five, six to one consolidation ratio in your traditional environment. You move to a modern underlying storage subsystem and it delivers a five to one more efficient footprint and data reduction. That then frees up power, cooling, floor space, budget to go make the investments in AI. And that's a little bit of that Dell is positioned in a couple of these places helping customers to lead through those pivots and shifts and another way that I think we're working with customers to ready what's next and next and next in the AI pivots.
John Furrier
>> Yeah, when Michael was here inside the studio, Michael was sitting in that chair too. He was here for the investor meeting. He laid out what I thought was pretty brilliant, I mean he always gets the vision right in terms of, "Okay, we got AI, agentic, and then physical AI." And you look at NVIDIA's roadmap, I mean the three power loss scaling laws that Jensen laid out at GTC in Washington is right in line with what you guys are doing. You got the power scale, you got the servers. A customer can take Dell's stuff today and get new stuff with the GPU factories and factories and get going and grow the functionality, not figure out how to just, "Ship me a factory. What's loaded on it?" It's not that easy.
Kyle Leciejewski
>> No, and I think this is one of those things where we're not standing still, right? We're pumping three plus billion dollars into the R&D machine, right? There was almost 2,000 patents that were granted last year. It ranked us as the 15th largest patent holder last year. We're trying to continue to push the envelope on these things. So you had mentioned power scale, and we're going with parallel file system, high performance object, the interconnects on front-end, InfiniBand, the next version of the NVIDIA chip, the liquid cooling integration, the L11 and L12 rack integration capability. These are all the things that you can't stand still. Every version you got to get better and faster and innovate further, because the customers are going to continue to push and challenge you to the next and the next and the next. And that's what's been really fun in this is every couple weeks, every couple months, you're working on a new, exciting, fun challenge to move this industry forward.
John Furrier
>> Well, you guys, your core competency of, I've been watching Dell since the beginning when Michael started. Obviously he's my age and followed his career, bought Dell Systems, but you guys always been involved in storage, compute, and networking, and that's the holy trinity as we say. But now add in databases that are now diverse data sources. So if you got storage, compute, and networking, now you got GPU, compute XPU, and data is now that third element. That's what changes, in our opinion, changes everything. You guys have laid that out. EHAB's been on theCUBE, he's leading the effort there. John Rose laid it out. You guys have been early on this. Do you feel that, I won't say too early, but what's your feeling on the progression from being a good bet, which I think is a good bet by Dell, and you're seeing results? How do you feel you guys are now on that piece?
Kyle Leciejewski
>> Yeah, look, I think what you alluded to there is certainly, again, the data is what's fueling all of the AI outcomes. If you look at our heritage from a storage standpoint, from a market share position, you're bigger than two, three, and four combined. We've got a right to plan, a right to win in this space. And I think the innovation portfolio, whether it's on parallel file system or high performance object or the AI data platform, which takes the storage substrates and the data substrates. And are working to build that as an integrated outcome for customers are how you continue to elevate above the infrastructure layer, and then you got a huge partner ecosystem that participates with you to drive the outcomes.
John Furrier
>> I was joking with Michael Dell. I end on this note. Get your reaction. I was saying, "Michael, AI factory it's like the days when you design the first PC, it's like a PC. You got a bus, you got storage, subsystem. It's just one big monster computer." We kind of laugh, but kidding aside, that's essentially it's a system. I mean, this AI factory is now a system, so you got to think of it, the storage is still going to do storage, but in a different way.
Kyle Leciejewski
>> Right. And I think that system mindset and being a meaningful supplier in storage and network and compute and the data layer and the software ecosystem to go deliver it, there's some magic in how you drive the integration to accelerate the outcomes for customers. And we're going to keep doubling down on some of those elements and driving those outcomes. And we think this is, we say this is one of the biggest waves we've ever seen in 40 years. This is the biggest inflection point in 40 plus years of the company. This is our moment. It also feels like we're in the middle of the bullseye of this moment. Some of the things in the internet and the smartphone, you were close to the bullseye. You are in the bullseye, and this has been so exciting for us.
John Furrier
>> The customer's getting on the wrong side of history or the right side of history. This is one of those waves that standing, being a fast follower isn't an option in our opinion. NVIDIA's a good call too, the AI factory. I love how you guys co-opted that with NVIDIA. I'd love that name. I'll ask you this question. I'm going to ask you this question. I know you won't answer. How's sales doing?
Kyle Leciejewski
>> We'll leave the sales questions to the earnings call, which is just a few short weeks. What I will say is we're proud to service our customers. We're proud to build these incredible outcome-driven data centers together. We're proud of the partner network with NVIDIA and the broader ecosystem at large. We think we're in the early innings of what is going to be an amazing AI wave, and we're proud to continue to serve at the capacity we have.
John Furrier
>> Kyle, great to have you on. Thanks for joining me. We'll be talking to a lot of the end user practitioners, so we'll keep in touch. Thanks for coming on.
Kyle Leciejewski
>> Love it. Thank you.
John Furrier
>> Okay. This is the AI Factory Series, again, where the leaders are making it happen. An infrastructure changeover and extension. It's not a pivot, it's an extension. It's a new trajectory to build on the existing compute, storage, and networking. Again, the system design, the system thinking is going next level. Of course, we're doing our part to bring it to you. My name is John Furrier, host of theCUBE. Thanks for watching.
John Furrier